What is the clinical significance of tumor markers?

Because tumor markers can be used to predict the response of a tumor to treatment and for prognosis , researchers have hoped that they might also be useful in screening tests that aim to detect cancer early, before there are any symptoms.

What is a clinical policy bulletin?

Clinical policy bulletins are used by Medicare Advantage plans and commercial payers to describe the services and procedures they cover and under what circumstances , says Denise Wilson, vice president of clinical audit and appeal services at AppealMasters in Towson, Maryland.

What are clinical assays for tumor markers most important for?

Tumor marker tests are mainly used to learn more about a known cancer . But in certain cases, they may be used to screen for cancer or to help diagnose the disease.

What is Aetna policy on 76377?

According to CMS policy, 3D rendering with interpretation and reporting of CT, MRI, US, or other tomographic modality (76376, 76377), requires an approved secondary diagnosis . A qualifying procedure for the 3D rendering should also be included on the same date of service, or in the previous three days.

What are the four 4 clinical applications of tumor markers?

Clinical application of tumor markers can be broadly classified into 4 groups: Screening and early detection, diagnostic confirmation, prognosis and prediction of therapeutic response and monitoring disease and recurrence .

Why do doctors order tumor markers?

But doctors mainly use tumor marker tests to: Decide which type of treatment or combination of treatments will work best for a certain type of cancer. Figure out how well a treatment is working. Predict a person's chance of recovery.

What is Aetna Clinical policy Bulletin?

Our Clinical Policy Bulletins (CPBs) explain the medical, dental and pharmacy services we may or may not cover . They are based on objective, credible sources, such as the scientific literature, guidelines, consensus statements and expert opinions.

What are Milliman clinical guidelines?

The Milliman Care Guidelines span the continuum of patient care providing access to evidence-based knowledge and best practices relevant to patients in a broad range of care settings .

What is CAPP in healthcare?

The Council of Accountable Physician Practices (CAPP) is a coalition of organized multispecialty medical groups and health systems . We believe that improving the health of all Americans requires the reshaping of the care delivery system.

Why Tumour markers are not recommended as screening tests?

Tumour markers are not recommended for screening asymptomatic patients for malignancy because they generally: Lack specificity – many patients may have an elevated result due to benign disease. Lack sensitivity – many patients with malignancy will have a normal result.

What are the best tumor markers?

Tumor Markers in Common Use ALK gene rearrangements and overexpression. ... Alpha-fetoprotein (AFP) ... B-cell immunoglobulin gene rearrangement. ... BCL2 gene rearrangement. ... Beta-2-microglobulin (B2M) ... Beta-human chorionic gonadotropin (Beta-hCG) ... Bladder Tumor Antigen (BTA) ... BRCA1 and BRCA2 gene mutations. More items... May 11, 2021

Why are most tumor markers not used for screening?

Tumor marker tests are not perfect. They are often not specific for cancer and may not be sensitive enough to pick up a cancer recurrence. The presence of tumor markers alone is not enough to diagnose cancer. You will probably need other tests to learn more about a possible cancer or recurrence.

What is the difference between 76376 and 76377?

CPT code 76376 can be reported when 3D rendering is performed by a radiologist or a specially-trained technologist at the acquisition scanner. CPT code 76377 is reported when the 3D post-processing images are reconstructed on an independent workstation with concurrent physician supervision.

What is Aetna policy 0047?

This policy will assure high- quality prenatal care for Aetna members while allowing family physicians the opportunity to continue their care through the 27th week of pregnancy .

What are the guidelines for 76706?

CPT code description for 76706 The U.S Preventive Services task Force recommends one-time screening for abdominal aortic aneurysm (AAA) by ultrasonography in men aged 65 to 75 who have ever smoked . This imaging technique involves the screening ultrasound of abdominal aorta.

Tumor Markers - Medical Clinical Policy Bulletins (2023)

Table of Contents

table of contents Policy policy scope medical need Experimental and Research Related Policies CPT Codes / HCPCS Codes / ICD-10 Codes Prostate Specific Antigen (PSA): Covered CPT codes if selection criteria are met: CPT codes not covered for indications listed in the CPB: HCPCS codes covered if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: carcinoembryonic antigen (CEA): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: CDH1 and TP53: CPT codes not covered for indications listed in the CPB: CDH1 and TP53- no specific code: ICD-10 codes not covered for indications listed in the CPB: Adenomatous polyposis coli (APC): Covered CPT codes if selection criteria are met: ICD-10 covered if selection criteria are met: Analysis confirms thyroid FNAB: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Androgen receptor 7 splicing variant (AR-V7): Covered CPT codes if selection criteria are met: Androgen receptor 7 splicing variant (AR-V7)- no specific code: ICD-10 codes covered if selection criteria are met: BCL2 e BCL6 : CPT codes covered when selection criteria are met: BCL6- no specific code: ICD-10 codes covered if selection criteria are met: Ensaio FISH do gene BCR/ABL: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Cancer antigen 125 (CA 125): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: CA 15-3 serial measurements (also known as CA 27-29 or Truquant RIA): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: CA 19-9: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for CPB-listed indications (not all included): Cardioembryonic antigen cellular adhesion molecule-7 (CEACAM-7)- No specific code: ICD-10 codes not covered for indications listed in the CPB: Molecular intelligence services, including MI Profile and MI Profile PLUS (formerly Target Now Molecular Profiling Test, including Target Now Select and Target Now Comprehensive)- No specific code: Cyfra21-1 (a fragment of cytokeratin 19) p53 and squamous cell carcinoma antigen (SCC-Ag)- No specific code: ICD-10 codes not covered for indications listed in the CPB: Vascular endothelial growth factor (VEGF) : Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: Evaluation of Human Epidermal Growth Factor Receptor 2 (HER2): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for CPB-listed indications (not all included): IGH™ (immunoglobulin heavy chain locus): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: IGK@ (kappa immunoglobulin light chain locus): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Serial measurements of human chorionic gonadotropin (HCG): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Serial AFP measurements for diagnosis of germ cell tumors or diagnosis and follow-up of hepatocellular carcinoma: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for CPB-listed indications (not all included): Serial measurements of AFP and HCG together to diagnose and monitor testicular cancer: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Measurement of estrogen and progesterone receptors and steroid receptor: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Measurement of progesterone receptors: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Targeted hematologic genomic sequencing panel (5-50 genes) for myelodysplastic syndromes (eg, MedFusion Myeloid Malignancy Analysis Panel): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Specific solid organ genomic sequencing panel (5-50 genes): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Oncomine™ Dx objective test: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: T cell receptor gene rearrangements: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Thyroid oncogene panel ThyGenEXT and ThyraMIR: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Thyroseq: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: TP53: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: test U2AF1: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: K-ras (KRAS) with BRAF reflex test: Covered CPT codes if selection criteria are met: Other CPT codes related to the CPB: Other CPB related HCPCS codes: ICD-10 codes covered if selection criteria are met: Incompatibility Fix (MSI/dMMR, MLH1, MSH2, MSH6): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Hypermethylation of the MLH1 tumor promoter: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met:: MPL (myeloproliferative leukemia protein): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Dual mouse minute 2 (MDM2): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: MYD88: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Panel NGS de MyMRD: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Next generation sequencing of tumor DNA (e.g. ClonoSeq): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: MSK-IMPACT: CPT codes not covered for indications listed in the CPB: MUC1- no specific code: ICD-10 codes not covered for indications listed in the CPB: ALK gene fusion: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ALK gene rearrangement: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ALK: Covered CPT codes if selection criteria are met: Other CPT codes related to CPB: ICD-10 codes covered if selection criteria are met: Urokinase plasminogen activator (uPA) and plasminogen activator inhibitor 1 (PAI-1): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: a bloodbath- no specific code: CPT codes not covered for indications listed in the CPB: ICD-10 codes covered if selection criteria are met: DC 117 (set c): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: disco 20: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: disco 25: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: CD 31- no specific code: Other CPT codes related to the CPB: ICD-10 codes covered if selection criteria are met: CD 33: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: disco 52: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Cyclin D1: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: Decoding test (an RNA biomarker assay): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: DecisionDx-UM: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Endopredict (score of 12 genes): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: FIP1L1-PDGFRA merger: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: FIP1L1-PDGFRA gene rearrangements: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: FLT3 gene mutation: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Fas-associated protein with death domain FADD- No specific code: ICD-10 codes not covered for indications listed in the CPB: Prostate PX, post-operative PX: Other CPT codes related to the CPB: ICD-10 codes not covered for indications listed in the CPB: NRAS mutation: Covered CPT codes if selection criteria are met: Other CPT codes related to the CPB: ICD-10 codes covered if selection criteria are met: NTRK: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Ras oncogenes (except KRAS and BRAF)- No specific code: Epidermal Growth Factor Receptor Test (EGFR): Covered CPT codes if selection criteria are met: Other CPT codes related to the CPB: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: ROS-1- No specific code: ICD-10 codes covered if selection criteria are met: ZAP-70: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: test ZRSR2: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Oncotipo Dx: Covered CPT codes if selection criteria are met: CPT codes not covered for indications listed in the CPB: Other CPT codes related to the CPB: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: Myeloperoxidase (MPO) Immunostaining FLT3-ITD, CEBPA Mutation, NPM1 Mutation, and KIT Mutation: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: NPM1: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: PDGFRA: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: LMP/RARA: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: You are talking: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ProMark: Covered CPT codes if selection criteria are met: ProMark- no specific code: ICD-10 codes covered if selection criteria are met: Placental alkaline phosphatase (PLAP): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Bladder Tumor Antigen (BTA) Stat Test, Nuclear Matrix Protein Test (NMP22), Fibrin/Fibrinogen Degradation Products Test (Aura-Tek FDP), Bladder FISH Test for Pathnostics, or Fluorescent Hybridization Test in situ (FISH) UroVysion, BTA TRAK: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for CPB-listed indications (not all included): immunocytes (uCyt)- No specific code: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: Janus Kinase 2 (JAK2) mutations: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: KRAS: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: BRAF, V600 mutation analysis: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Loss of heterozygosity (LOH) assay on the long arm of chromosome 18 (18q) or colon cancer (DCC) deleted protein (18q-LOH/DCC) for colorectal cancer: ICD-10 codes not covered for indications listed in the CPB: Biodesix BDX-XL2, Nodify XL2, Nodify Lung, Nodify CDT: CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for indications listed in the CPB: OvaCheck tests: ICD-10 codes not covered for indications listed in the CPB: Ovasure- No specific code: Other CPT codes related to the CPB: Circulating cell-free nucleic acids- No specific code: ICD-10 codes not covered for indications listed in the CPB: Circulating tumor cells (CTC) (eg, CELLSEARCH tests): CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for CPB-listed indications (not all included): Cofilina (CFL1)- no specific code: ICD-10 codes not covered for indications listed in the CPB: ColonSentry- no specific code: ICD-10 codes not covered for indications listed in the CPB: Prueba Oncotype DX® Mama DCIS Score™: CPT codes not covered for indications listed in the CPB: CDT-early lung test: CPT codes not covered for indications listed in the CPB: Galectina-3: CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for CPB-listed indications (not all included): Insight tipo TNBC: CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for indications listed in the CPB: Ki67 : CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for indications listed in the CPB: breast cancer index: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: BTK: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: mother's impression: Covered CPT codes if selection criteria are met: HCPCS codes covered if selection criteria are met: ICD-10 codes covered if selection criteria are met: Lymph2CX e Lymph3Cx: CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for CPB-listed indications (not all included): mucin 4 expression: CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for indications listed in the CPB: Mucina 5AC (MUC5AC)- No specific code: ICD-10 codes not covered for indications listed in the CPB: NF1, RET e SDHB: CPT codes not covered for indications listed in the CPB: NF1, RET e SDHB- no specific code: ICD-10 codes not covered for indications listed in the CPB: Microarray-based gene expression profiling tests: Other CPT codes related to the CPB: OVA1: CPT codes not covered for indications listed in the CPB: p16 protein expression- No specific code: ICD-10 codes not covered for indications listed in the CPB: Pathwork origin fabric test: CPT codes not covered for indications listed in the CPB: PreOvar test for the KRAS variant [to determine ovarian cancer risk]: ICD-10 codes not covered for indications listed in the CPB: Prueba ProOnc Tumor Source Dx- No specific code: Roma: CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for CPB-listed indications (not all included): Panel of 76 Rotterdam signature genes: HCPCS codes not covered for CPB listed indications: Serum amyloid A: CPT codes not covered for indications listed in the CPB: Other CPT codes related to the CPB: ICD-10 codes not covered for indications listed in the CPB: Breast cancer gene expression rate (HOXB13:IL17BR): HCPCS codes not covered for CPB listed indications: PAM50 ROR (Prosigna Breast Cancer Prognostic Gene Signature Assay) : Covered CPT codes if selection criteria are met: CPT codes not covered for indications listed in the CPB: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: PTEN: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: Breast Lymph Node GeneSearch (BLN) Assay- No specific code: feel shy- No specific code: Other CPT codes related to the CPB: Topographic genotype (PathfinderTG)- No specific code: Translation of Biomarkers (BT)- No specific code: ICD-10 codes not covered for indications listed in the CPB: BRAF e EGFR: CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for indications listed in the CPB: HE4: CPT codes not covered for indications listed in the CPB: Other CPT codes related to the CPB: ICD-10 codes not covered for indications listed in the CPB: HERmark- No specific code: ICD-10 codes not covered for indications listed in the CPB: Gene Expression TargetPrint: Other CPT codes related to the CPB: HCPCS codes not covered for CPB listed indications: ICD-10 codes not covered for indications listed in the CPB: TP53- No specific code: ICD-10 codes not covered for indications listed in the CPB: CK5, CK14, p63 e Racemasa P504S: Other CPT codes related to the CPB: ICD-10 codes not covered for indications listed in the CPB: EML4-ALK: Other CPT codes related to the CPB: ICD-10 codes not covered for indications listed in the CPB: Coloprint, CIMP, Line-1 hypomethylation and immune cells- No specific code: ICD-10 codes not covered for indications listed in the CPB: Confirm MDx: CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for indications listed in the CPB: Des-gamma-carboxyl prothrombin (DCP): CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for CPB-listed indications (not all included): 5-Hydroxyindoleacetic acid (5-HIAA): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Beta-2 microglobulina (B2M): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Expression of CALCA (Calcitonin): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Expression of CALR (calreticulin): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Expression of CALB2 (calretinin): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: CHGA (Chromogranin A) expression: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Beta human chorionic gonadotropin (beta-hCG): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Isocitrate dehydrogenase 1 and 2 (IDH1, IDH2): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: INHA expression (inhibin): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Lactate dehydrogenase (LDH): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: testes PDGFRB : Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Phosphatidylinositol-4,5-bisphosphonate 3-kinase, polypeptide catalytic subunit alpha gene (PIK3CA): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for indications listed in the CPB: PLCG2: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Quest Diagnostic's Thyroid Cancer Mutation Panel: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: RUNX1: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: tests SF3B1: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: teste SRSF2: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: thymidine kinase: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: thyroglobulin antibody: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Thyroglobulin (TG) expression: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: Thyroid transcription factor 1 (TTF-1): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: WT-1 gene expression- No specific code: ICD-10 codes covered if selection criteria are met: Test the tumor the test the HPV (p16): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ezh2: Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for CPB-listed indications (not all included): TERT (telomerase transcriptase reversa): Covered CPT codes if selection criteria are met: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for CPB-listed indications (not all included): Carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) (eg, benign diagnostic risk test)- No specific code: ICD-10 codes not covered for CPB-listed indications (not all included): CDX2: ICD-10 codes not covered for CPB-listed indications (not all included): CxBladder tests: CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for CPB-listed indications (not all included): 4Kscore: CPT codes not covered for indications listed in the CPB: Fibrinogen Degradation Products (FDP) test (eg, DR-70 or Onko-Sure)- No specific code: ICD-10 codes not covered for CPB-listed indications (not all included): HMGB1 e RAGE- No specific code: ICD-10 codes not covered for indications listed in the CPB: IHC4 (p. ej., NexCourse IHC4)- No specific code: ICD-10 codes not covered for CPB-listed indications (not all included): Lectin-reactive alpha-fetoprotein (AFP-L3): CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for CPB-listed indications (not all included): Biopsia líquida (p. ej., CancerIntercept, GeneStrat, Colvera, Neolab Prostate, FoundationACT, FoudationOne Liquid CDx, Guardant360CDx): Covered CPT codes if selection criteria are met: CPT codes not covered for indications listed in the CPB: ICD-10 codes covered if selection criteria are met: ICD-10 codes not covered for CPB-listed indications (not all included): long non-coding RNA- No specific code: ICD-10 codes not covered for indications listed in the CPB: Proteomic profiling based on mass spectrometry (eg Xpresys Lung): CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for CPB-listed indications (not all included): OncoVantage: CPT codes not covered for indications listed in the CPB: Select MDX- No specific code: ICD-10 codes not covered for indications listed in the CPB: Targeted paired rearrangements (whole genome next-generation sequencing): CPT codes not covered for indications listed in the CPB: ICD-10 codes not covered for indications listed in the CPB: Experimental and Investigational Tumor Markers: CPT codes not covered for indications listed in the CPB: There are no specific codes for the tumor markers listed below.: Bottom Examples of tumor markers include Myeloproliferative Neoplasms and Myelodysplastic Syndromes PSA PCA3 B15 CEA CA-125 HE4 CA 15-3 CA 19-9 Catepsinas disco 20 disco 25 CD 33 disco 52 CD 117, kit c HCG AFP emergencies, public relations NSE ABSENT p53 Zap-70 UPA hIgV mutation status Cadena Ligera Kappa / Lambda KRAS Bladder cancer: BTA-stat, NMP22, Urovysion, ImmunoCyt Cxvejiga Mama OncotypeDx Mom Print plano DestinoImprimir Sinfonia Panel of 76 Rotterdam signature genes Breast Cancer Gene Expression Index/Breast Cancer Index breast milk OvaChek ovaseguro Thrombospondin-1 GCC forecast feel shy VEGF ProstatePx Circulating tumor cells (eg, CELLSEARCH) she-2/new PancraGen (formerly PathFinderTG - Pancreas) Gene expression profiles for cancer of unknown primary origin GeneSearch BLN test BT Bcl-2 CD31 top2a TSP-1 mdr1 MRP-1 FLAP MPO DCP NMP66 HERmark MDM2 OVA1 ColonSentry Px prostate postoperative px EGFR CEACAM-7 CFL1 Early CDT-Lung ecad EML4-ALK MUC4 ProOnc TumorSourceDx CLIMATE Caris Target Now / Caris Molecular Profiling Service Racemasa CoA (P504S) and HMWCK (34betaE12) P504S FLT3 ColoImpresión DecisionDx-UM Molecular diagnosis of thyroid cancer Roma ERCC1 GSTP1 CEACAM6 DCIS Recurrence Score MiPRS SNA Phosphatidylinositol-4,5-bisphosphonate 3-kinase, polypeptide catalytic subunit alpha gene (PIK3CA) Cyclin D1 and FADD You are talking Oncotype Dx Prostate Oncotype DX AR-V7 core detection assay for men with metastatic castration-resistant prostate cancer prostavision PAM50 and Prosigna Oncotype Dx colon Decipher miRNA for prostate cancer Galectina-3 MLH1 promoter methylation p16 MUC5AC Tp53 Ki67 a bloodbath Multiplex Tests for Myeloid Hematopathological Disorders AnswerDx 4K Score Confirm MDx BioespeciFx HeproDx-TM network test Pulmotype NexCourse IHC4 EndoPredict Immunohistochemistry 4 (IHC4) Tumor infiltrated lymphocytes Other markers to guide adjuvant therapy in breast cancer Active surveillance of prostate cancer in men with "favorable" intermediate-risk disease CDX2 as a prognostic biomarker in colon cancer testes PDGFRB guardian360 CancerIntercept Circulating cell-free nucleic acids in colorectal cancer HMGB1 and RAGE in cutaneous malignancy Ki-67 in upper urinary tract carcinoma Non-coding long RNA in gallbladder cancer and non-small cell lung cancer MUC1 in gastric cancer Percepta Bronchial Genomic Classifier (Veracyte) Select MDx Prostate ExoDx (IntelliScore) Next generation sequencing and solid cancers Dx Oncomine Destination Test RAS (KRAS and NRAS) and BRAF mutation testing for colon cancer MSK-IMPACT Merkel SmT oncoprotein antibody titer Merkel virus VP1 capsid antibody MyAML Next Generation Sequencing Dashboard Advanced DNA and RNA sequencing OmniSeq (OmniSeq and LabCorp) Prostate Cancer Risk Panel (Mayo Clinic FISH analysis) Testing for human papillomavirus (HPV) in tumors Sentinel's Chest miR-31ahora BBDRisk Dx Dx-Melanoma decision FoundationOne CDx Assa BRAF and EGFR for esophageal carcinoma CA 19-9 for ovarian cyst CA 19-9 to predict prognosis or effect of treatment in bladder (urothelial) cancer Colvera (circulating tumor DNA/ctDNA) Galectin-3 for breast cancer Galectin-3 for ovarian cancer Galectin-3 for pancreatic cancer NF1, RET and SDHB for ovarian cancer BTK (Brution Typrosine Kinase) e PLCG2 (Fosfolipase C Gama 2) EZH2 (Polycomb Zeste 2 repressive complex subunit 2 enhancer) MYD88 (Primary Myeloid Differentiation Response 88) TERT (telomerase transcriptase reversa) Liquid biopsy for lung cancer BCR/ABL fluorescent in situ hybridization (FISH) Magnetic nanosensor immunoassay for lung cancer diagnosis Measurement of circulating tumor cells (e.g. CellMax Life and FirstSightCRC) for detection of colorectal cancer mRNA gene expression profiles for cutaneous melanoma My Prostate Score (MiPS) Lymph2Cx and Lymph3Cx Lymphoma Molecular Classification Assay Biodesix BDX-XL2, Nodify Lung, Nodify CDT or Nodify XL2Test to distinguish benign from malignant lung nodules myPath myriad melanoma NantHealth GPS Cancer Panels Natera Signatera Molecular Monitoring (MRD) for Breast Cancer ProMark Insight tipo TNBC Panel NGS de MyMRD Directed proteomic assay OncoOmicDx ctDx Lung Resolution Xpression Atlas Claims decipher bladder Mayo Clinic Laboratories Urinary Steroids Profile MI Cancer Research PreciseDx breast cancer test miR sentinel prostate cancer test Signatera FIP1L1-PDGFRA fusion oncogene Oncotype MAP PanCancerae Oncomap Extra prostate PanGIA Immunoscore to estimate recurrence risk or determine adjuvant therapy in patients with colon cancer OncoSignal Test for Analysis of Solid Tumors DCISionRT DetermaRx Cancer mRNA Detect Praxis Whole Genome Somatic Sequencing Praxis Somatic Optical Genome Mapping Praxis somatic transcriptome Testing RadTox cfDNA 3D forecast dashboards Augusta Hematology Optical mapping of the genome Teste BostonGene Tumor Portrait DecisionDx DiffDx-Melanoma, DecisionDx-Melanoma e DecisionDx-SCC LungLB and LungLife IA PancreaSeq Genomic Classifier (GC) Melanoma DAWN I EpiSwitch CiRT (Checkpoint Inhibitor Response Test) Guardant360 TissueSiguiente helium liver test Teste IMMray PanCan-d IsoPSA NavDx for cancer recurrence surveillance in HPV-associated oropharyngeal cancer Px Thyroid Guide glossary of terms Appendix References FAQs Videos

Number: 0352

Policy
Applicable CPT / HCPCS / ICD-10 codes
Bottom
References

Policy

policy scope

This Clinical Policy Bulletin addresses tumor markers, including somatic (acquired) mutations, in oncology.

For criteria related to germline (inherited) mutations, seeCPB 0140 - Genetic test.

medical need
1. Aetna considersanyof the following clinically necessary tumor markers for the indicated indication:
  1. Molecular cytogenetic analysis of 1p19q codelection for astrocytomas and gliomas;
  2. 5-hydroxyindoleacetic acid (5-HIAA) for neuroendocrine tumors;
  3. Affirms thyroid FNA analysis to evaluate fine needle aspiration specimens of indeterminate thyroid nodules; experimental for other indications. Repeat tests are considered experimental and investigative;
  4. ALK expression for pancreatic adenocarcinoma, pediatric Hodgkin's lymphoma, inflammatory myofibroblastic tumor (IMT) with ALK translocation, breast implant-associated ALCL, peripheral T-cell lymphoma, and uterine sarcoma;
  5. ALK gene fusion as a molecular biomarker in non-small cell lung cancer;
  6. ALK gene rearrangement for diffuse large B-cell lymphoma, anaplastic thyroid carcinoma, primary cutaneous CD30+ T-cell lymphoproliferative disorders, post-transplant lymphoproliferative disorder, and non-small cell lung cancer;
  7. Alpha-fetoprotein (AFP) testing for hepatocellular carcinoma in patients with hepatitis B, or for people with cirrhosis and one or more of the following risk factors: alcohol use; alpha-1 antitrypsin deficiency; Asian female at least 50 years old; Asian male at least 40 years old; family history of HCC; genetic hemochromatosis; Hepatitis C; non-alcoholic steatohepatitis; and stage 4 primary biliary cirrhosis;
  8. Alpha-fetoprotein (AFP) for the following indications: hepatocellular carcinoma; mediastinal mass; ovarian cancer; pelvic mass; Testicular cancer; testicular mass; thymic carcinoma; and thymoma;
  9. Alpha Fetoprotein (AFP): Serial measurements for the diagnosis of germ cell tumors in limbs with adenocarcinoma or unspecified carcinoma involving mediastinal lymph nodes; o Diagnosis and follow-up of hepatocellular carcinoma (eg, before considering a liver transplant);
  10. Androgen receptor 7 (AR-V7) splicing variant in circulating tumor cells to select therapy in metastatic castration-resistant prostate cancer after progression with abiraterone or enzalutamide;
  11. APC for familial adenomatous polyposis when criteria are met inCPB 0140 - Genetic test; and for desmoid fibromatosis; experimental for other indications;
  12. BCL2 and BCL6 for the diagnosis of non-Hodgkin's lymphoma and Castleman's disease;
  13. BCR/ABL fluorescence in situ hybridization (FISH) for lymphoblastic lymphoma, acute myeloid leukemia, acute lymphocytic leukemia, chronic myeloid leukemia, and suspected myeloproliferative neoplasia; experimental for other indications;
  14. Beta-2 microglobulin (B2M) for multiple myeloma, non-Hodgkin's lymphoma, and Waldenström/lymphoplasmacytic macroglobulinemia;
  15. BIRC3 and MALT1 for gastric MALT lymphoma, non-gastric MALT lymphoma, nodal marginal zone lymphoma, and splenic marginal zone lymphoma;
  16. BRAF V600 mutation for indeterminate thyroid nodules, hairy cell leukaemia; gastrointestinal stromal tumors; colorectal cancer, Lynch syndrome; non-small cell lung cancer; thyroid carcinoma; infiltrating glioma, pancreatic adenocarcinoma, and melanoma (seeCPB 0715 - Pharmacogenomic and pharmacodynamic tests); or Lynch syndrome for people who meet the criteria inCPB 0140 - Genetic test; and colorectal cancer if KRAS is not mutated; experimental for other indications;
  17. breast cancer indexFootnote2**to assess the need for adjuvant chemotherapy or adjuvant endocrine therapy in women or men with newly diagnosed breast tumors, whereallof the following criteria are met:
    1. Breast cancer is not metastatic (nodule negative) or has 1 to 3 ipsilateral axillary lymph nodes involved;y
    2. The breast tumor is estrogen receptor and/or progesterone receptor positive;y
    3. The breast tumor is negative for the HER2 receptor;y
    4. Adjuvant therapy is not excluded due to any other factors (eg advanced age and/or significant comorbidities);y
    5. The member and the physician (prior to the test) discussed the possible test results and agreed to use the results to guide therapy;
    BCI is also considered medically necessary for people with 0-3 lymph node positive breast cancer who have received 5 years of endocrine therapy without recurrence to guide decisions about extended endocrine therapy.
  18. BTK (Bruton tyrosine kinase) for chronic lymphocytic leukemia/small lymphocytic lymphoma;
  19. CA 15-3: Serial measurements of CA 15-3 (also known as CA 27-29 or Truquant RIA) during treatment in women diagnosed with breast cancer, especially advanced metastatic breast cancer (an increasing level of CA 15-3 may suggest treatment failure);
  20. CA 19-9 for the following indications:
    1. To monitor clinical response to therapy or detect early disease recurrence in members with known gastric cancer, pancreatic cancer, gallbladder cancer, cholangiocarcinoma, ovarian cancer, small bowel adenocarcinoma, or adenocarcinoma of the ampulla of Vater;o
    2. To rule out cholangiocarcinoma in people with primary sclerosing cholangitis undergoing liver transplantation;o
    3. For evaluation of jaundice, abnormal liver function tests (LFTs) or obstruction/imaging abnormality;o
    4. As a tumor marker for mucinous carcinoma of the appendix;
  21. CALB2 (calretinin) expression for primary occult and lung cancer;
  22. CALCA (calcitonin) expression for medullary thyroid cancer or for adenocarcinoma or anaplastic/undifferentiated head and neck tumors;
  23. CALR (calreticulin) for chronic myelogenous leukemia (chronic phase, adult), myelodysplastic syndrome or myeloproliferative neoplasms;
  24. Cancer antigen 125 (CA 125) levels foranyof the following:
    1. As a preoperative diagnostic aid in women with ovarian masses suspected of being malignant, so that intraoperative availability of a gynecological oncologist can be arranged if CA 125 is increased;o
    2. As a screening test for ovarian cancer when there is a family history of hereditary ovarian cancer syndrome (a pattern of ovarian cancer clusters in two or more generations), where the test is performed concurrently with transvaginal ultrasound and salpingo has not been performed -prophylactic oophorectomy. For this indication, screening is considered clinically necessary every six months starting at age 30 or 10 years before the earliest age of first diagnosis of ovarian cancer in the family;o
    3. Diagnosis of ovarian cancer in women with new symptoms (bloating, pelvic or abdominal pain, difficulty eating or feeling full quickly, or urinary frequency and urgency) that persist for three or more weeks, where the doctor performed a pelvic exam and rectal examination and suspicion of ovarian cancer;o
    4. In members with adenocarcinoma of unknown primary origin, to rule out ovarian cancer;o
    5. In members with known ovarian cancer, as an aid in disease control, response to treatment, detection of recurrent disease, or assessing the value of revision surgery;
  25. Carcinoembryonic antigen (CEA) foranyof the following:
    1. As a preoperative prognostic indicator in limbs with known colorectal carcinoma or mucinous carcinoma of the appendix, when it will aid in staging and planning surgical treatment;o
    2. CEA pancreatic cyst fluid to distinguish mucinous from non-mucinous malignant pancreatic cysts;o
    3. To detect asymptomatic recurrence of colorectal cancer after surgical and/or medical treatment for the diagnosis of colorectal cancer (not as a screening test for colorectal cancer);o
    4. Monitor response to treatment of metastatic colorectal cancer;o
    5. For cholangiocarcinoma, gallbladder cancer, lung cancer, medullary thyroid cancer, metastatic breast cancer, primary and mucinous occult ovarian cancer;o
    6. For evaluation of jaundice, abnormal liver function tests (LFTs), or bile duct obstruction/abnormality on liver imaging;
  26. CBFB for acute myelogenous leukemia;
  27. CCND1 (cyclin D1) for B-cell lymphomas, primary cutaneous B-cell lymphomas, chronic lymphocytic leukemia/small lymphocytic lymphoma, and hairy cell leukemia;
  28. CD20, to determine eligibility for anti-CD20 treatment (rituximab) (seeCEC 0314 - Rituximabe);
  29. CD 25, to determine eligibility for treatment with denyleucine diftitox (Ontak);
  30. CD 31 immunostaining, for diagnosis of angiosarcoma;
  31. CD33, for lymphoblastic lymphoma and to determine eligibility for anti-CD33 treatment (gemtuzumab, Mylotarg);
  32. CD52, for post-transplant lymphoproliferative disorder, T-cell prolymphocytic leukemia, and to determine eligibility for anti-CD52 treatment (alemtuzumab, Campath);
  33. CD117 (c-kit), for acute myeloid leukemia, cutaneous melanoma, gastrointestinal stromal tumors and systemic mastocytosis;
  34. expression of CHGA (chromogranin A) for neuroendocrine tumors, non-small cell lung cancer, small cell lung cancer, Merkel cell carcinoma and occult primary;
  35. DecipherFootnote 3***for the following indications:
    1. Post-biopsy in men with very low-risk, low-risk, and intermediate-risk NCCN favorable prostate cancer who have a life expectancy greater than 10 years who have not received treatment for prostate cancer and are candidates for active surveillance or definitive therapy;o
    2. Post-biopsy in men with intermediate-risk prostate cancer when deciding whether to add androgen deprivation therapy to radiation;o
    3. Men with undetectable PSA after prostatectomy for prostate cancer, to determine adjuvant versus salvage radiotherapy or to determine whether to initiate systemic therapies;
  36. DecisionDx-UM (Castle Biosciences, Phoenix, AZ) for risk stratification of people with localized uveal melanoma;
  37. EndoPredict (also known as 12 gene scoring)Footnote2**to assess the need for adjuvant chemotherapy in women or men with newly diagnosed breast tumors, whereallof the following criteria are met:
    1. Breast cancer is not metastatic (nodule negative) or has 1 to 3 ipsilateral axillary lymph nodes involved;y
    2. The breast tumor is estrogen receptor positive;y
    3. The breast tumor is negative for the HER2 receptor;y
    4. Adjuvant chemotherapy is not excluded due to any other factors (eg advanced age and/or significant comorbidities);y
    5. The member and the physician (prior to the test) discussed the possible test results and agreed to use the results to guide therapy;
  38. Epidermal growth factor receptor (EGFR) mutation test to predict response to EGFR-targeted tyrosine kinase inhibitors (erlotinib (Tarceva), gefitinib (Iressa), afatinib (Gilotrif), osimertinib (Tagrisso)) in lung cancer of non-small cells;
  39. EZH2 (Polycomb zest 2 repressive complex subunit 2 tensor) for the treatment of:
    1. myelodysplastic syndrome (MDS),y
    2. myeloproliferative neoplasms (MPN) to assess higher-risk mutations associated with disease progression in members with primary myelofibrosis (PMF);
    EZH2 is considered experimental and experimental by Aetna for all other indications, including diffuse large B-cell lymphomas;
  40. FIP1L1-PDGFRA fusion oncogene for systemic mastocytosis with peripheral blood eosinophilia;
  41. FIP1L1-PDGFRA gene rearrangements for myeloid/lymphoid neoplasms with peripheral blood eosinophilia and tyrosine kinase fusion genes;
  42. FLT3 gene mutation test for acute lymphoblastic leukemia, acute myeloid leukemia (AML), myelodysplastic syndromes, myeloproliferative neoplasms, and myeloid/lymphoid neoplasms with eosinophilia and tyrosine kinase fusion genes;
  43. Human chorionic gonadotropin (HCG), serial measurement to diagnose germ cell tumors in limbs with unspecified adenocarcinoma or carcinoma involving mediastinal lymph nodes, or to monitor treatment in limbs with known trophoblastic tumors (invasive hydatidiform moles and choriocarcinomas) and cell tumors germ cells (teratocarcinoma and embryonic cell carcinoma) of the ovaries or testes, or to control relapse after achieving remission;
  44. human chorionic gonadotropin, beta (beta-HCG) for mediastinal mass; ovarian cancer; pelvic mass; testicular mass; Testicular cancer; thymoma; or thymic carcinoma;
  45. Evaluation of human epidermal growth factor (HER2) receptor 2 (ERBB2) in breast, gastric, colorectal, esophageal, esophagogastric junction, salivary gland tumors and non-small cell lung cancer - seeCPB 0313 - Trastuzumabe (Herceptin e biossimilares), Trastuzumabe e Hyaluronidase-oysk (Herceptin Hylecta);
  46. Human papillomavirus (HPV) tumor test (p16) for head and neck cancer (including oropharyngeal cancer) or occult primary cancers;
  47. IGH™ (immunoglobulin heavy chain locus), gene reassortment analysis to detect abnormal clonal populations in non-Hodgkin's lymphomas, chronic lymphocytic leukemia, hairy cell leukemia, and post-transplant lymphoproliferative disorder;
  48. IGK@ (immunoglobulin kappa light chain locus), gene rearrangement analysis, evaluation of abnormal clonal populations for non-Hodgkin's lymphoma, systemic light chain amyloidosis;
  49. INHA (inhibin) expression for ovarian cancer or pelvic mass;
  50. Mutation of the gene for isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) for AML, chondrosarcomas, myelodysplastic syndromes, myeloproliferative neoplasms, orgiomas and glioblastomas;
  51. K-ras mutation analysis (KRAS), with BRAF reflex testing, to predict non-response to cetuximab (Erbitux) and panitumumab (Vectibix) in the treatment of anal adenocarcinoma, metastatic colorectal cancer, and small bowel adenocarcinoma; K-ras mutation analysis (KRAS) to predict non-response to Erlotinib (Tarceva) in the treatment of non-small cell lung cancer; experimental for all other indications;
  52. KRAS for metastatic colorectal cancer, myelodysplastic syndromes, non-small cell lung cancer, pancreatic adenocarcinoma, and uterine sarcoma;
  53. lactate dehydrogenase (LDH) for acute lymphoblastic leukemia (ALL), bone cancer, kidney cancer, kidney mass, lung cancer, multiple myeloma, non-Hodgkin's lymphoma, pelvic mass, ovarian cancer, testicular cancer or testicular mass;
  54. Liquid biopsy (up to 50 genes) (p. an oncogenic factor is not identified; large liquid biopsy panels (greater than 50 genes) are considered experimental and investigational for non-small cell lung cancer; for non-small cell lung cancer Guardant360CDx and FoundationOne Liquid CDx for Non-Small Cell Lung Cancer Lung Cancer and Prostate Cancer (seeCPB 0715 - Pharmacogenetic and pharmacodynamic tests);
  55. mother's impressionFootnote2**to assess the need for adjuvant chemotherapy in women or men with newly diagnosed breast tumors when all of the following criteria are met:
    1. Breast cancer is not metastatic (negative nodulefootnote 1*) o con 1-3 affected ipsilateral axillary lymph nodes;y
    2. The breast tumor is estrogen receptor positive or progesterone receptor positive;y
    3. The breast tumor is HER2 receptor negative (Rationale: Adjuvant chemotherapy with trastuzumab (Herceptin) is considered medically necessary regardless of the Mammaprint score for HER2 receptor positive lesions);y
    4. The limb is determined to be at "high clinical risk" for recurrence usingAssistant! In line(see page 20 ofMINDACT studysupplement for high clinical risk definitions);y
    5. Adjuvant chemotherapy is not excluded due to any other factors (eg advanced age and/or significant comorbidities);y
    6. The member and the physician (prior to the test) discussed the possible test results and agreed to use the results to guide therapy;
  56. Measurement of estrogen receptors (ESR1) for breast cancer, endometrial carcinoma, non-small cell lung cancer, occult primary cancer, ovarian cancer or uterine sarcoma;
  57. Measurement of progesterone receptors (PGR) for breast cancer, non-small cell lung cancer, primary sarcoma or occult uterine;
  58. Mismatch Repair (MSI/dMMR) (MLH1, MSH2, MSH6, PMS2) test for tumors (somatic mutations) for breast cancer, ovarian cancer, colorectal cancer, small bowel adenocarcinoma, esophageal cancer, esophagogastric junction cancer, cancer gastric, pancreatic cancer, cholangiocarcinoma, gallbladder cancer, pancreatic adenocarcinoma, cervical cancer, uterine cancer, prostate cancer, testicular cancer, penile cancer, myelodysplastic syndromes, Ewing's sarcoma and occult primary; for the medical necessity of germline mutation screening for HNPCC/Lynch syndrome with MLH1, MSH2, MSH6, seeCPB 0140 - Genetic test;
  59. MLH1 tumor promoter hypermethylation for endometrial cancer;
  60. MPL (myeloproliferative leukemia protein) for chronic myelogenous leukemia (chronic phase, adult), myelodysplastic syndromes or myeloproliferative neoplasms;
  61. murine double minute 2 (MDM2) for uterine sarcoma and soft tissue sarcoma;
  62. Mycosis fungoides, diagnosis: polymerase chain reaction (PCR) for rearrangement of the T cell receptor gamma chain gene as an adjuvant in the histopathological diagnosis of mycosis fungoides;
  63. MYD88 (primary myeloid differentiation response 88) to differentiate Waldenstrom's macroglobinemia (WM) versus marginal zone lymphoma (MZL) if there is plasma cell differentiation for gastric MALT lymphoma, nodal marginal zone lymphoma, non-gastric MALT lymphoma, and splenic lymphoma of marginal zone; and for multiple myeloma;
  64. myeloperoxidase (MPO), CEBPA mutation, and KIT mutation immunostaining for diagnosis of acute myelogenous leukemia;
  65. MyMRD NGS panel for comprehensive prognostic assessment in people with acute myelogenous leukemia (AML) or myelodysplastic syndromes (MDS);
  66. Next-generation sequencing of tumor DNA (eg, ClonoSeq) to detect or quantify minimal residual disease in people with multiple myeloma or acute lymphocytic leukemia;
  67. NPM1 in acute myeloid leukemia (AML), chronic myelogenous leukemia (chronic, adult phase), myelodysplastic syndromes, or myeloproliferative neoplasms; experimental for other indications;
  68. NRAS for colorectal cancer, myelodysplastic syndrome, or blastic plasmacytoid dendritic cell neoplasia (BPDCN);
  69. NTRK for all solid tumors;
  70. OncotypeDx Breast (also known as 21 gene RT-PCR test) to assess the need for adjuvant chemotherapy in women or men with newly diagnosed breast tumors, whereallof the following criteria are met:
    1. Breast cancer is not metastatic (negative nodulefootnote 1*) o con 1-3 affected ipsilateral axillary lymph nodes;y
    2. The breast tumor is estrogen receptor positive;y
    3. The breast tumor is HER2 receptor negative or the breast tumor is HER2 receptor positive and is less than 1 cm in diameter. (Rationale: Adjuvant chemotherapy with trastuzumab (Herceptin) is considered clinically necessary regardless of the Oncotype Dx Breast score for HER2 receptor-positive lesions 1 cm or larger in diameter);y
    4. Adjuvant chemotherapy is not excluded due to any other factors (eg advanced age and/or significant comorbidities);y
    5. The member and the physician (prior to the test) have discussed the possible test results and agree to use the results to guide therapy (ie, the member will not receive adjuvant chemotherapy if the Oncotype Dx Breast score is low);
  71. Oncotype DX ProstateFootnote 3***for the following indications after biopsy:
    1. Men with very low-risk, low-risk, and favorable intermediate-risk NCCN prostate cancer who have a life expectancy greater than 10 years and who have not received treatment for prostate cancer and are candidates for active surveillance or definitive therapy;o
    2. Men with intermediate-risk prostate cancer when deciding whether to add androgen deprivation therapy to radiation;
  72. PAM50 Recurrence Risk Score (ROR) (also known as Prosigna Breast Cancer Prognostic Gene Signature Assay)Footnote2**to assess the need for adjuvant chemotherapy in women or men with newly diagnosed breast tumors, whereallof the following criteria are met:
    1. Breast cancer is not metastatic (negative node);y
    2. The breast tumor is estrogen receptor positive;y
    3. The breast tumor is negative for the HER2 receptor;y
    4. Adjuvant chemotherapy is not excluded due to any other factors (eg advanced age and/or significant comorbidities);y
    5. The member and the physician (prior to the test) discussed the possible test results and agreed to use the results to guide therapy;
  73. PDGFRA for gastrointestinal stromal tumors (GISTs) and pediatric acute lymphoblastic leukemia (see also previous entry for FIP1L1-PDGFRA gene rearrangements and fusions);
  74. PDGFRB test for myelodysplastic syndromes (MDS), dermatofibrosarcoma protuberans, acute lymphoblastic leukemia, and myeloid/lymphoid neoplasms with peripheral blood eosinophilia and tyrosine kinase fusion genes;
  75. Phosphatidylinositol-4,5-bisphosphonate 3-kinase, alpha polypeptide catalytic subunit (PIK3CA) gene for breast cancer and uterine sarcoma;
  76. Placental alkaline phosphatase (PLAP), to diagnose seminoma and non-seminoma germ cell tumors in unknown primary cancers;
  77. PLCG2 (phospholipase C gamma 2) for chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL);
  78. PML/RARE for acute promyelocytic leukemia; experimental for all other indications;
  79. You are talkingFootnote 3***for the following post-biopsy indications:
    1. Men with very low-risk, low-risk, and favorable intermediate-risk NCCN prostate cancer who have a life expectancy greater than 10 years and who have not received treatment for prostate cancer and are candidates for active surveillance or definitive therapy;o
    2. Men with intermediate-risk prostate cancer when deciding whether to add androgen deprivation therapy to radiation;
  80. ProMarkFootnote 3***for the following post-biopsy indications:
    1. Men with favorable intermediate or very low risk NCCN prostate cancer who have a life expectancy greater than 10 years and who have not received treatment for prostate cancer and are candidates for active surveillance or definitive therapy;o
    2. Men with intermediate-risk prostate cancer when deciding whether to add androgen deprivation therapy to radiation;
  81. Prostate-specific antigen (PSA) for the detection of prostate cancer (seeCPB 0521 - Detection of prostate cancer), staging, monitoring response to treatment and detection of disease recurrence;
  82. PTEN for uterine sarcoma and for people who meet criteria for testing for Cowden syndrome inCPB 0140 - Genetic test; experimental for all other indications;
  83. Quest Diagnostics Thyroid Cancer Mutation Panel to evaluate fine needle aspiration specimens of thyroid nodules that are indeterminate; experimental for other indications. Repeat tests are considered experimental and investigative;
  84. ROS-1 to predict response to crizotinib (Xalkori) for the treatment of non-small cell lung cancer (NSCLC);
  85. RUNX1 for acute myeloid leukemia, myelodysplastic syndrome and systemic mastocytosis;
  86. SF3B1 (splicing factor 3b subunit 1) for chronic myelogenous leukemia (chronic, adult phase), myelodysplastic syndromes, myeloproliferative neoplasms, or uveal melanoma;
  87. SRSF2 (serine-arginine-rich splicing factor 2) for chronic myelogenous leukemia (chronic phase, adult), myelodysplastic syndromes, myeloproliferative neoplasms, or systemic mastocytosis;
  88. steroid hormone receptor status in pre- and postmenopausal members to identify individuals most likely to benefit from endocrine forms of adjuvant therapy and therapy for metastatic or recurrent breast cancer;
  89. Targeted Hematologic Genomic Sequencing Panel (5-50 genes) for Acute Lymphocytic Leukemia, Acute Myeloid Leukemia, Chronic Myeloid Leukemia, Myelodysplastic Syndromes (MDS), and Myeloproliferative Neoplasms (MPN) (eg, MedFusion Myeloid Malignancy Analysis Panel);
  90. Targeted solid organ genomic sequencing panel (5-50 genes) for colorectal cancer, cutaneous melanoma, pancreatic cancer, prostate cancer, and non-small cell lung cancer (including Oncomine Dx target assay (Thermo Fisher Scientific, Carlsbad, CA ));
  91. T-cell receptor gene rearrangements (TRA™, TRB™, TRD™, TRG™) for T-cell prolymphocytic leukemia, T-cell large granular lymphocytic leukemia, extranodal NK/nasal-type T-cell lymphoma, gamma-delta T - hepatosplenic T-cell lymphoma, peripheral T-cell lymphoma, primary cutaneous CD30+ T-cell lymphoproliferative disorders, myelodysplastic syndromes, Castleman's disease, mycosis fungoides/Sézary syndrome, and myeloid/lymphoid neoplasms with eosinophilia and tyrosine kinase fusion genes;
  92. TERT (telomerase reverse transcriptase) clinically necessary for the study of:
    1. Gliomas (ie, supratentorial infiltrating astrocytoma/oligodendroglioma, anaplastic gliomas/glioblastoma),y
    2. Myelodysplastic syndrome (MDS).
    Aetna considers TERT experimental and experimental for all other indications, including thyroid carcinoma.
  93. ThyGenEXT Thyroid Oncogene Panel (formerly eg ThyGenX, miRInform thyroid test) and ThyraMIR microRNA Classifier to evaluate fine needle aspiration specimens of thyroid nodules that are indeterminate; experimental for other indications; repeat tests are considered experimental and investigative;
  94. thymidine kinase for chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL);
  95. Thyroglobulin antibodies for thyroid cancer;
  96. Thyroglobulin (TG) expression for thyroid cancer, occult primary and adenocarcinoma or anaplastic/undifferentiated head and neck tumors
  97. thyroid transcription factor 1 (TTF-1) for lung cancer or neuroendocrine tumors;
  98. Thyroseq to evaluate fine needle aspiration samples of indeterminate thyroid nodules; experimental for other indications. Repeat tests are considered experimental and investigative;
  99. TP53 for acute myelogenous leukemia; adult medulloblastoma; chronic lymphocytic leukemia/small lymphocytic lymphoma; chronic myelogenous leukemia (chronic phase, adult); endometrial carcinoma; malignant peritoneal or pleural mesothelioma; mantle cell lymphoma; myelodysplastic syndromes; myeloproliferative neoplasms; IM occult; pediatric acute lymphoblastic leukemia; peripheral T-cell lymphomas; lymphoma of the marginal zone of the spleen; uterine sarcoma; or well-differentiated grade 3 neuroendocrine tumors;
  100. U2AF1 (U2 small nuclear RNA helper factor 1) for blastic plasmacytoid dendritic cell neoplasia (BPDCN), chronic myeloid leukemia (chronic phase, adult), myelodysplastic syndromes or myeloproliferative neoplasms;
  101. Urokinase plasminogen activator (uPA) and plasminogen activator inhibitor 1 (PAI-1)Footnote2**to assess the need for adjuvant chemotherapy in women or men with newly diagnosed breast tumors, whereallof the following criteria are met:
    1. Breast cancer is not metastatic (negative node);y
    2. The breast tumor is estrogen receptor positive;y
    3. The breast tumor is negative for the HER2 receptor;y
    4. Adjuvant chemotherapy is not excluded due to any other factors (eg advanced age and/or significant comorbidities);y
    5. The member and the physician (prior to the test) discussed the possible test results and agreed to use the results to guide therapy;
    In addition, urokinase plasminogen activator (uPA) and plasminogen activator inhibitor-1 (PAI-1) are considered clinically necessary to determine prognosis in people with newly diagnosed node-negative breast cancer;
  102. vascular endothelial growth factor (VEGF) expression for Castleman disease;
  103. Veristrat proteomics test for patients with advanced NSCLC, whose tumors do not have EGFR mutations or anaplastic kinase (ALK) lymphoma, who have progressed after at least one chemotherapy regimen, and for whom erlotinib was considered an appropriate treatment;
  104. WT-1 gene expression for desmoplastic round cell tumors, clear cell ovarian carcinomas, non-small cell lung cancer and occult primary;
  105. ZAP-70, to assess prognosis and need for aggressive therapy in people with chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL);
  106. ZRSR2 (CCCH type zinc finger, RNA binding motif and rich in serine/arginine 2) for chronic myeloid leukemia (chronic phase, adult) or myelodysplastic syndromes.
2. Aetna considers somatic genomic testing for Janus Kinase 2 (JAK2) mutations in people with chronic myeloproliferative disorders (CMPD) to be clinically necessary for the following indications:
  1. Qualitative evaluation of the JAK2-V617F sequence variant using methods with detection thresholds of up to 5% for the initial diagnostic evaluation of adult patients with symptoms of CMPD;
  2. Diagnostic evaluation of polycythemia vera in adults;y
  3. Differential diagnosis of essential thrombocytosis and primary myelofibrosis from reactive conditions in adults.
  Somatic genomic testing for Janus Kinase 2 (JAK2) mutations in people with chronic myeloproliferative disorders (CMPD) is considered experimental and investigational by Aetna for all other indications, including:
  1. Diagnostic evaluation of myeloproliferative disorders in children;
  2. Quantitative assessment of JAK2-V617F allele burden subsequent to qualitative JAK2-V617F detection.
3. Aetna considers the use of fluorescence immunocytology (eg, ImmunoCyt/uCyt) to be medically necessary as an adjunct to cystoscopy or cytology in the treatment of individuals with bladder cancer.
  Aetna considers the ImmunoCyte/uCyt immunohistochemistry test to be experimental and investigational in the evaluation of hematuria, the diagnosis of bladder cancer, or the detection of bladder cancer in asymptomatic individuals.
4. Aetna considers urinary biomarkers (e.g. Bladder Tumor Antigen (BTA) (e.g. BTA Stat and BTA TRAK), Nuclear Matrix Protein Test (NMP22), Fibrin/Fibrinogen Degradation Products (Aura -Tek FDfP ) or fluorescent in situ hybridization (FISH) (eg, Pathnostics Bladder FISH test, UroVysion Medically Necessary Bladder Cancer test inanyof the following conditions:
  1. Accompanying the treatment of bladder cancer;o
  2. Surveillance for eradication of bladder cancer;o
  3. Recurrences after eradication.
  The BTA Stat test, the NMP22 test, the Aura-Tek FDP test, or the UroVysion fluorescent in situ hybridization (FISH) test are considered experimental and investigational by Aetna for bladder cancer screening, hematuria evaluation, and bladder cancer diagnosis in symptomatic people. and all other indications.
footnote1* Standard node dissection negative by haematoxylin and eosin (H&E) staining or sentinel node negative by H&E staining (if the sentinel node is negative by haematoxylin and eosin (H&E) but the immunoassay is positive, then the node is still considered negative for this film). Also, women with isolated tumor cells in lymph nodes (micrometastases) are considered lymph node negative.
More than one Oncotype Dx test may be clinically necessary for people with breast cancer who have two or more histologically distinct tumors that meet medical necessity criteria. Repeating the Oncotype Dx test or testing multiple tumor sites in the same person has no proven value for other indications. Oncotype Dx is considered experimental and experimental for ductal carcinoma in situ (OncotypeDx DCIS), colon cancer (OncotypeDx Colon) and all indications other than breast cancer and prostate cancer.
footnote2**Aetna considers the use of more than one type of test to determine the need for adjuvant therapy in breast cancer (Oncotype Dx Breast, Breast Cancer Index, EndoPredict, PAM50, Mammaprint or uPA and PAI-1) experimental and investigational .
footnote3***Aetna considers retesting or using more than one type of test to assess the risk of prostate cancer progression (Oncotype Dx Prostate, Decipher, Prolaris, or ProMark) experimental and experimental.

Experimental and Research

Aetna considers each of the following experimental and investigative. The peer-reviewed medical literature does not support that these tests have the necessary sensitivity or specificity to define their clinical role:
1. 3D prediction ovarian doublet panel;
2. 3D Predictive Ovarian PARP Panel;
3. 4K score;
4. Xpression Atlas states;
5. AFP for diagnosis of trophoblastic tumors and oncologic indications other than those listed in Section I;
6. Loss of heterozygosity (LOH) assay on the long arm of chromosome 18 (18q) or colon cancer (DCC) deleted protein (18q-LOH/DCC) for colorectal cancer;
7. Augusta Hematology Optical Genome Mapping;
8. BBDRisk Dx;
9. Test Biodesix BDX-XL2, Nodify CDT, Nodify Lung or Nodify XL2 to distinguish benign from malignant lung nodules;
10. Biomarker Translation Test (BT) for Breast Cancer and Other Indications;
11. BioSpeciFx, including Comprehensive Tumor Profiling for any indication;
12. Teste BostonGene Tumor Portrait;
13. BRAF and EGFR for esophageal carcinoma;
14. Breast cancer gene expression ratio (HOXB13:IL17BR);
15. breast sentinel;
16. CA 125 for all other indications, including use as a screening test for colorectal cancer or ovarian cancer (except as noted in Section I) or for the differential diagnosis of limbs with symptoms of colonic disease;
17. CA 19-9 for all other indications not listed in Section I, including breast, colorectal, esophageal, gastroesophageal, hepatic, or uterine cancer; ovarian cyst, nasal cavity midline NUT carcinoma, prognostic prognosis or effect of treatment in people with bladder (urothelial) cancer, screening people with primary sclerosing cholangitis without signs or symptoms of cholangiocarcinoma; screening people with primary sclerosing cholangitis for the development of cholangiocarcinoma;
18. carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) (eg, benign diagnostic risk test) for atypical breast hyperplasia and to predict breast cancer risk;
19. Carcinoembryonic antigen cell adhesion molecule-7 (CEACAM-7) expression as a predictive marker of rectal cancer recurrence;
20. Caris Molecular Intelligence/Caris Target Now Molecular Profiling Test;
21. myPath Melanoma de Castle Biosciences (anteriormente Myriad myPath Melanoma);
22. CDH1 for ovarian cancer;
23. CDX2 as a prognostic biomarker for colon cancer;
24. CEA used for all other indications not listed in Section I, includinganyof the following:
  1. As a screening test for colorectal cancer;o
  2. As the sole determinant of treating a limb with colorectal cancer with adjuvant therapy or systemic therapy for suspected metastatic disease;o
  3. For the diagnosis of esophageal carcinoma;o
  4. For routine detection, diagnosis, staging or surveillance of gastric cancer;
25. Circulating cell-free nucleic acids in colorectal cancer;
26. Circulating tumor cell (CTC) assays (e.g., CELLSEARCH Circulating Multiple Myeloma Cell (CMMC) and CELLSEARCH HER2 Circulating Tumor Cell (CTC-HER2)) for all indications, including but not limited to metastatic breast, colorectal, melanoma cancer and prostate;
27. CK5, CK14, p63, and Racemase P504S tests for prostate cancer;
28. c-Met expression to predict prognosis in people with advanced NSCLC and colorectal cancer and other indications;
29. Cyfra21-1 (a fragment of cytokeratin 19), p53, squamous cell carcinoma antigen (SCC-Ag) and vascular endothelial growth factor C (VEGF-C) for the diagnosis of esophageal carcinoma;
30. Cofilin (CFL1) as a Prognostic and Drug Resistance Marker in Non-Small Cell Lung Cancer;
31. ColonSentry test for colorectal cancer screening;
32. ColoPrint, CIMP, LINE-1 hypomethylation and immune cells for colon cancer;
33. Colorectal Cancer DSA (Almac Diagnostics, Craigavon, UK);
34. ConfirmMDx for prostate cancer;
35. Cxbladder tests (eg, Cxbladder Triage) for bladder cancer;
36. cyclin D1 and FADD (Fas-associated protein with death domain) for head and neck squamous cell carcinoma;
37. Melanoma DAWN I;
38. DCIS recurrence score;
39. DCISionRT;
40. Unshuffle the bladder;
41. DecisionDx DiffDx-Melanoma (Castle Biosciences, Phoenix, AZ);
42. DecisionDx-Melanoma (Castle Biosciences, Phoenix, AZ);
43. DecisionDx-SCC (Castle Biosciences, Phoenix, AZ);
44. Des-gamma-carboxy prothrombin (DCP) (also known as "prothrombin produced by absence or antagonism of vitamin K II" [PIVKA II]) for diagnosing and monitoring hepatocellular carcinoma (HCC) and other indications;
45. DetermaRx;
46. EarlyCDT-Pulmonary test;
47. analysis of EGFR gene expression for transitional cell (urothelial) cancer;
48. EGFRVIII para glioblastoma multiforme;
49. EML4-ALK as a diagnostic tool for stage IV non-small cell lung cancer;
50. Envisia genomic classifier;
51. EpiSwitch CiRT (checkpoint inhibitor response test);
52. Excision repair cross-complement group 1 (ERCC1) protein for people with NSCLC, colon or gastric cancer being considered for treatment with platinum-based chemotherapy and other indications;
53. ExoDx Prostate/ExosomeDx Prostate (IntelliScore);
54. fibrin/fibrinogen degradation products (FDP) test (eg, DR-70 or Onko-Sure) for colorectal cancer;
55. FoundationOne, FoundationOneCDx and FoundationOne Heme (except when FoundationOne CDx is used as an add-on diagnostic test for somatic/tumor BRCA testing, seeCPB 0227 - BRCA Testing, Prophylactic Mastectomy and Prophylactic OophorectomyyCPB 0715 - Pharmacogenetic and pharmacodynamic tests);
56. galectin-3 for breast cancer, myelodysplastic syndrome, osteosarcoma, ovarian cancer, pancreatic cancer, and prostate cancer;
57. Hypermethylation of genes for prostate cancer;
58. GeneKey (GeneKey Corp., Boston, MA);
59. Ensayo GeneSearch Maternal Lymph Node (BLN);
60. glutathione-S-transferase P1 (GSTP1) for screening, detection and treatment of prostate cancer;
61. guanylyl cyclase c (GCC or GUCY2C) (eg, Previstage GCC Colorectal Cancer State Test) for colorectal cancer;
62. Guardant360 TissueSiguiente;
63. Teste HelioLiver;
64. HeproDx;
65. HER2 test for appendix cancer;
66. HERmark tests for breast cancer and other indications;
67. HMGB1 and RAGE in skin malignancies (eg, basal cell carcinoma, melanoma, and squamous cell carcinoma);
68. Human epididymal protein 4 (HE4) (e.g., Elecsys HE4 assay) for endometrial cancer, ovarian cancer, or evaluation of a pelvic mass, including assistance in determining referral for surgery to a gynecological oncologist or general surgery, and for other indications ;
69. IHC4 (eg NexCourse IHC4 by AQUA Technology) for breast cancer;
70. IMMray PanCan-d to detect pancreatic ductal adenocarcinoma;
71. Immunoassay using magnetic nanosensor for the diagnosis of lung cancer;
72. Immunoscore to estimate recurrence risk or determine adjuvant therapy in people with colon cancer;
73. Breast Cancer Profile Insight DX;
74. Insight tipo TNBC;
75. IsoPSA;
76. Ki67 for breast cancer;
77. Ki-67 in upper tract urinary carcinoma;
78. lectin-reactive alpha-fetoprotein (AFP-L3) for liver cancer;
79. Liquid biopsy (e.g., CancerIntercept, Colvera, GeneStrat, FoundationACT, FoundationOne Liquid, Guardant360, NeolabProstate) for any indication (except small panels (less than 50 genes) for non-small cell lung cancer), including but not limited to breast cancer, colorectal cancer, melanoma, ovarian cancer or prostate cancer (for EGFR liquid biopsy for non-small cell lung cancer (eg cobas EGFR Mutation Test v2) and PIK₃Teste CA (therascreen PIK₃CA RGQ PCR Kit) for breast cancer, seeCPB 0715 - Pharmacogenetic and pharmacodynamic tests);
80. long non-coding RNA in gallbladder cancer;
81. LungLB and LungLife AI;
82. Lymph2CX and Lymph3Cx Lymphoma Molecular Classification Assay to Distinguish Primary Mediastinal B-Cell Lymphoma (PMBCL) and Diffuse Large B-Cell Lymphoma (DLBCL);
83. Breast milk;
84. Proteomic profile based on mass spectrometry for indeterminate pulmonary nodules;
85. Targeted MatePair rearrangements (next-generation whole genome sequencing) for hematolymphoid neoplasia and solid organ neoplasia;
86. Mayo Clinic Laboratories Urinary Steroids Profile for the Treatment of Adrenal Malignancies;
87. Measurement of circulating tumor cells (eg, CellMax Life and FirstSightCRC) for detection of colorectal cancer;
88. Titer of antibodies against Merkel SmT oncoprotein;
89. Merkel virus VP1 capsid antibody;
90. MI cancer research;
91. Microarray-Based Gene Expression Profiling Tests Using the MyPRS Test for Multiple Myeloma;
92. Micro-ARN (miARN) miRview mets e miRview mets2 (Rosetta Genomics Laboratories, Filadélfia, PA; Rosetta Genomics Ltd., Rehovot, Israel);
93. Mi-Prostate Score (MiPS), a TMPRSS2:ERG (T2:ERG) gene fusion assay, post-DRE urinary PCA3 expression, and serum PSA (KLK3);
94. miR-31ahora;
95. miR sentinel prostate cancer test;
96. Molecular intelligence services, including MI Profile and MI Profile X (formerly Target Now Molecular Profiling Test, including Target Now Select and Target Now Comprehensive);
97. Molecular subtyping profiling (eg, BluePrint) for breast cancer;
98. mRNA gene expression profiling for cutaneous melanoma;
99. MSK-IMPACT;
100. MUC1 in gastric cancer;
101. Mucin 4 expression as a predictor of survival in colorectal cancer;
102. Mucin 5AC (MUC5AC) as a serum marker for bile duct cancer;
103. My Prognostic Risk Signature (MyPRS) (Signal Genetics LLC, Nueva York, NY);
104. MyAML Next Generation Sequencing Panel;
105. NantHealth GPS Cancer Panels;
106. Natera Signatera Molecular Monitoring (MRD) for breast cancer;
107. NavDx for cancer recurrence surveillance in HPV-associated oropharyngeal cancer;
108. NeoLAB Prostate Liquid Biopsy;
109. network test;
110. NF1, RET and SDHB for ovarian cancer;
111. NRAS mutation to select people with metastatic colorectal cancer who may benefit from the anti-VEGF antibody bevacizumab; predict disease prognosis and select persons with melanoma who may benefit from tyrosine kinase inhibitor therapies and other indications;
112. OmniSeq Advanced DNA and RNA Sequencing (OmniSeq and LabCorp);
113. OncInsights (Intervention Perspectives, Grand Rapids, MI);
114. Oncomap ExTra (formerly known as Oncotype MAP);
115. directed proteomic assay OncoOmicDx;
116. OncoSignal test for analysis of solid tumors;
117. OncoTarget/OncoTreat;
118. Oncotype MAP PanCancer Tissue Test;
119. Onco Advantage;
120. OVA1/Over tests;
121. OvaCheck tests;
122. OvaSure;
123. PancreaSeq genomic classifier;
124. PanGIA Prostate to determine whether an individual should undergo a prostate biopsy;
125. Pathwork Origin Tissue Test/ResponseDx Origin Tissue Test;
126. Percepta bronchial genomic classifier;
127. PGDx whole tissue elio (Personal Genome Diagnostics, Inc.) for tumor mutation profiling;
128. Phosphatidylinositol-4,5-bisphosphonate 3-kinase, polypeptide catalytic subunit alpha (PIK3CA) gene to predict disease prognosis and select individuals with metastatic colorectal cancer being considered for treatment with EGFR antagonists cetuximab and panitumumab and indications other than cancer breast and uterine sarcoma;
129. PLCG2 (phospholipase C gamma 2) for all indications other than chronic lymphocytic leukemia (CLL);
130. Praxis Somatic Combination Whole genome sequencing and optical genome mapping;
131. Optical Mapping of the Praxis Somatic Genome;
132. Praxis somatic transcriptome;
133. Praxis Whole Genome Somatic Sequencing;
134. PreciseDx Breast Cancer Test;
135. PreOvar test for the KRAS variant to determine ovarian cancer risk;
136. ProOnc TumorSourceDx test (Prometheus Laboratories, San Diego, CA) to identify tissue or source of metastatic tumor;
137. prostatic nucleus mitotic test;
138. Prostate Px and Post-Op Px to Predict Prostate Cancer Recurrence;
139. Prostate Cancer Risk Panel (Mayo Clinic FISH analysis);
140. Proveri Prostate Cancer Test (PPCA);
141. PSA for screening women for breast cancer or for differentiating benign from malignant breast masses;
142. PTEN gene expression for non-small cell lung cancer;
143. test RadTox cfDNA;
144. Ras oncogenes (except KRAS, NRAS and BRAF);
145. Answer Dx Two points;
146. Ribonucleotide reductase M1 subunit (RRM1) for people with NSCLC considered for treatment with gemcitabine-based chemotherapy and other indications;
147. ROMA (Ovarian Malignancy Risk Algorithm) for ovarian cancer;
148. panel of 76 Rotterdam Signature genes;
149. Salivary metatranscriptome analysis for oral cancer (ie CancerDetect mRNA);
150. SelectMDx for prostate cancer;
151. Prostate Sentinel Test for detection of prostate cancer and determination of the level of risk of the disease;
152. Serum amyloid A as a biomarker for endometrial endometrial carcinoma to monitor disease recurrence and objective response to therapy;
153. Signatera for people with stage II/III colorectal cancer who are considering adjuvant chemotherapy and/or who are being monitored for relapse after treatment;
154. TargetPrint gene expression test for assessing estrogen receptor, progesterone receptor, and HER2 receptor status in breast cancer;
155. The 41-gene signature assay;
156. Theros CancerType ID (bioTheranostics Inc., San Diego, CA);
157. thymidylate synthase;
158. Px Thyroid Guide;
159. TMPRSS fusion genes for prostate cancer;
160. topographic genotyping (Pancragen (formerly PathFinderTG));
161. Total (complete) sequencing of genes for cancer;
162. TP53 mutation analysis for ovarian cancer;
163. UroCor cytology panels (DD23 and P53) for bladder cancer;
164. vascular endothelial growth factor (VEGF), except for Castleman disease;
165. Vascular endothelial growth factor receptor 2 (VEGFR2) expression to identify persons with colorectal cancer likely to respond to VEGF inhibition and other indications;
166. Whole exome sequencing (somatic mutations) (eg, EXaCT-1Whole Exome Testing) for cancer;

Any of the following circulating tumor markers are also considered experimental and investigational for cancer screening in asymptomatic individuals, diagnosis, staging, routine cancer surveillance, and monitoring response to treatment:

a2-PAG	CA-SCC	MAM-6	LABELTA12
AMACR	Catepsina-D, Catepsina-L	Motility Related Protein (MRP)	etiqueta72
	Cyclin E (fragments or full length)	Multidrug resistance glycoprotein (Mdr1)	RÓTULOTA72.3
BCM	TU-PAN-2		RÓTULOTA72.5
CA195	Prostate cancer early antigen (EPCA)	NSE	TATI
CA242	Guanylyl cyclase C (Previstage GCC molecular test)		Trombospondina-1 (THBS-1)
CA50	of everything	PCA3 (DD3) / UpM3	thymosin B15
CA549	Human kallikrein 2 (HK2)	SUN/ELA	TNF-a
CA72-4	ABSENT	Prostate stem cell antigen (PSCA)	Topoisomerasa II Alfa (TOP2A)
CAM17-1	LPA	SCC	TPA
CAM26	M 26	SLEX	thymosin B15
CAM29	M 29	SPAN-1	Nuclear matrix protein 66 (NMP66)
COCHE-3	MSA	SLX	Anti-malignant antibody screening test (AMAS)
VOLUME 21-1	ACM	ST-439

Related Policies

CPB 0140 - Genetic test
CPB 0227 - BRCA Testing, Prophylactic Mastectomy and Prophylactic Oophorectomy
CPB 0245 - Tumor chemosensitivity assays
CPB 0313 - Trastuzumabe (Herceptin e biossimilares), Trastuzumabe e Hyaluronidase-oysk (Herceptin Hylecta)
CEC 0314 - Rituximabe
CPB 0319 - Testing the proto-oncogenes RET
CPB 0521 - Detection of prostate cancer
CPB 0715 - Pharmacogenetic and pharmacodynamic tests
CPB 0758 - Tumor chemoresistance assays

Mesa:
CPT Codes / HCPCS Codes / ICD-10 Codes
Code	code description
*Prostate Specific Antigen (PSA)*:
Covered CPT codes if selection criteria are met:
84152	prostate specific antigen (PSA); complexed (direct measurement)
84153	total
84154	book
CPT codes not covered for indications listed in the CPB:
81313	PCA3/KLK3 ratio (prostate cancer antigen 3 [non-coding protein]/kallikrein-related peptidase 3 [prostate-specific antigen]) (eg, prostate cancer)
HCPCS codes covered if selection criteria are met:
G0103	Prostate cancer screening; prostate specific antigen (PSA) test
ICD-10 codes covered if selection criteria are met:
C61	malignant neoplasm of prostate
D07.5	Carcinoma in situ from prostate
D40.0	Neoplasm of uncertain behavior of the prostate
R97,20 - R97,21	Elevated prostate specific antigen [PSA]
Z12.5	Prostate Malignant Neoplasia Screening Meeting
Z85.46	Personal history of malignant neoplasm of the prostate
ICD-10 codes not covered for indications listed in the CPB:
C50.011 - C50.929	Mama malignant neoplasm
D05.00 - D05.92	Carcinoma in situ of breast
D24.1 - D24.9	benign mama neoplasm
D48.60 - D48.62	Breast neoplasm of uncertain behavior
D49.3	Neoplasm of unspecified behavior of the breast
Z12.39	Screening meeting for other breast malignancies
*carcinoembryonic antigen (CEA)*:
Covered CPT codes if selection criteria are met:
82378	carcinoembryonic antigen (CEA)
ICD-10 codes covered if selection criteria are met:
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
C22.1	Intrahepatic bile duct carcinoma [cholangiocarcinoma]
C23 - C24.9	Malignant neoplasm of gallbladder and other unspecified parts of biliary tract
C25.0 - C25.9	malignant neoplasm of the pancreas
C34.00 - C34.92	Malignant neoplasm of the bronchi and lungs
C50.011 - C50.929	Mama malignant neoplasm
C56.1 - C56.9	malignant neoplasm of ovary
C73	Malignant neoplasm of the thyroid gland [medullary thyroid cancer]
C80.0 - C80.1	Disseminated neoplasia and other malignancies, unspecified
D01.0	Colon carcinoma in situ
D01.5	Carcinoma in situ of the liver, gallbladder and bile ducts
D02.20 - D02.22	Bronchial and lung carcinoma in situ
D05.00 - D05.92	Carcinoma in situ of breast
D07.39	Carcinoma in situ of other female genital organs [ovarian]
D09.3	Carcinoma in situ of the thyroid and other endocrine glands
D13.4	Benign neoplasm of liver [intrahepatic bile ducts]
D13.6	benign neoplasm of pancreas
D13.7	Benign neoplasm of the endocrine pancreas [benign neoplasm of the islets of Langerhans]
D24.1 0 D24.9	benign mama neoplasm
D27.0 - D27.9	Benign ovarian neoplasm
D34	benign thyroid neoplasm
K86.2 - K86.3	Cyst and pseudocyst of the pancreas
R17	Carcinoma in situ of other female genital organs [ovarian]
R93.2	Abnormal imaging findings of the liver and bile ducts
R94,5	Abnormal results of liver function studies
Z85.030 - Z85.048	Personal history of malignancy of the large intestine, rectum, rectosigmoid junction, and anus
ICD-10 codes not covered for indications listed in the CPB:
C15.3 - C15.9	Malignant neoplasm of esophagus
D48.60 - D48.62	Breast neoplasm of uncertain behavior
D49.3	Neoplasm of unspecified behavior of the breast
Z12.2	Screening Meeting for Malignant Neoplasms of the Respiratory Organs
Z12.11 - Z12.12	Meeting for screening of malignant neoplasm of the colon and rectum
Z12.39	Screening meeting for other breast malignancies
*CDH1 and TP53*:
CPT codes not covered for indications listed in the CPB:
CDH1 and TP53- no specific code:
ICD-10 codes not covered for indications listed in the CPB:
C56.1 - C56.9	malignant neoplasm of ovary
*Adenomatous polyposis coli (APC)*:
Covered CPT codes if selection criteria are met:
81201 - 81203	Adenomatous polyposis coli (APC) gene analysis (eg, familial adenomatous polyposis [FAP], attenuated FAP)
ICD-10 covered if selection criteria are met:
D12.0 - D12.9	benign neoplasm of the colon
D48.1	Neoplasm of uncertain behavior of connective tissue and other soft tissues [desmoid fibromatosis]
Z83.71	Family history of colonic polyps
*Analysis confirms thyroid FNAB*:
Covered CPT codes if selection criteria are met:
0208U	Oncology (medullary thyroid carcinoma), mRNA, gene expression analysis of 108 genes, using fine needle aspiration, algorithm reported positive or negative for medullary thyroid carcinoma
ICD-10 codes covered if selection criteria are met:
D44.0	Neoplasm of uncertain behavior of the thyroid gland [indeterminate thyroid nodules] [not covered for repeat testing of indeterminate thyroid nodules]
E04. 0 - E04.9	Other non-toxic goiter
*Androgen receptor 7 splicing variant (AR-V7)*:
Covered CPT codes if selection criteria are met:
Androgen receptor 7 splicing variant (AR-V7)- no specific code:
ICD-10 codes covered if selection criteria are met:
C61	malignant neoplasm of prostate
*BCL2 e BCL6* :
CPT codes covered when selection criteria are met:
BCL6- no specific code:
81278	IGH@/BCL2 (t(14;18)) translocation analysis (eg, follicular lymphoma), major breakpoint region (MBR) and minor clustering region (mcr) breakpoints, qualitative or quantitative
ICD-10 codes covered if selection criteria are met:
C82.00 - C88.9	Follicular lymphoma, non-follicular lymphoma, mature NK/T cell lymphomas, other specified and unspecified types of non-follicular lymphoma, other specified types of NK/T cell lymphoma and immunoproliferative malignancies, and certain other B-cell lymphomas [non-lymphomas follicular]. Hodgkins]
D47. Z2	Castleman's disease
Z12.89	Encounter for detection of malignancy from other sites [for diagnosis of non-Hodgkin lymphoma and Castleman disease]
*Ensaio FISH do gene BCR/ABL*:
Covered CPT codes if selection criteria are met:
0040U	BCR/ABL1 (t(9;22)) (eg chronic myelogenous leukemia) translocation analysis, main hotspot, quantitative
81206 - 81208	BCR/ABL1 (t(9;22)) translocation analysis (eg chronic myelogenous leukemia)
88271	molecular cytogenetics; DNA probe, each (e.g. FISH)
88275	interphase in situ hybridization, analyze 100-300 cells
ICD-10 codes covered if selection criteria are met:
C83.50 - C83.59	Lymphoblastic lymphoma (diffuse)
C91.00 - C91.02	acute myeloblastic leukemia
C91.10 - C91.12	B-cell type chronic lymphocytic leukemia
C92.00 - C92.12	myelogenous leukemia
C92.20 - C92.62	Atypical chronic myeloid leukemia BCR/ABL: negative, myeloid sarcoma, acute promyelocytic leukemia, acute myelomonocytic leukemia and acute myeloid leukemia with 11q23 anomaly
C92.A0 - C92.A2	Acute myeloid leukemia with multilineage dysplasia
C92.Z0 - C92.Z2	Other myeloid leukemia
C94.40 - C94.42	Acute panmyelosis with myelofibrosis
D45	polycythemia vera
D47.1	chronic myeloproliferative disease
D47.4	osteomielofibrose
D69.3	Immune Thrombocytopenic Purpura
D75.81	mielofibrose
*Cancer antigen 125 (CA 125)*:
Covered CPT codes if selection criteria are met:
86304	immunoassay for tumor antigen, quantitative; CA 125
ICD-10 codes covered if selection criteria are met:
C56.1 - C56.9	malignant neoplasm of ovary
D39.10 - D39.12	Ovarian neoplasm of uncertain behavior
Z12.73	Ovarian Malignancy Screening Meeting
Z80.41	Family history of ovarian neoplasia
ICD-10 codes not covered for indications listed in the CPB:
Z12.11 - Z12.12	Meeting for screening of malignant neoplasm of the colon and rectum
Z85.43	Personal history of ovarian neoplasia
*CA 15-3 serial measurements (also known as CA 27-29 or Truquant RIA)*:
Covered CPT codes if selection criteria are met:
86300	immunoassay for tumor antigen, quantitative; AC 15-3 (27.29)
ICD-10 codes covered if selection criteria are met:
C50.011 - C50.019 C50.111 - C50.119 C50.211 - C50.219 C50.311 - C50.319 C50.411 - C50.419 C50.511 - C50.519 C50.611 - C50.619 C50.811 - C50.819 C50.911 - C50.919	Malignant neoplasm of the female breast
D05.00 - D05.92	Carcinoma in situ of breast
Z85.3	Personal history of breast malignancy
ICD-10 codes not covered for indications listed in the CPB:
Z12.31 - Z12.39	Breast Malignancy Screening Meeting
*CA 19-9*:
Covered CPT codes if selection criteria are met:
86301	immunoassay for tumor antigen, quantitative; AC 19-9
ICD-10 codes covered if selection criteria are met:
C16.0 - C16.9	malignant neoplasm of the stomach
C17.0 - C17.9	Malignant neoplasm of small intestine [adenocarcinoma of small intestine]
C18.1	Malignant neoplasm of the appendix [mucinous carcinoma of the appendix]
C22.1	Intrahepatic bile duct carcinoma [cholangiocarcinoma]
C23 - C24.9	Malignant neoplasm of gallbladder and other unspecified parts of biliary tract
C25.0 - C25.9	malignant neoplasm of the pancreas
C30.0	Malignant neoplasm of nasal cavity [NUT midline carcinoma]
C56.1 - C56.9	malignant neoplasm of ovary
D00.2	stomach carcinoma in situ
D01.5	Carcinoma in situ of the liver, gallbladder, and bile ducts [covered by gallbladder and bile ducts]
D01.7 - D01.9	Carcinoma in situ of other and unspecified digestive organs
R17	unspecified jaundice
R93.2	Abnormal imaging findings of the liver and bile ducts
R94,5	Abnormal results of liver function studies
Z76.82	Awaiting organ transplant status
Z85.028	Personal history of other stomach malignancy
Z85.07 - Z85.09	Personal history of malignancy of the pancreas and other digestive organs
ICD-10 codes not covered for CPB-listed indications (not all included):
C15.3 - C15.9	Malignant neoplasm of esophagus
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
C22.0, C22.2 - C22.9	malignant neoplasm of the liver
C50.011 - C50.929	Mama malignant neoplasm
C53.0 - C55, C58	malignant neoplasm of uterus
C67.0 - C67.9	bladder malignancy
D01.0	Colon carcinoma in situ
D01.5	Carcinoma in situ of liver, gallbladder and bile ducts [not covered for liver]
D05.00 - D05.92	Carcinoma in situ of breast
N83.00 - N83.299	ovarian cysts
Cardioembryonic antigen cellular adhesion molecule-7 (CEACAM-7)- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C19 - C21.8	Malignant neoplasm of rectum, rectosigmoid junction and anus
D01.1 - D01.2	Carcinoma in situ of rectosigmoid junction and rectum
Z85.048	Personal history of another malignancy of the rectum, rectosigmoid junction, and anus
Molecular intelligence services, including MI Profile and MI Profile PLUS (formerly Target Now Molecular Profiling Test, including Target Now Select and Target Now Comprehensive)- No specific code:
Cyfra21-1 (a fragment of cytokeratin 19) p53 and squamous cell carcinoma antigen (SCC-Ag)- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C15.3 - C15.9	Malignant neoplasm of esophagus
*Vascular endothelial growth factor (VEGF)* :
Covered CPT codes if selection criteria are met:
VEGF-No specific code:
ICD-10 codes covered if selection criteria are met:
D47. Z2	Castleman's disease
ICD-10 codes not covered for indications listed in the CPB:
C15.3 - C15.9	Malignant neoplasm of esophagus
*Evaluation of Human Epidermal Growth Factor Receptor 2 (HER2)*:
Covered CPT codes if selection criteria are met:
83950	oncoprotein; Her-2/neu
ICD-10 codes covered if selection criteria are met:
C15.3 - C15.9	Malignant neoplasm of esophagus
C16.0 - C16.9	malignant neoplasm of the stomach
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell]
C50.011 - C50.929	Malignant neoplasm of the breast [see criteria]
ICD-10 codes not covered for CPB-listed indications (not all included):
C18.1	Malignant neoplasm of the appendix
*IGH™ (immunoglobulin heavy chain locus)*:
Covered CPT codes if selection criteria are met:
81168	CCND1/IGH (t(11;14)) translocation analysis (eg, mantle cell lymphoma), primary hotspot, qualitative and quantitative, if performed
81261	IGH™ (immunoglobulin heavy chain locus) (eg, leukemias and lymphomas, B cells), gene reassortment analysis to detect abnormal clonal populations; amplified methodology (eg polymerase chain reaction)
81278	IGH@/BCL2 (t(14;18)) translocation analysis (eg, follicular lymphoma), major breakpoint region (MBR) and minor clustering region (mcr) breakpoints, qualitative or quantitative
ICD-10 codes covered if selection criteria are met:
C85.10 - C85.99	Other specified and unspecified types of non-Hodgkin's lymphoma
C91.40 - C91.42	hairy cell leukemia
D47. z1	Post-transplant lymphoproliferative disorder (PTLD)
E85.9	Amyloidosis, unspecified [systemic light chain]
*IGK@ (kappa immunoglobulin light chain locus)*:
Covered CPT codes if selection criteria are met:
81264	IGK@ (immunoglobulin kappa light chain locus) (eg, leukemia and lymphoma, B cells), gene reassortment analysis, evaluation of abnormal clonal populations
83521	Immunoglobulin light chains (i.e., kappa, lambda), free, each
ICD-10 codes covered if selection criteria are met:
C85.10 - C85.99	Other specified and unspecified types of non-Hodgkin's lymphoma
C91.40 - C91.42	hairy cell leukemia
E85.9	Amyloidosis, unspecified [systemic light chain]
*Serial measurements of human chorionic gonadotropin (HCG)*:
Covered CPT codes if selection criteria are met:
84702	chorionic gonadotropin (hCG); quantitative
ICD-10 codes covered if selection criteria are met:
C56.1 - C56.9	malignant neoplasm of ovary
C58	Malignant neoplasm of the placenta (eg, choriocarcinoma)
C62.00 - C62.92	testicular malignant neoplasm
C77.1	Secondary malignant neoplasm of intrathoracic lymph nodes [mediastinal nodes]
D07.30 - D07.39	Carcinoma in situ of other and unspecified female genital organs [germ cell tumors (teratocarcinoma and embryonic cell carcinoma) of ovaries] [tumors (teratocarcinoma and embryonic cell carcinoma) of ovaries]
D07.60 - D07.69	Carcinoma in situ of other and unspecified male genital organs
D39.2	Neoplasm of uncertain behavior from placenta
O01.9	Hydatidiform mole, unspecified
Z85.43	Personal history of ovarian neoplasia
Z85.47	Personal history of testicular malignancy
*Serial AFP measurements for diagnosis of germ cell tumors or diagnosis and follow-up of hepatocellular carcinoma*:
Covered CPT codes if selection criteria are met:
82105	alpha-fetoprotein (AFP); sweat
ICD-10 codes covered if selection criteria are met:
B17.10 - B17.11	Acute hepatitis C with or without hepatic coma
B18.2	Chronic viral hepatitis C
B19.20 - B19.21	Unspecified viral hepatitis C with or without hepatic coma
C22.0 - C22.9	Malignant neoplasm of liver and intrahepatic bile ducts
C37	Malignant neoplasm of thymus
C56.1 - C56.9	malignant neoplasm of ovary
C62.00 - C62.92	Malignant neoplasm of testicles
C77.1	Secondary malignant neoplasm of intrathoracic lymph nodes [mediastinal nodes]
D01.5	Carcinoma in situ of the liver, gallbladder and bile ducts
D07.30 - D07.39	Carcinoma in situ of other and unspecified female genital organs [germ cell tumors]
D07.60 - D07.69	Carcinoma in situ of other and unspecified male genital organs
D15.0	Benign thymic neoplasm
E83.110	hereditary hemochromatosis
E88.01	alpha-1-antitrypsin deficiency
F10.10 - F10.99	alcohol related disorders
K70.30 - K70.31	Alcoholic liver cirrhosis without or with ascites
K74.3	Brackets for Primary Biliary Cirrhosis [Stage 4 Primary Biliary Cirrhosis]
K74.60 - K74.69	Liver cirrhosis, unspecified or other
K75.81	Non-alcoholic steatohepatitis (NASH)
N50.8	Other specified disorders of male genital organs [testicular mass]
R19,00	Intra-abdominal and pelvic swelling, mass, nodule, site unspecified
R19,07 - R19,09	Generalized intra-abdominal and pelvic swelling, mass and nodule and others
R22.2	Localized swelling, mass and nodule, trunk
Z12.89	Meeting for malignancy screening from other sites
Z22.51	Hepatitis B viral carrier
Z80.0	Family history of malignant neoplasm of digestive organs [family history of hepatocellular carcinoma]
Z85.43	Personal history of ovarian neoplasia
Z85.47	Personal history of testicular malignancy
ICD-10 codes not covered for CPB-listed indications (not all included):
C58	Malignant neoplasm of the placenta (eg, choriocarcinoma)
D39.2	Neoplasm of uncertain behavior from placenta
O01.9	Hydatidiform mole, unspecified
*Serial measurements of AFP and HCG together to diagnose and monitor testicular cancer*:
Covered CPT codes if selection criteria are met:
82105	alpha-fetoprotein (AFP); sweat
84702	chorionic gonadotropin (hCG); quantitative
ICD-10 codes covered if selection criteria are met:
C62.00 - C62.92	Malignant neoplasm of testicles
D07.60 - D07.69	Carcinoma in situ of other and unspecified male genital organs
Z12.71	Testicular malignancy screening appointment
*Measurement of estrogen and progesterone receptors and steroid receptor*:
Covered CPT codes if selection criteria are met:
84233	Receiver Assay; estrogen
ICD-10 codes covered if selection criteria are met:
C50.011 - C50.929	Mama malignant neoplasm
C53.0 - C55, C58	Malignant neoplasm of uterus [sarcoma]
C56.1 - C56.9	malignant neoplasm of ovary
C80.1	Malignant neoplasm (primary), unspecified [primary occult]
D05.00 - D05.92	Carcinoma in situ of breast
Measurement of progesterone receptors:
Covered CPT codes if selection criteria are met:
84234	Receiver Assay; progesterone
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell lung cancer]
C50.011 - C50.929	Mama malignant neoplasm
C53.0 - C55, C58	Malignant neoplasm of uterus [sarcoma]
C80.1	Malignant neoplasm (primary), unspecified [primary occult]
D05.00 - D05.92	Carcinoma in situ of breast
*Targeted hematologic genomic sequencing panel (5-50 genes) for myelodysplastic syndromes (eg, MedFusion Myeloid Malignancy Analysis Panel)*:
Covered CPT codes if selection criteria are met:
81450	Target genomic sequence analysis panel, neoplasia or hematolymphoid disorder, DNA analysis and RNA analysis when performed, 5-50 genes (e.g., BRAF, CEBPA, DNMT3A, EZH2, FLT3, IDH1, IDH2, JAK2, KRAS, KIT , MLL, NRAS, NPM1, NOTCH1), interrogation of sequence variants and copy number variants or rearrangements, or isoform expression or mRNA expression levels, if performed
81451	Targeted genomic sequence analysis panel, neoplasm or hematolymphoid disorder, 5-50 genes (e.g., BRAF, CEBPA, DNMT3A, EZH2, FLT3, IDH1, IDH2, JAK2, KIT, KRAS, MLL, NOTCH1, NPM1, NRAS), interrogation of sequence variants and copy number, variants or rearrangements, or expression of isoforms or mRNA expression levels, if performed; RNA analysis
ICD-10 codes covered if selection criteria are met:
D45	polycythemia vera
D46.0 - D46.9	Myelodysplastic syndromes
D47.1	chronic myeloproliferative disease
D47.3	Essential (hemorrhagic) thrombocythemia
*Specific solid organ genomic sequencing panel (5-50 genes)*:
Covered CPT codes if selection criteria are met:
81445	Targeted genomic sequence analysis panel, solid organ neoplasia, DNA analysis and RNA analysis when performed, 5-50 genes (e.g. ALK, BRAF, CDKN2A, EGFR, ERBB2, KIT, KRAS, NRAS, MET, PDGFRA , PDGFRB, PGR, PIK3CA , PTEN, RET), interrogation of sequence variants and copy number variants or rearrangements, if performed
81449	Target genomic sequence analysis panel, solid organ neoplasia, 5-50 genes (eg, ALK, BRAF, CDKN2A, EGFR, ERBB2, KIT, KRAS, MET, NRAS, PDGFRA, PDGFRB, PGR, PIK3CA, PTEN, RET ), interrogation for sequence variants and copy number variants or rearrangements, if performed; RNA analysis
ICD-10 codes covered if selection criteria are met:
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
C25.0 - C25.9	malignant neoplasm of the pancreas
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell]
C43.0 - C43.9	cutaneous melanoma
C61	malignant neoplasm of prostate
*Oncomine™ Dx objective test*:
Covered CPT codes if selection criteria are met:
0022U	Targeted genomic sequence analysis panel, non-small cell lung cancer, DNA and RNA analysis, 23 genes, interrogation of variants and sequence rearrangements, reported as presence/absence of variants and associated therapy(s) to consider [Oncomine™ Dx Target Test]
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell]
*T cell receptor gene rearrangements*:
Covered CPT codes if selection criteria are met:
81340	TRB™ (T cell receptor antigen, beta) (eg, leukemia and lymphoma), gene reassortment analysis to detect abnormal clonal populations; using amplification methodology (e.g. polymerase chain reaction)
81341	using direct probe methodology (e.g. Southern blot)
81342	TRG@ (T cell receptor antigen, gamma) (eg, leukemia and lymphoma), genetic reassortment analysis, evaluation of abnormal clonal populations
ICD-10 codes covered if selection criteria are met:
C84.00 - C84.09	mycosis fungoides
C84.10 - C84.19	sezary's disease
C84.40 - C84.49	Peripheral T-cell lymphoma, unclassified
C86.0	Extraganglionic T/NK cell lymphoma, nasal type
C86.1	Hepatosplenic T-cell lymphoma
C86.6	CD30-positive T-cell primary cutaneous lymphoproliferations
C91.60 - C91.62	T-cell type prolymphocytic leukemia
C91.Z0 - C91.Z2	Other lymphoid leukemia with information in parentheses [large lymphocytic granular T-cell]
D46.0 - D46.9	Myelodysplastic syndromes
D47. Z2	Castleman's disease
D47. Z9	Other specified neoplasms of uncertain or unknown behavior of lymphoid, hematopoietic and related tissues
*Thyroid oncogene panel ThyGenEXT and ThyraMIR*:
Covered CPT codes if selection criteria are met:
0018U	Oncology (thyroid), microRNA profiling by 10-sequence microRNA RT-PCR using fine needle aspiration, algorithm reported as positive or negative for moderate to high risk of malignancy
0245U	Oncology (thyroid), mutation analysis of 10 genes and 37 RNA fusions and expression of 4 mRNA markers by next generation sequencing, fine needle aspiration, report includes associated risk of malignancy expressed as a percentage
ICD-10 codes covered if selection criteria are met:
D44.0	Neoplasm of uncertain behavior of the thyroid gland [indeterminate thyroid nodules]
E04.0 - E04.9	Other non-toxic goiters [thyroid nodules]
*Thyroseq*:
Covered CPT codes if selection criteria are met:
0026U	Oncology (thyroid), DNA and mRNA of 112 genes, next generation sequencing, fine needle aspirate of thyroid nodule, algorithmic analysis reported as categorical result ("Positive, high probability of malignancy" or "Negative, low probability of malignancy")
0287U	Oncology (thyroid), DNA and mRNA, next-generation sequencing analysis of 112 genes, fine needle aspirate or formalin-fixed, paraffin-embedded tissue (FFPE), algorithmic prediction of cancer recurrence, reported as a categorical risk outcome ( low, intermediate, high)
ICD-10 codes covered if selection criteria are met:
D44.0	Neoplasm of uncertain behavior of the thyroid gland [indeterminate thyroid nodules]
E04.0 - E04.9	Other non-toxic goiter [thyroid nodules] [not covered for repeat testing of indeterminate thyroid nodules]
*TP53*:
Covered CPT codes if selection criteria are met:
81351	TP53 (tumor protein 53) gene analysis (eg, Li-Fraumeni syndrome); complete genetic sequence
81352	target sequence analysis (eg 4 oncology)
ICD-10 codes covered if selection criteria are met:
C45.0	mesotelioma pleural
C45.1	peritoneum mesothelioma
C53.0 - C55, C58	Malignant neoplasm of uterus [sarcoma]
C71.0 - C71.9	Malignant neoplasm of the brain [medulloblastoma]
C80.1	Malignant neoplasm (primary), unspecified [primary occult]
C7A.00 - C7A.8	Malignant neuroendocrine tumors
C83.00 - C83.09	Small B-cell lymphoma [splenic marginal zone lymphoma]
C83.10 - C83.19	mantle cell lymphoma
C84.40 - C84.49	Peripheral T-cell lymphoma, unclassified
C91.00 – C91.02	Acute lymphoblastic leukemia [ALL] [pediatric]
C91.10 - C91.12	B-cell type chronic lymphocytic leukemia
C92.20 - C92.22	Atypical chronic myelogenous leukemia, negative BCR/ABL
C92.60 - C92.62	Acute myelogenous leukemia with 11q23 abnormality
C92.A0 - C92.A2	Acute myeloid leukemia with multilineage dysplasia
C94.00 - C94.02	Acute erythroid leukemia [acute myelogenous leukemia]
C94.20 - C94.22	Acute megakaryoblastic leukemia [acute myeloid leukemia]
C94.6	Myelodysplastic disease, not elsewhere classified [myeloproliferative neoplasms]
D3A.00 - D3A.8	Benign neuroendocrine tumors
D45	Polycythemia vera [myeloproliferative neoplasms]
D46.0 - D46.Z	Myelodysplastic syndromes
D47.3	Essential (hemorrhagic) thrombocythemia [myeloproliferative neoplasms]
D75.81	Myelofibrose [myeloproliferative neoplasms]
test U2AF1:
Covered CPT codes if selection criteria are met:
81357	U2AF1 (U2 small nuclear RNA helper factor 1) gene analysis (eg, myelodysplastic syndrome, acute myeloid leukemia), common variants (eg, S34F, S34Y, Q157R, Q157P)
ICD-10 codes covered if selection criteria are met:
C86.4	blastic NK cell lymphoma
C92.10 – C92.12	Chronic myeloid leukemia, BCR/ABL-positive
C92.20 - C92.22	Atypical chronic myelogenous leukemia, negative BCR/ABL
C94.6	Myelodysplastic disease, not elsewhere classified [myeloproliferative neoplasms]
D45	Polycythemia vera [myeloproliferative neoplasms]
D46.0 – D46.Z	Myelodysplastic syndromes
D47.3	Essential (hemorrhagic) thrombocythemia [myeloproliferative neoplasms]
D75.81	Myelofibrose [myeloproliferative neoplasms]
*K-ras (KRAS) with BRAF reflex test*:
Covered CPT codes if selection criteria are met:
81210	BRAF (murine sarcoma B1 oncogene homologue v-raf) (eg, colon cancer), genetic analysis, variant V600E
81275	KRAS (Kirsten rat sarcoma viral oncogene) gene analysis (eg, carcinoma); variants in exon 2 (e.g. codons 12 and 13)
81276	KRAS (Kirsten rat sarcoma viral oncogene) gene analysis (eg, carcinoma); additional variant(s) (e.g. codon 61, codon 146)
Other CPT codes related to the CPB:
88363	Examination and selection of retrieved (i.e., previously diagnosed) archival tissues for molecular analysis (i.e., KRAS mutational analysis)
Other CPB related HCPCS codes:
J9055	Injection, cetuximab, 10 mg [to predict non-response to cetuximab (Erbitux) and panitumumab (Vectibix) in the treatment of anal adenocarcinoma]
J9303	Injection, panitumumab, 10 mg [to predict non-response to cetuximab (Erbitux) and panitumumab (Vectibix) in the treatment of anal adenocarcinoma]
ICD-10 codes covered if selection criteria are met:
C17.0 - C17.9	Malignant neoplasm of small intestine [adenocarcinoma of small intestine]
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum [metastatic colorectal cancer]
C21.0 - C21.1	Malignant Neoplasm of Anal Canal and Anus [Anal Adenocarcinoma]
C34.00 - C34.92	Malignant neoplasm of the bronchi and lungs
D01.1 - D01.2	Rectal carcinoma in situ [if KRAS wild type] [Lynch syndrome (HNPCC)]
D12.7 - D12.9	Benign neoplasm of rectum and anal canal [if KRAS wild type] [Lynch syndrome (HNPCC)]
D44.0	Neoplasm of uncertain behavior of the thyroid gland [indeterminate thyroid nodules]
E04.0 - E04.9	Other non-toxic goiter [thyroid nodules] [not covered for repeat testing of indeterminate thyroid nodules]
*Incompatibility Fix (MSI/dMMR, MLH1, MSH2, MSH6)*:
Covered CPT codes if selection criteria are met:
81292 - 81294	MLH1 gene analysis
81295 - 81297	MSH2 gene analysis
81298 - 81300	MSH6 gene analysis
81301	Analysis of microsatellite instability (eg, hereditary non-polyposis colorectal cancer, Lynch syndrome) of mismatch repair deficiency markers (eg, BAT25, BAT26), includes comparison of neoplastic and normal tissue, if done
ICD-10 codes covered if selection criteria are met:
C15.3 - C15.9	Malignant neoplasm of esophagus
C16.0 - C16.9	malignant neoplasm of the stomach
C17.0 - C17.9	Malignant neoplasm of small intestine [adenocarcinoma of small intestine]
C18.0 - C18.9	Colon malignant neoplasm [Lynch syndrome (HNPCC)] [all people with stage 2 colon cancer]
C19 - C21.8	Malignant neoplasm of rectum, rectosigmoid junction, and anus [Lynch syndrome (HNPCC)] [all people with stage 2 colon cancer] [less than 50 years old]
C23	Gallbladder malignant neoplasm
C25.0 - C25.9	malignant neoplasm of the pancreas
C41.0 – C41.9	Malignant neoplasm of bone and articular cartilage of other and unspecified locations [Ewing's sarcoma]
C50.011 - C50.929	Mama malignant neoplasm
C53.0 - C53.9	Malignant neoplasm of the cervix
C54.0 - C54.9	Malignant neoplasm of the body of the uterus
C60.0 - C60.9	malignant neoplasm of the penis
C61	malignant neoplasm of prostate
C62.0 - C62.92	testicular malignant neoplasm
C80.1	Malignant neoplasm (primary), unspecified [primary occult]
D01.1 - D01.2	Challenge carcinoma in situ [less than 50 years]
D12.7 - D12.9	Benign neoplasm of rectum and anal canal [under 50 years of age]
D46.0 - D46.9	Myelodysplastic syndromes
*Hypermethylation of the MLH1 tumor promoter*:
Covered CPT codes if selection criteria are met:
81288	MLH1 gene analysis (mutl homologous 1, colon cancer, non-polyposis type 2) (eg, hereditary non-polyposis colorectal cancer, Lynch syndrome); promoter methylation analysis
ICD-10 codes covered if selection criteria are met::
C54.1	endometrial malignancy
MPL (myeloproliferative leukemia protein):
Covered CPT codes if selection criteria are met:
81338	MPL gene analysis (MPL proto-oncogene, thrombopoietin receptor) (eg, myeloproliferative disorder); common variants (e.g. W515A, W515K, W515L, W515R)
81339	MPL gene analysis (MPL proto-oncogene, thrombopoietin receptor) (eg, myeloproliferative disorder); sequence analysis, exon 10
ICD-10 codes covered if selection criteria are met:
C92.10 – C92.12	Chronic myeloid leukemia, BCR/ABL-positive
C92.20 - C92.22	Atypical chronic myelogenous leukemia, negative BCR/ABL
C94.6	Myelodysplastic disease, not elsewhere classified [myeloproliferative neoplasms]
D45	polycythemia vera
D46.0 – D46.Z	Myelodysplastic syndromes
D47.3	Essential (hemorrhagic) thrombocythemia
D75.81	mielofibrose
*Dual mouse minute 2 (MDM2)*:
Covered CPT codes if selection criteria are met:
Dual mouse minute 2 (MDM2)– No specific code
ICD-10 codes covered if selection criteria are met:
C49.0 - C49.9	Malignant neoplasm of other connective and soft tissues [sarcoma]
C53.0 - C55, C58	Malignant neoplasm of uterus [sarcoma]
*MYD88*:
Covered CPT codes if selection criteria are met:
81305	Primary myeloid differentiation response 88 (MYD88) gene analysis (eg, Waldenstrom's macroglobulinemia, lymphoplasmacytic leukemia), p.Leu265Pro (L265P) variant
ICD-10 codes covered if selection criteria are met:
C83.00 - C83.09	Small B cell lymphoma
C88.0	Macroglobulinemia de Waldenström
C88.4	Extranodal B-cell lymphoma of the marginal zone of mucosa-associated lymphoid tissue [MALT lymphoma]
C90.00 - C90.02	multiple myeloma
*Panel NGS de MyMRD*:
Covered CPT codes if selection criteria are met:
0171U	Targeted Genomic Sequence Analysis Panel, Acute Myeloid Leukemia, Myelodysplastic Syndrome, and Myeloproliferative Neoplasms, DNA analysis, 23 genes, interrogation of sequence variants, rearrangements, and minimal residual disease, reported as presence/absence
ICD-10 codes covered if selection criteria are met:
C92.00 - C92.02 C92.40 - C92.A2	Aguda myeloid leukemia (AML)
D46.0 - D46.Z	Myelodysplastic syndromes
*Next generation sequencing of tumor DNA (e.g. ClonoSeq)*:
Covered CPT codes if selection criteria are met:
Next generation sequencing of tumor DNA (e.g. ClonoSeq)– no specific code
ICD-10 codes covered if selection criteria are met:
C90.00 - C90.02	multiple myeloma
C91.00 - C91.02	Acute lymphoblastic leukemia [ALL]
*MSK-IMPACT*:
CPT codes not covered for indications listed in the CPB:
0048U	Oncology (solid organ neoplasm), DNA, targeted sequencing of protein-coding exons from 468 cancer-associated genes, including interrogation for somatic mutations and microsatellite instability, paired with normal samples, using paraffin-embedded, formalin-fixed tumor tissue, report of clinical results significant mutations
MUC1- no specific code:
ICD-10 codes not covered for indications listed in the CPB:
C16.0 - C16.9	malignant neoplasm of the stomach
*ALK gene fusion*:
Covered CPT codes if selection criteria are met:
ALK gene fusion- no specific code
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell cancer]
*ALK gene rearrangement*:
Covered CPT codes if selection criteria are met:
ALK gene rearrangement- no specific code
ICD-10 codes covered if selection criteria are met:
C34.00 – C34.92	Malignant neoplasm of bronchus and lung [non-small cell]
C83.30 - C83.39	Diffuse large B-cell lymphoma
C84.40 - C84.49	Peripheral T-cell lymphoma, unclassified
D47. z1	Post-transplant lymphoproliferative disorder (PTLD)
*ALK*:
Covered CPT codes if selection criteria are met:
ALK expression- no specific code
Other CPT codes related to CPB:
81401	Molecular pathology procedure, level 2 (eg, 2 to 10 SNPs, 1 methylated variant, or 1 somatic variant [typically using target variant analysis without sequencing] or detection of a dynamic mutation/triplet repeat disorder)
ICD-10 codes covered if selection criteria are met:
C25.0 - C25.9	malignant neoplasm of the pancreas
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell lung cancer]
C53.0 - C55	Malignant neoplasm of cervix, uterine body and uterus, part unspecified
C81.00 – C81.99	Hodgkin's lymphoma [pediatric only]
C84.40 - C84.49	Peripheral T-cell lymphoma, unclassified
C84.60 – C84.69	Anaplastic large cell lymphoma, ALK positive [breast implant associated]
C84.70 – C84.79	Anaplastic large cell lymphoma, negative ALK [breast implant associated]
*Urokinase plasminogen activator (uPA) and plasminogen activator inhibitor 1 (PAI-1)*:
Covered CPT codes if selection criteria are met:
85415	fibrinolytic and inhibitory factors; plasminogen activator
ICD-10 codes covered if selection criteria are met:
C50.011 - C50.929	Malignant breast neoplasm [negative node]
D05.00 - D05.92	Carcinoma in situ of breast
a bloodbath- no specific code:
CPT codes not covered for indications listed in the CPB:
81538	Oncology (lung), mass spectrometry of 8 proteins, including amyloid A, using serum, prognosis and predictive algorithm reported as good versus poor overall survival
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [for people with advanced NSCLC, whose tumors do not have EGFR or ALK mutations, who have progressed after at least one chemotherapy regimen, and for whom erlotinib is considered an appropriate treatment]
*DC 117 (set c)*:
Covered CPT codes if selection criteria are met:
81272	KIT (Hardy-Zuckerman 4 homologous feline sarcoma viral oncogene v-kit) (e.g., gastrointestinal stromal tumor [GIST], acute myeloid leukemia, melanoma), gene analysis, target sequence analysis (e.g., exons 8, 11, 13, 17, 18)
81273	KIT (Hardy-Zuckerman v-Kit 4 feline sarcoma viral oncogene homologue) (eg, mastocytosis), genetic analysis, variant(s) D816
88184	Flow cytometry, cell surface, cytoplasmic or nuclear marker, technical component only; first bullet
+88185	each additional marker (list separately in addition to the first marker code)
ICD-10 codes covered if selection criteria are met:
[to determine eligibility for Gleevac treatment]
C15.3 - C15.9	Malignant neoplasm of esophagus
C43.0 - C43.9	cutaneous melanoma
C49.4	Malignant neoplasm of connective and soft tissue of abdomen [gastrointestinal stromal tumors]
C92.00 - C92.12	myelogenous leukemia
D47.02	systemic mastocytosis
*disco 20*:
Covered CPT codes if selection criteria are met:
88184	Flow cytometry, cell surface, cytoplasmic or nuclear marker, technical component only; first bullet
+88185	each additional marker (list separately in addition to the first marker code)
ICD-10 codes covered if selection criteria are met:
[to determine eligibility for Rituxan treatment]
C81.00 - C86.6 C88.4 C91.10 - C91.12 C91.40 - C91.42 C96.0 - C96.4 C96.a - C96.9	Lymphoid, hematopoietic and related tissue malignancies
*disco 25*:
Covered CPT codes if selection criteria are met:
88184	Flow cytometry, cell surface, cytoplasmic or nuclear marker, technical component only; first bullet
+88185	each additional marker (list separately in addition to the first marker code)
ICD-10 codes covered if selection criteria are met:
[to determine eligibility for Ontak treatment]
C84.00 - C84.49	Mycosis fungoides, Sezary's disease, and peripheral T-cell lymphoma, unclassified
CD 31- no specific code:
Other CPT codes related to the CPB:
88341 - 88344	Immunohistochemistry or immunocytochemistry, per sample
ICD-10 codes covered if selection criteria are met:
C49.0 - C49.9	Malignant neoplasm of other connective and soft tissues [angiosarcoma]
*CD 33*:
Covered CPT codes if selection criteria are met:
88184	Flow cytometry, cell surface, cytoplasmic or nuclear marker, technical component only; first bullet
+88185	each additional marker (list separately in addition to the first marker code)
88187	Flow cytometry, interpretation; 2 to 8 markers
88189	16 or more markers
88341	immunohistochemistry or immunocytochemistry, per sample; each additional single antibody staining procedure (list separately in addition to primary procedure code)
88342	immunohistochemistry or immunocytochemistry, per sample; initial single antibody staining procedure
ICD-10 codes covered if selection criteria are met:
[to determine eligibility for treatment with Mylotarg]
C83.50 - C83.59	Lymphoblastic lymphoma (diffuse)
C91.00 - C91.02	Acute lymphoblastic leukemia [ALL]
C92.00 - C92.02 C92.40 - C92.a2	acute myeloid leukemia
C93.00 - C93.02	Acute monoblastic/monocytic leukemia
C94.00 - C94.02	Acute erythroid leukemia
C95.00 - C95.02	Acute leukemia of unspecified cell type
*disco 52*:
Covered CPT codes if selection criteria are met:
88184	Flow cytometry, cell surface, cytoplasmic or nuclear marker, technical component only; first bullet
+88185	each additional marker (list separately in addition to the first marker code)
88187	Flow cytometry, interpretation; 2 to 8 markers
88342	immunohistochemistry or immunocytochemistry, per sample; initial single antibody staining procedure
ICD-10 codes covered if selection criteria are met:
[to determine eligibility for Campath treatment]
C82.00 - C82.99 C83.10 - C83.89 C84.00 - C84.49 C84.a0 - C84.99 C85.10 - C86.6 C91.10 - C91.12 C91.40 - C91.42	Lymphoid, hematopoietic and related tissue malignancies
C91.60 - C91.62	T-cell type prolymphocytic leukemia
D47. Z1	Post-transplant lymphoproliferative disorder (PTLD)
*Cyclin D1*:
Covered CPT codes if selection criteria are met:
81168	CCND1/IGH (t(11;14)) translocation analysis (eg, mantle cell lymphoma), primary hotspot, qualitative and quantitative, if performed
81401	Level 2 molecular pathology procedure (eg, 2 to 10 SNPs, 1 methylated variant, or 1 somatic variant [typically using target variant analysis without sequencing] or detection of a dynamic mutation/triplet repeat disorder) (EML4/ALK inv (2) ) (e.g., non-small cell lung cancer), translocation or inversion analysis
ICD-10 codes covered if selection criteria are met:
C83.10 - C83.19	Mantle cell lymphoma [diagnosis and prediction of disease recurrence]
ICD-10 codes not covered for indications listed in the CPB:
C44.02, C44.121 - C44.129, C44.221 - C44.229, C44.320 - C44.329, C44.42	Squamous cell carcinoma of lip, eyelid, ear and external canal, face, scalp and neck
*Decoding test (an RNA biomarker assay)*:
Covered CPT codes if selection criteria are met:
81542	Oncology (prostate), mRNA, microarray gene expression profiling of 22 genes of content, using formalin-fixed paraffin-embedded tissue, algorithm reported as metastasis risk score
ICD-10 codes covered if selection criteria are met:
C61	Malignant neoplasm of the prostate [not covered for repeat testing to assess risk of prostate cancer progression]
*DecisionDx-UM*:
Covered CPT codes if selection criteria are met:
81552	Oncology (uveal melanoma), mRNA, real-time RT-PCR gene expression profiling of 15 genes (12 content and 3 maintenance), by fine-needle aspirate or formalin-fixed paraffin-embedded tissue, algorithm reported as risk of metastases
ICD-10 codes covered if selection criteria are met:
C69.30 - C69.42	Malignant neoplasm of choroid and ciliary body [localized uveal melanoma]
*Endopredict (score of 12 genes)*:
Covered CPT codes if selection criteria are met:
81522	Oncology (breast), mRNA, RT-PCR gene expression profiling of 12 genes (8 content and 4 maintenance), using formalin-fixed paraffin-embedded tissue, algorithm reported as recurrence risk score
ICD-10 codes covered if selection criteria are met:
C50.011 - C50.929	Mama malignant neoplasm
*FIP1L1-PDGFRA merger*:
Covered CPT codes if selection criteria are met:
88271	molecular cytogenetics; DNA probe, each (e.g. FISH)
88275	interphase in situ hybridization, analyze 100-300 cells
ICD-10 codes covered if selection criteria are met:
D47.02	systemic mastocytosis
*FIP1L1-PDGFRA gene rearrangements*:
Covered CPT codes if selection criteria are met:
88374	Morphometric analysis, in situ hybridization (quantitative or semi-quantitative), using computer-assisted technology, per specimen; each multiplex probe staining procedure
88377	Morphometric analysis, in situ hybridization (quantitative or semi-quantitative), manual, per specimen; each multiplex probe staining procedure
ICD-10 codes covered if selection criteria are met:
D47. Z9	Other specified neoplasms of uncertain or unknown behavior of lymphoid, hematopoietic and related tissues
*FLT3 gene mutation*:
Covered CPT codes if selection criteria are met:
0046U	FLT3 (fms-related tyrosine kinase 3) (eg, acute myelogenous leukemia) tandem internal duplication (ITD) variants, quantitative
81245	FLT3 (fms-related tyrosine kinase 3) (eg, acute myeloid leukemia), genetic analysis; tandem duplication internal variants (ITDs) (i.e., exons 14, 15)
81246	FLT3 (fms-related tyrosine kinase 3) (eg, acute myeloid leukemia), genetic analysis; tyrosine kinase (TKD) domain variants (e.g. D835, I836)
ICD-10 codes covered if selection criteria are met:
D47. Z9	Other specified neoplasms of uncertain or unknown behavior of lymphoid, hematopoietic and related tissues
Fas-associated protein with death domain FADD- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C44.02, C44.121 - C44.129, C44.221 - C44.229, C44.320 - C44.329, C44.42	Squamous cell carcinoma of lip, eyelid, ear and external canal, face, scalp and neck
*Prostate PX, post-operative PX*:
Other CPT codes related to the CPB:
88305	Level IV - Surgical pathology, macroscopic and microscopic examination
88313	Special coloring including interpretation and report; Group II, other (e.g. iron trichrome), except staining for microorganisms, staining for enzyme constituents, or immunocytochemistry and immunohistochemistry
88323	Consultation and report on forwarded material that requires slide preparation
88341 - 88344	Immunohistochemistry or immunocytochemistry, per sample
88350	immunofluorescence, per sample; each additional single antibody staining procedure (list separately in addition to primary procedure code)
ICD-10 codes not covered for indications listed in the CPB:
C61	malignant neoplasm of prostate
*NRAS mutation*:
Covered CPT codes if selection criteria are met:
81311	NRAS (neuroblastoma viral oncogene [v-ras] homologue of RAS) (p.
Other CPT codes related to the CPB:
81404	Molecular pathology procedure, level 5 (e.g., analysis of 2-5 exons using DNA sequence analysis, mutation scanning or deletion/duplication variants of 6-10 exons, or characterization of a dynamic mutation/triplet repeat by analysis from southern blot)
ICD-10 codes covered if selection criteria are met:
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
C86.4	Blast NK cell lymphoma [blastic plasmacytoid dendritic cell neoplasm (BPDCN)]
D46.0 - D46.9	Myelodysplastic syndromes
*NTRK*:
Covered CPT codes if selection criteria are met:
81194	NTRK (neurotrophic tropomyosin receptor tyrosine kinase 1, 2 and 3) translocation analysis (e.g. solid tumors)
ICD-10 codes covered if selection criteria are met:
C11.0 - C88.4	Malignant Neoplasms - Solid Tumors
Ras oncogenes (except KRAS and BRAF)- No specific code:
*Epidermal Growth Factor Receptor Test (EGFR)*:
Covered CPT codes if selection criteria are met:
81235	EGFR (epidermal growth factor receptor) gene analysis (e.g. non-small cell lung cancer), common variants (e.g. exon 19 deletion LREA, L858R, T790M, G719A, G719S, L861Q)
Other CPT codes related to the CPB:
88341 - 88344	Immunohistochemistry or immunocytochemistry, per sample
88381	Microdissection (ie, preparation of samples from the microscopically identified target); manual
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell lung cancer]
ICD-10 codes not covered for indications listed in the CPB:
C71.0 - C71.9	Malignant neoplasm of the brain [Glioblastoma multiforme]
D09.0	Bladder carcinoma in situ [urothelial carcinoma]
D09.10 - D09.19	Carcinoma in situ of other and unspecified urinary organs (ureter, renal pelvis) [urothelial carcinoma]
ROS-1- No specific code:
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell lung cancer]
*ZAP-70*:
Covered CPT codes if selection criteria are met:
88184	Flow cytometry, cell surface, cytoplasmic or nuclear marker, technical component only; first bullet
+88185	each additional marker (list separately in addition to the first marker code)
ICD-10 codes covered if selection criteria are met:
C91.10 - C91.12	B-cell chronic lymphocytic leukemia [evaluation of prognosis and need for aggressive therapy]
test ZRSR2:
Covered CPT codes if selection criteria are met:
81360	ZRSR2 (CCCH-like zinc finger, RNA-binding motif, and serine/arginine-rich) (p.
ICD-10 codes covered if selection criteria are met:
C92.10 – C92.12	Chronic myeloid leukemia, BCR/ABL-positive
C92.20 - C92.22	Atypical chronic myelogenous leukemia, negative BCR/ABL
D46.0 – D46.Z	Myelodysplastic syndromes
*Oncotipo Dx*:
Covered CPT codes if selection criteria are met:
0047U	Oncology (prostate), mRNA, real-time RT-PCR gene expression profiling of 17 genes (12 content and 5 cleanup), using formalin-fixed paraffin-embedded tissue, algorithm reported as risk score
81518	Oncology (breast), mRNA, real-time RT-PCR gene expression profiling of 11 genes (7 content and 4 maintenance), using formalin-fixed, paraffin-embedded tissue, algorithms reported as percentage risk of metastatic recurrence and probability benefit of extended endocrine therapy
81519	Oncology (breast), mRNA, real-time RT-PCR gene expression profiling of 21 genes, using formalin-fixed paraffin-embedded tissue, algorithm reported as recurrence score
CPT codes not covered for indications listed in the CPB:
81525	Oncology (colon), mRNA, real-time RT-PCR gene expression profiling of 12 genes (7 content and 5 maintenance), using formalin-fixed paraffin-embedded tissue, algorithm reported as recurrence score
Other CPT codes related to the CPB:
88360	Morphometric analysis, tumor immunohistochemistry (eg, Her-2/neu, estrogen receptor/progesterone receptor), quantitative or semi-quantitative, per sample, each individual antibody staining procedure; manual
88361	using computer aided technology
88367 - 88377	Morphometric analysis, in situ hybridization, (quantitative or semi-quantitative)
ICD-10 codes covered if selection criteria are met:
C50.011 - C50.019 C50.111 - C50.119 C50.211 - C50.219 C50.311 - C50.319 C50.411 - C50.419 C50.511 - C50.519 C50.611 - C50.619 C50.811 - C50.819 C50.911 - C50.919	Malignant neoplasm of female breast [except lymph node positive] [HER2 negative breast cancer, estrogen receptor positive, lymph node negative]
C50.021 - C50.029 C50.121 - C50.129 C50.221 - C50.229 C50.321 - C50.329 C50.421 - C50.429 C50.521 - C50.529 C50.621 - C50.629 C50.821 - C50.829 C50.921 - C50.929	Malignant neoplasm of male breast
C61	Malignant neoplasm of the prostate [not covered for repeat testing to assess risk of prostate cancer progression]
C77.3	Secondary and unspecified malignancy of lymph nodes of axilla and upper extremities [1-3 ipsilateral axillary lymph nodes involved]
ICD-10 codes not covered for indications listed in the CPB:
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
D01.0	Colon carcinoma in situ
D05.10 - D05.12	Intraductal carcinoma in situ from breast
D07.5	Carcinoma in situ from prostate
Z85.030 - Z85.048	Personal history of malignancy of the large intestine, rectum, rectosigmoid junction, and anus
*Myeloperoxidase (MPO) Immunostaining FLT3-ITD, CEBPA Mutation, NPM1 Mutation, and KIT Mutation*:
Covered CPT codes if selection criteria are met:
0046U	FLT3 (fms-related tyrosine kinase 3) (eg, acute myelogenous leukemia) tandem internal duplication (ITD) variants, quantitative
0049U	NPM1 (nucleophosmin) gene analysis (eg, acute myelogenous leukemia), quantitative
81245 - 81246	FLT3 (fms-related tyrosine kinase 3) (eg, acute myeloid leukaemia), genetic analysis
83876	Mieloperoxidasa (MPO)
ICD-10 codes covered if selection criteria are met:
C92.00 - C92.02 C92.40 - C92.a2	acute myeloid leukemia
*NPM1*:
Covered CPT codes if selection criteria are met:
0049U	NPM1 (nucleophosmin) gene analysis (eg, acute myelogenous leukemia), quantitative
81310	NPM1 (nucleophosmin) gene analysis (eg, acute myelogenous leukemia), exon 12 variants
ICD-10 codes covered if selection criteria are met:
C92.00 - C92.02, C92.40 - C92.A2	acute myeloid leukemia
C92.10 – C92.12	Chronic myeloid leukemia, BCR/ABL-positive
C92.20 - C92.22	Atypical chronic myelogenous leukemia, negative BCR/ABL
C94.6	Myelodysplastic disease, not elsewhere classified [myeloproliferative neoplasms]
D45	polycythemia vera
D46.0 - D46.Z	Myelodysplastic syndromes
D47.3	Essential (hemorrhagic) thrombocythemia
D75.81	mielofibrose
*PDGFRA*:
Covered CPT codes if selection criteria are met:
81314	PDGFRA (platelet-derived growth factor receptor alpha polypeptide) (eg, gastrointestinal stromal tumor [GIST]), gene analysis, target sequence analysis (eg, exons 12, 18)
ICD-10 codes covered if selection criteria are met:
C49.4	Malignant neoplasm of connective and soft tissues of the abdomen
C91.00 - C91.02	Acute lymphoblastic leukemia (ALL) [pediatric]
D47.02	systemic mastocytosis
D47. Z9	Other specified neoplasms of uncertain or unknown behavior of lymphoid, hematopoietic and related tissues
*LMP/RARA*:
Covered CPT codes if selection criteria are met:
81315 - 81316	PML/RARalpha, (t(15;17)), (promyelocytic leukemia/retinoic acid receptor alpha) (eg, promyelocytic leukemia) translocation analysis
ICD-10 codes covered if selection criteria are met:
C92.00 - C92.02	acute myeloblastic leukemia
*You are talking*:
Covered CPT codes if selection criteria are met:
81541	Oncology (prostate), real-time RT-PCR mRNA gene expression profiling of 46 genes (31 content and 15 maintenance), using formalin-fixed, paraffin-embedded tissue, algorithm reported as a mortality risk score disease specific
ICD-10 codes covered if selection criteria are met:
C61	Malignant neoplasm of the prostate [not covered for repeat testing to assess risk of prostate cancer progression]
*ProMark*:
Covered CPT codes if selection criteria are met:
ProMark- no specific code:
ICD-10 codes covered if selection criteria are met:
C61	Malignant neoplasm of the prostate [not covered for repeat testing to assess risk of prostate cancer progression]
*Placental alkaline phosphatase (PLAP)*:
Covered CPT codes if selection criteria are met:
84080	alkaline phosphatase; isoenzymes
ICD-10 codes covered if selection criteria are met:
C56.1 - C56.9	malignant neoplasm of ovary
C62.00 - C62.92	Malignant neoplasm of testicles
D07.30 - D07.39	Carcinoma in situ of other and unspecified female genital organs [germ cell tumors]
D07.60 - D07.69	Carcinoma in situ of other and unspecified male genital organs
Z85.43	Personal history of ovarian neoplasia
Z85.47	Personal history of testicular malignancy
Bladder Tumor Antigen (BTA) Stat Test, Nuclear Matrix Protein Test (NMP22), Fibrin/Fibrinogen Degradation Products Test (Aura-Tek FDP), Bladder FISH Test for Pathnostics, or Fluorescent Hybridization Test in situ (FISH) UroVysion, BTA TRAK:
Covered CPT codes if selection criteria are met:
85362 - 85380	fibrin breakdown products
86294	Immunoassay for tumor antigen, qualitative or semiquantitative (eg, vesicular tumor antigen)
86386	Nuclear matrix protein 22 (NMP22) qualitative
88120	Cytopathology, in situ hybridization (eg FISH), urinary tract sample with morphometric analysis, 3-5 molecular probes, each sample; manual
88121	using computer aided technology
88364 - 88366	In situ hybridization (e.g. FISH), each probe
ICD-10 codes covered if selection criteria are met:
C67.0 - C67.9	bladder malignancy
D09.0	Carcinoma in situ from bladder
Z85.51	Personal history of bladder malignancy
ICD-10 codes not covered for CPB-listed indications (not all included):
R31,0 - R31,9	hematuria
Z12.6	Bladder malignancy screening appointment
immunocytes (uCyt)- No specific code:
ICD-10 codes covered if selection criteria are met:
C67.0 - C67.9	bladder malignancy
ICD-10 codes not covered for indications listed in the CPB:
R31,0 - R31,9	hematuria
Z12.6	Bladder malignancy screening appointment [diagnosis or screening in asymptomatic persons]
*Janus Kinase 2 (JAK2) mutations*:
Covered CPT codes if selection criteria are met:
0027U	JAK2 (Janus kinase 2) gene analysis (eg, myeloproliferative disorder), exon 12-15 targeting sequence analysis
81270	JAK2 (Janus kinase 2) gene analysis (eg, myeloproliferative disorder), p.Val617Phe (V617F) variant [not covered for diagnostic evaluation of myeloproliferative disorders in children; and Quantitative Assessment of JAK2-V617F Allele Load After Qualitative Detection of JAK2-V617F]
81279	JAK2 (Janus kinase 2) (eg, myeloproliferative disorder) targeted sequence analysis (eg, exons 12 and 13)
ICD-10 codes covered if selection criteria are met:
C92.10 - C92.12	Chronic myeloid leukemia, BCR/ABL-positive
D45	polycythemia vera
D47.1	chronic myeloproliferative disease
D47.3	Essential (hemorrhagic) thrombocythemia
D47.4	osteomielofibrose
D75.81	mielofibrose
*KRAS*:
Covered CPT codes if selection criteria are met:
81275	KRAS (Kirsten rat sarcoma viral oncogene) gene analysis (eg, carcinoma); variants in exon 2 (e.g. codons 12 and 13)
81276	KRAS (Kirsten rat sarcoma viral oncogene) gene analysis (eg, carcinoma); additional variant(s) (e.g. codon 61, codon 146)
ICD-10 codes covered if selection criteria are met:
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
C21.8	Malignant neoplasm of overlapping sites of rectum, anus and anal canal
C25.0 - C25.9	malignant neoplasm of the pancreas
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell]
C53.0 - C55, C58	Malignant neoplasm of uterus [sarcoma]
D46.0 - D46.9	Myelodysplastic syndromes
*BRAF, V600 mutation analysis*:
Covered CPT codes if selection criteria are met:
81210	BRAF (murine sarcoma B1 oncogene homologue v-raf) (eg, colon cancer), genetic analysis, variant V600E
ICD-10 codes covered if selection criteria are met:
C18.0 - C21.8	Malignant neoplasm of colon, rectosigmoid junction, rectum, anus and anal canal
C25.0 - C25.9	malignant neoplasm of the pancreas
C34.00 - C34.92	Malignant neoplasm of the bronchi and lungs
C43.0 - C43.9	Skin melanoma [for consideration of Vemurafenib, Dabrafenib, and Trametinib]
C49.4	Malignant neoplasm of connective and soft tissue of abdomen [gastrointestinal stromal tumors]
C71.0 - C71.9	Malignant neoplasm of the brain [infiltrating glioma]
C73	Malignant neoplasm of the thyroid gland
C91.40 - C91.42	hairy cell leukemia
D44.0	Neoplasm of uncertain behavior of the thyroid gland [indeterminate thyroid nodules]
*Loss of heterozygosity (LOH) assay on the long arm of chromosome 18 (18q) or colon cancer (DCC) deleted protein (18q-LOH/DCC) for colorectal cancer*:
no specific code
ICD-10 codes not covered for indications listed in the CPB:
C18.0 - C20	Malignant neoplasm of colon, rectum and rectosigmoid junction
*Biodesix BDX-XL2, Nodify XL2, Nodify Lung, Nodify CDT*:
CPT codes not covered for indications listed in the CPB:
Nodify Lung e Nodify CDT– no specific code
0080U	Oncology (lung), mass spectrometry analysis of galectin-3 binding protein and M130 cysteine-rich receptor scavenging protein M130 type 1, with five clinical risk factors (age, smoking, nodule diameter, nodule spiculation status and nodule location), using plasma, algorithm reported as a categorical probability of malignancy
ICD-10 codes not covered for indications listed in the CPB:
C34.00 - C34.92	Malignant neoplasm of the bronchi and lungs
C78.00 - C78.02	Secondary malignant neoplasm of the lung
D14.30 - D14.32	Benign neoplasm of bronchi and lungs
R91.1	solitary lung nodule
R91.8	Other nonspecific abnormal finding in the lung field
*OvaCheck tests*:
no specific code
ICD-10 codes not covered for indications listed in the CPB:
C56.1 - C56.9	malignant neoplasm of ovary
Z12.73	Ovarian Malignancy Screening Meeting
Ovasure- No specific code:
Other CPT codes related to the CPB:
82985	glycosylated protein
83520	Immunoassay, quantitative analyte; otherwise not specified
84146	prolactin
84305	somatomedina
86304	immunoassay for tumor antigen, quantitative; CA 125
Circulating cell-free nucleic acids- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
*Circulating tumor cells (CTC) (eg, CELLSEARCH tests)*:
CPT codes not covered for indications listed in the CPB:
0091U	Oncologic (colorectal) screening, cellular enumeration of circulating tumor cells, using whole blood, algorithm, to detect the presence of adenoma or cancer, reported as positive or negative
0337U	Oncology (plasma cell disorders and myeloma), immunological screening of circulating plasma cells, identification, morphological characterization and enumeration of plasma cells based on differential expression of protein biomarkers CD138, CD38, CD19 and CD45, peripheral blood
0338U	Oncology (solid tumor), selection of circulating tumor cells, identification, morphological characterization, detection and enumeration based on differential EpCAM, cytokeratins 8, 18 and 19 and CD45 protein biomarkers, and quantification of cells that express the HER2 protein biomarker, peripheral blood
86152	Cell count by immunological selection and identification in fluid samples (eg circulating tumor cells in the blood)
86153	interpretation and medical report, when necessary
88346	immunofluorescence, per sample; initial single antibody staining procedure
88361	Morphometric, tumor immunohistochemical analysis (eg, Her-2/neu, estrogen receptor/progesterone receptor), quantitative or semi-quantitative, for each antibody; using computer aided technology
ICD-10 codes not covered for CPB-listed indications (not all included):
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
C43.0 - C43.9	malignant skin melanoma
C50.011 - C50.929	Mama malignant neoplasm
C61	malignant neoplasm of prostate
C79.81	Secondary malignant neoplasm of the breast
Cofilina (CFL1)- no specific code:
ICD-10 codes not covered for indications listed in the CPB:
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell lung cancer]
ColonSentry- no specific code:
ICD-10 codes not covered for indications listed in the CPB:
Z12.11 - Z12.12	Meeting for screening of malignant neoplasm of the colon and rectum
*Prueba Oncotype DX® Mama DCIS Score™*:
CPT codes not covered for indications listed in the CPB:
0045U	Oncology (ductal breast carcinoma in situ), mRNA, real-time RT-PCR gene expression profiling of 12 genes (7 content and 5 maintenance), using formalin-fixed paraffin-embedded tissue, algorithm reported as recurrence score
*CDT-early lung test*:
CPT codes not covered for indications listed in the CPB:
83520	Immunoassay for analytes other than antibodies to infectious agents or antigens to infectious agents; quantitative, not otherwise specified [such as a test for early detection of lung cancer]
*Galectina-3*:
CPT codes not covered for indications listed in the CPB:
82777	Galectina-3
ICD-10 codes not covered for CPB-listed indications (not all included):
C25.0 - C25.9	malignant neoplasm of the pancreas
C40.00 - C41.9	Malignant neoplasm of bone and articular cartilage [osteosarcoma]
C50.011 - C50.929	Mama malignant neoplasm
C56.1 - C56.9	malignant neoplasm of ovary
C61	malignant neoplasm of prostate
D46.0 - D46.Z	Myelodysplastic syndromes
*Insight tipo TNBC*:
CPT codes not covered for indications listed in the CPB:
0153U	Oncology (breast), mRNA, gene expression profiling by next-generation sequencing of 101 genes using formalin-fixed, paraffin-embedded tissue, algorithm reported as clinical subtype(s) of triple negative breast cancer with information on the involvement of immune cells
ICD-10 codes not covered for indications listed in the CPB:
C50.011 - C50.929	Mama malignant neoplasm
*Ki67* :
CPT codes not covered for indications listed in the CPB:
88360	Morphometric analysis, tumor immunohistochemistry (eg, Her-2/neu, estrogen receptor/progesterone receptor), quantitative or semi-quantitative, per sample, each individual antibody staining procedure; manual
88361	using computer aided technology
ICD-10 codes not covered for indications listed in the CPB:
C50.011 - C50.929	Mama malignant neoplasm
C64.1 0 C66.9	Malignant neoplasm of kidney, renal pelvis and ureter
*breast cancer index*:
Covered CPT codes if selection criteria are met:
81518	Oncology (breast), mRNA, real-time RT-PCR gene expression profiling of 11 genes (7 content and 4 maintenance), using formalin-fixed paraffin-embedded tissue, algorithms reported as percentage risk of metastatic recurrence and probability of benefit from endocrine therapy extension
ICD-10 codes covered if selection criteria are met:
C50.011 - C50.929	Mama malignant neoplasm
*BTK*:
Covered CPT codes if selection criteria are met:
81233	BTK (Bruton tyrosine kinase) gene analysis (eg chronic lymphocytic leukemia), common variants (eg C481S, C481R, C481F)
ICD-10 codes covered if selection criteria are met:
C83.00 - C83.09	Small B cell lymphoma
C91.10 - C91.12	B-cell type chronic lymphocytic leukemia
*mother's impression*:
Covered CPT codes if selection criteria are met:
81521	Oncology (breast), mRNA, microarray gene expression profiling of 70 content genes and 465 housekeeping genes, using formalin-fixed paraffin-embedded or frozen fresh tissue, algorithm reported as index related to distant metastasis risk
81523	Oncology (breast), mRNA, next-generation sequencing gene expression profiling of 70 content genes and 31 housekeeping genes, using formalin-fixed paraffin-embedded tissue, algorithm reported as index related to distant metastasis risk
HCPCS codes covered if selection criteria are met:
S3854	Gene expression profiling panel for use in breast cancer treatment management
ICD-10 codes covered if selection criteria are met:
C50.011 - C50.929	Mama malignant neoplasm
Z17.0	Positive Estrogen Receptor Status [ER+]
Z17.1	Negative Estrogen Receptor Status [ER-]
*Lymph2CX e Lymph3Cx*:
CPT codes not covered for indications listed in the CPB:
0017U	Oncology (Hematolymphoid Neoplasm), JAK2 Mutation, DNA, PCR Amplification of Exons 12-14 and Sequence Analysis, Blood or Bone Marrow, Report of Missing or Detected JAK2 Mutation
ICD-10 codes not covered for CPB-listed indications (not all included):
C85.20 - C85.29	Mediastinal (thymic) large B-cell lymphoma
C83.30 - C83.39	Diffuse large B-cell lymphoma
*mucin 4 expression*:
CPT codes not covered for indications listed in the CPB:
88313	Group II, all others (eg, iron, trichrome), except immunocytochemical and immunoperoxidase stains, including interpretation and reporting, each
ICD-10 codes not covered for indications listed in the CPB:
C18.0 - C20	Malignant neoplasm of colon, rectum and rectosigmoid junction
Mucina 5AC (MUC5AC)- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C22.1	intra-hepatic carcinoma
C24.0 - C24.9	Malignant neoplasm of other and unspecified parts of the bile ducts
*NF1, RET e SDHB*:
CPT codes not covered for indications listed in the CPB:
NF1, RET e SDHB- no specific code:
ICD-10 codes not covered for indications listed in the CPB:
C56.1 - C56.9	malignant neoplasm of ovary
*Microarray-based gene expression profiling tests*:
Other CPT codes related to the CPB:
81406	Molecular pathology procedure, level 7 (eg, analysis of 11-25 exons using DNA sequence analysis, scanning for mutations or duplication/deletion variants of 26-50 exons, cytogenomic matrix analysis for neoplasia)
*OVA1*:
CPT codes not covered for indications listed in the CPB:
0003U	Oncologic (ovarian) biochemical assays of five proteins (apolipoprotein A-1, CA 125 II, follicle-stimulating hormone, human epididymal protein 4, transferrin), using serum, algorithm reported as a probability score
81503	Oncology (ovarian), biochemical assays of five proteins (CA-125, apolipoprotein A1, beta-2 microglobulin, transferrin, and prealbumin), using serum, algorithm reported as risk score
p16 protein expression- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C00.0 - C14.8	Malignant neoplasms of the lip, oral cavity, and pharynx [non-oropharyngeal squamous cell carcinoma]
*Pathwork origin fabric test*:
CPT codes not covered for indications listed in the CPB:
81504	Oncology (tissue of origin), microarray gene expression profiling of >2000 genes, using formalin-fixed, paraffin-embedded tissue, algorithm reported as tissue similarity scores
*PreOvar test for the KRAS variant [to determine ovarian cancer risk]*:
ICD-10 codes not covered for indications listed in the CPB:
C56.1 - C56.9	malignant neoplasm of ovary
Z85.43	Personal history of ovarian neoplasia
Prueba ProOnc Tumor Source Dx- No specific code:
*Roma*:
CPT codes not covered for indications listed in the CPB:
81500	Oncology (ovarian), biochemical assays of two proteins (CA-125 and HE4), using serum, with menopausal status, algorithm reported as risk score
86304	immunoassay for tumor antigen, quantitative; CA 125
86305	Human epididymal protein 4 (HE4)
ICD-10 codes not covered for CPB-listed indications (not all included):
C56.1 - C56.9	malignant neoplasm of ovary
*Panel of 76 Rotterdam signature genes*:
HCPCS codes not covered for CPB listed indications:
S3854	Gene expression profiling panel for use in breast cancer treatment management
*Serum amyloid A*:
CPT codes not covered for indications listed in the CPB:
88342	immunohistochemistry or immunocytochemistry, each antibody separately identifiable by block, cytology preparation or blood smear; first antibody separately identifiable by slide
Other CPT codes related to the CPB:
88341 - 88344	Immunohistochemistry or immunocytochemistry, per sample
ICD-10 codes not covered for indications listed in the CPB:
C54.0 - C54.8	Malignant neoplasm of the uterine body, isthmus and uterus
Z85.42	Personal history of malignant neoplasm of the uterus
*Breast cancer gene expression rate (HOXB13:IL17BR)*:
HCPCS codes not covered for CPB listed indications:
S3854	Gene expression profiling panel for use in breast cancer treatment management
*PAM50 ROR (Prosigna Breast Cancer Prognostic Gene Signature Assay)* :
Covered CPT codes if selection criteria are met:
0008M	Oncology (breast), mRNA analysis of 58 genes using hybrid capture, in formalin-fixed, paraffin-embedded (FFPE) tissue, prognostic algorithm reported as a risk score [Prosigna]
81520	Oncology (breast), mRNA gene expression profiling by hybrid capture of 58 genes (50 content and 8 cleanup), using formalin-fixed paraffin-embedded tissue, algorithm reported as a recurrence risk score
CPT codes not covered for indications listed in the CPB:
81406	Molecular pathology procedure, level 7 (e.g., analysis of 11-25 exons using DNA sequence analysis, scanning for mutations or duplication/deletion variants of 26-50 exons, cytogenomic matrix analysis for neoplasia) [when specified as follows]: PALB2 (BRCA2 partner and locator) (eg, breast and pancreas cancer), complete genetic sequence
ICD-10 codes covered if selection criteria are met:
C50.011 - C50.929	Mama malignant neoplasm
ICD-10 codes not covered for indications listed in the CPB:
C61	malignant neoplasm of prostate
*PTEN*:
Covered CPT codes if selection criteria are met:
81321 - 81323	Tensin phosphatase (PTEN) homologous gene analysis (eg, Cowden syndrome, PTEN hamartoma tumor syndrome)
ICD-10 codes covered if selection criteria are met:
C53.0 - C55, C58	malignant neoplasm of uterus
Q85.81 - Q85.89	Other phakomatoses not elsewhere classified [Cowden syndrome]
ICD-10 codes not covered for indications listed in the CPB:
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell lung cancer]
Breast Lymph Node GeneSearch (BLN) Assay- No specific code:
feel shy- No specific code:
no specific code
Other CPT codes related to the CPB:
88341 - 88344	Immunohistochemistry or immunocytochemistry, per sample
88360	Morphometric analysis, tumor immunohistochemistry (eg, Her-2/neu, estrogen receptor/progesterone receptor), quantitative or semi-quantitative, per sample, each individual antibody staining procedure; manual
88361	using computer aided technology
Topographic genotype (PathfinderTG)- No specific code:
Translation of Biomarkers (BT)- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C50.011 - C50.929	Mama malignant neoplasm
*BRAF e EGFR*:
CPT codes not covered for indications listed in the CPB:
81210	BRAF (B-Raf proto-oncogene, serine/threonine kinase) (eg, colon cancer, melanoma), genetic analysis, variant(s) V600
81235	EGFR (epidermal growth factor receptor) gene analysis (e.g. non-small cell lung cancer), common variants (e.g. exon 19 deletion LREA, L858R, T790M, G719A, G719S, L861Q)
ICD-10 codes not covered for indications listed in the CPB:
C15.3 - C15.9	Malignant neoplasm of esophagus
*HE4*:
CPT codes not covered for indications listed in the CPB:
81500	Oncology (ovarian), biochemical assays of two proteins (CA-125 and HE4), using serum, with menopausal status, algorithm reported as risk score
86305	Human epididymal protein 4 (HE4)
Other CPT codes related to the CPB:
86316	immunoassay for tumor antigen; other antigen, quantitative (e.g., CA 50, 72-4, 549), each
ICD-10 codes not covered for indications listed in the CPB:
C54.1	endometrial malignancy
C56.1 - C56.9	malignant neoplasm of ovary
R19,00	Intra-abdominal and pelvic swelling, mass, nodule, site unspecified [not covered for pelvic mass evaluation, including assistance in determining referral for surgery to a gynecologic oncologist or general surgery]
R19,07 - R19,09	Generalized intra-abdominal and pelvic swelling, mass, and nodule [not covered for evaluation of a pelvic mass, including assistance in determining referral for surgery to a gynecological oncologist or general surgery]
HERmark- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C50.011 - C50.929	Mama malignant neoplasm
D05.00 - D05.92	Carcinoma in situ of breast
*Gene Expression TargetPrint*:
Other CPT codes related to the CPB:
88360	Morphometric analysis, tumor immunohistochemistry (eg, Her-2/neu, estrogen receptor/progesterone receptor), quantitative or semi-quantitative, per sample, each individual antibody staining procedure; manual
88361	using computer aided technology
88367 - 88377	Morphometric analysis, in situ hybridization (quantitative or semi-quantitative)
HCPCS codes not covered for CPB listed indications:
S3854	Gene expression profiling panel for use in breast cancer treatment management
ICD-10 codes not covered for indications listed in the CPB:
C50.011 - C50.929	Mama malignant neoplasm
TP53- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C56.1 - C56.9	malignant neoplasm of ovary
*CK5, CK14, p63 e Racemasa P504S*:
Other CPT codes related to the CPB:
88341 - 88344	Immunohistochemistry or immunocytochemistry, per sample
ICD-10 codes not covered for indications listed in the CPB:
C61	malignant neoplasm of prostate
*EML4-ALK*:
Other CPT codes related to the CPB:
88381	Microdissection (ie, preparation of samples from the microscopically identified target); manual
ICD-10 codes not covered for indications listed in the CPB:
C34.0 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell lung cancer]
Coloprint, CIMP, Line-1 hypomethylation and immune cells- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
*Confirm MDx*:
CPT codes not covered for indications listed in the CPB:
81551	Oncology (prostate), real-time PCR promoter methylation profiling of 3 genes (GSTP1, APC, RASSF1), using formalin-fixed paraffin-embedded tissue, algorithm reported as probability of detecting prostate cancer on repeat biopsy
ICD-10 codes not covered for indications listed in the CPB:
C61	malignant neoplasm of prostate
*Des-gamma-carboxyl prothrombin (DCP)*:
CPT codes not covered for indications listed in the CPB:
83951	oncoprotein; des-gamma-carboxy-prothrombin (DCP)
ICD-10 codes not covered for CPB-listed indications (not all included):
C22.0	Hepatic cell carcinoma
D01.5	Carcinoma in situ of the liver and biliary system
*5-Hydroxyindoleacetic acid (5-HIAA)*:
Covered CPT codes if selection criteria are met:
83497	Hydroxyindoleacetic acid, 5-(HIAA)
ICD-10 codes covered if selection criteria are met:
C7A.00 - C7A.8	Malignant neuroendocrine tumors
D3A.00 - D3A.8	Benign neuroendocrine tumors
*Beta-2 microglobulina (B2M)*:
Covered CPT codes if selection criteria are met:
82232	Beta-2 microglobulina
ICD-10 codes covered if selection criteria are met:
C85.10 - C85.99	Other specified and unspecified types of non-Hodgkin's lymphoma
C88.0	Macroglobulinemia de Waldenström
C90.00 - C90.02	multiple myeloma
*Expression of CALCA (Calcitonin)*:
Covered CPT codes if selection criteria are met:
82308	calcitonin
ICD-10 codes covered if selection criteria are met:
C73	thyroid malignancy
C76.0	Malignant neoplasm of head, face and neck
Expression of CALR (calreticulin):
Covered CPT codes if selection criteria are met:
81219	CALR (calreticulin) (eg, myeloproliferative disorders), gene analysis, common variants in exon 9
ICD-10 codes covered if selection criteria are met:
C92.10 – C92.12	Chronic myeloid leukemia, BCR/ABL-positive
C92.20 - C92.22	Atypical chronic myelogenous leukemia, negative BCR/ABL
C94.6	Myelodysplastic disease, not elsewhere classified [myeloproliferative neoplasms]
D45	polycythemia vera
D46.0 – D46.Z	Myelodysplastic syndromes
D47.3	Essential (hemorrhagic) thrombocythemia
D75.81	mielofibrose
*Expression of CALB2 (calretinin)*:
Covered CPT codes if selection criteria are met:
88342	immunohistochemistry or immunocytochemistry, each antibody separately identifiable by block, cytology preparation or blood smear; first antibody separately identifiable by slide
88341	each additional single antibody staining procedure (list separately in addition to primary procedure code)
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of the bronchi and lungs
C80.0 - C80.1	Disseminated neoplasia and other malignancies, unspecified
*CHGA (Chromogranin A) expression*:
Covered CPT codes if selection criteria are met:
86316	immunoassay for tumor antigen; other antigen, quantitative (e.g., CA 50, 72-4, 549), each
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of the bronchi and lungs
C4A.0 - C4A.9	Merkel cell carcinoma
C7A.00 - C7A.8	Malignant neuroendocrine tumors
C80.0 - C80.1	Disseminated neoplasia and other malignancies, unspecified
D3A.00 - D3A.8	Benign neuroendocrine tumors
*Beta human chorionic gonadotropin (beta-hCG)*:
Covered CPT codes if selection criteria are met:
84704	chorionic gonadotropin (hCG); free beta chain
ICD-10 codes covered if selection criteria are met:
C37	Malignant neoplasm of thymus
C56.1 - C56.9	malignant neoplasm of ovary
C62.00 - C62.92	testicular malignant neoplasm
D07.39	Carcinoma in situ of other female genital organs
D07.69	Carcinoma in situ of other male genital organs [testicles]
D15.0	Benign thymic neoplasm
D27.0 - D27.9	Benign ovarian neoplasm
D29.20 - D29.22	Benign testicular neoplasm
N50.8	Other specified disorders of male genital organs [testicular mass]
R19,00	Intra-abdominal and pelvic swelling, mass, nodule, site unspecified
R19,07 - R19,09	Generalized intra-abdominal and pelvic swelling, mass and nodule and others
R22.2	Localized swelling, mass and nodule, trunk
*Isocitrate dehydrogenase 1 and 2 (IDH1, IDH2)*:
Covered CPT codes if selection criteria are met:
81120	IDH1 (isocitrate dehydrogenase 1 [NADP+], soluble) (e.g., glioma), common variants (e.g., R132H, R132C)
81121	IDH2 (isocitrate dehydrogenase 2 [NADP+], mitochondrial) (eg, glioma), common variants (eg, R140W, R172M)
83570	Isocitric dehydrogenase (IDH)
ICD-10 codes covered if selection criteria are met:
C40.00 - C41.9	Malignant neoplasm of bone and articular cartilage [chondrosarcoma]
C71.0 - C71.9	Malignant neoplasm of brain, spinal cord, cranial nerves, and other parts of central nervous system [glioma] [glioblastoma]
C92.00 - C92.02, C92.40 - C92.a2	Aguda myeloid leukemia (AML)
D46.0 - D46.9	Myelodysplastic Syndromes (MDS)
D47.1	chronic myeloproliferative disease
*INHA expression (inhibin)*:
Covered CPT codes if selection criteria are met:
86336	Inhibin A
ICD-10 codes covered if selection criteria are met:
C56.1 - C56.9	malignant neoplasm of ovary
D07.39	Carcinoma in situ of other female genital organs
D27.0 - D27.9	Benign ovarian neoplasm
R19,00	Intra-abdominal and pelvic swelling, mass, nodule, site unspecified
R19,07 - R19,09	Generalized intra-abdominal and pelvic swelling, mass and nodule and others
*Lactate dehydrogenase (LDH)*:
Covered CPT codes if selection criteria are met:
83615	Lactate dehydrogenase (LD), (LDH)
83625	isoenzymes, separation and quantification
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of the bronchi and lungs
C40.00 - C41.9	Malignant neoplasm of bone and articular cartilage
C56.1 - C56.9	malignant neoplasm of ovary
C62.00 - C62.92	testicular malignant neoplasm
C64.1 - C65.9	Malignant neoplasm of the kidney and renal pelvis
C85.10 - C85.99	non-Hodgkin's lymphoma
C90.00 - C90.02	multiple myeloma
C91.00 - C91.02	Acute lymphoblastic leukemia (ALL)
D02.20 - D02.22	Bronchial and lung carcinoma in situ
D07.39	Carcinoma in situ of other female genital organs
D07.69	Carcinoma in situ of other male genital organs [testicles]
D14.30 - D14.32	Benign neoplasm of bronchi and lungs
D16.0 - D16.9	Benign neoplasm of bone and articular cartilage
D27.0 - D27.9	Benign ovarian neoplasm
D29.20 - D29.22	Benign neoplasm of testicles
D30.00 - D30.12	Benign neoplasm of kidney and renal pelvis
N28.89	Other specified disorders of kidney and ureter [kidney mass]
N50.8	Other specified disorders of male genital organs [testicular mass]
R19,00	Intra-abdominal and pelvic swelling, mass, nodule, site unspecified
R19,07 - R19,09	Generalized intra-abdominal and pelvic swelling, mass and nodule and others
*testes PDGFRB* :
Covered CPT codes if selection criteria are met:
testes PDGFRB- No specific code
ICD-10 codes covered if selection criteria are met:
C91.00 - C91.02	Acute lymphoblastic leukemia [ALL]
D46.0 - D46.9	Myelodysplastic Syndromes (MDS)
D47. Z9	Other specified neoplasms of uncertain or unknown behavior of lymphoid, hematopoietic and related tissues
D48.5	Neoplasm of uncertain behavior from skin [dermatofibrossarcoma]
*Phosphatidylinositol-4,5-bisphosphonate 3-kinase, polypeptide catalytic subunit alpha gene (PIK3CA)*:
Covered CPT codes if selection criteria are met:
0155U	PIK3CA (phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic alpha subunit) gene analysis (eg, breast cancer) (ie, p.C420R, p.E542K, p.E545A, p.E545D [g. 1635G>T only ], p.E545G, p.E545K, p.Q546E, p.Q546R, p.H1047L, p.H1047R, p.H1047Y)
81309	PIK3CA (phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic alpha subunit) (e.g., breast and colorectal cancer), target sequence analysis (e.g., exons 7, 9, 20)
ICD-10 codes covered if selection criteria are met:
C50.011 - C50.929	Mama malignant neoplasm
C53.0 - C55, C58	malignant neoplasm of uterus
ICD-10 codes not covered for indications listed in the CPB:
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
*PLCG2*:
Covered CPT codes if selection criteria are met:
81320	PLCG2 (phospholipase C gamma 2) gene analysis (eg, chronic lymphocytic leukemia), common variants (eg, R665W, S707F, L845F)
ICD-10 codes covered if selection criteria are met:
C83.00 - C83.09	Small B cell lymphoma
C91.10 - C91.12	B-cell type chronic lymphocytic leukemia
*Quest Diagnostic's Thyroid Cancer Mutation Panel*:
Covered CPT codes if selection criteria are met:
81445	Targeted genomic sequence analysis panel, solid organ neoplasia, DNA analysis and RNA analysis when performed, 5-50 genes (e.g. ALK, BRAF, CDKN2A, EGFR, ERBB2, KIT, KRAS, NRAS, MET, PDGFRA , PDGFRB, PGR, PIK3CA , PTEN, RET), interrogation of sequence variants and copy number variants or rearrangements, if performed
ICD-10 codes covered if selection criteria are met:
D44.0	Neoplasm of uncertain behavior of the thyroid gland
E04.0 - E04.9	Other non-toxic goiter [thyroid nodules] [not covered for repeat testing of indeterminate thyroid nodules]
*RUNX1*:
Covered CPT codes if selection criteria are met:
81334	RUNX1 (runt-related transcription factor 1) (e.g., acute myelogenous leukemia, familial platelet disorder with associated myeloid malignancy), gene analysis, target sequence analysis (e.g., exons 3-8)
81401	Molecular pathology procedure, level 2 (eg, 2 to 10 SNPs, 1 methylated variant, or 1 somatic variant [typically using target variant analysis without sequencing] or detection of a dynamic mutation/triplet repeat disorder)
ICD-10 codes covered if selection criteria are met:
C92.00 - C92.02, C92.40 - C92.A2	acute myeloid leukemia
D46.0 - D46.9	Myelodysplastic Syndromes (MDS)
D47.02	systemic mastocytosis
tests SF3B1:
Covered CPT codes if selection criteria are met:
81347	SF3B1 (splicing factor [3b] subunit B1) gene analysis (e.g. myelodysplastic syndrome/acute myelogenous leukemia), common variants (e.g. A672T, E622D, L833F, R625C, R625L)
ICD-10 codes covered if selection criteria are met:
C69.30–C69.32	malignant neoplasm of choroid
C69.40–C69.42	Malignant neoplasm of the ciliary body
C92.10 – C92.12	Chronic myeloid leukemia, BCR/ABL-positive
C92.20 - C92.22	Atypical chronic myelogenous leukemia, negative BCR/ABL
C94.6	Myelodysplastic disease, not elsewhere classified [myeloproliferative neoplasms]
D45	polycythemia vera
D46.0 – D46.Z	Myelodysplastic syndromes
D47.3	Essential (hemorrhagic) thrombocythemia
D75.81	mielofibrose
teste SRSF2:
Covered CPT codes if selection criteria are met:
81348	SRSF2 (serine-arginine-rich splicing factor 2) gene analysis (e.g. myelodysplastic syndrome, acute myeloid leukemia), common variants (e.g. P95H, P95L)
ICD-10 codes covered if selection criteria are met:
C92.10 – C92.12	Chronic myeloid leukemia, BCR/ABL-positive
C92.20 - C92.22	Atypical chronic myelogenous leukemia, negative BCR/ABL
C94.6	Myelodysplastic disease, not elsewhere classified [myeloproliferative neoplasms]
C96.21	aggressive systemic mastocytosis
D45	polycythemia vera
D46.0 – D46.Z	Myelodysplastic syndromes
D47.3	Essential (hemorrhagic) thrombocythemia
D75.81	mielofibrose
*thymidine kinase*:
Covered CPT codes if selection criteria are met:
81405	Molecular pathology procedure, level 6 (e.g., analysis of 6-10 exons using DNA sequence analysis, scanning for mutations or duplication/deletion variants of 11-25 exons, regionally targeted cytogenomic array analysis)
ICD-10 codes covered if selection criteria are met:
C91.10 - C91.12, C91.90 - C91.91	Chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL)
*thyroglobulin antibody*:
Covered CPT codes if selection criteria are met:
86800	thyroglobulin antibody
ICD-10 codes covered if selection criteria are met:
C73	Malignant neoplasm of the thyroid gland
D09.3	Carcinoma in situ of the thyroid and other endocrine glands
D34	benign thyroid neoplasm
*Thyroglobulin (TG) expression*:
Covered CPT codes if selection criteria are met:
84432	tiroglobulina
ICD-10 codes covered if selection criteria are met:
C73	Malignant neoplasm of the thyroid gland
C76	Malignant neoplasm of head, face and neck
C80.0 - C80.1	Disseminated neoplasia and other malignancies, unspecified
D09.3	Carcinoma in situ of the thyroid and other endocrine glands
D34	benign thyroid neoplasm
*Thyroid transcription factor 1 (TTF-1)*:
Covered CPT codes if selection criteria are met:
88342	immunohistochemistry or immunocytochemistry, each antibody separately identifiable by block, cytology preparation or blood smear; first antibody separately identifiable by slide
88341	each additional single antibody staining procedure (list separately in addition to primary procedure code)
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of the bronchi and lungs
C7A.00 - C7A.8	Malignant neuroendocrine tumors
D02.20 - D02.22	Bronchial and lung carcinoma in situ
D14.30 - D14.32	Benign neoplasm of bronchi and lungs
D3A.00 - D3A.8	Benign neuroendocrine tumors
WT-1 gene expression- No specific code:
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of bronchus and lung [non-small cell lung cancer]
C48.2	Peritoneal malignant neoplasm, unspecified [desmoplastic round cell tumor]
C56.1 - C56.9	Malignant neoplasm of ovary [clear cell carcinoma of ovary]
C80.0 - C80.1	Disseminated neoplasia and other malignancies, unspecified
*Test the tumor the test the HPV (p16)*:
Covered CPT codes if selection criteria are met:
87624	Detection of infectious agents by nucleic acid (DNA or RNA); Human papillomavirus (HPV), high-risk types (eg, 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68)
ICD-10 codes covered if selection criteria are met:
C00.0 - C14.8	Malignant neoplasm of lip, oral cavity and pharynx
C76.0	Malignant neoplasm of head, face and neck
C80.1	Malignant neoplasm (primary), unspecified
*ezh2*:
Covered CPT codes if selection criteria are met:
81236	EZH2 (polycomb zest 2 repressive complex subunit 2 tensor) (eg, myelodysplastic syndrome, myeloproliferative neoplasms) gene analysis, complete gene sequence
81237	EZH2 (polycombzest-2 repressive complex subunit 2 enhancer) gene analysis (e.g., diffuse large B-cell lymphoma), common variant(s) (e.g., codon 646)
ICD-10 codes covered if selection criteria are met:
D46.20 - D46.9	myelodysplastic syndrome
D45	polycythemia vera
D69.3	Immune Thrombocytopenic Purpura
C94.40 - C94.42	Acute panmyelosis with myelofibrosis
D47.1	chronic myeloproliferative disease
D47.4	osteomielofibrose
D75.81	mielofibrose
C92.10 - C92.12	Chronic myeloid leukemia, BCR/ABL-positive
ICD-10 codes not covered for CPB-listed indications (not all included):
C83.30 - C83.39	Diffuse large B-cell lymphoma
*TERT (telomerase transcriptase reversa)*:
Covered CPT codes if selection criteria are met:
81345	TERT (telomerase reverse transcriptase) gene analysis (e.g. thyroid carcinoma, glioblastoma multiforme), target sequence analysis (e.g. promoter region)
ICD-10 codes covered if selection criteria are met:
C71.0 - C71.9	malignant neoplasm of the brain
D46.20 - D46.9	myelodysplastic syndrome
ICD-10 codes not covered for CPB-listed indications (not all included):
C73	Malignant neoplasm of the thyroid gland
Carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) (eg, benign diagnostic risk test)- No specific code:
ICD-10 codes not covered for CPB-listed indications (not all included):
N62	Breast hypertrophy [atypical breast hyperplasia]
*CDX2*:
ICD-10 codes not covered for CPB-listed indications (not all included):
C18.0 - C18.9	colon malignancy
D01.0	Colon carcinoma in situ
D12.0 - D12.9	benign neoplasm of the colon
*CxBladder tests*:
CPT codes not covered for indications listed in the CPB:
0012M	Oncology (urothelial), mRNA, real-time PCR quantitative gene expression profiling of five genes (MDK, HOXA13, CDC2 [CDK1], IGFBP5, and XCR2), using urine, algorithm reported as urothelial carcinoma risk score
0013M	Oncology (urothelial), mRNA, real-time PCR quantitative gene expression profiling of five genes (MDK, HOXA13, CDC2 [CDK1], IGFBP5, and CXCR2), using urine, algorithm reported as risk score for recurrent urothelial carcinoma
ICD-10 codes not covered for CPB-listed indications (not all included):
C67.0 - C67.9	bladder malignancy
*4Kscore*:
CPT codes not covered for indications listed in the CPB:
81539	Oncology (high-grade prostate cancer), four-protein biochemical assay (total PSA, free PSA, intact PSA, and human kallikrein-2 [hK2]), using plasma or serum, prognostic algorithm reported as a probability score
Fibrinogen Degradation Products (FDP) test (eg, DR-70 or Onko-Sure)- No specific code:
ICD-10 codes not covered for CPB-listed indications (not all included):
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
HMGB1 e RAGE- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C43.0 - C44.99	Melanoma and other skin neoplasms
IHC4 (p. ej., NexCourse IHC4)- No specific code:
ICD-10 codes not covered for CPB-listed indications (not all included):
C50.011 - C50.929	Mama malignant neoplasm
*Lectin-reactive alpha-fetoprotein (AFP-L3)*:
CPT codes not covered for indications listed in the CPB:
82107	alpha-fetoprotein (AFP); AFP-L3 isoform fraction and total AFP (including ratio)
ICD-10 codes not covered for CPB-listed indications (not all included):
C22.0, C22.2 - C22.9	malignant neoplasm of the liver
*Biopsia líquida (p. ej., CancerIntercept, GeneStrat, Colvera, Neolab Prostate, FoundationACT, FoudationOne Liquid CDx, Guardant360CDx)*:
Covered CPT codes if selection criteria are met:
0179U	Oncology (non-small cell lung cancer), cell-free DNA, specific sequence analysis of 23 genes (single nucleotide variations, insertions and deletions, fusions without prior partner knowledge/breakpoint, copy number variations), with significant mutation(s) report (covered up to 50 genes)
CPT codes not covered for indications listed in the CPB:
Neolab Prostate-no specific code
0011M	Oncology, prostate cancer, 12-gene mRNA expression assay (10 content and 2 maintenance), RT-PCR testing using blood plasma or urine, algorithms to predict risk of high-grade prostate cancer
0326U	Targeted genomic sequence analysis panel, solid organ neoplasia, circulating cell-free DNA analysis of 83 or more genes, sequence variant interrogation, gene copy number amplifications, gene rearrangements, microsatellite instability, and mutational load of the tumor
86152	Cell count by immunological selection and identification in fluid samples (eg circulating tumor cells in the blood)
86153	interpretation and medical report, when necessary
ICD-10 codes covered if selection criteria are met:
C34.00 - C34.92	Malignant neoplasm of the bronchi and lungs
ICD-10 codes not covered for CPB-listed indications (not all included):
C18.0 - C20	Malignant neoplasm of colon, rectosigmoid junction and rectum
C43.0 - C43.9	malignant skin melanoma
C50.011 - C50.929	Mama malignant neoplasm
C56.1 - C56.9	malignant neoplasm of ovary
C61	malignant neoplasm of prostate
long non-coding RNA- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C23	Gallbladder malignant neoplasm
*Proteomic profiling based on mass spectrometry (eg Xpresys Lung)*:
CPT codes not covered for indications listed in the CPB:
0174U	Oncology (solid tumor), mass spectrometry 30 protein targets, formalin-fixed, paraffin-embedded tissue, prognosis and predictive algorithm reported as likely, unlikely, or uncertain benefit of 39 chemotherapy and specific therapeutic oncologic agents
81538	Oncology (lung), mass spectrometry of 8 proteins, including amyloid A, using serum, prognosis and predictive algorithm reported as good versus poor overall survival
ICD-10 codes not covered for CPB-listed indications (not all included):
R91.8	Other non-specific abnormal lung field finding [undetermined lung nodules]
*OncoVantage*:
CPT codes not covered for indications listed in the CPB:
81445	Targeted genomic sequence analysis panel, solid organ neoplasia, DNA analysis and RNA analysis when performed, 5-50 genes (e.g. ALK, BRAF, CDKN2A, EGFR, ERBB2, KIT, KRAS, NRAS, MET, PDGFRA , PDGFRB, PGR, PIK3CA , PTEN, RET), interrogation of sequence variants and copy number variants or rearrangements, if performed
Select MDX- No specific code:
ICD-10 codes not covered for indications listed in the CPB:
C61	malignant neoplasm of prostate
*Targeted paired rearrangements (whole genome next-generation sequencing)*:
CPT codes not covered for indications listed in the CPB:
0013U	Oncology (neoplasia of solid organs), detection of genetic rearrangements using next-generation sequencing of the whole genome, fresh or frozen DNA, cells or tissues, reports of specific genetic rearrangements
0014U	Hematology (hematolymphoid neoplasm), detection of genetic rearrangements by next-generation whole-genome sequencing, DNA, whole blood or bone marrow, reports of specific genetic rearrangements
0056U	Hematology (acute myelogenous leukemia), DNA, next-generation whole-genome sequencing to detect genetic rearrangements, blood or bone marrow, report of specific genetic rearrangements
ICD-10 codes not covered for indications listed in the CPB:
C81.00 - C96.9	Neoplasia hematolinfóide
C00.0 - C43.9, C44.00 - C80.2	solid organ neoplasm
*Experimental and Investigational Tumor Markers*:
CPT codes not covered for indications listed in the CPB:
0006M	Oncology (liver), mRNA expression levels of 161 genes, using fresh hepatocellular carcinoma tumor tissue, with alpha-fetoprotein level, algorithm reported as risk classifier [Heprodx]
0007M	Oncology (gastrointestinal neuroendocrine tumors), real-time PCR expression analysis of 51 genes, using whole peripheral blood, algorithm reported as a tumor disease index nomogram [Netest]
0015M	Adrenal cortical tumor, biochemical assay of 25 steroid markers, using 24-hour urine sample and clinical parameters, prognostic algorithm reported as a clinical risk and integrated clinical risk of steroids for adrenocortical carcinoma, adenoma or other adrenal malignancy
0016M	Oncology (Bladder), mRNA, 209-gene microarray gene expression profiling, using formalin-fixed paraffin-embedded tissue, algorithm reported as molecular subtype (luminal, luminal infiltrate, basal, low basal claudin, neuroendocrine type)
0005U	Oncology (prostate) gene expression profiling by real-time RT-PCR of 3 genes (ERG, PCA3 and SPDEF), urine, algorithm reported as risk score
0013U	Oncology (neoplasia of solid organs), detection of genetic rearrangements using next-generation sequencing of the whole genome, fresh or frozen DNA, cells or tissues, reports of specific genetic rearrangements
0019U	Oncology, RNA, gene expression by whole transcriptome sequencing, formalin-fixed paraffin-embedded tissue or fresh frozen tissue, predictive algorithm reported as potential targets for therapeutic agents
0037U	Targeted genomic sequence analysis, solid organ neoplasia, DNA analysis of 324 genes, interrogation of sequence variants, gene copy number amplifications, gene rearrangements, microsatellite instability, and tumor mutational load
0050U	Targeted Genomic Sequence Analysis Panel, Acute Myeloid Leukemia, DNA analysis, 194 genes, interrogation of sequence variants, copy number variants or rearrangements
0053U	Oncology (prostate cancer), 4-gene FISH analysis (ASAP1, HDAC9, CHD1, and PTEN), needle biopsy sample, algorithm reported as higher grade tumor probability
0057U	Oncology (solid organ neoplasm), mRNA, gene expression profiling by massively parallel sequencing for analysis of 51 genes, using formalin-fixed paraffin-embedded tissue, algorithm reported as normalized percentile rank
0058U	Oncology (Merkel cell carcinoma), detection of antibodies against Merkel cell polyoma virus oncoprotein (small T antigen), serum, quantitative
0059U	Oncology (Merkel cell carcinoma), detection of antibodies against Merkel cell polyoma virus (VP1) capsid protein, serum, reported as positive or negative
0067U	Oncology (breast), immunohistochemistry, protein expression profiling of 4 biomarkers (matrix metalloproteinase-1 [MMP-1], carcinoembryonic antigen-related cell adhesion molecule 6 [CEACAM6], hyaluronoglucosaminidase [HYAL1], highly expressed protein [ HEC1]), formalin-fixed, paraffin-embedded precancerous breast tissue, algorithm reported as carcinoma risk score
0069U	Oncology (colorectal), microRNA, RT-PCR expression profiling of miR-31-3p, formalin-fixed paraffin-embedded tissue, algorithm reported as expression score
0090U	Oncology (cutaneous melanoma), mRNA RT-PCR gene expression profiling of 23 genes (14 content and 9 cleaning), using formalin-fixed paraffin-embedded tissue, algorithm reported as categorical (ie, benign, indeterminate). , evil)
0092U	Oncology (lung), three protein biomarkers, magnetic nanosensor technology immunoassay, plasma, algorithm reported as malignancy likelihood risk score
0113U	Oncology (prostate), measurement of urine PCA3 and TMPRSS2-ERG and serum PSA after prostate massage, by RNA amplification and fluorescence-based detection, algorithm reported as risk score
0120U	Oncology (B-cell lymphoma staging), mRNA, fluorescent probe hybridization gene expression profiling of 58 genes (45 content and 13 maintenance), formalin-fixed, paraffin-embedded tissue, algorithm reported as probability of primary lymphoma mediastinal B-cell lymphoma (PMBCL) and diffuse large B-cell lymphoma (DLBCL) with cell subtyping of origin in the latter
0174U	Oncology (solid tumor), mass spectrometry 30 protein targets, formalin-fixed, paraffin-embedded tissue, prognosis and predictive algorithm reported as likely, unlikely, or uncertain benefit of 39 chemotherapy and specific therapeutic oncologic agents
0204U	Oncology (thyroid), mRNA, gene expression analysis of 593 genes (including BRAF, RAS, RET, PAX8 and NTRK) for sequence variants and rearrangements, using fine needle aspiration, reported as detected or not detected
0211U	Oncology (pan-tumor), DNA and RNA by next-generation sequencing using formalin-fixed, paraffin-embedded tissue, interpretive reporting for single nucleotide variants, copy number changes, tumor mutational load, and microsatellite instability, with therapeutic association
0220U	Oncology (breast cancer), image analysis with artificial intelligence assessment of 12 histological and immunohistochemical features, reported as recurrence score
0228U	Oncology (prostate), multianalyte molecular profiling by photometric detection of macromolecules adsorbed on nanosponge matrix slides with machine learning, using first morning urine, algorithm reported as prostate cancer probability
0229U	BCAT1 (branched chain amino acid 1) or IKZF1 (IKAROS family zinc finger 1) promoter methylation analysis (e.g., colorectal cancer)
0242U	Targeted genomic sequence analysis panel, solid organ neoplasia, circulating cell-free DNA analysis of 55-74 genes, interrogation of sequence variants, gene copy number amplifications and gene rearrangements
0244U	Oncology (solid organ), DNA, comprehensive genomic profiling, 257 genes, single nucleotide variant interrogation, insertions/deletions, copy number changes, gene rearrangements, tumor mutational load, and microsatellite instability, using formalin-fixed tumor tissue and embedded in paraffin
0250U	Oncology (solid organ neoplasm), DNA analysis of targeted genomic sequence of 505 genes, interrogation of somatic changes (SNV [single nucleotide variant], small insertions and deletions, one amplification and four translocations), microsatellite instability and tumor mutation
0261U	Oncology (colorectal cancer), image analysis with artificial intelligence assessment of 4 histological and immunohistochemical features (CD3 and CD8 within tumor stromal border and tumor core), tissue, reported as immune response and recurrence risk score
0262U	Oncology (solid tumor), 7-way real-time RT-PCR gene expression profiling (ER, AR, PI3K, MAPK, HH, TGFB, Notch), formalin-fixed, paraffin-embedded (FFPE), algorithm reported as via genic activity score
0285U	Oncology, radiation response, cell-free DNA, quantitative branched-chain DNA amplification, plasma, reported as radiation toxicity score
0288U	Oncology (lung), mRNA, quantitative PCR analysis of 11 genes (BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR, WNT3A) and 3 reference genes (ESD, TBP, YAP1), formalin - paraffin-embedded tumor tissue (FFPE), algorithmic interpretation reported as a recurrence risk score
0295U	Oncology (ductal carcinoma of the breast in situ), protein expression profile by immunohistochemistry of 7 proteins (COX2, FOXA1, HER2, Ki-67, p16, PR, SIAH2), with 4 clinicopathological factors (size, age, status of margin, palpability), using formalin-fixed paraffin-embedded tissue (FFPE), algorithm reported as a recurrence risk score
0296U	Oncology (oral and/or oropharyngeal cancer), RNA sequencing gene expression profiling of at least 20 molecular traits (e.g., human and/or microbial mRNA), saliva, algorithm reported as positive or negative for signature associated with malignancy
0297U	Oncology (pantumor), whole-genome sequencing of normal and corresponding malignant DNA samples, fresh or formalin-fixed paraffin-embedded (FFPE) tissue, blood or bone marrow, comparative sequence analysis and variant identification
0298U	Oncology (pantumor), whole transcriptome sequencing of paired normal and malignant RNA specimens, fresh or formalin-fixed and paraffin-embedded (FFPE) tissue, blood or bone marrow, comparative sequence and expression level analysis, and chimeric transcript identification
0299U	Oncology (pantumor), optical whole-genome mapping of malignant and corresponding normal DNA samples, fresh frozen tissue, blood or bone marrow, comparative identification of structural variants
0300U	Oncology (pan tumor), whole genome sequencing and optical genome mapping of combined samples of malignant and normal DNA, fresh tissue, blood or bone marrow, comparative sequence analysis and variant identification
0313U	Oncology (pancreas), 74-gene DNA and mRNA next-generation sequencing analysis and CEA gene expression analysis (CEACAM5), pancreatic cyst fluid, algorithm reported as categorical (i.e., negative, low probability of neoplasia) or positive, high probability of neoplasia )
0314U	Oncology (cutaneous melanoma), mRNA gene expression profiling by RT-PCR of 35 genes (32 content and 3 maintenance), using formalin-fixed, paraffin-embedded (FFPE) tissue, algorithm reported as categorical (i.e., benign, intermediate, malignant)
0315U	Oncology (cutaneous squamous cell carcinoma), mRNA gene expression profiling by RT-PCR of 40 genes (34 content and 6 cleaning), using formalin-fixed, paraffin-embedded (FFPE) tissue, algorithm reported as a result of categorical risk (i.e., Class 1, Class 2A, Class 2B)
0317U	Oncology (lung cancer), 4-probe FISH assay (3q29, 3p22.1, 10q22.3, 10cen), whole blood, algorithmically generated predictive assessment reported as decreased or increased risk of lung cancer
0324U	Oncology (ovarian), spheroid cell culture, 4-drug panel (carboplatin, doxorubicin, gemcitabine, paclitaxel), predictive response to tumor chemotherapy for each drug
0325U	Oncology (ovarian), spheroid cell culture, poly (ADP-ribose) polymerase (PARP) inhibitors (niraparib, olaparib, rucaparib, velparib), predicting tumor response to each drug
0329U	Oncology (Neoplasia), Exome and Transcriptome Sequence Analysis for Sequence Variants, Gene Copy Number Amplifications and Deletions, Gene Rearrangements, Microsatellite Instability, and Mutational Load of Tumors Using Tumor DNA and RNA with Normal Blood DNA or saliva for subtraction, report of clinically significant mutation(s) with therapeutic associations
0331U	Oncology (hematolymphoid neoplasia), optical genome mapping for copy number changes and genetic rearrangements using DNA from blood or bone marrow, reports of clinically significant changes
0332U	Oncology (pan-tumor), genetic profiling of 8 DNA regulatory (epigenetic) markers by quantitative polymerase chain reaction (qPCR), whole blood, reported as having a high or low probability of responding to immune control endpoint inhibitor therapy
0333U	Oncology (liver), surveillance of hepatocellular carcinoma (HCC) in high-risk patients, analysis of methylation patterns in circulating cell-free DNA (cfDNA) plus measurement of serum AFP/AFP-L3 and des-gamma-carboxy oncoprotein-prothrombin (DCP ), algorithm reported as normal or abnormal result
0334U	Oncology (solid organ), targeted genomic sequence analysis, formalin-fixed, paraffin-embedded (FFPE) tumor tissue, DNA analysis, 84 or more genes, interrogation of sequence variants, gene copy number amplifications, gene rearrangements, microsatellite instability, and tumor mutational load
0342U	Oncology (pancreatic cancer), multiplex immunoassay for C5, C4, cystatin C, factor B, osteoprotegerin (OPG), gelsolin, IGFBP3, CA125, and multiplex electrochemiluminescent immunoassay (ECLIA) for CA19-9, serum, diagnostic algorithm reported qualitatively positive , negative or limit
0343U	Oncology (prostate), exosome-based analysis of 442 small non-coding RNAs (sncRNAs) by quantitative reverse transcription polymerase chain reaction (RT-qPCR), urine, reported as molecular evidence of zero, low, intermediate, or high risk of prostate cancer
0356U	Oncology (oropharyngeal), evaluation of 17 DNA biomarkers by digital droplet PCR (ddPCR), cell-free DNA, algorithm reported as prognostic risk score for cancer recurrence
0357U	Oncology (melanoma), artificial intelligence (AI) enabled quantitative mass spectrometry analysis of 142 unique pairs of glycopeptides and product fragments, plasma, prognosis, and predictive algorithm reported as likely, unlikely, or uncertain benefit of immunotherapy agents
0359U	Oncology (prostate cancer), analysis of all prostate-specific antigen (PSA) structural isoforms by phase separation and immunoassay, plasma, cancer risk algorithm reports
0360U	Oncology (lung), 7-autoantibody enzyme-linked immunosorbent assay (ELISA) (p53, NY-ESO-1, CAGE, GBU4-5, SOX2, MAGE A4, and HuD), plasma, algorithm reported as categorical outcome for malignancy risk
0362U	Oncology (papillary thyroid cancer), gene expression profiling by means of targeted hybrid capture enrichment, RNA sequencing of 82 content genes and 10 housekeeping genes, formalin-fixed, paraffin-embedded (FFPE) tissue, algorithm reported as one of three molecular subtypes
0363U	Oncology (urothelial), mRNA, real-time PCR quantitative gene expression profiling of 5 genes (MDK, HOXA13, CDC2 [CDK1], IGFBP5 and CXCR2), using urine, algorithm incorporates age, sex, smoking history and frequency of gross hematuria, reported as a risk score for urothelial carcinoma
81218	CEBPA (CCAAT/enhancer binding protein [C/EBP], alpha) (eg, acute myelogenous leukemia), genetic analysis, complete genetic sequence
81455	Target genomic sequence analysis panel, hematolymphoid or solid organ neoplasm, DNA analysis and RNA analysis when performed, 51 or more genes (e.g. ALK, BRAF, CDKN2A, CEBPA, DNMT3A, EGFR, ERBB2, EZH2, FLT3 , IDH1, IDH2, JAK2 , KIT, KRAS, MLL, NPM1, NRAS, MET, NOTCH1, PDGFRA, PDGFRB, PGR, PIK3CA, PTEN, RET), interrogation of sequence variants and copy number variants or rearrangements, if any performed
81456	Target Genomic Sequence Analysis Panel, Hematolymphoid or Solid Organ Disorder or Neoplasm, 51 or more genes (e.g. ALK, BRAF, CDKN2A, CEBPA, DNMT3A, EGFR, ERBB2, EZH2, FLT3, IDH1, IDH2, JAK2, KIT , KRAS, MET , MLL, NOTCH1, NPM1, NRAS, PDGFRA, PDGFRB, PGR, PIK3CA, PTEN, RET), interrogation of sequence variants and copy number variants or rearrangements, or expression isoforms or mRNA expression levels, if performed; RNA analysis
81529	Oncology (cutaneous melanoma), mRNA, real-time RT-PCR gene expression profiling of 31 genes (28 content and 3 maintenance), using formalin-fixed, paraffin-embedded tissue, algorithm reported as recurrence risk, including probability sentinel lymph node metastasis [DecisionDx-Melanoma]
81540	Oncology (tumor of unknown origin), mRNA, real-time RT-PCR gene expression profiling of 92 genes (87 content and 5 maintenance) to classify tumor into major and subtype of cancer using paraffin-embedded tissue fixed in formalin , algorithm reported as a probability of a predicted major cancer type and subtype
82387	Catepsina-D
84275	sialic acid
86316	immunoassay for tumor antigen; other antigen, quantitative (e.g., CA 50, 72-4, 549), each
88342	immunohistochemistry or immunocytochemistry, each antibody separately identifiable by block, cytology preparation or blood smear; first separately identifiable antibody per slide [cyclin E (fragments or full length)]
There are no specific codes for the tumor markers listed below.:
anti-VEGF antibody bevacizumab; BluePrint Molecular Subtyping Profile for Breast Cancer; breast sentinel; C-Met expression; glutathione S-transferase P1 (GSTP1); mammostrate; Percepta bronchial genomic classifier; Phosphatidylinositol-4,5-bisphosphonate 3-kinase; Proveri Prostate Cancer Test (PPCA); ribonucleotide reductase M1 subunit (RRM1); ROS1 rearrangements; Planned GCC; prostatic nucleus mitotic test; UroCor cytology assay (DD23 and P53); BioSpecifx; core detection assay; Envisia genomic classifier, Myriad myPath Melanoma; NantHealth; Signatory Natera; PCa Sentinel Test; signatory; Salivary Metatranscriptome Analysis for Oral Cancer (i.e. CancerDetect mRNA), BostonGene Tumor Portrait Test

Bottom

A tumor marker is a substance such as a protein, antigen or hormone in the body that can indicate the presence of cancer. These markers are usually specific for certain types of cancer and can be detected in blood, urine, and tissue samples. The body can make the marker in response to the cancer, or the tumor itself can make the marker. Detection of tumor markers can be used to determine a diagnosis or as an indicator of disease (cancer) progression. It can also be used to document clinical response to treatment. Tumor markers include, but are not limited to, alpha-fetoprotein (AFP), CA 15-3/CA 27.29, CA 19-9, CA-125, carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA).

Tumor markers are normally produced in small amounts in the cells of the body. Detection of a higher than normal serum level by radioimmunoassay or immunohistochemical techniques usually indicates the presence of a certain type of cancer. Currently, the main use of tumor markers is to assess the cancer's response to treatment and to detect recurrence. In some cancers, tumor marker levels can reflect the extent or stage of the disease and can be useful in predicting how well the disease will respond to treatment. A decrease or return to normal in the level of a tumor marker may indicate that the cancer has responded favorably to therapy. If the tumor marker level rises, it could indicate that the cancer is spreading. Finally, measurements of tumor marker levels can be used after completion of treatment as part of follow-up to check for recurrence.

However, in many cases, the literature states that measurements of tumor marker levels alone are insufficient to diagnose cancer for the following reasons:

tumor marker levels may be elevated in people with benign conditions;
Levels of tumor markers are not elevated in all people with cancer, especially in the early stages of the disease; It is
many tumor markers are not specific for a particular type of cancer; It is
the level of a tumor marker may be elevated for more than one type of cancer.

Examples of tumor markers include

5-Hydroxyindoleacetic acid (5-HIAA) – main serotonin metabolite, used as a marker in the evaluation of carcinoid tumors;
Beta-2-Microglobulina (B2M)–A protein found on the surface of many cells. Elevated B2M levels are an indicator of certain types of cancer, including chronic lymphocytic leukemia, non-Hodgkin's lymphoma, and multiple myeloma or kidney disease;
Beta Human Chorionic Gonadotropin (beta HCG)–A type of tumor marker that may be found in higher than normal amounts in people with some types of cancer.
Calcitonin: hormone secreted by the thyroid that reduces calcium in the blood;
Calretinin: A calcium-binding protein that is used as a marker in the evaluation of lung cancer and other diseases.
Chromogranin A: A protein found in neuroendocrine cells that releases chromogranin A and other hormones into the blood. Chromogranin A can be found in higher than normal amounts in people with certain neuroendocrine tumors, small cell lung cancer, prostate cancer and other conditions
Guanylyl cyclase c (GCC): Enzyme that can be expressed only in the cells that line the intestine, from the duodenum to the rectum.
Inhibin: One of two hormones (called inhibin-A and inhibin-B) secreted by the gonads (by Sertoli cells in males and by granulosa cells in females) and inhibits the production of follicle-stimulating hormone (FSH) by the pituitary gland. gland;
Lactate dehydrogenase (LDH): marker used to monitor testicular cancer treatment;
Mucin-1 (MUC-1): Elevated carbohydrate antigen in people with breast, ovarian, lung, and prostate tumors, as well as other disorders;
Napsin A – Protein used as a marker in the evaluation of lung cancer;
Pre-albumin – Marker of nutritional status and sensitive indicator of protein synthesis. Also known as transthyretin;
Prostate Specific Antigen (PSA) – Substance produced by the prostate. PSA blood levels are often increased in men with prostate cancer.
Thyroglobulin: A protein found in the thyroid gland. Some thyroglobulin may be found in the blood, and this amount may be measured after thyroid surgery to determine whether the thyroid cancer has returned;
Thyroid Transcription Factor-1 (TTF-1): protein used as a tumor marker in the evaluation of lung cancer;
Transferrin: A protein in blood plasma that transports iron derived from food to the liver, spleen, and bone marrow.

Tumors can be evaluated with histology, which involves examining the structure, especially the microscopic structure, of body tissues. Methods for detecting tumor markers include, but are not limited to: Fluorescent in situ hybridization (FISH): A laboratory technique used to detect small deletions or rearrangements in chromosomes. Immunohistochemical analysis (IHC): A laboratory process for detecting an organism in tissues with antibodies.

Supposedly, genetic mutation testing can be used to find somatic mutations in cancer cells that are not inherited. Some examples of genes that may have somatic mutations include: IDH1 and IDH2 genes (associated with acute myelogenous leukemia [AML], gliomas, and chondrosarcomas); NPM1 and FLT3 genes (associated with AML).

Individualized molecular tumor profiling is a laboratory method of testing a panel of tumor markers, which may include genetic and biochemical markers, to establish a personalized molecular profile of a tumor to recommend treatment options.

Mass spectrometry-based proteomic profiling (eg, Veristrat, Xpresys Lung) is a multivariate serum protein test that uses mass spectrometry and proprietary algorithms to analyze proteins in an individual's serum. Xpresys is no longer on the market.

Next-generation sequencing (NGS) testing uses selected genes to purportedly identify molecular growth drivers to improve risk stratification and targeted therapies. Examples include: FoundationOne and OncoVantage for solid tumor cancers; FoundationOne Heme for blood cancer and sarcomas; and ThyGenX for indeterminate thyroid nodules.

Liquid biopsy refers to testing serum for fragments of DNA that are shed by cancer cells and released into the bloodstream. This method is purportedly used for the detection, diagnosis, and/or monitoring of cancer cells that might otherwise require a tissue sample.

Multianalytical assays with algorithmic analysis (MAAA) are laboratory measurements that use a mathematical formula to analyze various markers that may be associated with a particular disease state and are designed to assess an individual's disease activity or disease risk. The lab performs an algorithmic analysis using test results and sometimes other information, such as gender and age, and converts the information into a numerical score, which is transmitted in a lab report. MAAAs are usually unique to a single lab that owns the algorithm. MAAAs have been proposed for the evaluation of pelvic masses, including assistance in determining referral for surgery to a gynecological oncologist or general surgeon.

Topographic genotyping (eg, PathFinderTG) is a test that examines a panel of 15 to 20 genetic markers in a tissue biopsy or other tissue sample to presumably aid in the determination of uncertain or equivocal cancer diagnoses.

Myeloproliferative Neoplasms and Myelodysplastic Syndromes

Myeloproliferative neoplasms and myelodysplastic syndromes are diseases of blood cells and both carry an increased risk of transformation into acute myelogenous leukemia (AML). Myelodysplastic syndromes (MDS) refer to a heterogeneous group of myeloid disorders characterized by variable reductions in the production of red blood cells, platelets, and mature granulocytes that can also exhibit functional (i.e., qualitative) defects.

In contrast, myeloproliferative neoplasms (MPN) refer to a group of heterogeneous disorders characterized by overproduction of one or more types of blood cells. MPNs include polycythemia vera, essential thrombocythemia, chronic myelogenous leukemia, primary myelofibrosis, chronic neutrophilic leukemia, and other less defined entities such as chronic eosinophilic leukemia that are not otherwise classified.

A third category, myelodysplastic/myeloproliferative neoplasms (MDS/MPN), includes disorders that manifest both dysplastic and proliferative features. These include chronic myelomonocytic leukemia, juvenile myelomonocytic leukemia, atypical CML (aCML, BCR-ABL1 negative), MDS/MPN with ringed sideroblasts and thrombocytosis, and unclassifiable MDS/MPN.

PSA

Prostate Specific Antigen (PSA) is a substance produced by the prostate. PSA blood levels are often increased in men with prostate cancer. Elevated levels of prostate-specific antigen (PSA) can also be found in the blood of men with benign prostate conditions such as prostatitis and benign prostatic hyperplasia (BPH). Although the PSA cannot distinguish between benign prostate conditions and cancer, an elevated PSA level may indicate that other tests are needed to determine whether cancer is present. PSA levels have been shown to be useful in monitoring the effectiveness of prostate cancer treatment and in checking for recurrence after treatment has ended. The use of PSA for screening remains highly controversial. Although researchers are studying the PSA value in conjunction with digital rectal examinations for routine prostate cancer screening in men aged 55 to 74 years; and the literature still does not show whether the use of PSA to detect prostate cancer actually reduces the number of deaths caused by this cancer. The American Cancer Society recommends that physicians and patients consider PSA screening and digital rectal examination for African-American men and men with a familial tendency age 40 years and older and all men age 50 years and older.

The Cancer Care Ontario guidelines on active surveillance for prostate cancer (Morash, et al., 2015) state that the active surveillance protocol should include the following tests: PSA test every 3 to 6 months; digital rectal exam each year and confirmatory transrectal ultrasound (TRUS) biopsy of 12 to 14 cores (including anterior targeted cores) within 6 to 12 months, then serial biopsy at least every 3 to 5 years thereafter. The guidelines state that "current evidence shows that PSA kinetics does not reliably predict disease stability or reclassification to a higher-risk state. There is conflicting evidence as to whether PSA is a good predictor of disease progression." found in the ability of different PSA measures such as PSA velocity, PSA density, and PSA doubling time to predict progression or reclassification PSA control is considered a necessary component of an AS protocol, but a PSA increase may be better seen as a trigger for reassessment (e.g., MRI, repeat biopsy) rather than triggering an intervention."

PCA3

Prostate cancer antigen 3 (PCA3, also known as DD3) is a gene that has been found to be highly overexpressed in prostate cancer. This gene has been investigated as a potential diagnostic marker for prostate cancer. However, there are no published clinical outcome studies on the effectiveness of the PCA3 gene in detecting, diagnosing, or treating prostate cancer.

Prostate Cancer Antigen 3 (PCA3) (Progensa, Gene-Probe, Inc.) encodes a prostate-specific mRNA. It is one of the most prostate cancer-specific genes identified, with overexpression in approximately 95% of the analyzed cancers. The PCA3 urine assay is an amplified nucleic acid assay that uses transcription-mediated amplification (TMA) to quantify PCA3 and PSA mRNA in prostate cells found in urine samples. The PCA3 score is calculated as the ratio of PCA3 mRNA to PSA. The primary target population for this non-invasive test is men with an elevated PSA but a negative prostate biopsy. Other target groups include men with slightly elevated PSA as well as men with signs and symptoms suggestive of prostate cancer.

van Gils and colleagues (2007) stated that PCA3 is a promising prostate cancer marker. These investigators conducted a multicenter study to validate the diagnostic performance of the PCA3 urine test established in a previous single-institution study. The first voided urine after digital rectal examination (DRE) was collected from a total of 583 men with serum PSA levels between 3 and 15 ng/ml who were due to undergo a prostate biopsy. These investigators determined the PCA3 score in these samples and correlated the results with the results of prostate biopsies. A total of 534 men (92%) had an informative sample. The area under the receiver operating characteristic curve, a measure of the diagnostic accuracy of a test, was 0.66 for the PCA3 urine test and 0.57 for the serum PSA. The sensitivity of the urine PCA3 test was 65%, the specificity 66% (versus 47% for serum PSA), and the negative predictive value 80%. The authors concluded that the findings of this multicenter study validated the diagnostic performance of the PCA3 urine test in the largest group studied to date using a test based on the PCA3 gene.

Marks and Associates (2007) examined the potential usefulness of investigational PCA3 urinalysis for predicting the outcome of a repeat biopsy. Post-DRE urine (3 swabs per lobe) was collected from 233 men with persistent serum PSA levels of 2.5 ng/mL or greater and at least one previous negative biopsy. PCA3 scores were determined using a highly sensitive quantitative assay with TMA. The ability of the PCA3 score to predict biopsy outcome was evaluated and compared with serum PSA levels. The RNA yield was adequate for analysis in urine samples from 226 of 233 men (ie, the informative sampling rate was 97%). Repeat biopsy revealed prostate cancer in 60 (27%) of the remaining 226 subjects. Analysis of the receiver operating characteristic curve produced an area under the curve of 0.68 for the PCA3 score. In contrast, the area under the curve for serum PSA was 0.52. Using a PCA3 cutoff of 35, the assay sensitivity was 58% and specificity 72%, with an odds ratio of 3.6. With PCA3 scores less than 5, only 12% of men had prostate cancer at repeat biopsy; with PCA3 scores greater than 100, the risk of a positive biopsy was 50%. The authors concluded that in men who underwent repeat prostate biopsy to rule out cancer, urine PCA3 score was superior to serum PSA in predicting biopsy outcome. The high specificity and informative rate suggest that the PCA3 assay may have an important role in the diagnosis of prostate cancer.

Groskopf et al (2007) reported that the PCA3 score is independent of prostate volume and highly correlated with the risk of a positive biopsy. The PCA3 test was performed on 529 men scheduled for prostate biopsy. Overall, the PCA3 score had a sensitivity of 54% and a specificity of 74%. A PCA3 score of less than 5 was associated with a 14% risk of a positive biopsy, while a PCA3 score of greater than 100 was associated with a 69% risk of a positive biopsy.

Haese et al (2007) presented preliminary results from a multicenter European study of PCA3. Included patients had a PSA level less than or equal to 2.5 ng/mL, had 1 or 2 previous negative biopsies, and were scheduled for repeat biopsy. The specificity of the PCA3 score (cutoff 35) was 78% and the sensitivity 67%. Patients with a PCA3 score greater than or equal to 35 had a 33% chance of a new positive biopsy, compared with a 6% chance for those with a PCA3 score less than 35.

In a review on biomarkers for prostate cancer detection, Parekh, et al. (2007) stated that prostate stem cell antigen, alpha-methyl coenzyme-A racemase, PCA3, early prostate cancer antigen, hepsin and human kallikrein 2 are promising markers currently under validation.

An evaluation by the BlueCross BlueShield Association's Technology Assessment Center (BCBSA, 2008) found that, overall, PCA3 trial results to date are preliminary; interpretation of results has not been standardized, and no studies of clinical utility in decision-making for initial biopsy, repeat biopsy, or treatment have been reported.

Tosoian et al (2010) evaluated the relationship between PCA3 and prostate biopsy results in men in a surveillance program. Urine samples were obtained from 294 men with prostate cancer enrolled in the Johns Hopkins surveillance program. The follow-up protocol included biannual total and free PSA determinations, digital rectal examination, and annual follow-up prostate biopsy. Cox proportional hazards regression was used to assess the association between PCA3 results and progression on surveillance biopsy (defined as Gleason pattern 4 or 5, more than 2 positive biopsy cores, or greater than 50% impairment) . of any nucleus with cancer). Patients with progression on biopsy (12.9%) had a mean PCA3 score similar to those without progression (60.0 vs 50.8, p = 0.131). Analysis of receptor operating characteristics suggested that PCA3 alone could not be used to identify men with biopsy progression (area under the curve = 0.589, 95% CI 0.496 to 0.683, p = 0.076). After adjustment for age and date of diagnosis, PCA3 was not significantly associated with progression on biopsy (p = 0.15). The authors concluded that in men with low-risk prostate cancer who were carefully screened for surveillance, the PCA3 score was not significantly associated with short-term biopsy progression. They stated that further analyzes are needed to assess the usefulness of PCA3 in combination with other biomarkers or in selected subsets of patients under surveillance.

While there are studies examining the positive and negative predictive values of the PCA3 urine test, evidence for the effect of this test in the treatment of people with or suspected prostate cancer is currently lacking. The urine PCA3 assay shows promise as a diagnostic tool for prostate cancer; however, more research is needed to determine the clinical value of this assay for screening and diagnostic purposes.

A review of PCA3 prepared for the Agency for Healthcare Research and Quality (2013) concluded: "For diagnostic accuracy, there was little evidence that PCA3 had better diagnostic accuracy for positive biopsy results than tPSA elevations, but insufficient evidence that this would lead to better health outcomes in the medium and long term. For all other settings, comparators and outcomes, there was insufficient evidence."

The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group (2013) found insufficient evidence to recommend testing for prostate cancer antigen 3 (PCA3) to inform decisions about when to re-biopsy patients. to inform decisions to perform initial biopsies for prostate cancer in men at risk (eg, previous elevated PSA test or suspicious digital rectal exam). The EGAPP working group found insufficient evidence to recommend PCA3 testing in men with positive cancer biopsies to determine whether the disease is indolent or aggressive in order to develop an optimal treatment plan. The EGAPP working group concluded that, based on the available evidence, the overall certainty of clinical validity for predicting the diagnosis of prostate cancer using PCA3 is considered "low". The EGAPP working group discouraged clinical use for diagnosis unless more evidence supports greater clinical validity. The EGAPP working group also found that, based on available evidence, overall certainty of net health benefit is considered "low". The EGAPP working group advises against clinical use unless more evidence supports better clinical outcomes.

The European Association of Urology guidelines (2015) state that "biomarkers, including urine markers such as PCA3, the TMPRSS2:ERG fusion gene, or PSA isoforms such as the Phi index, look promising, as does genomics" in the sample. of fabric. , more study data will be needed before these markers can be used in standard clinical practice."

A Cancer Care Ontario guideline on prostate cancer surveillance (Morash, et al., 2015), which was endorsed by the American Society of Clinical Oncology (2016), did not include the level of PCA3 in its recommendation because evidence of PCA3 for predict the reclassification of disease in the prostate was missing cancer.

The National Institute for Health and Care Excellence (NICE) Clinical Practice Guideline on "Prostate Cancer Diagnosis: PROGENSA PCA3 Assay and Prostate Health Index" (2015) stated that "Use of the PROGENSA PCA3 Assay is not recommended and the prostate health index in people who have investigations for suspected prostate cancer, who have a negative or inconclusive transrectal prostate biopsy." The review cited studies that found that adding the PCA3 score to clinical assessment and MRI had very little effect on the size of the reported area under the curve, with minimal change in sensitivity and specificity derived for clinical assessment with MRI. with clinical evaluation by magnetic resonance imaging and the PCA3 test.

In a Lancet review of prostate cancer, Attard, et al. (2016) stated that "several studies have so far been inconclusive on whether PCA3 is useful for selectively detecting aggressive prostate cancers".

B15

Hutchinson et al (2005) stated that, in tissue-based assays, thymosin beta15 (B15) has been shown to correlate with prostate cancer and the recurrence of malignancy. To be clinically effective, thymosin B15 must be released by the tumor into body fluids in detectable concentrations. These researchers developed a quantitative assay that can measure clinically relevant levels of thymosin B15 in human urine. Sixteen recombinant thymosin B15 antibodies and/or peptide conjugates were generated. An antibody, which had stable characteristics over a wide range of pH and salt concentrations found in urine and minimal cross-reactivity with other thymosin betas, was used to develop a competitive enzyme-linked immunosorbent assay (ELISA). Urinary thymosin B15 concentration was determined for control groups; normal (n = 52), prostatic intraepithelial neoplasia (PIN, n = 36) and patients with prostate cancer; no treatment (n = 7) with subsequent biochemical failure, radiotherapy (n = 17) with risk of biochemical recurrence. The operating range of the competitive ELISA was between 2.5 and 625 ng/ml. Recoveries exceeded 75% and intra-assay and inter-assay coefficients of variability were 3.3% and 12.9%, respectively. No cross-reactivity with other urinary proteins was observed. A stable thymosin B15 signal has been recovered from urine samples stored at -20 degrees C for up to 1 year. With a threshold of 40 (ng/dl)/microg protein/mg creatinine), the assay had a sensitivity of 58% and a specificity of 94%. Relative to control groups, thymosin B15 levels were above this threshold in a significant fraction of patients with prostate cancer (p < 0.001), including 5 of 7 patients who subsequently experienced PSA recurrence. The authors concluded that an ELISA capable of detecting thymosin B15 at clinically relevant concentrations in the urine of patients with prostate cancer has been established. They noted that the study will provide a tool for future clinical studies to validate urinary thymosin B15 as a predictive marker for recurrent prostate cancer.

CEA

Carcinoembryonic antigen (CEA) is a normal cellular product overexpressed in adenocarcinomas, mainly of the colon, rectum, breast and lung. It is normally found in small amounts in the blood of most healthy people, but it can increase in people with cancer or some benign conditions.

CEA is an oncofetal glycoprotein present in the gastrointestinal tract and body fluids of the embryo and fetus (Chin, et al., 2006). It is also present in certain adult gastrointestinal cells, including mucous cells in the colon and rectum, with small amounts in the blood. Blood levels are often elevated in patients with disseminated cancers and in some patients with non-malignant disease.

According to the available literature, the main use of CEA is in the control of colorectal cancer, especially when the disease presents metastasis. CEA is also used after treatment to detect recurrence of colorectal cancer. However, the literature indicates that a wide variety of other types of cancer can produce elevated levels of this tumor marker, including melanoma; lymphoma; and cancers of the breast, lung, pancreas, stomach, cervix, bladder, kidney, thyroid, liver, and ovary. Elevated levels of CEA can also occur in patients with non-cancerous conditions such as inflammatory bowel disease, pancreatitis and liver disease.

Updated American Society of Clinical Oncology (ASCO) recommendations for the use of tumor markers in gastrointestinal cancer (Gershon, et al., 2006) indicated that post-operative CEA levels should be performed every 3 months for advanced stages. II and III. disease for at least 3 years if the patient is a potential candidate for surgery or chemotherapy for metastatic disease.

CA-125

Cancer antigen 125 (CA-125) is a test that evaluates the treatment of ovarian cancer. CA-125 is a protein found more in ovarian cancer cells than in other cells. CA-125 is expressed in >80% of non-mucinous ovarian epithelial neoplasms (Chin et al, 2006). About half of women with metastatic ovarian cancer have an elevated level of CA-125.

The Gynecologic Cancer Foundation, the Society of Gynecologic Oncologists and the American Cancer Society issued a consensus statement to promote early detection of ovarian cancer, which recommends that women who experience symptoms such as bloating, pelvic or abdominal pain, difficulty eating or feeling full quickly, and urinary frequency and urgency, consultation with a gynecologist is advised if symptoms are new and persist for more than three weeks (ACS, 2007; SGO, 2007). Ovarian cancer is among the deadliest types of cancer because diagnosis often comes very late, after the cancer has spread. If the cancer is found and surgically removed before it has spread outside the ovary, the five-year survival rate is 93%. However, only 19% of cases are detected early for this type of successful intervention. It is estimated that in 2007, 22,430 new cases and 15,280 deaths were reported (ACS, 2007). The recommendations in the consensus statement are based on studies showing that the above symptoms occurred in women with ovarian cancer more than in other women (Goff, et al., 2004; Daly & Ozols, 2004). The recommendations acknowledge that there is no consensus on what clinicians should do when patients experience these symptoms. According to a consensus statement issued by the Gynecologic Cancer Foundation, pelvic and rectal examination in women with symptoms is the first step. If cancer is suspected, the next step may be a transvaginal ultrasound to check the ovaries for abnormal growths, enlargement, or pockets of fluid that could indicate cancer. Testing for CA-125 levels should also be considered.

There is no evidence available that CA-125 measurement can be used effectively for widespread screening to reduce ovarian cancer mortality, nor that the use of this test results in a decrease rather than an increase in morbidity and mortality. According to the available literature, not all women with elevated CA 125 levels have ovarian cancer. CA 125 levels can also be elevated by cancers of the uterus, cervix, pancreas, liver, colon, breast, lung and digestive tract. Non-cancerous conditions that can cause elevated CA 125 levels include endometriosis, pelvic inflammatory disease, peritonitis, pancreatitis, liver disease and any condition that inflames the pleura. Menstruation and pregnancy can also cause an increase in CA 125. However, based on the available literature, changes in CA 125 levels can be used effectively in the treatment of ovarian cancer. In women with ovarian cancer treated with chemotherapy, the literature suggests that a drop in the CA 125 level generally indicates that the cancer is responding to treatment and a longer survival is expected. On the other hand, increased CA 125 levels during or after treatment may suggest that the cancer is not responding to therapy or that residual cancer remains. According to the available literature, the failure of the CA 125 level to return to normal after three cycles of chemotherapy indicates residual tumor, early treatment failure and decreased survival. Based on accepted guidelines, CA 125 levels can also be used to monitor patients for recurrence of ovarian cancer. Although an elevated CA 125 level is highly correlated with the presence of ovarian cancer, the literature suggests that a normal value does not exclude residual or recurrent disease.

Aetna's preventive services guidelines are based on recommendations from leading organizations of primary care physicians and federal public health agencies. None of these organizations recommend routine screening for ovarian cancer in asymptomatic average-risk women with serum CA-125 levels. These organizations concluded that serum CA-125 levels are not sensitive or specific enough to be used as a screening test for ovarian cancer and that the harms of such screening outweigh the benefits.

The American College of Obstetricians and Gynecologists (2002) stated that "[un]fortunately, there is no ovarian cancer screening test that has been shown to be effective in detecting asymptomatic low-risk women. a pelvic ultrasound (abdominal and transvaginal) has been the two most highly evaluated tests. Negative tests were modest at best. Due to the low incidence of the disease, reported in approximately one case per 2,500 women per year, it is estimated that a test even with 100% sensitivity and 99% specificity would have a positive predictive value of only 4.8%, meaning that 20 out of 21 women undergoing surgery would not have primary ovarian cancer. Unfortunately, no available test comes close to this level of sensitivity. ability, specificity, or specificity."

The National Cancer Institute (2004) stated, "There is insufficient evidence to establish that screening for ovarian cancer with serum markers such as CA 125 levels, transvaginal ultrasound, or pelvic examinations would result in decreased mortality." the harm is a false-positive test result, which can lead to anxiety and invasive diagnostic procedures. ".

The US Preventive Services Task Force (2004) recommends against routine screening with serum CA-125 levels for ovarian cancer. The task force concluded that the potential harms of this detection outweigh the potential benefits.

HE4

Human epididymal protein 4 (HE4) is a secreted glycoprotein that is being studied as a potential marker for ovarian cancer.

A variety of other tumor markers have been investigated for early detection of ovarian cancer, as well as different combinations of tumor markers complementary to CA 125 that could potentially offer greater sensitivity and specificity than CA 125 alone. Preliminary studies on HE4 (human epididymal protein 4), an ovarian cancer marker, reported similar sensitivity to CA 125 when comparing ovarian cancer cases to healthy controls, and greater sensitivity when comparing ovarian cancer cases. ovarian disease with benign gynecological disease (Hellstrom, et al., 2003 and 2008; Moore, et al., 2008;) However, an evaluation of genomic testing for ovarian cancer prepared by Duke University for the Agency for Research and Quality of Health Care (AHRQ, 2006) stated, "While the research remains promising, the adaptation of genomic testing to clinical practice must await properly designed and powered studies in relevant clinical settings." . More studies are needed to determine whether HE4 significantly increases the sensitivity of CA 125 while maintaining high specificity.

The National Comprehensive Cancer Network (NCCN) guidelines (2016) state that data show that HE4 and several other markers do not rise early enough to be useful in detecting early-stage ovarian cancer.

CA 15-3

Cancer antigen 15-3 (CA 15-3) is a serum cancer antigen that has been used in the treatment of patients with breast cancer. Based on the available literature, its low detection rate in the early stage of the disease indicates that CA 15-3 cannot be used to detect or diagnose patients with breast cancer. It has been widely used to monitor the effectiveness of metastatic cancer treatment. Elevated serum concentrations of CA 15-3 are found in 5% of stage I, 29% of stage II, 32% of stage III, and 95% of stage IV breast carcinoma (Chin, et al., 2006). The majority (96%) of patients with an increase in CA 15-3 greater than 25% have disease progression. The majority (nearly 100 percent) of patients with a CA 15-3 decline of more than 50 percent are responding to treatment.

Ovarian, lung, and prostate cancers can also increase CA 15-3 levels. The literature indicates that elevated levels of CA 15-3 may be associated with non-cancerous conditions such as benign breast or ovarian disease, endometriosis, pelvic inflammatory disease and hepatitis.

Like the CA 15-3 antigen, CA 27-29 is found in the blood of most breast cancer patients. The literature indicates that CA 27-29 levels can be used in conjunction with other procedures (such as mammograms and measurements of other tumor marker levels) to check for recurrence in women previously treated for stage II and III breast cancer . CA 27-29 levels can also be elevated by cancers of the colon, stomach, kidney, lung, ovary, pancreas, uterus and liver. First trimester pregnancy, endometriosis, ovarian cysts, benign breast disease, kidney disease and liver disease are non-cancerous conditions that can also increase CA 27-29 levels.

Elevated levels of CA 27.29 are primarily associated with metastatic breast cancer, where it can be used to monitor disease course, response to treatment, and detect disease recurrence (Chin, et al., 2006). Elevated serum concentrations of CA 27,29 are found in 95 percent of stage IV breast cancer. Additionally, CA 27.29 was found elevated in lung (43 percent), pancreatic (47 percent), ovarian (56 percent) and liver (55 percent) cancers.

CA 19-9

Cancer antigen 19-9 (CA 19-9) is a mucin glycoprotein first identified from a human colorectal carcinoma cell line and is present in epithelial tissue of the stomach, gallbladder, pancreas and colon. prostate (Chin, et al., 2006). Concentrations are increased in patients with pancreatic, gastric and colon cancer, as well as in some non-malignant conditions. Rising levels generally indicate disease progression, while falling levels suggest a therapeutic response.

Initially found in patients with colorectal cancer, CA 19-9 has also been identified in patients with pancreatic, stomach, hepatocellular, and bile duct cancers. In those who have pancreatic cancer, the literature indicates that higher levels of CA 19-9 tend to be associated with more advanced disease. Although the sensitivity of the CA 19-9 level in patients with pancreatic cancer is relatively high, the specificity is reduced by elevations that occur in patients with benign pancreatic or liver disease. Non-cancerous conditions that can increase CA 19-9 levels include gallstones, pancreatitis, liver cirrhosis and cholecystitis. Although excellent correlations between CA 19-9 measurements and recurrence have been reported in patients with pancreatic cancer followed after surgical resection, no patient was saved by early diagnosis of recurrence, a fact that reflects the lack of effective therapy.

The National Comprehensive Cancer Network (NCCN, 2010) guidelines state that CA 19-9 measurement should be considered when evaluating patients with intrahepatic or extrahepatic cholangiocarcinoma and gallbladder cancer. The guidelines note that CA 19-9 is often elevated in people with cholangiocarcinoma or gallbladder cancer, although this marker is not specific for these cancers. Nehls et al. (2004) considered CA19-9 as one of the new potential tumor markers for the diagnosis of cholangiocarcinoma. Levi et al. (2005) aimed to characterize the properties of the CA 19-9 test and a change in CA 19-9 over time in predicting cholangiocarcinoma in patients with primary sclerosing cholangitis. The charts of 208 patients were reviewed. Fourteen patients had cholangiocarcinoma. Median CA 19-9 was higher with cholangiocarcinoma (15 vs 290 U/mL, p < 0.0001). A cutoff of 129 U/mL provided: sensitivity 78.6%, specificity 98.5%, adjusted positive predictive value 56.6% and negative predictive value 99.4%. The mean change over time was 664 U/mL in cholangiocarcinoma compared to 6.7 U/mL in isolated CEP (p < 0.0001). A cutoff of 63.2 U/mL for CA 19-9 change provided: 90% sensitivity, 98% specificity, and 42% positive predictive value.

CA 19-9 is produced by adenocarcinomas of the pancreas, stomach, gallbladder, colon, ovary and lung and passes into the circulation. Although numerous studies have addressed the potential usefulness of CA 19-9 in colon and rectal adenocarcinoma, the sensitivity of CA 19-9 has always been lower than that of the CEA test for all stages of the disease. Its use for screening asymptomatic populations has been hampered by a false positive rate of 15% to 30% in patients with non-neoplastic diseases of the pancreas, liver and biliary tract. Only a few studies addressed the use of CA 19-9 in the post-primary therapy follow-up of patients. Significant postoperative reductions are observed for CA 19-9, but these decreases were not correlated with survival or disease-free interval. By monitoring response to treatment, it was found that reductions in CEA more accurately reflect response to therapy than reductions in CA 19-9. Progressive increases in markers may signal disease progression in 25% of patients expressing the CA 19-9 marker, but this follow-up only provides a minimum lead time of 1 to 3 months. Monitoring with CA 19-9 has not been shown to improve the treatment of patients with colorectal cancer. The serum level of CA 19-9 does not provide significant information compared to that provided by CEA, which is currently considered the marker of choice for this neoplasm.

Sinakos and colleagues (2011) evaluated long-term outcomes in Mayo Clinic patients with primary sclerosing cholangitis (PSC) between 2000 and 2010 (n=73) for the incidence of cholangiocarcinoma (CCA). Results showed that baseline levels of CA 19-9 in patients without CCA were significantly lower than those in patients with CCA (p < 0.0001). No known factors affecting CA 19-9 levels were identified in 33% of patients without CCA; Endoscopic treatment and recurrent bacterial cholangitis were associated with CA 19-9 levels in 26% and 22% of these patients, respectively.

Juntarmanns (2011) prospectively analyzed a database of bile duct tumors and retrieved records from 238 patients operated on between 1999 and 2008. Their findings included that preoperative serum levels of CA19-9 did not show a statistically reliable differentiation between benign or evil. The authors concluded that current diagnoses cannot differentiate malignant from benign tumor masses in the hepatic hilum with the necessary reliability. The authors further concluded that the administration of CIK cells, thymus factor, IL-2 and IFN-alpha after AHSCT can improve the immune function of patients, and the TH1/TH2 ratio can virtually reflect the immune status of patients, but more information is available. mandatory. to make prognostic assessments of immune reconstruction and long-term survival rate.

Sarbia et al (1993) investigated 69 adenocarcinomas of the esophagogastric junction and found high rates of antigen expression for the "gut" markers CA 19-9 (55.5-100%) and BW 494 (42.9-100%) 86 .7%. . The authors concluded that these data, in combination with the expression of CK-20, PGII and 2B5, indicate that the distribution of adenocarcinomas with gastric or intestinal differentiation at the esophagogastric junction forms a continuum without sharp borders. This study has not been replicated and the NCCN guidelines for cancers of the esophagus and gastroesophageal junction do not include recommendations for CA 19-9 testing for these indications (NCCN, 2011).

The update of the American Society of Clinical Oncology (ASCO) recommendations for the use of tumor markers in gastrointestinal cancer (Gershon, et al., 2006) indicated that, for pancreatic cancer, CA 19-9 can be measured every 1 to 3 months for patients with locally advanced or metastatic disease receiving active therapy.

Mucinous carcinoma of the appendix is a rare entity that is most often associated with primary tumors of the appendix and colon, and whose spread is usually limited to the abdominal cavity (Andreopoulou et al, 2007). Imaging evaluation of these mucinous lesions is difficult, and recent studies have explored the use of tumor markers as clinical tools in the evaluation of appendiceal mucinous carcinoma.

Carmignani et al (2004) evaluated patients with intraperitoneal and synchronous systemic dissemination of appendix cancer treated with cytoreductive surgery and perioperative regional chemotherapy with a mean follow-up of 42.6 months. The results of this study indicated that patients with elevated levels of CEA and CA 19-9 had a shorter median survival (p = 0.0083 and p = 0.0193, respectively). In a subsequent study, Carmingnani et al (2004) prospectively recorded tumor markers CEA and CA19-9 one week before definitive treatment. Investigators found elevated CEA in 56% of 532 patients and elevated CA19-9 in 67.1% of these patients. They reported that "although the absolute level of the tumor marker does not correlate with prognosis, a normal value indicates better survival." Their findings included elevated CEA in 35.2% of 110 patients who had recurrent disease and elevated CA 19-9 in 62.9%, while 68.2% of patients had at least one of the markers.

Current guidelines indicate that, for liver transplantation for primary sclerosing cholangitis, rigorous efforts should be made to detect superimposed cholangiocarcinoma, including measurement of CA 19-9 (Devlin and O'Grady, 1999).

Carmignani et al (2004a) conducted a study to report the role of combined treatments, including cytoreductive surgery and perioperative regional chemotherapy, in patients with intraperitoneal and synchronous systemic spread of appendix cancer. Study participants were treated with debulking surgery and perioperative regional chemotherapy, and statistical analysis of variables used survival as the endpoint and included demographic characteristics, previous surgical score (PSS), tumor marker levels, peritoneal cancer index (PCI ) and completion of cytoreduction (CC). With a median follow-up of 42.6 months, the median survival time (MST) for 15 patients was 28 months and the 5-year survival rate was 29.4%. Female patients had longer MST than male patients (p = 0.0199) and survival was better in patients with PSS 0 and 1 (p = 0.0277). Patients with high levels of CEA and CA 19-9 had shorter MST (p = 0.0083 and p = 0.0193, respectively), while comparisons of PCI and CC did not show significant differences. The morbidity rate (n = 2) was 13.3% and the mortality rate (n = 2) was also 13.3%. The authors concluded that "Acceptable morbidity and mortality and a 5-year survival rate of 29.4% allow consideration of cytoreductive surgery and regional chemotherapy as a treatment option for selected patients with synchronous intraperitoneal and systemic spread of breast cancer. appendix".

Carmignani et al (2004b) in an additional publication on gastrointestinal cancer, stated that the tumor markers carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA 19-9) have found selected clinical application. The authors noted that the use of these tumor markers in mucinous epithelial tumors of the appendix has not been previously determined. Therefore, the authors performed a study in which, in patients with peritoneal dissemination of a mucinous epithelial malignancy of the appendix, tumor markers CEA and CA 19-9 were prospectively recorded preoperatively in the week before treatment. , the tumor marker was determined. The primary endpoint was the accuracy of these two tumor markers in treating this disease for these two specific clinical situations. CEA was elevated in 56% of the 532 patients and CA 19-9 was elevated in 67.1% of these patients. Although the absolute level of the tumor marker did not correlate with prognosis, a normal value indicated better survival. CEA was elevated in 35.2% of the 110 patients with recurrent disease and CA 19-9 was elevated in 62.9%. At least one of the tumor markers was elevated in 68.2% of the patients. An elevated tumor marker CEA at the time of recurrence indicated a poor prognosis, and tumor markers CEA and CA 19-9 were elevated in most of these patients. This should be a valuable previously underutilized diagnostic tool in this group of patients. These tumor markers were also beneficial in assessing prognosis because a normal level indicated a better prognosis. At the time of a reoperative procedure, tumor markers CEA and CA 19-9 provided information on disease progression and are of practical value in the management of epithelial appendix malignancy with peritoneal dissemination.

Andreopoulou et al (2007) stated that mucinous carcinoma of the appendix is a rare entity with a distinct natural history that poses diagnostic and therapeutic challenges and that mucinous peritoneal carcinomatosis is most commonly associated with primary tumors of the appendix and colon. The authors stated that the spread usually remains confined to the abdominal cavity and that the imaging evaluation of these mucinous lesions is difficult, while tumor markers (CEA and CA19.9) can be surrogates for the extent of the disease.

Recruitment for large-scale studies, given the rare nature of appendiceal mucinous carcinoma, would be challenging. However, the available evidence illustrates a benefit of using CA 19-9 in patients with mucinous carcinoma of the appendix.

The National Comprehensive Cancer Network clinical practice guideline on "Hepatobiliary Cancers" (version 1.2021) states that CEA and Ca 19-9 are gold standard tests and should not be performed to confirm the diagnosis of gallbladder cancer or cholangiocarcinoma (extra -hepatic or intrahepatic). ).

An UpToDate review on "Tumors of the Nasal Cavity" (Dagan et al, 2021) does not mention CA 19-9.

Furthermore, the National Comprehensive Cancer Network Compendium of Biomarkers (2021) does not list nasal cavity NUT midline carcinoma tumor associated with CA 19-9 expression.

Catepsinas

This enzyme plays a key role in protein catabolism and tissue remodeling (Chin, et al., 2006). Overexpression is associated with non-ductal carcinoma and metastasis in the diagnosis of breast cancer. Elevated levels may have clinical significance in predicting decreased metastasis-free survival and decreased overall survival in women with node-negative breast cancer.

Svatek et al (2008) examined the role of urinary cathepsins B and L in the detection of bladder urothelial cell carcinoma. These investigators concluded that urinary cathepsin L is an independent predictor of bladder cancer presence and invasion in patients with a history of bladder urothelial carcinoma. They stated that further evaluation of this marker is needed before its use as an adjunct to cystoscopy for urothelial bladder carcinoma.

disco 20

CD 20 is used to determine eligibility for rituximab (Rituxan; anti-CD20) treatment in patients with B-cell non-Hodgkin lymphomas (NHL) (Chin, et al., 2006). Rituximab is a genetically engineered chimeric murine/human monoclonal antibody directed against the CD20 antigen found on the surface of normal and malignant B-cell lymphocytes. As non-Hodgkin's lymphoma (NHL) subtypes can differ in their response to rituximab, determining drug susceptibility is important in choosing treatment.

disco 25

CD 25 is used to determine eligibility for treatment with denileucine diftitox in patients with persistent or recurrent CTCL (Chin, et al., 2006). Denileukin diftitox (Ontak) is a therapy for cutaneous T-cell lymphoma (CTCL) that targets the high-affinity interleukin-2 (IL-2) receptor. The IL-2 receptor can exist in a low affinity form (CD25), an intermediate affinity form (CD122/CD132) and a high affinity form (CD25/CD122/CD132). Patients whose malignant cells express the CD25 component of the IL-2 receptor may respond to treatment with Ontak.

CD 33

CD 33 is used to determine eligibility for gemtuzumab (Mylotarg, anti-CD33) treatment in patients with acute myelogenous leukemia (Chen, et al., 2006). Gemtuzumab consists of a recombinant humanized kappa IgG antibody conjugated to a cytotoxic antitumor antibiotic, calicheamicin, which specifically binds to the CD33 antigen. This antigen is found on the surface of leukemic blast cells and normal immature cells of the myelomonocytic lineage, but not on normal hematopoietic stem cells.

disco 52

CD52 is used to determine eligibility for alemtuzumab (Campath, anti-CD52) treatment in patients with chronic lymphocytic leukemia (Chen, et al., 2006). CD52 is an antigen that can be expressed in high density on the surface of malignant CLL cells. Alemtuzumab is a humanized antibody directed against CD52 and its binding is required for cell death and therapeutic response.

CD 117, kit c

CD 117 is used to determine eligibility for treatment with imatinib mesylate in patients with c-kit positive gastrointestinal stromal tumors (GISTs) (Chen, et al., 2006). c-kit glycoprotein (CD117) is a member of the subclass III receptor tyrosine kinase family and has been implicated in several malignancies. Imatinib mesylate, a tyrosine kinase inhibitor, is effective in treating GISTs and other tumors that express c-kit.

HCG

Human chorionic gonadotropin (HCG) is normally produced in increasing amounts by the placenta during pregnancy. Accepted guidelines state that HCG levels can be used to detect choriocarcinoma in women at high risk for the disease and to monitor treatment for trophoblastic disease. The literature states that high levels of HCG can also indicate the presence of testicular, ovarian, liver, stomach, pancreatic and lung cancer.

Accepted guidelines state that alpha-fetoprotein (AFP) and b-HCG measurements are valuable for determining prognosis and monitoring therapy in patients with non-seminomatous germ cell cancer. Due to the low incidence of elevated AFP and b-HCG levels in early-stage cancer, the literature suggests that these markers are of no value in detecting testicular cancer. However, the specificity of these markers is such that, when measured simultaneously, at least one marker will be positive in 85% of patients with active cancer. The value of AFP and b-HCG as markers is enhanced by a low frequency of false positive results and testicular cancer chemoactivity. The literature states that patients with other types of cancer rarely have elevated levels of AFP. Non-cancerous conditions that can cause elevated AFP levels include benign liver conditions such as cirrhosis or hepatitis, ataxia, telangiectasia, Wiscott-Aldrich syndrome, and pregnancy.

AFP

Alpha-fetoprotein (AFP) is a protein that is normally elevated in pregnant women because it is produced by the fetus; however, AFP is generally not found in the blood of adults. In men and women who are not pregnant, an elevated AFP level can indicate liver, ovarian, or testicular cancer..

Alpha-fetoprotein is normally produced by a developing fetus. Alpha-fetoprotein levels begin to decline shortly after birth and are usually undetectable in the blood of healthy adults, except during pregnancy. According to accepted guidelines, an elevated AFP level strongly suggests the presence of primary liver cancer or ovarian or testicular germ cell cancer. Since the AFP is an established test for the diagnosis and monitoring of hepatoma, it is used as a screening tool to rule out the presence of liver neoplasia before considering liver transplantation. This is especially relevant in cases (eg cirrhosis) where there is an increased risk of developing a primary liver tumour.

Elevated serum AFP levels are most closely associated with non-seminomatous testicular cancer and hepatocellular cancer (Chin, 2006). The serum clearance rate after treatment is an indicator of the effectiveness of the therapy. On the other hand, the rate of growth of progressive disease can be monitored by serially measuring serum AFP concentrations over time.

emergencies, public relations

Estrogen receptor (ER) and progesterone receptor (PR) predict response to hormone therapy for women with advanced breast cancer and those receiving adjuvant treatment and predict the aggressiveness of a tumor (Chin, 2006).

Estrogen receptor and progesterone receptor are intracellular receptors measured directly in tumor tissue. These receptors are polypeptides that bind to their respective hormones, translocate to the nucleus and induce the expression of specific genes. Breast cancers depend on estrogen and/or progesterone for growth and this effect is mediated by ERs and progesterone receptors (ER/PR) (Chin, et al., 2006). Both receptors may be overexpressed in malignant breast tissue. Most oncologists have used the estrogen receptor and also the progesterone receptor not only to predict the likelihood of response to hormone therapy at the time of metastatic disease, but also to predict the likelihood of recurrent disease and to predict the need for hormone therapy or adjuvant chemotherapy. . Although these latter uses of estrogen and progesterone receptors are commonly accepted by most oncologists, the data on which these conclusions are based are controversial.

NSE

Neurospecific enolase (NSE) has been detected in patients with neuroblastoma, small cell lung cancer, Wilms tumor, melanoma, and cancers of the thyroid, kidney, testis, and pancreas. However, studies of NSE as a tumor marker have mainly focused on patients with neuroblastoma and small cell lung cancer. According to the available literature, measurement of the NSE level in patients with these diseases cannot be correlated with the extent of the disease, the patient's prognosis or the patient's response to treatment due to the low sensitivity of this marker.

ABSENT

LASA is a complex marker that measures the amount of sialic acid in the serum and can be elevated in the serum of patients with many different malignancies. Elevations in blood levels of LASA have been reported in patients with breast (63 percent), gastrointestinal (65 percent), lung (79 percent), and ovarian (94 percent) cancer, as well as in those with leukemia (86 percent). ), lymphoma (87 percent). percent), melanoma (84 percent), sarcoma (97 percent), and Hodgkin's disease (91 percent). As a result, this assay may not have the high specificity or sensitivity required for cancer detection (Chen, et al., 2006). This serum cancer marker has not been widely accepted for use in the detection or prognosis of colorectal carcinoma. There is no practical information about the outcome and use of LASA in the medical literature. Although several articles describe the use of LASA in the diagnosis of colorectal cancer and its association with tumor-node-metastasis (TNM) stage, it has been shown that patients with colorectal polyps and colorectal carcinoma have elevated levels of LASA and that levels have returned to baseline. basis after removal of polyps or carcinomas.

p53

p53 is a tumor suppressor gene on the short arm of chromosome 17 that encodes a protein important in regulating cell division. Although the full role of p53 in normal and neoplastic cells is unknown, there is evidence that the gene product is important in preventing the division of cells that contain damaged DNA. Deletion or mutation of the p53 gene is a frequent event along with other molecular abnormalities in colorectal carcinogenesis. The literature on p53 abnormality and prognosis in colorectal cancer suffers from a paucity of reported data and the use of a variety of statistical analyzes and assay techniques in the small number of cases analyzed. For these reasons, the literature generally does not recommend p53 testing as a routine approach to assist in the management of patients with colorectal cancer.

American Society of Clinical Oncology guidelines (2016) advise against using p53 to guide adjuvant chemotherapy in breast cancer. This is a moderate-strength recommendation based on intermediate-quality evidence.

Zap-70

Protein kinase 70 associated with the zeta chain, used as a prognostic marker in CLL.

Zap-70 is indicated to assess prognosis and the need for aggressive therapy in patients with chronic lymphocytic leukemia (CLL) (Chin, et al., 2006). ZAP-70 is a 70 kD member of the Syk family of protein tyrosine kinases. It is expressed primarily on T cells and natural killer (NK) cells and is critical for signal transduction following T cell receptor engagement. In CLL B cells, elevated expression of ZAP-70 appears to predict the need for therapy in a form as effective as the IgVH mutation status. Although ZAP-70 expression is strongly correlated with IgVH mutation status, the combination of the two markers may provide greater prognostic value than either marker alone. Positive ZAP-70 results predict an aggressive disease course.

UPA

Urokinase serine protease plasminogen activator (uPA) and its primary inhibitor, plasminogen activator inhibitor-1 (PAI-1), have shown promise for risk assessment and prediction of therapeutic response in primary breast cancer (Chin, et al. ., 2006). ). High levels of uPA or PAI-1 in primary tumor tissue are associated with an aggressive disease course and poor prognosis in node-positive and node-negative breast cancer.

A report by the Belgian Center for Health Knowledge (KCE) (San Miguel, et al., 2015) found no studies reporting the impact of uPA/PAI-1 on clinical management (clinical utility).

The American Society of Clinical Oncology (2016) guidelines state: "If a patient has ER/PgR-positive, HER2-negative (node negative) breast cancer, the physician may use urokinase plasminogen activator and urokinase activator inhibitor. type 1 plasminogen to guide decisions about adjuvant systemic therapy". This is a weak recommendation based on high-quality evidence. ASCO guidelines recommend the use of urokinase-type plasminogen activator and type 1 plasminogen activator inhibitor to guide decisions about adjuvant systemic therapy in patients with HER2-positive breast cancer or TN.

hIgV mutation status

Patients with chronic lymphocytic leukemia (CLL) can be divided into two basic groups based on the mutational status of the immunoglobulin heavy chain variable region (IgVH) gene in the leukemic cells (Chin, 2006). Patients with IgVH mutations have longer survival than those without IgVH mutation. Therefore, mutation analysis can be useful for planning management strategies.

Cadena Ligera Kappa / Lambda

Elevated serum levels of monoclonal free light chains are associated with malignant plasma cell proliferation (eg, multiple myeloma), primary amyloidosis, and light chain storage disease (Chen et al, 2006). The appearance of higher levels of free light chains in the urine may be indicative of kidney disease or malignant lymphoproliferative disease such as multiple myeloma. These tests have been used to screen for multiple myeloma.

KRAS

Ras proto-oncogenes are normal cellular components believed to be important for signal transduction necessary for proliferation and differentiation. The ras oncogene family has 3 members: H-ras, K-ras and N-ras. Ras gene mutations can be found in a variety of tumor types, although the incidence varies greatly. The highest incidences are found in adenocarcinomas of the pancreas (90%), colon (50%) and lung (30%); thyroid tumors (50%) and myeloid leukemia (30%).

Investigators have established an association between some K-ras oncogene (KRAS) genotypes and response to treatment with cetuximab or panitumumab (Lievre et al, 2006 and 2008; Di Fiore et al, 2007; Gonçalves et al, 2008; De Roock et al. al, 2008). Patients whose tumors express specific forms of the KRAS gene have significantly reduced responses to cetuximab and panitumumab. It has been theorized that cetuximab and panitumumab do not target the epidermal growth factor receptor (EGFR) associated with these specific KRAS mutations and therefore cannot block its activation. It has been suggested that the KRAS genotype be considered as a selection factor for cancer patients who are candidates for treatment with cetuximab or panitumumab.

Karapetis and colleagues (2008) stated that treatment with cetuximab improves overall survival (OS) and progression-free survival (PFS) and preserves quality of life in patients with colorectal cancer who have not responded to chemotherapy. The status of the K-ras gene mutation in the tumor may affect the response to cetuximab and have prognostic value regardless of treatment. cetuximab plus best supportive care or best supportive care only, to look for activating mutations in exon 2 of the K-ras gene. They assessed whether K-ras gene mutation status was associated with survival in the cetuximab and supportive care groups. Of the tumors evaluated for K-ras mutations, 42.3% had at least one mutation in exon 2 of the gene. Cetuximab efficacy was significantly associated with K-ras mutation status (p = 0.01 and p < 0.001 for interaction of K-ras mutation status with OS and PFS, respectively). In patients with wild-type K-ras tumours, treatment with cetuximab compared with supportive care alone significantly improved OS (median 9.5 vs. 4.8 months; hazard ratio for death, 0.55; interval between 95% confidence [CI], 0.41 to 0.74; p < 0.001) and PFS (median 3.7 months versus 1.9 months; risk ratio for progression or death, 0.40 95% CI 0, 30 to 0.54, p < 0.001). Among patients with K-ras mutation tumors, there was no significant difference between those who received cetuximab treatment and those who received only supportive care with respect to OS (hazard ratio, 0.98; p = 0.89) or PFS (hazard ratio, 0.99; p = 0.96). In the group of patients who received only the best supportive care, K-ras gene mutation status was not significantly associated with OS (risk of death ratio, 1.01; p = 0.97). The authors concluded that patients with colorectal tumors with a K-ras mutation did not benefit from cetuximab, whereas patients with wild-type K-ras tumors benefited from cetuximab. K-ras gene mutation status did not influence survival in patients treated with only the best supportive care.

ASCO's interim clinical opinion on testing for KRAS gene mutations in patients with metastatic colorectal carcinoma to predict response to anti-EGFR monoclonal antibody therapy (Allegra et al, 2009) indicated that, based on systematic reviews of the relevant literature , all patients with colorectal carcinoma who are candidates for anti-EGFR antibody therapy should be tested for KRAS mutations in a CLIA-accredited laboratory. If a KRAS mutation is detected at codon 12 or 13, patients with metastatic colorectal carcinoma should not receive anti-EGFR antibody therapy as part of their treatment.

The KRAS oncogene mutation test is intended to assist in formulating treatment decisions for patients who may be candidates for treatment of metastatic epithelial cancers with anti-EGFR therapies such as cetuximab or panitumumab. Several tests for the KRAS mutation are currently available in the United States; however, at this time, the FDA has not approved any KRAS genotyping test kits.

Data from 540 patients with metastatic colorectal cancer in the randomized phase III trial CRYSTAL were presented at the 2008 annual meeting of the American Society of Clinical Oncology (ASCO). Among 192 patients with KRAS mutations, there was no improvement in overall responses or PFS with the addition of cetuximab to standard chemotherapy. In patients with normal KRAS, the 1-year PFS rate was 43% for patients receiving cetuximab versus 25% for those receiving standard chemotherapy alone, and the overall response rate was 59% versus 43%, respectively (van Cutsem, 2008). Also at the 2008 ASCO meeting, data from 233 patients with metastatic colorectal cancer were presented, confirming the correlation of KRAS status with patient response to anti-EGFR therapy. No benefit was found after adding cetuximab to standard chemotherapy with FOLFOX (the combination of fluorouracil, leucovorin, and oxaliplatin) in patients with KRAS mutants; however, the addition of cetuximab to FOLFOX increased the response rate and PFS in patients with a wild-type (ie, non-mutated) KRAS gene (Bokemeyer, 2008). Response to panitumumab was correlated with KRAS status in a published phase III study. A total of 427 patients with metastatic colorectal cancer received panitumumab or best supportive care. Panitumumab exhibited a 17% response rate among patients with normal KRAS but a 0% response among patients with KRAS mutations (Amado, 2008).

A meta-analysis of results from 8 studies involving 817 patients with colorectal cancer found that the presence of the KRAS mutation predicted a lack of response to treatment with anti-EGFR monoclonal antibodies (e.g., panitumumab or cetuximab), since both as standard therapy alone or in combination with chemotherapy (Linardou et al, 2008). This analysis also provided empirical evidence that k-RAS mutations are highly specific negative predictors of response (de novo resistance) to single agent EGFR tyrosine kinase inhibitors in advanced patients with non-small cell lung. Cancer; and similarly to anti-EGFR monoclonal antibodies alone or in combination with chemotherapy in patients with metastatic colorectal cancer.

The Medical Advisory Panel of the Blue Cross and Blue Shield Association Technology Assessment Center (BCBSA, 2008) concluded that the use of KRAS mutation analysis meets TEC criteria for predicting non-response to anti-EGFR monoclonal antibodies cetuximab and panitumumab for treat metastatic colorectal cancer. The ECT evaluation found that the evidence is sufficient to conclude that patients with KRAS-mutant tumors in the setting of metastatic colorectal cancer do not respond to anti-EGFR monoclonal antibody therapy. The review explained that the data show that the clinical benefit of using EGFR inhibitors in the treatment of metastatic colorectal cancer, either as monotherapy or in combination with other treatment regimens, is not seen in patients with KRAS-mutated tumors. The review found, "These data support knowing a patient's tumor mutation status before considering the use of an EGFR inhibitor in the treatment regimen. Identifying patients whose tumors express mutant KRAS will prevent patient drug exposure ineffective, you drug toxicities and accelerate the use of the best available alternative therapy."

The National Comprehensive Cancer Network (NCCN, 2010) colorectal cancer guidelines recommend consideration of BRAF reflex testing in patients with wild-type KRAS. The NCCN guidelines explain that several small studies suggest that patients with wild-type KRAS and a BRAF mutation are unlikely to respond to anti-EGFR therapies such as cetuximab and panitumumab. The guidelines explain that patients with a known BRAF mutation are unlikely to respond to anti-EGFR antibodies, although the data are somewhat inconsistent. Studies show that in patients with metastatic colorectal cancer, about 8 percent have mutations in the BRAF gene. Testing for the BRAF V600E mutation is performed by PCR amplification and direct DNA sequence analysis.

Ratner et al (2010) stated that ovarian cancer (OC) is the most lethal form of cancer in women, usually presenting as advanced disease at the time of diagnosis, in part due to the lack of risk factors, risk markers genetic or known. The KRAS oncogene and altered let-7 microRNA (miRNA) levels are associated with an increased risk of developing solid tumors. In this study, these investigators investigated a hypothetical association between an increased risk of OC and a KRAS variant allele in rs61764370, called a KRAS variant, which disrupts a let-7 miRNA binding site in this oncogene. Samples obtained were tested for the presence of the KRAS variant from unselected OC patients in 3 independent cohorts, 2 independent case-control studies of ovarian and OC patients with hereditary breast and ovarian cancer (HBOC) syndrome, as well as their families. . . Results indicated that the KRAS variant is associated with more than 25% of unselected OC cases. Furthermore, these investigators found that it is a marker of a significantly increased risk of developing OC, as confirmed by 2 independent case-control analyses. Finally, they determined that the KRAS variant was present in 61% of HBOC patients without BRCA1 or BRCA2 mutations, previously considered uninformative, as well as in their relatives with cancer. These findings supported the hypothesis that the KRAS variant is a genetic marker for increased risk of developing OC and suggested that the KRAS variant may be a new genetic marker of cancer risk for HBOC families without other known genetic abnormalities.

Hollestelle et al (2011) noted that a variant allele in the 3'UTR of the KRAS gene (rs61764370 T>G) was recently shown to be associated with an increased risk of developing non-small cell lung cancer, as well as OC, and was enriched even more so in patients with OC from HBOC families. This functional variant has been shown to disrupt a let-7 miRNA binding site, leading to increased KRAS expression in vitro. In the current study, these investigators genotyped this KRAS variant in breast cancer index cases from 268 BRCA1 families, 89 BRCA2 families, 685 non-BRCA1/BRCA2 families, and 797 geographically matched controls. The allele frequency of the KRAS variant was found to be increased among BRCA1 breast cancer patients, but not among BRCA2 or non-BRCA1/BRCA2 families compared to controls. Because most BRCA1 carriers develop ER-negative breast cancer, these investigators also examined variant allele frequency among indices of BRCA1/BRCA2-negative families with ER-negative breast cancer. However, the prevalence of the KRAS variant did not increase significantly compared to controls, suggesting that the variant allele is not only associated with ER-negative breast cancer. Subsequent expansion of the number of BRCA1 carriers with breast cancer to include family members beyond the index cases resulted in a loss of significance for the association between the variant allele and BRCA1 mutant breast cancer. In this same cohort, the KRAS variant did not appear to modify breast cancer risk for BRCA1 carriers. More importantly, the results of the current study suggested that KRAS variant frequencies may be increased among BRCA1 carriers, but robust evidence requires confirmation in a larger cohort of BRCA1 carriers.

The Therascreen KRAS RGQ PCR Kit (Qiagen) is designed to detect 7 mutations in codons 12 and 13 of the KRAS gene (Raman, et al., 2013). The kit uses two technologies, ARMS and Scorpions, for real-time PCR mutation detection. The therascreen KRAS RGQ PCR Kit is being developed as a diagnostic companion to assist clinicians, through detection of KRAS mutations, in identifying patients with metastatic colorectal cancer (mCRC) who are most likely to benefit from cetuximab.

PreOvar™ (Mira Dx) testing for the KRAS variant and will help identify patients with ovarian cancer whose female relatives should also be tested for the KRAS variant (Raman, et al., 2013). PreOvar™ can also help assess the relative risk of developing ovarian cancer in women with a family history of ovarian cancer without a living proband (ancestor with the disease). The KRAS variant is present in 6-10% of the general population and in 25% of unselected women with epithelial ovarian cancer. In addition, the KRAS variant has been identified in over 60% of hereditary breast and ovarian cancer (HBOC) patients who were previously classified as "non-reporting" or negative for other known genetic markers of ovarian cancer risk. The test determines whether the KRAS variant might put someone at greater risk of developing ovarian cancer.

The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group (EWG) (2013) found that for patients with metastatic colorectal cancer (mCRC) who are being considered for treatment with cetuximab or panitumumab, there is compelling evidence to recommend clinic use ofKRASmutation analysis to determine which patients areKRASpositive mutation and therefore unlikely to benefit from these agents prior to initiation of therapy. The level of certainty of the evidence was considered high, and the magnitude of the net health benefit of avoiding potentially ineffective and harmful treatment, as well as promoting more immediate access to what may be the next most effective treatment, is at least moderate. .

The EWG found insufficient evidence to recommend for or againstBRAFV600E tests for the same clinical scenario (EGAPP, 2013). The level of certainty forBRAFThe V600E test to guide anti-epidermal growth factor receptor (EGFR) therapy was considered low. The EWG encourages further study of the test's potential value in mCRC patients who have wild-type (mutation negative) tumors forKRASto predict response to therapy.

Bladder cancer: BTA-stat, NMP22, Urovysion, ImmunoCyt

In the United States, bladder cancer is the fourth most common cancer in men and the eighth most common in women. Patients usually present with urinary tract symptoms (eg, macroscopic or microscopic hematuria or irritative voiding symptoms such as polluria, dysuria, and urinary urgency). Evaluations of these patients usually involve voiding cytology, cystoscopy, and upper urinary tract imaging such as intravenous pyelography, renal ultrasound, or retrograde pyelography. Most newly diagnosed bladder cancers are superficial (ie, do not invade beyond the lamina propria on histological examination) and are known as transitional cell carcinoma (TCC). These superficial bladder cancers are usually treated by transurethral resection. However, the literature shows that approximately 50 to 75% of treated CCTs recur. In addition, 10 to 15% of TCCs progress to muscle-invasive bladder cancer. According to the literature, the prevalence of recurrence after initial treatment, as well as the natural history of TCC, require long-term follow-up. After treatment, accepted guidelines stipulate that patients previously diagnosed with TCC should undergo urinary cytology/cystoscopy every 3 months for the first year, every 6 months for the second year, and once a year thereafter.

Currently, urinary cytology with confirmatory cystoscopy represents the cornerstone for the identification of bladder tumors. However, the subjectivity and low sensitivity of cytology led to the development of several urine tests as a complement to cytology/cystoscopy for the diagnosis and follow-up of patients with TCC. These tests include the BTA Stat Test (Bard Diagnostic, Redmond, WA), the NMP22 Test (Matritech, Newton, MA), the Aura-Tek FDP Test (PerImmune, Rockville, MD), and the Vysis UroVysion FISH Test (Vysis, Inc ., Downers Grove, IL). They are usually objective, qualitative (BTA Stat and Aura-Tek FDP) or quantitative (NMP22, UroVysion) and have higher sensitivity than cytology, but some have lower specificity. To date, no bladder tumor marker has emerged as the generally accepted test of choice and none has established itself as a screening tool for bladder malignancy.

Urine-based markers, such as proteins with increased expression by cancer cells or chromosomal abnormalities in the urine, can be detected by a variety of laboratory methods to aid in the treatment of bladder cancer. The following markers/tests are currently available:

Vesicular tumor antigen (BTA) (for example, BTA stat and BTA TRAK)
Fluorescence immunocytology (eg, ImmunoCyt/uCyt+)
Fluorescent in situ hybridization (FISH) (eg, UroVysion)
mRNA quantification using RT-qPRC tests (eg CxBladder)
Nuclear matrix protein 22 (NMP22) (eg, NMP22 BladderChek and Matritech NMP22 Test).

Urine-based markers have a role in detecting bladder cancer recurrence in people with a history of bladder cancer and are used adjunct with urinary cytology and cystoscopy. These tests have also been proposed for the detection of bladder cancer, diagnosis of bladder cancer in individuals symptomatic of bladder cancer, and for the evaluation of hematuria.

The UroVysion Bladder Cancer Kit (UroVysion Kit) (Baycare Laboratories) is designed to detect aneuploidy for chromosomes 3, 7, 17 and loss of the 9p21 locus by fluorescent in situ hybridization (FISH) in urine samples from individuals with hematuria suspected to have bladder cancer (Raman, et al., 2013). FISH analysis is used in conjunction with cystoscopy to monitor recurrence among those with previously diagnosed bladder cancer. FISH analysis is a surveillance tool in established primary and secondary bladder adenocarcinoma.

ImmunoCyt is an immunocytochemical assay for the detection of tumor cells shed in the urine of patients previously diagnosed with bladder cancer (Chen, et al., 2006). This test is intended to increase the sensitivity of cytology for detecting tumor cells in the urine of people previously diagnosed with bladder cancer. The test has been used to detect tumor cells in the urine of people previously diagnosed with bladder cancer and for use in conjunction with cystoscopy to aid in the treatment of bladder cancer.

Although urinary cytology has proven to be less accurate than urinary biomarker tests, the familiarity with the method, as well as the ease of execution, ensure the former's continued routine use by primary care physicians, especially in patients with no history of cancer. of bladder. Urine-based biomarker tests have been shown to be accurate in detecting low-grade bladder tumors. In particular, these tests may be useful in deciding the need for further diagnostic evaluation in patients with a history of bladder cancer and negative urine cytology results. For example, elevated levels of bladder tumor markers in patients with a history of CBT may warrant earlier rather than later cystoscopic examination. On the other hand, lengthening the intervals between cystoscopic investigations can be considered when the values of these tumor markers are normal.

An evaluation by the Adelaide Health Technology Assessment (Mundy & Hiller, 2009) concluded that the NMP BladderCheck and UroVysion FISH assays, designed for the detection of bladder cancer in high-risk patients, have low sensitivity and low positive predictive values. The review recommended that these assays not be used in asymptomatic patients. However, the review suggests that these tests may be useful in following patients with transitional cell carcinoma between cytoscopies. The AHTA recommended that this technology not be evaluated further.

A review prepared by the Agency for Healthcare Research and Quality (Meleth, et al., 2014) found: "Although UroVysion is marketed as a diagnostic rather than prognostic test, limited evidence from two small studies (total N = 168) ranked How Low or Medium Risk of Bias Supported Associations Between Test Result and Recurrence Risk Prognosis. i.e., evidence of clinical validity), we found no evidence that use of the test was associated with better patient outcomes."

The American Urologic Association guideline on "Diagnosis, evaluation, and follow-up of asymptomatic microhematuria (AMH) in adults" (Davis et al, 2012) stated that "The use of urinary cytology and urinary markers (Nuclear Matrix Protein 22 [NMP22] , antigen Bladder Tumor Test [BTA]-stat and UroVysion Fluorescence In Situ Hybridization Assay [FISH]) are not recommended as part of the routine evaluation of the asymptomatic patient with microhematuria."

Chou et al (2015) systematically reviewed the evidence on the accuracy of urinary biomarkers for diagnosing bladder cancer in adults who have signs or symptoms of the disease or are under surveillance for recurrent disease. Data sources included Ovid MEDLINE (January 1990 to June 2015), Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and reference lists. A total of 57 studies evaluating the diagnostic accuracy of quantitative or qualitative nuclear matrix protein 22 (NMP22), qualitative or quantitative bladder tumor antigen (BTA), FISH, fluorescent immunohistochemistry (ImmunoCyt [Scimedx]), and Cxbladder (Pacific Edge Diagnostics USA) using cystoscopy and histopathology as a reference standard met the inclusion criteria; Case-control studies were excluded. Double extraction and quality assessment of individual studies were performed; the overall strength of evidence (SOE) was also assessed. For all biomarkers, sensitivities ranged from 0.57 to 0.82 and specificities from 0.74 to 0.88. Positive likelihood ratios ranged from 2.52 to 5.53, and negative likelihood ratios ranged from 0.21 to 0.48 (moderate SOE for quantitative NMP22, qualitative BTA, FISH, and ImmunoCyt; low SOE for others). For some biomarkers, sensitivity was higher for the initial diagnosis of bladder cancer than for the diagnosis of recurrence. Sensitivity increased with higher tumor stage or grade. Studies that directly compared the accuracy of quantitative NMP22 and qualitative BTA found no difference in diagnostic accuracy (moderate SOE); direct studies of other biomarkers have been limited. Urinary biomarkers plus cytological assessment were more sensitive than biomarkers alone, but missed about 10% of bladder cancer cases. The authors concluded that urinary biomarkers are missed by a substantial proportion of patients with bladder cancer and are subject to false-positive results in others; accuracy is poor for low-grade, low-stage tumors. They stated that research is needed to understand how the use of these biomarkers with other diagnostic tests affects the use of cystoscopy and clinical outcomes.

In an editorial accompanying the aforementioned study, Abbosh and Plimack (2015) stated that "Until urinary biomarkers are available and are accurate enough to supplant currently recommended screening algorithms in patients with negative biomarkers, they will not be a cost-effective solution. ". . as well as strategies for detecting bladder cancer.

In summary, urine-based bladder tumor marker tests have been shown to be useful as an adjunct to urinary cytology and cystoscopy in monitoring bladder cancer recurrences, but based on the available literature, they should not be used as a bladder malignancy screening tool. The US Preventive Services Task Force (USPSTF, 2004) concluded that the potential harms of screening for bladder cancer using available tests such as microscopic urinalysis, urine dipstick, urine cytology, or new tests such as bladder tumor antigen (BTA) or nuclear matrix protein immunoassay (NMP22) tests outweigh any potential benefit.

Cxvejiga

O'Sullivan and colleagues (2012) examined whether the uRNA® RNA assay and its Cxbladder® derivative have greater sensitivity for detecting bladder cancer than cytology, NMP22™ BladderChek™ and NMP22™ ELISA, and whether they are useful in screening of bladder cancer .risk stratification. A total of 485 patients presenting gross hematuria but no history of urothelial cancer were prospectively recruited from 11 urology clinics in Australasia. Urine samples were obtained prior to cystoscopy. The sensitivity and specificity of RNA tests were compared with cytology and NMP22 assays using cystoscopy as a reference. The ability of Cxbladder to distinguish between low-grade Ta urothelial carcinoma and more advanced urothelial carcinoma was also determined. uRNA detected 41 of 66 cases of urothelial carcinoma (sensitivity 62.1%, 95% confidence interval [CI] 49.3 to 73.8) compared with NMP22 ELISA (50.0%, 95% CI: 37, 4 to 62.6), BladderChek (37.9%, 95% CI: 26.2 to 50.7) and cytology (56.1%, 95% CI: 43.8 to 68.3). Cxbladder, which was developed from the study data, detected 82%, including 97% of high-grade tumors and 100% of stage 1 or higher tumors. Breakpoints for uRNA and Cxbladder were pre-specified to give 85% specificity. Cytology specificity was 94.5% (95% CI: 91.9 to 96.5), NMP22 ELISA 88.0% (95% CI: 84.6 to 91.0), and BladderChek 96.4% ( 95% CI: 94.2 to 98.0). Cxbladder distinguished between low-grade Ta tumors and other urothelial carcinomas detected with a sensitivity of 91% and a specificity of 90%. The authors concluded that uRNA and Cxbladder showed greater sensitivity for detecting urothelial carcinoma compared to NMP22 assays. Stratification with Cxbladder provides a potential method to prioritize patients for waiting list management.

An UpToDate review on "Clinical Presentation, Diagnosis, and Staging of Bladder Cancer" (Lotan and Choueiri, 2013) does not mention the use of mRNA biomarkers/PCR tests as a management tool for bladder cancer. Furthermore, the NCCN clinical practice guideline on "Bladder Cancer" (Version 1.2014) does not mention the use of mRNA biomarkers/PCR tests as a management tool for bladder cancer.

An assessment of urinary biomarkers for the diagnosis of bladder cancer prepared for the Agency for Healthcare Research and Quality (Chou, et al., 2016) identified only one Cxbladder study that met the inclusion criteria, classified as of moderate quality, with strength general evidence of 'low'.

Cxbladder Triage is a urine test designed to rule out the presence of bladder cancer in low-risk patients with hematuria. The test uses an algorithm to incorporate clinical risk factor markers (age, gender, smoking history, and gross hematuria frequency) and genetic information (mRNA), using real-time PCR quantitative gene expression profiling of 5 biomarker genes ( MDK). , HOXA13, CDC2 [CDK1], IGFBP5, and CXCR2), to determine a risk score for urothelial carcinoma.

Laukhtina et al (2021) conducted a systematic review and network meta-analysis (NMA) on the diagnostic accuracy of novel urinary biomarker (UBT) tests in non-essential invasive bladder cancer (NMIBC). PubMed, Web of Science, and Scopus We searched through April 2021 to identify studies addressing the diagnostic values of UBTs: Xpert bladder cancer, Adxbladder, Bladder EpiCheck, Uromonitor, and Cxbladder Monitor, and Triage andDetect. The primary objective was to evaluate pooled diagnostic values for disease recurrence in patients with IMVT using a DTA meta-analysis and compare them to cytology using an NMA. Secondary endpoints were diagnostic values for high-grade recurrence (HG) as well as early detection of bladder cancer. In the quantitative synthesis, 21 studies were included, with 7,330 patients. In most studies, there was a clear risk of bias. For NMIBC surveillance, new UBTs have demonstrated promising combined diagnostic values with sensitivities of up to 93%, specificities of up to 84%, positive predictive values of up to 67%, and negative predictive values of up to 99%. The pooled estimates for the diagnosis of GH recurrence were similar to those for the diagnosis of recurrence of any grade. Analysis of the number of potentially avoided cystoscopies during follow-up of 1000 patients showed that UBTs can be effective in reducing the number of preventable interventions in up to 740 cystoscopies. The NMA revealed that the diagnostic values (except for specificity) of the new UBT were significantly higher than those of the cytology for the detection of IMVT recurrence. There were too few data on UBT in the primary diagnostic setting to allow for statistical analysis. The authors concluded that their analyzes support a high diagnostic accuracy of the new UBTs studied, supporting their usefulness in the NMIBC surveillance setting. All of this could potentially help to safely prevent unnecessary cystoscopies. There are insufficient data to reliably assess its use in the primary diagnostic setting. These results need to be confirmed in a larger cohort as well as head-to-head comparative studies.

Mama OncotypeDx

Oncotype Dx (Genomic Health, Inc., Redwood City, CA) is a laboratory-developed diagnostic assay that quantifies the likelihood of breast cancer recurrence in women with newly diagnosed breast cancer, node-negative stage I or II, receptor positive estrogen, who will be treated with tamoxifen. The assay analyzes the expression of a panel of 21 genes and is designed to be used in conjunction with other conventional breast cancer screening methods. Along with staging, grading, and other tumor marker analyses, Oncotype Dx aims to provide a broader view of the likelihood of systemic disease recurrence. Clinical studies have evaluated the prognostic importance of the Oncotype Dx multigene assay in breast cancer (Paik et al, 2004; Esteva et al, 2003).

Oncotype Dx analyzes the patterns of 21 genes and is being applied as a quantification tool for the probability of breast cancer recurrence within 10 years of newly diagnosed breast cancer, stage I or II, lymph node negative and hormone receptor negative positive in women who will be treated with tamoxifen (Raman, et al., 2013). Oncotype is being applied as a quantification tool for the 10-year probability of breast cancer recurrence in women with newly diagnosed breast cancer. It is also intended to help make decisions about adjuvant chemotherapy based on the likelihood of recurrence.

Currently, there is a lack of evidence from prospective clinical studies on the impact of this test in the treatment of women with breast cancer demonstrating improvements in clinical outcomes (Lopez, et al., 2010; Romeo, et al., 2010; Tiwana, et al., 2010; al. al., 2013; IETS, 2013), Bastand Hortobagyi (2004) commented that "[b]before the use of the recurrence score [from the Oncotype Dx multigene assay] is applied to general patient care, however, Additional studies are needed." The National Cancer Institute is sponsoring a prospective, randomized, controlled clinical trial, the TAILORx trial, using the Oncotype Dx assay to help identify a group of patients at average risk of recurrence to determine whether treating patients with therapeutic hormone therapy alone is equivalent to treat them with hormone therapy in combination with adjuvant chemotherapy.

However, there is indirect evidence for the clinical utility of Oncotype Dx. Paik et al (2006) used tumor samples from banks of previous clinical studies of tamoxifen and adjuvant chemotherapy in early breast cancer to evaluate the performance of the Oncotype Dx multigene assay in predicting response to adjuvant chemotherapy. These investigators examined tumor samples from individuals enrolled in the National Surgical Adjuvant Breast and Bowel Project (NSABP) B20 study to determine whether there is a correlation between the recurrence score (RS) determined by Oncotype Dx in tumor samples and the subsequent response to adjuvant chemotherapy. A total of 651 patients (227 randomized to tamoxifen and 424 randomized to tamoxifen plus chemotherapy) could be evaluated. The interaction test between chemotherapy treatment and RS was statistically significant (p = 0.038). Patients with tumors with a high RS (RS greater than or equal to 31) (i.e., high risk of recurrence) had a greater benefit from chemotherapy (relative risk, 0.26; 95% confidence interval, 0.13 to 0. 53; absolute decrease in distance for 10 years). recurrence rate: average, 27.6%; standard error, 8.0%). Patients with low RS tumors (less than 18) had minimal benefit, if any, from chemotherapy treatment (relative risk, 1.31; 95% confidence interval, 0.46 to 3.78; absolute decrease in recurrence rate 10 years apart: mean, -1.1%; standard error, 2.2%). The investigators found that patients with intermediate RS tumors did not appear to benefit much, but concluded that uncertainty in estimation cannot exclude clinically important benefits.

A limitation of the Paik et al (2006) study is that the NASBP B20 trial was conducted before the advent of major advances in breast cancer chemotherapy, including the introduction of trastuzumab (Herceptin), which has been shown to improve overall response. illness. Free survival in breast cancer patients with HER2-positive tumors. Current guidelines recommend the use of adjuvant chemotherapy with trastuzumab in women with HER2-positive metastatic breast cancer and women with HER2-positive non-metastatic breast cancer 1 cm or larger in diameter. Therefore, the Oncotype Dx score would not influence the decision to use adjuvant trastuzumab in women with HER2-positive tumors 1 cm or larger in diameter.

Commenting on an initial report of this study by Paik et al, of Oncotype Dx presented in abstract form, the BlueCross BlueShield Association Technology Assessment Center assessment stated that "additional studies in different populations are needed to confirm whether the risk prediction it's accurate enough for clinicians "and for patients to confidently choose whether to discontinue adjuvant chemotherapy."

An international consensus group (Azim, et al., 2013) found the available evidence convincing for the analytical and clinical validity of Oncotype Dx Breast. However, neither Oncotype Dx nor any of the other genomic tests evaluated demonstrated strong evidence of clinical utility: they stated that it was not clear from current evidence that modifying treatment decisions based on the results of a particular genomic test could result in clinical improvement. result.

Selection criteria for the prospective OncotypeDx TailorRx study state that candidates must be axillary lymph node negative as determined by sentinel lymph node biopsy and/or axillary dissection as defined by the American Joint Committee on Cancer 6th Edition Staging System (NCI, 2009) . The American Joint Committee on Cancer (AJCC) 6th edition criteria redefined isolated tumor cells as negative nodules (the previous version of the criteria, AJCC 5th edition, classified isolated tumor cells as positive nodules). "Isolated tumor cells (single cells or deposits of cells) will now be defined as deposits of tumor cells no larger than 0.2 mm in diameter that may or may not (but generally do not show) histological evidence of malignant activity. Pending further information , isolated tumor cells will be classified as lymph node negative, because it is believed that the unknown benefits of providing treatment for these small lesions would not outweigh the morbidity caused by the treatment itself." (Singletary, et al., 2002). However, the stored tumor samples used in the study by Paik, et al. (2006) to validate the OncotypeDx were classified based on the AJCC 5th Ed criteria. In addition, there is new evidence showing that women with isolated tumor cells have a significantly increased risk of breast cancer. Investigators from the Netherlands found an association between isolated tumor cells and micrometastases in regional lymph nodes and the clinical outcome of breast cancer (de Boer, et al., 2009). These investigators identified all patients in the Netherlands who underwent sentinel lymph node biopsy for breast cancer before 2006 and had breast cancer with favorable primary tumor features and isolated tumor cells or micrometastases in regional lymph nodes. Node-negative patients were randomized between 2000 and 2001. The primary endpoint was disease-free survival. Investigators identified 856 patients with node-negative disease who did not receive adjuvant systemic therapy (adjuvant therapy cohort), 856 patients with isolated tumor cells or micrometastases who did not receive adjuvant systemic therapy (node-positive non-adjuvant therapy cohort). , and 995 patients with isolated tumor cells or micrometastases who received such treatment (the node-positive adjuvant therapy cohort). The mean follow-up was 5.1 years. The adjusted hazard ratio for disease events among patients with isolated tumor cells who did not receive systemic treatment, compared with women with negative lymph node disease, was 1.50 (95% confidence interval [CI], 1.15 at 1.94); among patients with micrometastases, the adjusted hazard ratio was 1.56 (95% CI, 1.15 to 2.13). Among patients with isolated tumor cells or micrometastases, the adjusted hazard ratio was 0.57 (95% CI, 0.45 to 0.73) in the node-positive adjuvant therapy cohort compared to the nodule-positive cohort. . adjuvant positive nodule therapy cohort. The investigators concluded that isolated tumor cells or micrometastases in regional lymph nodes were associated with a reduced 5-year disease-free survival rate among women with favorable early-stage breast cancer who did not receive adjuvant therapy. In patients with isolated tumor cells or micrometastases who received adjuvant therapy, disease-free survival was improved.

The Medical Advisory Panel of the BlueCross BlueShield Association Center for Technology Assessment (BCBCA, 2014) concluded that the use of Oncotype DX to determine the risk of recurrence in deciding whether to undergo adjuvant chemotherapy in women with unilateral hormone receptor-positive breast cancer and lymph node negative receiving hormone therapy meets the Blue Cross and Blue Shield Association Technology Assessment Center (TEC) criteria. A technology review by the BlueCross BlueShield Association (2014) stated, "The technical performance of the assay is well documented and is unlikely to be a significant source of variability; rather, tissue sampling is likely the greatest source of variability." Retrospective epidemiological analyzes indicated strong and independent associations between the outcome of the Oncotype DX Recurrence Score (RS) and distant disease recurrence or death from breast cancer. chemotherapy outweighs very small absolute benefit Two studies of the original validation data, in which conventionally classified patients were reclassified by the Oncotype DX result, suggested that the test provides significant information about the risk of recurrence, beyond the conventional criteria for risk classification of individual patients. Evidence indicated that Oncotype DX results are significantly associated with death from breast cancer in a community patient population, with patients at high risk of RS benefiting from chemotherapy, while benefits for other categories of RS were not statistically significant. . Therefore, the evidence was considered sufficient to allow conclusions about likely health outcomes.”

Oncotype Dx has also been promoted for use in women with ER-positive, lymph node-positive breast cancer. A BlueCross BlueShield Association review (2010) concluded that the use of Oncotype Dx to select adjuvant chemotherapy in node-positive breast cancer patients has not yet been shown to improve health outcomes. The report explained that the evidence for not selecting chemotherapy for women with low RS values is based on low event rates and wide confidence intervals that include the possibility of benefit from chemotherapy. Because the data allow for a possible benefit from chemotherapy in patients with low RS scores, it is unknown whether health outcomes would be better, the same, or worse if chemotherapy were stopped in these women. The report stated that due to the lack of clear and sufficient information, there is a need for a second confirmatory study. to determine the predictive ability of Oncotype Dx to identify which node-positive breast cancer patients will benefit from chemotherapy.

The clinical evidence base for OncotypeDX is considered to be the strongest. There was some evidence on the test's impact on decision-making and to support the case that OncotypeDX predicts the benefit of chemotherapy; however, few studies have been conducted in the UK and limitations of study design have been identified. OncotypeDX has a stronger evidence base, but more evidence on its impact on decision-making in the UK and the predictive ability of the test on an estrogen receptor positive (ER+) human epidermal growth factor receptor, lymph node negative ( LN-). 2 negative (HER2-) populations are required to receive current drug regimens.

The National Comprehensive Cancer Network (NCCN, 2015) guidelines state that "the 21-gene RT-PCR assay recurrence score may be considered in selected patients with 1 to 3 involved ipsilateral axillary lymph nodes to guide the addition of combination chemotherapy to the standard hormone therapy. A retrospective analysis of a prospective randomized trial suggests that the test is predictive in this group similar to its performance in node-negative disease." The NCCN guidelines (2015) explained: "Unplanned retrospective subset analysis of a single study A randomized trial in ALN-positive, ER-positive postmenopausal breast cancer found that the 21-gene RT-PCR assay can provide predictive information for chemotherapy other than tamoxifen [citing Albain, et al., 2010] Patients with high study scores benefited from chemotherapy, whereas patients with low scores did not appear to benefit from the addition of chemotherapy, regardless of lymph node number positive. The selection of patients for use in the assay remains controversial." "The RxPONDER trial will confirm data from the SWOG-8814 trial for women with ER-positive disease and positive lymph node disease treated with endocrine therapy with or without chemotherapy based on risk scores."

Guidance from the National Institute for Health and Care Excellence (2013) stated: "Oncotype DX is recommended as an option to guide adjuvant chemotherapy decisions for people with ER+, LN-, and HER2- estrogen-early breast cancer if: The person is assessed to be at intermediate risk, and the information about the biological characteristics of the cancer provided by Oncotype DX is likely to help predict the course of the disease and thus help make the decision about prescribing chemotherapy and the manufacturer provides Oncotype DX to the NHS in accordance with the confidentiality agreement agreed with the National Institute for Health and Care Excellence (NICE) NICE encourages the collection of additional data on the use of Oncotype DX in the NHS."

An evaluation by the Belgian Center for Health Knowledge (KCE) (San Miguel, et al., 2015) concluded that "the evidence for Oncotype DX is stronger than the evidence for other tests". The KCE report noted, however, that significant gaps in the evidence remain. The KCE review primarily identified studies that support the prognostic ability (clinical validity) of the test. The KCE considered these studies to be of moderate to high quality. Oncotype DX on long-term outcomes such as overall survival, while four studies indicated that Oncotype DX leads to changes in decision-making. The KCE identified two studies on the predictive benefit of the test, one for patients with lymph nodes. The KCE report also noted that early evidence relating to improvements in quality of life and reductions in patient anxiety as a result of using the test has been reported, but this is based on a small number of patients and more evidence is needed.

The American Society of Clinical Oncology (2016) guidelines state: "If a patient has ER/PgR-positive, HER2-negative (node negative) breast cancer, the clinician may use the 21-gene recurrence score (RS; Oncotype DX; Genomic Health, Redwood City, CA) to guide decisions about adjuvant systemic chemotherapy." This is a strong recommendation based on high-quality evidence. ASCO guidelines recommend that OncotypeDx Breast not be used to guide decisions about adjuvant systemic chemotherapy for patients with ER/PgR-positive and HER2-negative (node positive) breast cancer. The guidelines also recommend that OncotypeDx Breast not be used in women with HER2-positive breast cancer or TN breast cancer. Guidelines recommend against using OncotypeDx Mama to guide decisions about extended endocrine therapy for patients with ER/PgR-positive, HER-2-negative (node negative) breast cancer who have received 5 years of endocrine therapy with no evidence of recurrence.

Acceptance of 21-gene recurrence scoring assays as clinical decision-making tools in women or men with early-stage breast cancer is controversial due to lack of prospective validation studies; however, the 2007 guidelines of an ASCO-convened expert panel on tumor markers in breast cancer concluded that multiparameter gene expression analysis (ie, the Oncotype Dx assay) can be used to predict recurrence risk in women with newly diagnosed ER, lymph node negative - breast cancer positive. While it is reasonable to consider using a 21-gene recurrence scoring assay in men, none of the data generated to date has been conducted in men with breast cancer (Gradishar, 2010).

A 2009 abstract reviewing Oncotype Dx (BC) male breast cancer cases concluded: "This large genomic study of male breast cancer reveals a heterogeneous biology as measured by the standardized quantitative assay of oncotype Dx breast cancer, similar to that seen in women BC: Some differences were observed, which may reflect differences in hormonal biology between men and women, and warrant further study.” (Shak et al., 2009).

Mom Print

MammaPrint, a 70-gene profile that classifies breast cancer as either low-risk or high-risk for recurrence by measuring representative genes from all cancer metastasis pathways that were selected for their predictive relationship to the 10-fold probability of recurrence years (Raman, et al., 2013). MammaPrint is indicated for women with stage I or II breast cancer, positive or negative lymph nodes, positive or negative ER, and tumor size less than 5 centimeters. MammaPrint determines if the patient is a candidate for chemotherapy.

In February 2007, the Food and Drug Administration (FDA) approved Mammaprint (Agendia, Amsterdam), a DNA microarray-based test used to predict whether women with early breast cancer may experience the disease again. The test measures the activity of 70 genes, providing information about how likely the cancer is to come back. It measures each of these genes in a sample of a woman's breast cancer tumor and then uses a specific formula to produce a score that determines whether the patient is considered to be at low or high risk of metastasis. In clinical trials, 1 in 4 women considered high risk by Mammaprint had cancer recurrence within 5 years. However, there are doubts about the accuracy of this test. The positive predictive values at 5 and 10 years were 23% and 29%, respectively, while the corresponding negative predictive values were 95% and 90%, respectively.

Mammaprint was tested in 307 patients under 61 years of age undergoing surgery for stage I or II breast cancer with a tumor size of 5 cm or less and negative lymph nodes. The study found that Mammaprint more than doubled physicians' ability to predict breast cancer recurrence.

Cardoso et al (2016) conducted a study to evaluate the clinical utility of the 70-gene signature test (MammaPrint). The study was designed from a randomized phase III trial. In this study, of 6693 women enrolled with early-stage breast cancer, women at low clinical and genomic risk did not receive chemotherapy, while those at high risk received chemotherapy. Genomic risk was assessed for all study subjects using MammaPrint. The authors noted that "the primary objective was to assess whether, among patients with high-risk clinical features and a low-risk gene expression profile who did not receive chemotherapy, the lower bound of the 95% confidence interval for the rate of La 5 to 5-year survival without distant metastasis would be 92% (ie, the non-inferiority cutoff or higher). 5-year distant metastasis-free survival rate that was 1.5 percentage points lower than the chemotherapy rate.

A commentary by Hudis and Dickler (2016) indicated that it can be challenging to convince practitioners that chemotherapy is not necessary in a young, healthy population. They further noted that the primary study endpoint in a 70-gene signature test study was "to declare noninferiority to a predefined baseline of a 5-year metastasis-free survival rate in only one cohort: high-risk patients whose discordant low genomic risk led to the omission of standard chemotherapy." They concluded that while for selected patients providers may want to use MammaPrint, the actions they take as a result of this trial will be variable and may change over time as a result of further studies.

The study by Cardoso et al (2016) was a median 5-year follow-up outcome of the MINDACT study, which involves following subjects for 10 years. The authors noted that follow-up is ongoing to determine whether their findings remain valid for the long-term outcome. These investigators noted that "in the critical group of patients at high clinical risk and low genomic risk, the use of adjuvant chemotherapy led to a trend toward a higher 5-year outcome rate than without chemotherapy, including a distant metastasis of 1. 5 percentage points higher, a 2.8 percentage points higher disease-free survival rate, and a 1.4 percentage points higher overall survival rate with chemotherapy than without chemotherapy in the intent-to-treat population, and a metastasis-free survival rate distant 1.9 percentage points greater, a disease-free survival rate that was 3 percentage points greater, and an overall survival rate that was 1.5 points greater, percentage greater with chemotherapy than without chemotherapy in the general population by protocol population The study was not designed to assess the statistical significance of these differences.About 50% of study patients were defined as clinically low risk. co. In this group, we found no significant difference in the 5-year distant metastasis-free survival rate between patients at high genomic risk who received chemotherapy and those who did not receive chemotherapy. Based on these data, the 70-gene signature results do not provide evidence to make recommendations about chemotherapy for patients at low clinical risk."

In an editorial accompanying the aforementioned study, Hudis and Dickler (2016) stated that "a difference of 1.5 percentage points, if real, may mean more for one patient than for another. Therefore, the reported difference does not precisely exclude one benefit that clinicians and patients might find a meaningful randomization with adequate power or a higher threshold for 5-year metastasis-free survival may have provided a more convincing result, but would have posed other important challenges for researchers."

A focused update from the American Society of Clinical Oncology (ASCO) (Kopp, et al., 2017) states that if a patient has hormone receptor positive breast cancer, human epidermal growth factor receptor 2 (HER2) negative and for nodule negative, the MammaPrint assay can be used in people at high clinical risk to inform decisions about discontinuing adjuvant systemic chemotherapy due to its ability to identify a good prognosis population with potentially limited chemotherapy benefit. The guidelines state that if a patient has hormone receptor positive, HER2 negative, and node positive breast cancer, the MammaPrint assay can be used in patients with one to three positive nodes and high clinical risk to inform decisions about discontinuation of the systemic adjuvant chemotherapy. However, these patients should be informed that the benefit of chemotherapy cannot be excluded, particularly in patients with more than one lymph node involved. The guideline update was based on an evaluation of clinical utility data from the MINDACT trial plus other published literature.

plano

The molecular subtyping profile or BluePrint is proposed for the evaluation of the prognosis of an individual when diagnosed with breast cancer. Multigene profiling classifies breast cancer into molecular subclasses of basal-type, luminal-type, and ERBB-type (HER2/neu positive) to stratify an individual's risk to presumably aid in treatment decisions.

Agendia BluePrint has an 80-gene profile that classifies breast cancer into molecular subtypes (Raman, et al., 2013). The profile separates tumors into basal-type, luminal-type, and ERBB2-type subgroups, measuring the functionality of downstream genes for each of these molecular pathways to inform the clinician about the potential effect of adjuvant therapy.

Krijgsman et al (2012) noted that the classification of breast cancer into molecular subtypes may be important for appropriate selection of therapy, as tumors with apparently similar histopathology can have markedly different clinical outcomes. In this article, these researchers report the development of a molecular subtyping profile (BluePrint), which allows for rationalization in patient selection for chemotherapy or endocrine therapy prescription. An 80-gene molecular subtyping profile (BluePrint) was developed using 200 breast cancer patient samples and confirmed in 4 independent validation cohorts (n = 784). In addition, the profile was tested as a predictor of response to chemotherapy in 133 patients with breast cancer treated with neoadjuvant chemotherapy T/FAC. BluePrint scoring of a cohort of patients treated with neoadjuvant chemotherapy (n = 133) shows a better distribution of pathological complete response (pCR) among molecular subgroups compared to local pathology: 56% of patients had a pCR in the baseline subgroup, 3% in the MammaPrint low-risk luminal subgroup, 11% in the MammaPrint high-risk luminal subgroup, and 50% in the HER2 subgroup. The gene pool that identifies luminal breast cancer is highly enriched for genes that have an estrogen receptor binding site close to the promoter region, suggesting that these genes are direct targets of the estrogen receptor. Implementation of this profile may improve the clinical management of patients with breast cancer, allowing the selection of patients most likely to benefit from chemotherapy or endocrine therapy.

A review by the National Institute for Health Research (Ward, et al., 2013) concluded that the evidence for Blueprint is limited. Due to the limited available data identified for this test, the NIHR was unable to draw firm conclusions about its analytical validity, clinical validity (prognostic ability) and clinical utility. The report stated that more evidence was needed about the prognosis and predictive ability of this test.

A report by the Belgian Center for Health Knowledge (KCE) (San Miguel, et al., 2015) found limited evidence of the prognostic ability (clinical validity) of BluePrint. The KCE found insufficient evidence on the impact of BluePrint on clinical management (clinical utility).

Also, there is no information about BluePrint/Molecular Subtyping from the NCCN Clinical Practice Guideline on "Breast Cancer" (Version 2.2013).

DestinoImprimir

TargetPrint®, ER/PR/HER2 Expression Assay (Agendia) is a microarray-based gene expression test that provides a quantitative assessment of the level of estrogen receptor (ER), progesterone receptor (PR) and HER2/neu overexpression of a patient on her breast cancer (Raman, et al., 2013). TargetPrint is offered in conjunction with MammaPrint to provide the clinician with an even more comprehensive basis for treatment decisions. TargetPrint provides an additional benefit to the diagnostic process. Immunohistochemistry provides a semi-quantitative positive or negative result, while the gene expression result provided by TargetPrint allows clinicians to integrate the absolute level of ER, PR, and HER2 gene expression into treatment planning. TargetPrint determines if the patient is a candidate for hormone therapy.

TargetPrint is a microarray-based gene expression test that provides a quantitative assessment of estrogen receptor (ER), progesterone receptor (PR) and HER2/neu overexpression level in breast cancer. The manufacturer claims that TargetPrint is offered in conjunction with MammaPrint gene expression profiling to provide the clinician with an even more comprehensive basis for treatment decisions. The manufacturer claims that compared to immunohistochemistry (IHC), TargetPrint provides additional information. While the IHC provides a semi-quantitative positive or negative result, the gene expression result provided by TargetPrint provides data on the absolute level of ER, PR, and HER2 gene expression. Published information on TargetPrint is limited to studies examining its correlation with ER, PR, and HER2 receptor measurements (Gunven et al, 2011; Gevensleben et al, 2010; Roepman et al, 2009). There is a lack of evidence from published prospective clinical studies demonstrating that quantification of ER, PR, and HER2 gene expression by TargetPrint alters management in a way that improves clinical outcomes.

Sinfonia

Symphony (Agendia) provides a comprehensive tumor profile and is used to support therapeutic options for breast cancer (Raman, 2013). SYMPHONY includes four assays to support breast cancer treatment decisions: MammaPrint® determines recurrence risk. BluePrint™ determines molecular subtypes and TargetPrint® determines estrogen receptor (ER), progesterone receptor (PR) and HER2 status. TheraPrint™ identifies alternative types of therapy for metastatic disease. SYMPHONY provides genomic information that helps guide therapeutic decisions, even in cases classified as indeterminate, such as grade 2, small tumors, HER2 and/or positive lymph nodes. MammaPrint® determines if the patient is a candidate for chemotherapy. TargetPrint® determines if the patient is a candidate for hormone therapy. BluePrint® provides information on tumor subclassification that guides the choice of therapies and combinations of therapies. TheraPrint® identifies alternative types of therapy for metastatic disease.

Panel of 76 Rotterdam signature genes

The Rotterdam Signature Test (Veridex) is a 76 gene expression assay (Raman, 2013). Sixty genes are designed to evaluate estrogen receptor positive samples and 16 genes to evaluate estrogen receptor negative samples. In a validation study that tested the signature on samples of 148 women, 50 fell into the low-risk group and 98 into the high-risk group. The test had a specificity of 88% and a sensitivity of 39% for the low-risk group, with a risk rate for distant recurrence in 5 years of 5.74 when comparing the high-risk group with the low-risk group. The Rotterdam Firm identifies women at high and low risk of disease recurrence.

The Rotterdam Signature 76-Gene Panel (Veridex, LLC) is a multivariable index assay designed to help assess a patient's risk of systemic cancer recurrence after successful initial treatment of node-negative localized breast cancer with surgery and tamoxifen alone. This multigene assay was developed for use in patients with node negative breast cancer. The Rotterdam Signature Panel uses microarray processing to measure cellular mRNA concentrations in fresh tissue samples. The Rotterdam Signature panel uses the U133a Human Genome GeneChip (Affymetrix, Inc.) to identify patients who have gene expression signatures associated with low or high risk of developing metastatic disease. A multicenter study investigated the ability of the Rotterdam signature of 76 genes to identify patients at risk of distant metastasis within 5 and 10 years of first diagnosis, using frozen tissue samples from 180 patients with node-negative breast cancer who did not receive systemic chemotherapy (Foekens, et al., 2006). The Rotterdam 76 gene signature correctly identified 27 of 30 cases of relapse at 5 years (90% sensitivity) and 75 of 150 patients who did not relapse (50% specificity). An earlier abstract of the same study (Foekens, et al., 2005) reported a hazard ratio for distant metastasis-free survival comparing favorable versus unfavorable signature = 7.41 (95% confidence interval 2.63-20.9) ; p = 8.5 x 10-6). Overall survival hazard ratio comparing favorable to unfavorable signature = 5.45 (95% confidence interval 1.62 to 18.3); p = 0.002. There are no published studies that have evaluated the clinical utility of the Rotterdam 76 gene signature following the long-term outcomes of patients selected and not selected for chemotherapy based on study results.

Breast Cancer Gene Expression Index/Breast Cancer Index

The Breast Cancer Gene Expression Index (HOXB13:IL17BR, also known as H/I) (AviaraDx, Inc., Carlsbad, CA) is intended to predict the risk of disease recurrence in women with breast cancer receptor-positive lymph nodes. estrogen (ER) positive. - negative breast cancer. The breast cancer gene expression rate is based on the expression rate of two genes: the homeobox-B13 gene (HOXB13) and the interleukin-17B receptor gene (IL17BR). In breast cancers that are more likely to recur, the HOXB13 gene tends to be overexpressed, while the IL-17BR gene tends to be underexpressed.

Ma et al (2004) reported early validation of the HOXB13:IL17BR gene expression ratio. The researchers generated gene expression profiles of hormone receptor-positive primary breast cancers in a group of 60 patients treated with adjuvant tamoxifen monotherapy. An expression signature predictive of disease-free survival was reduced to a ratio of two genes, HOXB13 vs. IL17BR, which outperformed existing biomarkers. The researchers concluded that ectopic expression of HOXB13 in MCF10A breast epithelial cells increases motility and invasiveness in vitro, and its expression is increased in both primary invasive and pre-invasive breast cancer. The investigators suggested that the HOXB13:IL17BR expression ratio may be useful in identifying suitable patients for alternative therapeutic regimens in early-stage breast cancer.

In a retrospective study of 852 patients, Ma et al (2006) found that the HOXB13:IL17BR ratio (H:I expression ratio) independently predicted breast cancer recurrence in patients with node negative and ER positive cancers. The H:I expression ratio was found to be predictive in patients who received tamoxifen therapy as well as in those who did not. HOXB13, IL17BR, CHDH, estrogen receptor (ER) and progesterone receptor (PR) expression was quantified by real-time polymerase chain reaction (PCR) in 852 paraffin-embedded and paraffin-embedded primary breast cancers. formalin from 566 untreated and 286 treated with tamoxifen. patients treated for breast cancer. Gene expression and clinical variables were analyzed for association with relapse-free survival (RFS) using Cox proportional hazards regression models. The investigators reported that, across the cohort, HOXB13 expression was associated with shorter RFS (p = 0.008) and IL17BR and CHDH expression was associated with longer RFS (p < 0.0001 for IL17BR and p = 0. 0002 for CHDH). In ER-positive patients, the HOXB13:IL17BR ratio predicted clinical outcome regardless of treatment, but more strongly in node-negative patients. In multivariate analysis of the ER-positive, node-negative subgroup, including age, PR status, tumor size, S-phase fraction, and tamoxifen treatment, the two-gene score remained a significant predictor of RFS (hazard ratio [HR] = 3.9; 95% CI: 1.5 to 10.3, p = 0.007).

The value of the breast cancer gene expression rate was also evaluated in a study by Goetz et al (2006). This study found that a high ratio of H:I expression is associated with a higher rate of relapse and mortality in patients with ER-positive, node-negative cancer treated with surgery and tamoxifen. Goetz et al (2006) examined the association between the expression ratio of HOXB13 to IL17BR and clinical outcomes of relapse and survival in women with ER-positive breast cancer enrolled in a North Central Cancer Treatment Group (NCCTG) study of tamoxifen adjuvant. 89-30-52). Tumor blocks were obtained from 211 of 256 eligible patients, and quantitative reverse transcription-PCR profiles for HOXB13 and IL-17BR were obtained from 206 patients. In the positive node cohort (n = 86), the HOXB13/IL-17BR ratio was not associated with relapse or survival. In contrast, in the negative node cohort (n = 130), a high HOXB13/IL-17BR ratio was associated with significantly worse SRF [HR, 1.98; p = 0.031], disease-free survival (DFS) (HR, 2.03; p = 0.015) and OS (HR, 2.4; p = 0.014), regardless of standard prognostic markers.

The Blue Cross and Blue Shield Association (BCBSA) Technology Assessment Center (BCBSA, 2007) announced that its Medical Advisory Panel (MAP) has concluded that the use of breast cancer gene expression index gene expression profiling does not meet to ECT criteria.

The Working Group on Evaluating Genomic Applications in Practice and Prevention (EGAPP) (2009) did not find sufficient evidence to make a recommendation for or against using the H:I ratio test to improve outcomes in populations. defined in women with breast cancer. The EGAPP concluded that there is insufficient evidence to assess the balance of benefits and harms of the proposed uses of this test. The EWG has encouraged the development and evaluation of these technologies.

In a systematic review of gene expression profiling assays in early-stage breast cancer, Marchionni, et al. (2008) summarized the evidence on the validity and usefulness of 3 gene expression-based breast cancer prognostic tests: Oncotype Dx, MammaPrint, and H/I. The authors concluded that gene expression technologies hold great promise for improving prognostic predictions and treatment benefit for women with early-stage breast cancer. However, more information is needed about the extent of improvement in prediction, the characteristics of the women in whom the tests should be used, and the best way to incorporate test results into decision-making about disease treatment.

American Society of Clinical Oncology guidelines (Harris, et al., 2007) found that in newly diagnosed patients with estrogen receptor-positive, lymph node-negative breast cancer, the Oncotype Dx assay can be used to predict the risk of recurrence. patients treated with tamoxifen. ASCO guidelines concluded that Oncotype Dx can be used to identify patients who are anticipated to derive the greatest therapeutic benefit from adjuvant tamoxifen and who may not require adjuvant chemotherapy. The ASCO guidelines further found that patients with high recurrence scores appear to derive relatively greater benefit from adjuvant chemotherapy than from tamoxifen. ASCO found that there is currently not enough data to comment on whether these findings generalize to hormone therapies other than tamoxifen or whether this study applies to other chemotherapy regimens. American Society of Clinical Oncology guidelines (Harris, et al., 2007) concluded that the precise clinical usefulness and appropriate application for other multiparameter assays such as the MammaPrint, Rotterdam Signature, and Breast Cancer Gene Expression Ratio assays are under discussion. . investigation. ASCO also found insufficient data to recommend the use of proteomic standards for the treatment of patients with breast cancer.

Sgori et al. (2013) found that, in the absence of prolonged letrozole therapy, high H/A identifies a subset of disease-free ER-positive patients after 5 years of tamoxifen who are at risk for late recurrence. The researchers also found that when long-term endocrine therapy with letrozole is prescribed, a high H/A predicts the benefit of therapy and a lower likelihood of late disease recurrence. Sgori et al. performed a prospective-retrospective nested case-control design of 83 recurrences combined with 166 non-recurrences of patients treated with letrozole and placebo in the MA.17 study. H/I expression in primary tumors was determined by reverse transcription polymerase chain reaction with a prespecified cut-off. Investigators determined the predictive ability of H/A to determine the benefit of letrozole using multivariate conditional logistic regression that included standard clinicopathological factors as covariates. All statistical tests were two-tailed. The investigators reported that high H/A was statistically significantly associated with a decrease in late recurrence in patients receiving long-term letrozole therapy (odds ratio [OR] = 0.35; 95% confidence interval [CI] = 0. 16 at 0.75, P = 0.007). In a model adjusted with standard clinicopathologic factors, high H/A remained statistically significantly associated with patient benefit from letrozole (OR = 0.33, 95% CI = 0.15 to 0.73, P = 0.006). The reduction in absolute risk of recurrence at 5 years was 16.5% for patients with high H/A (p = 0.007). The interaction between H/A and letrozole treatment was statistically significant (p = 0.03).

The BioTheranostics Breast Cancer Index (BCI) is a prognostic biomarker that provides a quantitative assessment of the likelihood of distant recurrence in patients diagnosed with lymph node negative, estrogen receptor positive breast cancer (Raman, et al., 2013). In development and validation studies, BCI stratified approximately 50% of patients with ER+ negative breast cancer treated with tamoxifen into a low-risk group for recurrence 10 years apart. The BCI is a molecular assay developed from the combination of two indices: HOXB13:IL17BR and five indices of genes associated with the cell cycle (BUB1B, CENPA, NEK2, RACGAP1, RRM2) that assess the tumor grade. The test is performed on a formalin-fixed, paraffin-embedded (FFPE) tissue block.

Ma et al (2008) reported the development and initial validation of a five-gene reverse transcription PCR assay for molecular grade index (MGI) that was later incorporated into the BCI and is suitable for testing routine fixed clinical samples, in formalin and embedded in paraffin. . The researchers found that the combination of MGI and HOXB13:IL17BR alone outperformed and identified a subgroup (approximately 30%) of patients with early-stage estrogen receptor-positive breast cancer with very poor outcomes despite endocrine therapy. From their previously published list of genes whose expression correlates with tumor grade and tumor stage progression, the researchers selected five cell cycle-related genes to construct MGI and evaluated MGI on two publicly available microarray datasets. with a total of 410 patients. Using two additional cohorts (n = 323), the investigators developed a real-time reverse transcription PCR assay for MGI, validated its prognostic utility, and examined its interaction with HOXB13:IL17BR. The researchers reported that MGI consistently performed as a strong prognostic factor and was comparable to a more complex 97-gene genomic grade index across multiple datasets. In patients treated with endocrine therapy, MGI and HOXB13:IL17BR modified each other's prognostic performance. High MGI was associated with significantly worse outcome only in combination with high HOXB13:IL17BR, and similarly high HOXB13:IL17BR was significantly associated with poor outcome only in combination with high MGI.

Jerevall et al (2011) reported the development of the Breast Cancer Index, a dichotomous index that combines two gene expression assays, HOXB13:IL17BR (H:I) and molecular grade index (MGI), to assess the risk of recurrence in patients with breast cancer. . The aim of the study was to demonstrate the prognostic utility of the combined index in early-stage breast cancer. In a blinded retrospective analysis of 588 patients with ER-positive breast cancer treated and not treated with tamoxifen from the prospective randomized Stockholm study conducted between 1976 and 1990, H:I and MGI were measured by time-lapse RT-PCR. Association with patient outcome was assessed by Kaplan-Meier analysis and Cox proportional hazards regression. A continuous risk index was developed using the Cox model. The investigators found that dichotomous H:I+MGI was significantly associated with distant recurrence and death from breast cancer. More than 50% of tamoxifen-treated patients classified as low-risk had less than a 3% 10-year risk of recurrence. A continuous risk model (Breast Cancer Index (BCI)) was developed with the tamoxifen treated group and the proven prognostic performance in the untreated group was 53% of patients classified as low risk with 8.3% risk of recurrence at a distance of 10 years.

Jankowitz et al (2011) reported a study to validate the prognostic performance of BCI in patients with estrogen receptor positive breast cancer and node negative. The researchers found that, in this characteristically low-risk cohort, the BCI categorized high- and low-risk groups with an approximately five-fold difference in the 10-year risk of distant recurrence and breast-cancer-specific death. The researchers identified tumor samples from 265 tamoxifen-treated patients who were estrogen receptor positive and lymph node negative from the cancer research registry of a single academic institution. They took the BCI test and assigned scores based on a predetermined risk model. Researchers assessed risk using BCI and Adjuvant Online! (AO) and correlated them with clinical outcomes in the patient cohort. The investigators found that BCI was a significant predictor of outcome in this cohort of patients with estrogen receptor positive and node negative (median age: 56 years; median follow-up: 10.3 years) treated with adjuvant tamoxifen alone or tamoxifen with chemotherapy. (32%). BCI classified 55%, 21% and 24% of patients as low, intermediate and high risk, respectively. The 10-year recurrence rates were 6.6%, 12.1%, and 31.9%, and the breast cancer-specific mortality was 3.8%, 3.6%, and 22.1% in the low, intermediate, and high risk groups, respectively. In a multivariate analysis that included clinicopathologic factors, BCI was a significant predictor of distant recurrence (HR for 5-unit increase = 5.32 [CI 2.18-13.01; P = 0.0002]) and mortality . specific breast cancer (HR for 5-unit increase = 9.60 [CI 3.20-28.80; P < 0.0001]). AO was significantly associated with the risk of recurrence. In a separate multivariate analysis, both BCI and AO were significantly predictive of outcome. In a time-dependent (10-year) ROC curve accuracy analysis of recurrence risk, the addition of BCI and AO increased the predictive accuracy in all patients from 66% (AO only) to 76% (AO+BCI). and only with tamoxifen. treated patients from 65% to 81%. The authors concluded that BCI and AO are independent predictors with BCI having additional utility beyond the standard of care parameters included in AO. The authors acknowledge that this study is limited by the fact that it is a single-institution retrospective study and that the results may have been biased based on sample availability and patterns of referral to the tertiary academic center.

Mathieu et al (2012) evaluated the performance of BCI in predicting chemosensitivity based on pathologic complete response (pCR) and breast-conserving surgery (BCS). The authors performed the BCI assay on tumor samples from 150 breast cancer patients from a single institution treated with neoadjuvant chemotherapy. The authors used logistic regression and c-index to assess the predictive power and additive precision of the BCI in addition to clinicopathological factors. BCI classified 42% of patients as low risk, 35% as intermediate and 23% as high risk. The low-risk BCI group had a NPV of 98.4% for CRp and a NPV of 86% for BCS. The high versus low BCI group had 34 and 5.8 times greater odds of achieving pCR and BCS, respectively (P = 0.0055; P = 0.0022). BCI increased the c-index for pCR (0.875-0.924; p = 0.017) and BCS prediction (0.788-0.843; p = 0.027) in addition to clinicopathological factors. The authors concluded that BCI significantly predicted pCR and BCS in addition to clinicopathological factors. High NPVs indicate that the BCI can be a useful tool to identify patients with breast cancer who are not eligible for neoadjuvant chemotherapy. The authors concluded that "these results suggest that the BCI can be used to assess both chemosensitivity and BCS eligibility." The authors state that one of the main limitations of this study is that, in this retrospective analysis, patients were not selected on the basis of ER or HER2 expression for neoadjuvant chemotherapy indications. The authors explained that this may have increased the predictive power of the BCI, as this biomarker was initially developed and validated in patients with an ER+ negative node.

(Video) ONCOLOGY - Oncological Emergencies for Medical Students

Zhang et al (2013) examined the prognostic performance of BCI for predicting early (0-5 years) and late (greater than 5 years) risk of distant recurrence in patients with estrogen receptor positive (ER(+)) lymph node . negative tumors (LN(-)). The BCI model was validated by retrospective analyzes of tumor samples from patients treated with tamoxifen from a prospective randomized study (Stockholm TAM, n = 317) and a multi-institutional cohort (n = 358). Within the Stockholm TAM cohort, BCI risk groups stratified the majority (approximately 65%) of patients as low risk with less than 3% 0-5 years and 5 years to 10 years distant recurrence rate. In the multi-institutional cohort, which had larger tumors, 55% of patients were classified as low risk for BCI with a distant recurrence rate of less than 5% at 0 to 5 years and 5 to 10 years. Zhang and colleagues found that, for both cohorts, continuous BCI was the most significant prognostic factor beyond standard clinicopathological factors at 0 to 5 years and beyond 5 years. The authors concluded that the prognostic sustainability of the BCI to assess the risk of early and late distant recurrence at the time of diagnosis has clinical use for chemotherapy decisions at the time of diagnosis and for decisions of adjuvant endocrine therapy extended beyond five years.

Sgori et al (2013) compared the prognostic ability of the BCI, Oncotype DX Breast, and IHC4 assay for early and late recurrence in patients with estrogen receptor-positive, lymph node-negative (N0) disease who participated in the Arimidex, Tamoxifen, alone or in combination (ATAC). In this prospective comparison study, Sgori and colleagues obtained archival tumor blocks from the TransATAC tissue bank from all postmenopausal patients with estrogen receptor-positive breast cancer for whom Oncotype DX and IHC4 values had already been derived. The researchers performed BCI analyzes on samples paired with sufficient RNA using two BCI models: cubic (BCI-C) and linear (BCI-L), using previously validated thresholds. The primary objective of the prospectively defined study was to evaluate the overall prognostic performance (0-10 years) of the BCI-C model for DR in ER+ N0 patients. Secondary objectives were: 1) evaluation of the prognostic performance of the BCI-L model and its components, H/A and MGI, for general (0-10 years), early (0-5 years) and late (5-10 years) DR ). ); 2) comparative performance of BCI-L against Oncotype DX RS and IHC4. To assess the ability of biomarkers to predict recurrence beyond standard clinicopathological variables, the investigators calculated the change in odds ratio from Cox proportional hazards models. Suitable fabric was available. of 665 patients with estrogen receptor-positive N0 breast cancer by BCI analysis. The primary analysis showed significant differences in the risk of distant recurrence over 10 years in the BCI-C categorical risk groups (p<0 0001) with 6.8% (95% CI 4 4-10 0) of patients in the BCI-C group. low-risk, 17.3% (12.0-24.7) in the intermediate group and 22.2% (15.3-31.5) in the high-risk group with distant recurrence. The BCI-C analyzed as a continuous variable was not significantly associated with the overall risk (0-10 years) of RD when adjusted for CTS (interquartile HR = 1.39; 95% CI, 0.99 to 3.70; LR- Δχ2 = 3.70, P = 0.054 ). Comparison of the prognostic performance of BCI-L with BCI-C indicated that, unlike BCI-C, BCI-L was a significant predictor of risk of recurrence as a continuous variable, and HR after adjustment for CTS was of 2 19 versus 4.86 between high and low risk groups for BCI-C and BCI-L, respectively. Therefore, all subsequent analyzes were performed using BCI-L. Secondary analysis showed that BCI-L was a much stronger predictor for overall distant recurrence (0-10 years) compared with BCI-C (interquartile HR 2 30 [95% CI 1 62-3 27], likelihood ratio (LR)-Δχ (2)=22 69, p<0 0001). When compared to BCI-L, the Oncotype Dx breast score was less predictive (HR 1 48 [95% CI 1 22-1 78]; LR-Δχ(2) = 13 68; p = 0 0002) and IHC4 was similar (HR 1 69 [95% CI 1 51-2 56]; LR-Δχ(2)=22 83; p<0 0001). All subsequent analyzes were performed using the BCI-L model. In multivariate analysis, all assays had significant prognostic power for early distant recurrence (BCI-L HR 2.77 [95% CI, 1.63-4.70], LR-Δχ(2)=15.42, p< 0 0001; Oncotype Dx Breastscore HR 1 80 [1 42-2 29], LR-Δχ(2)=18 48, p<0 0001;IHC4 HR 2 90 [2 ·01-4·18], LR-Δχ( 2)=29·14, p<0·0001); however, only BCI-L was significant for late distant recurrence (BCI-L HR 1 95 [95% CI 1 22-3 14], LR-Δχ(2) = 7 97, p = 0 0048, recurrence score of 21 genes HR 1 13 [0 82-1 56], LR-Δχ(2)=0 48, p=0 47, IHC4 HR 1 30 [0 88-1·94], LR-Δχ(2)=1· 59, p=0·20). The authors concluded that the BCI-L was the only significant prognostic test for early and late distant recurrence risk and identified two risk populations for each time period. The BCI-L can help identify patients at high risk of late distant recurrence who may benefit from endocrine or other therapy. An important limitation is that BCI-L assessment was a secondary objective of this study; the main objective was the evaluation of the BCI-C.

An editorial (Ignatiadis, 2013) accompanying the study by Sgroi, et al. stated that the BCI test is "ready for prime time" in making treatment decisions for estrogen receptor-positive postmenopausal women who have undergone 5 years of hormone therapy. The editorial noted that there are other molecular diagnostic assays that have also been shown to predict late recurrence. In support, the editorial cited a study by Sestak, et al. (2013), who found that, in the last phase of follow-up, the Clinical Treatment Score (CTS) added most of the prognostic information for distant recurrence in years 5 to 10 for patients with breast cancer in the ATAC study. . Sestack et al. reported that, in a multivariate model incorporating CTS, PAM50 provided the strongest additional prognostic factor at the 5- to 10-year follow-up phase, followed by BCI, with IHC4 and RS adding less prognostic information.

A manufacturer-funded study (Gustavsen, et al., 2014) reported a model that found that BCI represents a cost savings from a third-party payer perspective, based on assumptions about the impact of BCI on adjuvant chemotherapy use, prolonged use of endocrine therapy and adherence to endocrine therapy. The authors developed two cost-effective models to project the cost-effectiveness of BCI in a hypothetical population of patients with estrogen receptor-positive, lymph node-negative breast cancer compared to standard clinical-pathological diagnostic modalities. The authors modeled the costs associated with adjuvant chemotherapy, toxicity, follow-up, endocrine therapy, and recurrence over 10 years. The models examined cost-utility compared to standard practice when used at diagnosis and in disease-free patients 5 years after diagnosis. The authors reported that the use of the BCI was designed to save costs in both models. In the newly diagnosed population, net cost savings were $3,803 per patient assessed. In the population 5 years after diagnosis, the BCI was projected to produce a net cost savings of $1803 per patient assessed. The authors reported that sensitivity analyzes demonstrated that BCI was cost-saving across a wide range of clinically relevant entry assumptions.

Preliminary data suggest that molecular approaches including gene expression platforms such as BCI can add classic clinical parameters including tumor size and lymph node status at diagnosis, but more research is needed (Smith, et al. al., 2014; Bianchini & Gianni, 2013; Ignatiadis and Sotiriou, 2013). The clinical usefulness of BCI and other molecular diagnostics in predicting late recurrence has yet to be established (Foukakis and Bergh, 2015). It also remains to be established which of several molecular diagnostic tests under development is most appropriate to detect late recurrence (Sestak & Kuzick, 2015). ).

A review by the National Institute for Health Research (Ward, et al., 2013) found that, based on the limited data available, no firm conclusions can be drawn about analytical validity, clinical validity (prognostic ability), and the clinical utility of the Index of Breast cancer. The evaluation indicated that more evidence is needed about the prognostic and predictive capacity of this test. An IETS evaluation (2013) and a consensus statement (Azim, et al., 2013) reached similar conclusions.

A review by the BlueCross BlueShield Association (2015) concluded that the evidence is insufficient to allow conclusions about the rate of breast cancer in health outcomes. While evidence supports the association of risk classes defined by the Breast Cancer Index and recurrence and survival outcomes, it remains to be seen whether the Breast Cancer Index adds incremental prognostic information to standard clinical risk classifiers.

An evaluation by the Belgian Center for Health Knowledge (KCE) (San Miguel, et al., 2015) found that the evidence for the H/I ratio assay is limited to studies supporting the prognostic ability (clinical validity) of to test They will find insufficient evidence of the impact of the H/I ratio test on clinical management (clinical utility).

A review published in the ASCO Educational Book (Smith, et al., 2014) reviewed BCI and other currently available molecular diagnostics for screening and determining the optimal duration of adjuvant endocrine therapy in women with estrogen receptor-positive breast cancer. early stage: "Further Research The application of molecular features and gene expression scores to standard clinico-pathological criteria to tailor extended endocrine therapy is now a high priority... .Preliminary data suggest that molecular approaches that include gene expression platforms, such as ROR, can be added to classic clinical parameters including tumor size and lymph node status at the time of diagnosis".

A local Palmetto Medicare (LCD) coverage determination allows breast cancer index coverage in certain postmenopausal women with estrogen receptor positive breast cancer, reasoning that the benefit defined by the BCI test data appears to be when a woman has significant side effects or other concerns regarding adjunctive tamoxifen therapy and is opposed to taking tamoxifen longer than 5 years or starting an AI (letrazole) after tamoxifen (CMS, 2014). score such that, "at the 95% confidence interval (CI), the risk in some people categorized in the low BCI group can be as high as 20%. Due to the complexity of the data, there is a significant possibility that a physician may consider all patients with BCI-L as of negligible risk and therefore not considering prolonged hormone therapy, and consequently keeping women from violating NCCN-recommended interventions, the false sense of security can deprive many women of life-saving therapy."

There is a lack of consensus among guidelines regarding the value of molecular assays in determining whether a longer duration of adjuvant endocrine therapy beyond 5 years is clinically indicated. for hormone receptor-positive breast cancer status: "Well-established clinical factors including tumor size, lymph node status, ER, PgR, and HER2 biomarkers, and molecular diagnostic assays serve as prognostic factors for breast cancer recurrence. However, , there are no biomarker measures that selectively predict early versus late recurrence, predict whether tamoxifen or AI therapy would be an appropriate treatment, or determine whether longer durations of therapy are clinically indicated as an endocrine adjuvant." The National Comprehensive Cancer Network Guidelines for Breast Cancer, Version 2, 2015 states, "Many other multigene or multigene expression assay systems have been developed. These systems are generally based on small retrospective studies, and the Panel believes that none are currently sufficiently validated to warrant inclusion in the guideline." The St. Gallen (Coates, et al., 2015) found that Oncotype DX, MammaPrint, PAM-50 ROR score, EndoPredict, and the Breast Cancer Index were considered useful for year 1 to 5 prognosis, but only Oncotype Dx scored the most in favor of its value in predicting the usefulness of chemotherapy. The panel agreed that the PAM50 ROR score was clearly prognostic beyond 5 years, and that Mammaprint was not prognostic beyond 5 years. The Panel was divided on the prognostic value of Breast Cancer Index, Oncotype DX and EndoPredict in this time period. The ESMO guidelines (Senkus, et al., 2013) state: "Molecular signatures for ER-positive breast cancer such as OncotypeDx, EndoPredict, Breast Cancer Index, or for all types of breast cancer (pNO-1) as MammaPrint and Genomic Grade Index are commercially available, but none have demonstrated robust clinical utility to date. Oncotype DX can be used in conjunction with all clinicopathological factors to aid in treatment decision making.”

As diretrizes da Sociedade Americana de Oncologia Clínica (2016) declaram: "Se uma paciente tem câncer de mama ER/PgR-positivo, HER2-negativo (nó negativo), o médico pode usar o índice de câncer de mama para orientar as decisões sobre a terapia sistêmica adjuvante. " Esta é uma recomendação de força moderada baseada em evidências de qualidade intermediária. As diretrizes da ASCO recomendam o uso do índice de câncer de mama para orientar as decisões sobre a terapia sistêmica adjuvante em pacientes com câncer de mama ER/PgR=positivo, HER2-negativo (nó positivo). As diretrizes também recomendam contra o uso do índice de câncer de mama em câncer de mama HER2-positivo ou câncer de mama TN. As diretrizes também recomendaram contra o uso do Índice de Câncer de Mama para orientar decisões sobre terapia endócrina estendida para pacientes com câncer de mama ER/PgR-positivo, HER-2-negativo (nó negativo) que receberam 5 anos de terapia endócrina sem evidência de recorrência .

breast milk

Mammostrat (Clarient) is a new test to estimate the risk of recurrence in hormone receptor-positive early-stage breast cancer, regardless of proliferation and grade (Raman, et al., 2013). Five biomarkers are combined with a defined mathematical algorithm that results in a risk index. Mammostrat is clinically validated and has been studied in over 4,500 total patients in numerous independent cohorts, including the NSABP B14 and B20 trials. Clinicians and patients face difficult decisions about adding toxic adjuvant chemotherapy in addition to standard endocrine therapy. Mammostrat can help clinicians understand the inherent aggressiveness of the tumor and the likelihood of tumor recurrence.

Mammostrat is a prognostic immunohistochemical (IHC) test that measures the risk of breast cancer recurrence in postmenopausal, node-negative, estrogen receptor-expressing patients receiving hormone therapy and considering adjuvant chemotherapy. The test analyzes five monoclonal antibody biomarkers and applies a diagnostic algorithm to assess whether patients are at high, moderate or low risk of recurrence after surgical removal of the breast cancer tumor and treatment with tamoxifen.

Bartlett et al (2010) tested the effectiveness of Mammostrat in a mixed population of cases treated at a single center with breast-conserving surgery and long-term follow-up. Tissue microarrays were collected from a consecutive series of 1812 women treated with wide local excision and postoperative radiotherapy. Of the 1390 cases scored, 197 received neither adjuvant hormone therapy nor chemotherapy, 1044 received tamoxifen alone, and 149 received a combination of hormone therapy and chemotherapy. The median age at diagnosis was 57 years, 71% were postmenopausal, 23.9% were node positive, and the median tumor size was 1.5 cm. Samples were stained using 0.6 mm2 tissue microarray cores in triplicate and tested for p53, HTF9C, CEACAM5, NDRG1, and SLC7A5 positivity. Each case was assigned a Mammostrat risk score, and distant recurrence-free survival (DRFS), relapse-free survival (RFS), and overall survival (OS) were analyzed using positivity and marker score. Increased Mammostrat scores were significantly associated with reduced DRFS, RFS, and OS in ER-positive breast cancer (p < 0.00001). In multivariate analyses, the risk score was independent of conventional risk factors for DRFS, RFS and OS (p < 0.05). In patients with negative lymph nodes treated with tamoxifen, the recurrence rates at 10 years were 7.6+/- 1.5% in the low risk group versus 20.0+/- 4.4% in the low risk group. high risk. Furthermore, exploratory analyzes revealed associations with outcomes in untreated, ER-negative patients. The authors concluded that Mammostrat may act as an independent prognostic tool for breast cancer treated with ER-positive tamoxifen, and study results revealed a possible association with outcome regardless of lymph node status and ER-negative tumors. .

There is not enough evidence to determine whether the Mammostrat test is better than conventional risk assessment tools in predicting breast cancer recurrence. Furthermore, neither NCCN nor ASCO have incorporated the test into their guidelines as a management tool. Guidance from the National Institute for Health and Clinical Excellence (NICE, 2013) states that Mammostrat "is only recommended for research use in people with ER+, LN-, and HER2- early breast cancer, to gather evidence about possible clinical outcomes. important and to determine the ability of the tests to predict the benefit of chemotherapy... The tests are not recommended for general use in these people due to uncertainty about their overall clinical benefit and, consequently, cost-effectiveness."

An evaluation by the Belgian Center for Health Knowledge (KCE) (San Miguel, et al., 2015) found that the evidence for Mammostrat is mostly limited to studies that support the prognostic ability (clinical validity) of the test. The KCE stated that these studies include a large sample size and appear to be of reasonable quality. The KCE study cited the clinical usefulness in terms of the test's predictive ability by risk group. "However, more evidence is needed."

The American Society of Clinical Oncology (2016) guidelines state: "If a patient has ER/PgR-positive, HER2-negative (node positive or node negative) breast cancer, the clinician should not use the five-protein assay ( Mammostrat; Clarient, a GE Healthcare company, Aliso Viejo, CA) to guide decisions about adjuvant systemic therapy." This is a moderate-strength recommendation based on intermediate-quality evidence. ASCO guidelines recommend against using Mammostrat to guide decisions about adjuvant systemic therapy for patients with HER2-positive breast cancer or TN.

OvaChek

OvaCheck™ (Correlogic Systems, Inc.) is a proteomic blood test for the early detection of ovarian cancer. A similar test, involving a different molecular pattern, was the subject of a 2002 study of 216 women with ovarian cancer. This study showed that the proteomic test had a specificity of 100% and a sensitivity of 95%, with a positive predictive value of 94% (Petricoin, et al., 2002). Although this study showed that a proteomics test detected ovarian cancer even when CA-125 levels were normal, this study only included women who had ovarian cancer detected by other means. There is not enough evidence that this test is effective in detecting women with undetected ovarian cancer.

Furthermore, there is concern, given the low prevalence of ovarian cancer, that this test is not specific enough to be used for screening. The National Cancer Institute explains that even a 99% specific test for ovarian cancer means that 1% of people without cancer would test positive, which is "too high a rate for commercial use" (NCI, 2004). For a rare disease such as ovarian cancer, which has a prevalence of approximately 1 in 2500 in the general population, a specificity of 99% and a sensitivity of 100% translates to 25 women being falsely identified for every true cancer found.

The OvaCheck™ test uses electrospray ionization (ESI) type of mass spectrometry using highly diluted denatured blood samples. This method differs from a matrix-assisted laser desorption ionization (MALDI) assay of undiluted native serum samples that was used in the Lancet study and is currently under investigation by the National Cancer Institute and the Food and Drug Administration (NCI, 2004). ). The NCI notes that "the class of molecules analyzed by these two approaches, and therefore the molecules that constitute the diagnostic standards, are expected to be completely different." Neither the NCI nor the FDA were involved in the design or validation of the OvaCheck™ methodology.

Because the Ovacheck test is performed as a "home" test by two national laboratories rather than a commercially available kit, FDA approval may not be required for the OvaCheck test. The Society of Gynecologic Oncologists (SGO, 2004) reviewed the literature on OvaCheck and concluded that "further research is needed to validate the effectiveness of the test before it is offered to the public". Similarly, the American College of Obstetricians and Gynecologists (2004) stated that "more research is needed to validate the effectiveness of the test before it is recommended to the public".

An evaluation of the Ovacheck test and other genomic tests for ovarian cancer prepared for the Agency for Healthcare Research and Quality by the Duke Center for Evidence-Based Practices (Myers, et al., 2006) reached the following conclusions: "Sensitivity of the genomic test / Specificity Estimates are limited by small sample sizes, spectrum bias, and an unrealistic prevalence of ovarian cancer; in particular, the positive predictive value estimates derived from most studies are substantially higher than would be expected in most scenarios. screening or diagnosis Evidence relevant to the question of the impact of genomic testing on health outcomes in asymptomatic women Although there is a relatively large literature on the association of test results and various clinical outcomes, the clinical utility of c has not been evaluated. based on these results." Specifically with respect to Ovacheck and other proteomic tests for ovarian cancer, the review found that, "[a]lthough all studies reported good discrimination for the specific protein profile studied, there were several recurring issues that limit the ability to make inferences about possible clinical applicability", in particular, technical problems with the trials themselves, variations in the analytical methods used between studies, and an unrealistically high prevalence of ovarian cancer in the datasets compared to what is reported.

ovaseguro

OvaSure is an ovarian cancer screening test that involves the use of 6 biomarkers (leptin, prolactin, osteopontin, insulin-like growth factor II, macrophage inhibitory factor and CA-125) to assess the presence of ovarian cancer. - women at risk. Visintin et al (2008) characterized and validated OvaSure to discriminate between disease-free and ovarian cancer patients. These researchers looked at 362 healthy controls and 156 patients with newly diagnosed ovarian cancer. Concentrations of leptin, prolactin, osteopontin, insulin-like growth factor II, macrophage inhibitory factor, and CA-125 were determined using a bead-based multiplex immunoassay system. The 6 markers were evaluated in a training set (181 control group samples and 113 ovarian cancer patient samples) and a test set (181 control group samples and 43 ovarian cancer samples). Multiplex and ELISA exhibited the same expression pattern for all biomarkers. None of the biomarkers alone were good enough to differentiate healthy cells from cancer cells. However, the combination of all 6 markers provided better differentiation than CA-125. Four models with less than 2% misclassification in the training sets had a significant improvement (84-98% sensitivity with 95% specificity) over CA-125 (72% sensitivity with 95% specificity) in the test set. The chosen model correctly classified 221 of the 224 specimens in the test set, with a classification accuracy of 98.7%. The authors noted that OvaSure is the first blood biomarker test with a sensitivity of 95.3% and specificity of 99.4% for the detection of ovarian cancer. Six markers provided significant improvement over CA-125 alone for detecting ovarian cancer. Validation was performed with a blinded cohort. They stated that this new multiplex platform has the potential for efficient screening in patients at high risk for ovarian cancer.

However, the Society of Gynecologic Oncologists (SGO, 2008) issued an opinion on OvaSure, stating that additional research is needed before the test is offered to women outside the context of a research study. In addition, SGO stated that it "will look forward to the results of further clinical validation of OvaSure."

Additionally, according to the FDA website, the FDA sent the Laboratory Corporation of America a warning letter stating that it is illegally marketing OvaSure for the detection of ovarian cancer. Per the FDA warning letter, your review indicates that this product is a device under section 201(h) of the Food, Drug, and Cosmetic Act (FDCA or Act), 21 U.S.C. 321(h) because it is intended for use in the diagnosis of disease or other conditions, or in the cure, treatment, prevention or mitigation of disease. The Act requires manufacturers of non-exempt devices to obtain FDA approval or marketing authorization for their products before they can offer them for sale. This helps protect public health by ensuring that new devices are safe and effective or substantially equivalent to other devices already legally marketed in this country for which approval is not required. Per the FDA warning letter, no such determination was made for OvaSure.

Members of the NCCN Guideline Panel (NCCN, 2016) believe that the OvaSure screening test should not be used to screen for ovarian cancer. The NCCN guidelines explain that the OvaSure test uses 6 biomarkers, including leptin, prolactin, osteopontin, insulin-like growth factor II, macrophage inhibitory factor, and CA-125.

Thrombospondin-1

Thrombospondin-1 (THBS-1), an angiogenesis inhibitor, has been identified as a potential monitoring marker in gynecological malignancies. In a randomized phase III trial of co-expression of angiogenic markers and their associations with prognosis in advanced epithelial ovarian cancer, Secord, et al. (2007) reported that a high THBS-1 level may be an independent predictor of worse progression-free survival and overall survival in women with advanced-stage EOC. However, the authors stated that "a larger prospective study is needed to validate these findings".

GCC forecast

Guanylyl cyclase C (GCC or GUCY2C) (Diagnocure) a gene that encodes a protein found in the cells lining the intestine from the duodenum to the rectum (Raman, et al., 2013). It is involved in water transport, crypt morphology, and suppression of tumorigenesis. It is not normally found in tissues elsewhere in the body and therefore GCC detected outside the intestine indicates the presence of metastatic colorectal cancer. Previous studies have indicated that the presence of GCC in the blood can be an early indicator of micrometastases that would otherwise escape detection by current standard monitoring methods. Early detection offers the opportunity for more immediate treatment or surgical intervention to potentially improve patient outcomes and survival rates. It is a diagnostic test for recurrence by identifying micrometastases in the blood.

Guanyl cyclase C (GCC) is a receptor protein that is normally expressed in high concentrations on the luminal surface of the gastrointestinal epithelium. GCC expression persists in mucosal cells that have undergone malignant transformation. Therefore, GCC has potential use as a marker to determine the spread of colorectal cancer to lymph nodes. A retrospective study of 21 patients after surgical resection of colorectal cancer found that all 11 of 21 patients who had been cancer-free for 5 years or longer were negative for GCC in lymph nodes, whereas all 10 of 21 patients whose cancer had returned within 3 years after surgery were positive for GCC. However, the value of GCC marker testing in the treatment of colorectal cancer needs to be evaluated in prospective clinical outcome studies. A large prospective study is currently underway to compare standard histological examination of lymph nodes with the GCC marker test.

Previstage™ Guanylyl Cyclase C (GCC or GUCY2C) (Diagnocure) is a gene that encodes a protein found in the cells lining the intestine from the duodenum to the rectum (Raman, et al., 2013). It is involved in water transport, crypt morphology, and suppression of tumorigenesis. It is not normally found in tissues elsewhere in the body and therefore GCC detected outside the intestine indicates the presence of metastatic colorectal cancer. GCC mRNA has been shown to be highly accurate in detecting colorectal cancer spread and recurrence in lymph nodes and blood, respectively, representing a significant improvement over traditional detection methods. Previstage is a predictive test for recurrence risk stratification and a prognostic marker of recurrence.

feel shy

Thymidylate synthase is a gene related to DNA synthesis. According to Compton (2008), the prognostic value of this promising and potentially clinically applicable molecular marker has been studied in colorectal cancer. Compton found that the independent influence of this marker on prognosis remains unproven. Compton explained that "variability in study methodology, conflicting results from multiple studies examining the same factor, and the prevalence of multiple small studies without statistically robust multivariate analyses, all contribute to the lack of conclusive data." Compton concluded that before this marker can be incorporated into clinically meaningful prognostic stratification systems, further studies are needed using multivariate analyses, well-characterized patient populations, current and reproducible methodology, and standardized reagents.

In a special report on the pharmacogenomics of cancer, the BlueCross and BlueShield Association Technology Evaluation Center (TEC) (2007) described the results of a meta-analysis on thymidylate synthase protein expression and survival in colorectal cancer which indicated that low expression of thymidylate synthase was significantly associated with better survival, but heterogeneity and potential bias precluded firm conclusions.

The American Society of Colon and Rectal Surgeons guidelines (2004) stated, "In the future, DNA analysis and intratumoral expression of specific chemicals," including thymidylate synthase, "may be used routinely to better assess prognosis or response to therapy". Furthermore, Shankaran et al (2008) stated in a review of the role of molecular markers in predicting response to therapy in patients with colorectal cancer: "Although to date no molecular feature has emerged as a consistent predictor of response to therapy, studies Retrospective studies have investigated the role of a variety of biomarkers, including microsatellite instability, 18q loss of heterozygosity, transforming growth factor receptor beta type II, thymidylate synthase, Kirsten-ras, and epidermal growth factor receptor (KRAS)".

VEGF

Tumor angiogenesis is associated with the invasiveness and metastatic potential of several types of cancer. Vascular endothelial growth factor (VEGF), the most potent and specific angiogenic factor identified to date, regulates both normal and pathological angiogenesis. A Cancer Care Ontario evidence report (Welch et al, 2008) on the use of the VEGF inhibitor bevacizumab in colorectal cancer explained that increased VEGF expression was correlated with metastasis, recurrence, and poor prognosis in many types of cancer, including colorectal cancer . . The guidelines of the National Institute for Health and Clinical Excellence (NICE, 2007) explained that bevacizumab (Avastin) is a recombinant humanized monoclonal IgG1 antibody that acts as an angiogenesis inhibitor. It targets the biological activity of VEGF, which stimulates the formation of new blood vessels in the tumor. However, neither FDA-approved bevacizumab labeling nor evidence-based guidelines recommend measuring VEGF to diagnose colorectal cancer or to select patients for treatment. In a special report on cancer pharmacogenomics, the BlueCross and BlueShield Association Technology Evaluation (TEC) Center (2007) stated that pretreatment VEGF levels do not appear to predict response to antiangiogenic therapy.

Shin and colleagues (2013) evaluated the inhibitory effects of bevacizumab on VEGF signaling and tumor growth in vitro and in vivo, and evaluated VEGF receptor 2 (VEGFR2) phosphorylation and downstream signaling in endothelial cells as markers. pharmacodynamics by phospho flow cytometry. These investigators also validated markers in mCRC patients treated with bevacizumab-based chemotherapy. In in vitro studies, bevacizumab inhibited human umbilical vein endothelial cell proliferation in association with reduced VEGF signaling. In particular, bevacizumab inhibited VEGF-induced phosphorylation of VEGFR-2, Akt, and extracellular signal-regulated kinase (ERK). In vivo treatment with bevacizumab inhibited the growth of xenograft tumors and attenuated VEGF-induced Akt and ERK phosphorylation. The median percentages of VEGFR2 + pAkt + and VEGFR2 + pERK + cells, determined by phospho flow cytometry, were approximately 3-fold greater in patients with mCRC than in healthy controls. Treatment with bevacizumab decreased VEGFR2 + pAkt + cells in 18 of 24 patients at day 3. The authors concluded that bevacizumab combined with chemotherapy decreased the number of VEGFR2 + pAkt + cells, reflecting impaired VEGFR2 signaling. Together, these data suggested that changes in the proportion of circulating VEGFR2 + pAkt + cells may be a potential pharmacodynamic marker of the effectiveness of antiangiogenic agents and may be useful in determining drug dosage and administration schedule.

ProstatePx

Donovan et al (2008) of Aureon, maker of Prostate Px, reported the development and validation of their pathological model systems to predict recurrence of prostate cancer after prostatectomy. The clinical utility of defining high risk of failure after radical prostatectomy is deciding whether patients need closer-than-average follow-up or whether adjuvant radiotherapy, hormone therapy, or chemotherapy would be beneficial. In this analysis, the concordance rate for the systems pathology approach used by Aureon was 0.83, but it was only slightly better than a 10-variable model that used only the usual clinical parameters, with a concordance ratio of 0.80. The corresponding hazard ratios for clinical failure were 6.37 for the 10-variable clinical model and 9.11 for the systems pathology approach. In an accompanying editorial, Klein, et al. (2009) questioned the clinical significance of these differences. They noted that "[although] the difference in concordance rates was statistically significant, the question is whether there is sufficient clinical relevance to justify the additional effort, expense, and clinical experience required for the systems approach... In practice In In contemporary clinical settings, a patient with a model-generated hazard ratio of 6.37 using easily derived and routinely reported clinical and pathologic parameters is just as likely to be a candidate for more rigorous monitoring or adjuvant therapy as one with a higher hazard ratio. of 9.11 generated by the systems approach " . .

Sutcliffe et al (2009) provided an evidence-based perspective on the prognostic value of the newer markers in localized prostate cancer and identified the best prognostic model, including the 3 classic markers, and investigated the models that incorporate newer markers are better. Eight electronic bibliographic databases were searched. Reference lists of relevant articles were reviewed and various resources related to health services research were consulted via the Internet. The search was restricted to publications from 1970 in English. Selected studies were assessed, data extracted using a standard template, and quality assessed using an adaptation of published criteria. Due to heterogeneity in relation to populations, results and study type, meta-analyses were not performed and results are presented in tabulated format with a narrative summary of results. A total of 30 articles met the inclusion criteria, of which 28 reported new prognostic markers and 5 prognostic models. A total of 21 new markers were identified from the 28 new marker studies. There was considerable variability in reported results, the quality of the studies was generally poor, and there was a paucity of studies in some categories. The marker with the most evidence of its prognostic significance was PSA velocity (or doubling time). There was a particularly strong association between PSA velocity and death from prostate cancer in both clinical and pathological models. In the clinical model, the hazard ratio for death from prostate cancer was 9.8 (95% CI 2.8 to 34.3, p < 0.001) in men with an annual PSA rate greater than 2 ng/mL versus an annual PSA of 2 ng/mL or less; likewise, the hazard ratio was 12.8 (95% CI 3.7 to 43.7, p < 0.001) in the pathology model. The quality of prognostic model studies was adequate and generally better than the quality of prognostic marker studies. Two issues have been poorly addressed in most or all studies of forecasting models:

inclusion of established markers, and
consideration of possible biases from study wear. Given the heterogeneity of the models, they cannot be considered comparable.

Only two models did not include a new marker, and one of them included multiple demographic and comorbid variables to predict all-cause mortality. Only two models reported a measure of model performance, the C statistic, and neither was calculated on an external dataset. It was not possible to assess whether models that included new markers performed better than those that did not. This review highlighted the low quality and heterogeneity of the studies, making many of the results inconclusive. He also noted the small proportion of models reported in the literature that are based on patient cohorts with a mean or median follow-up of at least 5 years, making long-term predictions unreliable. However, prostate-specific antigen velocity stood out in terms of the strength of the evidence supporting its prognostic value and the relatively high hazard rates. There is great interest in PSA velocity as a monitoring tool for active surveillance, but there is still no consensus on how it should be used and, mainly, what threshold should indicate the need for radical treatment.

In an editorial on clinically relevant prognostic markers for prostate cancer, Gelmann and Henshall (2009) stated that "until we have sufficiently discriminating markers to inform treatment decisions, the problem of who to treat will continue to grow exponentially as the number of low-risk patients cases of cancer detected by screening is increasing".

Circulating tumor cells (eg, CELLSEARCH)

The circulating tumor cell (CTC) test, CELLSEARCH, is a blood test that has been proposed as a method to determine prognosis, assess progression, and assess response to treatment in people with metastatic breast, colorectal, and prostate cancer. CTC assays were developed to detect cells that shed from tumors and enter the bloodstream.

The CellSearch™ Epithelial Cell Kit, in conjunction with the CellSpotter™ Analyzer (Veridex, LLC, Warren, NJ) is a device designed to automate the detection and enumeration of circulating tumor cells (CTCs) of epithelial origin (CD45-, EpCAM+ and cytokeratins ). ). 8, 18+ and/or 19+) in whole blood in patients with advanced breast cancer (Ellery, et al., 2010; Raman, et al., 2011). It is intended for use in adjuvant monitoring and prediction of cancer disease progression and response to therapy.

The CellSearch Epithelial Cell Kit received FDA 510(k) clearance on January 21, 2004. The FDA has concluded that the device is substantially equivalent to immunomagnetic circulating cancer cell enumeration and sorting systems. These devices consist of biological probes, dyes and other reagents, preservation and preparation devices, and semi-automated analytical instruments to screen and count circulating cancer cells in a sample prepared from whole blood.

The CellSearch Epithelial Cell Kit quantifies CTCs by tagging cancer cells with tiny protein-coated magnetic beads in whole blood. These cells are stained with fluorescent markers for identification and then distributed in a cartridge for analysis, where a strong magnetic field is applied to the mixture, causing the magnetically labeled cells to move towards the surface of the cartridge. The cartridge is then analyzed by the CellSpotter Analyzer. A medical professional rechecks the CTCs and the CellSpotter analyzer makes the final CTC count.

In a prospective multicenter study, Cristofanilli et al (2004) used the CellSearch system in 177 patients with metastatic breast cancer measurable for CTC levels before patients started a new line of therapy and during follow-up. Disease progression or response to treatment was determined using standard imaging studies at participating centers every nine to twelve weeks. Outcomes were evaluated based on baseline CTC levels before patients started new therapy. In the first study, patients with 5 or more CTCs per 7.5 mL of blood compared to a group with less than 5 CTCs had a shorter median PFS (2.7 months vs. 7.0 months). ) and shorter overall survival (10.1 months vs. 7.0 months). ). over 18 months). At the follow-up visit, approximately three to four weeks after starting therapy, the percentage of patients with more than 5 CTCs dropped from 49 to 30 percent, suggesting a benefit from therapy. The difference in PFS between the two groups remained constant (2.1 months for women with 5 or more CTCs vs. 7 months for women with less than 5 CTCs). Overall, survival in women with more than 5 CTCs was 8.2 months compared with more than 18 months in the cohort with less than 5 CTCs. Cristofanilli concluded that the number of CTCs before treatment was an independent predictor of progression-free survival and overall survival in patients with metastatic breast cancer. However, Cristofanilli also concluded that the results may not be valid for patients who have no measurable disease or for those starting a new regimen of hormone therapy, immunotherapy, or both. He states, "The prognostic implications of an elevated level of circulating tumor cells for patients with metastatic disease starting a new treatment may provide an opportunity to stratify these patients in research studies." Furthermore, the study did not address whether patients with high numbers of circulating tumor cells might benefit from other therapies. Therefore, this minimally invasive assay requires further evaluation as a prognostic marker of disease progression and response to therapy.

The clinical application of CTC quantification in the peripheral blood of patients with breast cancer is still unclear. Data published in the peer-reviewed medical literature are needed to determine how such measurements would guide treatment decisions and whether those decisions would result in beneficial patient outcomes (Kahn, et al., 2004; Abeloff, et al., 2004). An AETSA review of CellSearch (2006) concluded: "At the current stage of development of this technology, there is no evidence that it provides any advantage over existing technology for CTC identification, or indeed any other clinical use." American Society of Clinical Oncology guidelines (Harris, et al., 2007) concluded: "Measurement of circulating tumor cells (CTC) should not be used to make the diagnosis of breast cancer or to influence treatment decisions" in patients with breast cancer. Likewise, the CTC test (CellSearch Assay) recently approved by the US Food and Drug Administration (FDA) cannot be recommended for use in patients with metastatic breast cancer until further validation confirms the clinical value of this test."

An evaluation by the Canadian Agency for Medicines and Health Technologies (CADTH, 2012) found that studies indicate that CTC measurement using the CellSearch system can be used as a prognostic factor for disease progression and potential treatment in patients with ovarian cancer. No economic studies were identified, therefore the cost-effectiveness of the CellSearch system cannot be summarized.

Although studies link circulating tumor cells to prognostic indicators (see, for example, Cohen, et al., 2008; De Giorgi, et al., 2009), there is a lack of published prospective clinical studies demonstrating that measuring CTCs alters the management in ways that improve clinical outcomes. These clinical outcome studies are currently ongoing. Current National Comprehensive Cancer Network (NCCN) guidelines do not make recommendations for the use of circulating tumor cells.

The American Society of Clinical Oncology (2016) guidelines state, "The clinician should not use circulating tumor cells to guide decisions about adjuvant systemic therapy." This is a strong recommendation based on intermediate-quality evidence.

Scher et al (2015) noted that clinical trials in castration-resistant prostate cancer (CRPC) need new clinical endpoints that are valid surrogates for survival. These investigators evaluated circulating tumor cell count (CTC) as a surrogate outcome measure. Examination of CTCs alone and in combination with other biomarkers as surrogates for OS was a secondary endpoint of COU-AA-301, a Phase III multinational, randomized, double-blind study of abiraterone acetate plus prednisone versus prednisone alone in patients with metastases. CRPC previously treated with docetaxel. Biomarkers were measured at baseline and at 4, 8 and 12 weeks, with 12 weeks being the main measure of interest. Prentice criteria were applied to test candidate biomarkers as surrogates for OS at the individual patient level. A panel of biomarkers using CTC count and lactate dehydrogenase (LDH) level was shown to meet the 4 Prentice criteria for surrogacy at the individual level; 12-week surrogate biomarker data were available for 711 patients. The abiraterone acetate plus prednisone and prednisone alone groups demonstrated a significant difference in survival (p = 0.034); surrogate allocation at 12 weeks differed by treatment (p < 0.001); surrogate's discriminatory power to predict mortality was high (c-weighted index, 0.81); and adding the surrogate to the model removed the effect of treatment on survival. Overall, the 2-year survival of patients with CTCs less than 5 (low risk) versus patients with CTCs greater than or equal to 5 cells/7.5 mL blood and LDH greater than 250 U/L (high risk). risk) at 12 weeks was 46% and 2%, respectively. The authors concluded that a panel of biomarkers containing CTC number and LDH level proved to be a surrogate for survival at the individual patient level in this study of abiraterone acetate plus prednisone versus prednisone alone for patients with metastatic CRPC. They stated that independent phase III clinical trials are needed to validate these findings.

A review by the Institut National d'Excellence en Santé et Services Sociaux (INESSS) (Arsenault & Le Blanc, 2016) concluded: "Based on the identified scientific literature, the use of CellSearch tests as a predictive and prognostic biomarker in patients with -stage of cancer unproven breast cancer Evidence is insufficient to establish a concrete association between the presence of CTCs before and after treatment and patient survival For patients with metastatic breast cancer, review of the scientific literature suggests that enumeration of CTCs before treatment may be a prognostic biomarker for patient survival. Despite the prognostic value of CTC enumeration, according to studies, its clinical usefulness has not yet been confirmed. context of a clinical trial More studies are needed to determine whether the CellSearch test can play a clinically meaningful role. in the treatment of patients with breast cancer".

she-2/new

Estrogen and progestogen receptors are important prognostic markers in breast cancer, and the higher the percentage of positive cells overall and the higher the intensity, the better the prognosis. The positivity of estrogen and progesterone receptors on breast cancer cells is an indication that the patient may be a good candidate for hormone therapy. HER-2/neu is an oncogene. Its gene product, a protein, is overexpressed in approximately 20-30% of breast cancers. The overexpressed protein is present in abnormally high concentrations on the surface of some malignant breast cancer cells, causing these cells to proliferate rapidly. This is important because these tumors are amenable to treatment with Herceptin (trastuzumab), which specifically binds to this overexpressed protein. Herceptin blocks these protein receptors, thereby inhibiting continued replication and tumor growth. HER2/neu can also be expressed in the ovary, stomach, colorectal, endometrium, lung, bladder, prostate, and salivary gland (Chen, et al., 2006).

HER-2/neu is an oncogene that encodes an epidermal growth factor receptor-related growth factor receptor (EGFR) and is amplified in approximately 25-30 percent of node-positive breast cancers (Chin, et al. 2006). Overexpression of HER-2/neu is associated with decreased overall and disease-free survival. HER-2/neu overexpression can be used to identify patients who may benefit from trastuzumab (Herceptin™) and/or high-dose chemotherapy. Trastuzumab is a humanized monoclonal antibody directed against the HER 2/neu oncogene (c-erbB-2).

Her-2 has been used to: assess the prognosis of patients with lymph node positive stage II breast cancer; to predict overall and disease-free survival in patients with nodule-positive stage II breast cancer treated with adjuvant chemotherapy with cyclophosphamide, doxorubicin, and 5-fluorouracil; and to determine the patient's eligibility for treatment with Herceptin (Chen, et al., 2006). The College of American Pathologists (CAP) recommends FISH as an ideal method for HER2/neu testing; therefore, positive IHC results are usually confirmed by the FISH test.

There are complementary tests that can be used in cases of breast cancer, such as DNA ploidy, Ki-67 or other proliferation markers. However, most authorities believe that HER-2/neu, estrogen, and progesterone receptor status are the most important to assess first. The other tests have no therapeutic implications and, when compared with disease grade and stage, are not independently significant with respect to prognosis.

Harris et al (2007) updated the ASCO recommendations for the use of tumor marker tests in the prevention, detection, treatment and surveillance of breast cancer. Thirteen categories of breast tumor markers were considered, 6 of which were new to the guideline. The following categories have shown evidence of clinical utility and have been recommended for use in practice: CA 15-3, CA 27.29, CEA, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, urokinase plasminogen activator, urokinase inhibitor plasminogen activator 1 and certain Multiparameter Gene Expression Assays. However, not all apps for these bookmarks were supported. The following categories demonstrated insufficient evidence to support routine use in clinical practice: DNA/ploidy by flow cytometry, p53, cathepsin D, cyclin E (fragments or full length), proteomics, certain multiparameter assays, detection of bone marrow micrometastases, and circulating tumor cells (eg CellSearch assay). These guidelines found that current data are insufficient to recommend measurement of Ki67, cyclin D, cyclin E, p27, p21, thymidine kinase, topoisomerase II, or other proliferation markers to assign patients to prognostic groups. The guidelines also found insufficient data to recommend the evaluation of bone marrow micrometastases for the management of patients with breast cancer.

American Society of Clinical Oncology guidelines (2016) recommend against using soluble HER2 levels to guide selection of the type of adjuvant therapy in breast cancer. This is a moderately strong recommendation based on low-quality evidence. The guidelines also discourage the use of HER2 gene coamplification to guide the selection of adjuvant chemotherapy in breast cancer.

PancraGen (formerly PathFinderTG - Pancreas)

The manufacturer describes PathFinderTG (RedPath Integrated Pathology, Pittsburgh, PA), also known as topographic genotyping, as a quantitative genetic mutational analysis platform for solving diagnoses of "indeterminate, atypical, suspicious, equivocal, non-diagnostic specimens" of pathological specimens ( Red Road, 2007). The manufacturer claims that PathFinder TG "targets acquired mutational damage rather than inherited genetic predisposition to certain diseases, although there are certain NIH-recommended hereditary conditions that we test for." The manufacturer claims that the time sequence of acquired mutational damage revealed by the PathFinderTG test is an earlier demonstration of the biological aggressiveness of the tumor than current staging systems that are based on the depth of invasion already achieved by the tumor. The most available published evidence for topographic genotyping focuses on retrospective analyzes of pathologic specimens that examine correlations of test results with tumor characteristics (eg, Saad et al, 2008; Lin et al, 2008; Finkelstein et al. , 2003; Pollack et al., 2001; et al., 1998; Kounelis et al., 1998; Finkelstein et al., 1998; Holst et al., 1998; Jones et al., 1997; Holst et al., 1997; Pricolo et al. al. 1997; Przygodzki et al, 1997; Finkelstein et al, 1996; Kanbour-Shakir et al, 1996; Ribeiro et al, 1996; Pryzgodzki et al, 1996; Safatle-Ribeiro et al, 1996; Papadaki et al, 1996; Przygodzki et al. al, 1996; Pricolo et al, 1996; Finkelstein et al., 1994). There are no prospective clinical outcome studies on the use of topographic genotyping to guide patient management. Current evidence-based guidelines from leading medical professional organizations and public health agencies do not include recommendations for topographic genotyping. In a review on the molecular analysis of pancreatic cyst fluid, Shen and colleagues (2009) stated that a large study with validation of the PathFinderTG molecular test of pancreatic fluid will be required before a firm conclusion can be reached.

A systematic review of PathFinderTG evidence prepared for the Agency for Healthcare Research and Quality (Trikalinos, et al., 2010) reviewed the evidence available at the time and found that most studies on topographic genotyping based on loss of heterozygosity with PathfinderTG were excluded . because they only described the molecular profile of different tumors, without assessing the ability of the method to aid in diagnosis, prognosis or guide treatment. The review did not find any studies that directly measured whether the use of topographic genotyping based on loss of heterozygosity with PathfinderTG improves relevant clinical outcomes for the patient. The review reported that eligible studies on the diagnostic and prognostic power of topographic genotyping based on loss of heterozygosity with PathfinderTG were small in sample size and had manifest methodological limitations. The review reported that important features of their designs were not clearly reported. The report noted that topographic genotyping based on loss of heterozygosity with PathfinderTG is considered particularly useful in cases where conventional pathology cannot provide a conclusive diagnosis. However, the included studies were not designed to address this issue. Therefore, it is unclear whether the findings of the reviewed studies are directly applicable to patients with the same types of cancer but with an inconclusive diagnosis.

A subsequent study by Panarelli et al (2012) comparing PathFinderTG with cytology examination found agreement in 35% of cases. The authors concluded that the PathfinderTG panel can aid in the classification of pancreatic lesions, but it is often inaccurate and should not replace the cytological evaluation of these lesions.

The manufacturer has announced that PathginderTG - Pancreas has been renamed Pancragen.

Al Haddad et al (2015) reported a multicenter retrospective chart review study to determine the diagnostic accuracy of integrated molecular pathology (Pancragen) for pancreatic adenocarcinoma and the usefulness of the IMP test based on current guideline recommendations for the treatment of pancreatic cysts . determined the malignant potential of pancreatic cysts than a 2012 Sendai guideline management criteria model. retrospective of records (n = 492). Performance was determined by correlation between clinical outcome and prior Pancragen diagnosis ("benign"/"statistically indolent" vs. "statistically increased risk [SHR]"/"aggressive") or a guideline criterion model. International consensus (Sendai 2012) for "surveillance" versus "surgery". The Cox proportional hazards model determined risk ratios for malignancy. Benign and statistically indolent Pancragen diagnoses had a 97% probability of benign follow-up up to 7 years and 8 months from the initial Pancragen test. HRS and aggressive diagnoses had relative risk ratios for malignancy of 30.8 and 76.3, respectively (both P < 0.0001). The Sendai surveillance criteria had a 97% probability of benign follow-up for up to 7 years and 8 months, but for the surgical criteria the hazard ratio was only 9.0 (P < 0.0001). In patients who met the Sendai surgical criteria, benign and statistically indolent Pancragen diagnoses had a >93% probability of benign follow-up, with relative risk ratios for HRS and aggressive PDI diagnoses of 16.1 and 50.2, respectively (both P < 0.0001). The authors concluded that Pancragen may improve patient management by ensuring a more relaxed observation in patients who meet the Sendai surveillance criteria.

Loren et al (2016) used registry data to determine whether initial trial of adjuvant Pancragen influenced future real-world pancreatic cyst management decisions for intervention or surveillance against the 2012 International Consensus Guideline (ICG) recommendations and whether this benefited patient outcomes. Using data from the National Pancreatic Cyst Registry, researchers evaluated associations between real-world decisions (intervention versus surveillance), ICG model recommendations (surgery versus surveillance), and Pancragen diagnoses (high risk versus low risk). ) using 2 × 2 tables. Investigators used Kaplan Meier and hazard ratio analysis to assess time to malignancy. The odds ratios (OR) for the surgical decision were determined by logistic regression. Of the 491 patients, 206 received clinical intervention at follow-up (183 surgery, 4 chemotherapy, 19 presumably for malignant cytology). Overall, 13% (66/491) of patients had a malignant outcome and 87% (425/491) had a benign outcome at 2.9 years of follow-up. When ICG and Pancragen agreed for surveillance/surgery recommendations, 83% and 88% actually underwent surveillance or surgery, respectively. However, when discordant, Pancragen diagnoses were predictive of real-world decisions, with 88% of patients undergoing intervention when ICG recommended surveillance but Pancragen indicated high risk, and 55% undergoing surveillance when ICG recommended surgery but Pancragen indicated low risk. These management decisions associated with Pancragen benefited patient outcomes in these subgroups, with 57% malignancy and 99% benign outcomes at a median follow-up of 2.9 years. Pancragen was also more predictive of real-world decisions than ICG using multivariate analysis: OR 11.4 (95% CI 6.0- 23.7) versus 3.7 (2.4- 5.8), respectively.

Kowalski et al (2016) examined the usefulness of integrated molecular pathology (IMP) in managing pancreatic cyst surveillance based on results and false negative (FN) analysis of a previously published cohort (n = 492). On endoscopic ultrasound with fine needle aspiration (EUS-FNA) of cyst fluid without malignant cytology, IMP demonstrated better stratification of malignancy risk at approximately 3 years of follow-up than the International Consensus Guideline (Fukuoka) management recommendations 2012 in such cases. The investigators reviewed the patient outcomes and clinical features of the Fukuoka and IMP FN cases. Practical guidelines for appropriate surveillance intervals and surgical decisions using IMP were derived from follow-up data, considering EUS-FNA sampling limitations and observed high-risk clinical circumstances. Patient surveillance intervals based on the predictive value of PDI were compared with those of Fukuoka. Follow-up results of low-risk IMP diagnoses supported surveillance every 2 to 3 years, regardless of cyst size, when EUS-FNA sampling limitations or high-risk clinical circumstances were absent. In 10 of 11 patients diagnosed with NF IMP (2% of the cohort), there were EUS-FNA sampling limitations; Fukuoka identified high risk in 9 of 11 cases. In 4 of the 6 Fukuoka NF cases (1% of the cohort), the IMP identified high risk. Overall, 55% of cases had potential sampling limitations and 37% had high-risk clinical circumstances. The results support a more cautious management in these cases when using the IMP.

A technical review from the American Gastroenterological Association (Scheiman et al, 2014) stated: "Tests to detect molecular alterations in the fluid of pancreatic cysts are currently available and are reimbursed by Medicare under certain circumstances. Case series have confirmed that malignant cysts have a higher quantity and quality of molecular alterations, but no study has been adequately designed to identify how the test works in predicting outcome with respect to the need for surgery, surveillance, or predict interventions leading to better survival.This adjunct to fine needle aspiration (FNA) may provide value in a variety of clinical circumstances, such as as confirmation of a serous lesion due to missing KRAS or GNAS mutation in a macrocystic serous cystadenoma, but its routine use is not supported. this moment."

A guideline from the American Society for Gastrointestinal Endoscopy (Muthusamy, et al., 2016) stated, "A more recent study demonstrated that integrated molecular analysis of cyst fluid (i.e., the combination of molecular analysis with the results of clinical and imaging) was able to better characterize the malignant potential of pancreatic cysts compared to consensus guidelines for the treatment of mucinous cysts [citing Al Haddad, et al., 2015]... Molecular analysis (requires only 200 mL of fluid) may be more useful in patients with small cysts with non-diagnostic cytology, equivocal CEA results in cyst fluid, or when there is not enough fluid for CEA testing [citing Al Haddad, et al., 2014 However, further research are needed to determine the precise role that molecular analysis of cystic fluid will play in the evaluation of pancreatic cystic lesions."

Guidelines published in April 2015 by the American Gastroenterological Association (Vege, et al., 2015) do not present recommendations for the use of topographic genotyping for the evaluation of pancreatic cysts. Other guidelines (NCCN, 2015; Vege, et al., 2015; Del Chiaro, et al., 2013; Sahani, et al., 2013; Tanaka, et al., 2012) do not have firm recommendations for topographic genotyping to assess the indeterminate pancreatic. cysts

The international consensus guidelines for "Management of intraductal papillary mucinous neoplasia (IPMN) and mucinous cystic neoplasia (MCN) of the pancreas" (Tanaka et al, 2012) stated that endoscopic ultrasound (EUS) is recommended for all cysts with worrisome features or for cysts larger than 3 cm without these characteristics. Endoscopic ultrasound confirmation of a mural nodule, any feature of main duct involvement (intraductal mucin or main duct wall thickening), or cytology suspicious or positive for malignancy is an indication for surgical resection. Cysts with high-risk stigmata should be resected in patients who are clinically fit for surgery, although EUS is optional. Endoscopic ultrasound may be considered in smaller cysts without worrisome features, but is not required. Endoscopic US analysis should include at least cytology, amylase level, and CEA. The guidelines state that elevated CEA is a marker that distinguishes mucinous from non-mucinous, but non-benign from malignant cysts.

Khalid et al (2004) observed that brush cytology of biliary strictures for the diagnosis of pancreatobiliary malignancy has low sensitivity. These investigators attempted to improve the diagnostic performance of pancreaticobiliary brush cytology by analyzing the loss of heterozygosity (LOH) of the microsatellite marker linked to the tumor suppressor gene and the detection of mutations in codon 12 of k-ras. A total of 26 patients with biliary strictures underwent endoscopic retrograde cholangiography with brush cytology. A panel of 12 polymorphic microsatellite markers linked to 6 tumor suppressor genes was developed. Genomic DNA from cell pellets acquired from brush cytology specimens and microdissected normal and malignant surgical tissue was subjected to polymerase chain reaction (PCR) amplification; PCR products were compared for LOH and 12k-ras codon mutations. A total of 17 patients were confirmed to have pancreatobiliary adenocarcinoma; 9 patients had benign strictures (8 surgically proven, 1 by follow-up). The cytomorphological interpretation was positive for malignancy (n = 8), undetermined (n = 10) and negative for malignancy (n = 8). Selected malignant-appearing cytologic cell pools and microdissected histologic specimens of cancer showed abundant LOH characteristic of malignancy, whereas brushes from 9 cases without cancer showed no LOH (p < 0.001); The LOH and k-ras mutation profile of the cytology samples was almost always consistent with the tissue samples. The presence of the k-ras mutation predicted pancreatic malignancy (p < 0.001). The authors concluded that codon 12 LOH and k-ras mutation analysis of PCR-amplified DNA from biliary cytology discriminated reactive cells from malignant cells with 100% sensitivity, specificity, and accuracy. Small variations in LOH in brushes and at different sites within the same tumor probably reflect intratumoral mutational heterogeneity during clonal expansion of pre- and neoplastic lineages.

Nodit et al (2006) observed that the clinical course of the pancreatic endocrine tumor (PET) varies according to the aggressiveness of the tumor, the extension of the disease and the possible accumulated molecular alterations. Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) results, while accurate in diagnosing PET, correlated poorly with the PET result. The role of detection of key molecular abnormalities in predicting PET behavior and clinical outcome of EUS-FNA material remains unknown. This study included patients with confirmed PET who underwent EUS-FNA over a 32-month period. Patient demographic and clinical data were recorded, and follow-up information was obtained by contacting the physician to assess disease progression. Representative tumor cells were microdissected from the FNA material. DNA was collected and amplified, targeting a panel of 17 polymorphic microsatellite markers on chromosomes 1p, 3p, 5q, 9p, 10q, 11q, 17p, 17q, 21q and 22q. Polymerase chain reaction (PCR) products were subjected to fluorescent capillary gel electrophoresis to detect microsatellite loss. Fractional allelic loss (FAL) was calculated. A total of 25 patients were studied; 13 were classified histologically as benign PET limited to the pancreas and 12 as malignant PET (invasive or metastatic). The mean FAL on benign and malignant PET was 0.03 and 0.37 (p < 0.0001), respectively. Furthermore, the mean FAL was significantly higher in those with disease progression compared to patients with stable disease (0.45 vs 0.09, respectively, p < 0.0001). The authors concluded that microsatellite loss analysis of EUS-FNA PET material can be performed reliably and that a FAL value greater than 0.2 is associated with disease progression. These investigators stated that this technique may be of value in the preoperative assessment and risk stratification of patients with PET.

The authors stated that the small sample size and limited follow-up period were disadvantages of this study, which needed to be replicated in larger prospective studies with longer follow-up periods. The impact of individual microsatellite markers on the clinical course of PET also requires further study. In this study, certain microsatellites (3p26, 5q23, 17q23, and 21q23) were only missed on malignant PET scans, but with the small number of samples studied, the significance of this was unclear. Interestingly, both malignant PET scans with a single allele loss (5q23 and 17q23) involved microsatellites that were not lost on the benign PET scan.

Finkelstein et al (2012) intended to complement the microscopic examination of bile brush samples to improve sensitivity through mutational profiling (MP) of:

selected cells microdissected from cytology slides; It is
corresponding cell-free DNA (cfDNA) in the residual supernatant fluid.

From 43 patients with brushing of bile duct or pancreatic strictures, DNA was extracted from microdissected cells and 1 to 2 mL of supernatant fluid from cytocentrifugation. Mutation analysis focused on 17 genomic sites associated with pancreatobiliary cancer, including sequencing for the KRAS point mutation and LOH analysis of microsatellites located at 1p, 3p, 5q, 9p, 10q, 17p, 17q, 21q, and 22q. Mutations were found in 25/28 patients with malignancy and no mutations were found in 5/5 patients with benign surgical outcomes. Cell-free supernatant fluid generally contained higher levels and quality of DNA, resulting in greater mutation detection in most patients. KRAS mutations occurred only in patients with pancreatic cancer; MP of supernatant fluid samples resulted in high sensitivity and specificity for malignancy, improving malignancy detection compared to cytology alone. The authors concluded that in this study they showed that malignancy-free DNA was present in the extracellular compartment even when a given cytology sample lacked sufficient cellularity to allow a definitive diagnosis. Most importantly, the cell-free supernatant, available as a residual sample after cytocentrifugation, should be considered a potentially valuable source of information due to its content of adequate amounts of free DNA for robust mutational analysis with the ability to resolve problems related to cell-free DNA. sampling. . variation and to detect neoplasms at an early stage of development.

The authors stated that this study had several limitations. First, the total number of test samples was relatively small and the results shown here require confirmation with additional samples. In particular, adding more confirmed negative samples would strengthen the sensitivity findings. Second, this study was restricted to the use of the Saccomanno fixation which, although commonly used, was not the only fixative used in the practice of cytology. While each fixative warrants individual testing for its ability to provide adequate levels of intact, representative supernatant DNA for MP, it was reasonable to expect favorable results with other sample preparation methods, since most cytology fixatives were alcohol-based. and were not expected to induce DNA degradation. Indeed, additional unpublished work in our laboratory involving testing of other supernatant fluids indicated that the most common cytology fixatives produced amplifiable DNA (with the notable exception of CytoRich Red). This was consistent with their previous experience of genotyping microdissected cytology slides, where most slides yielded amplifiable DNA regardless of the cytology fixative used. Although none of the supernatant specimens tested in this study did not provide sufficient DNA for MP, it was expected that a small proportion of markedly hypocellular specimens, likely from non-neoplastic states, would not meet the lower threshold for the amount of DNA for analysis. It should be noted that in this study, when microdissection alone was used, 2/18 cases were false negatives for mutation detection (1 cholangiocarcinoma and 1 pancreatic adenocarcinoma). Although no false negative cases of malignant stenosis were observed in Cohort 2A in which microdissection analysis and supernatant fluid were used, in 2 patients the supernatant fluid manifested a lesser degree of mutational change than that present in the corresponding cytological cells. microdissected stains. These findings emphasized that sampling variation and other limitations may, however, be present in individual cases that limit or prevent cancer detection. It remained essential to integrate all information, including clinical and imaging findings, to optimize each patient's diagnosis.

Finkelstein et al (2014) noted that the diagnosis of fine needle aspiration of solid pancreatic masses is complicated by many factors that keep the false negative rate high. Their new approach analyzes cell-free cytospin supernatant, currently a discarded portion of the sample. Supernatants and cytology slides were collected from 25 patients: 11 cases with confirmed results [5 positive (adenocarcinoma) and 6 negative (inflammatory states)], plus 14 without confirmed results. Slides were microdissected, DNA was extracted from microdissections and corresponding supernatants, and all were tested for KRAS point mutation and loss of heterozygosity. In particular, higher levels of free DNA were found in the supernatants than in the corresponding microdissected cells. The supernatants contained enough DNA for mutational profiling even when the samples contained few or no cells. Mutations were present in 5/5 of malignant tumors and there were no mutations in inflammatory states. The authors concluded that these findings support the use of the supernatant for mutational genotyping when diagnostic confirmation is required for solid pancreatic masses. These investigators stated that the data presented suggest that the supernatant fluid should be considered a valuable source of information that can address many diagnostic issues and can serve as a useful supplementary tool for pathologists when microscopic examination is suboptimal.

The authors stated that several limitations of this molecular analysis of the cytocentrifugation supernatant were recognized. The total number of test samples was not large and the promising results shown here need to be evaluated with a larger number of samples. Furthermore, this study was restricted to the use of the Saccomano fixation. Ideally, each commonly used fixative should be individually tested for its ability to provide adequate levels of representative supernatant DNA for mutational profiling. However, it is reasonable to expect favorable results with other sample preparation methods, since most cytology fixatives are alcohol-based and are not expected to induce significant DNA degradation. Consistently, previous work has shown that cytology specimens based on microdissected stained cytology cells are especially suitable for mutational analysis.

Deftereos et al (2014) stated that FNAB of pancreatic solid masses can be significantly affected by sampling variation. Molecular analysis of the tumor's DNA can be an aid to a more definitive diagnosis. These investigators evaluated how molecular analysis of cell-free cytocentrifuge supernatant DNA can help reduce sampling variability and increase diagnostic throughput. A total of 23-FNA smears of solid pancreatic masses were taken. The remaining aspirates were washed to prepare slides or cytospun cell blocks. DNA was extracted from the supernatant fluid and the amount of DNA was evaluated spectrophotometrically and the amplifiability by quantitative PCR (qPCR). Supernatants with suitable DNA were analyzed for mutations using PCR/capillary electrophoresis for a broad panel of markers (KRAS point mutation by sequencing, microsatellite fragment analysis for loss of heterozygosity (LOH) of 16 markers in 1p, 3p, 5q, 9p, 10q, 17p, 17q, 21q and 22q). In selected cases, microdissections of stained cytological smears and/or cytocentrifugation slides were analyzed and compared. In total, 5/23 samples cytologically confirmed as adenocarcinoma showed detectable mutations in the slide-microdissected cytological cells and in the cytocentrifugation supernatant. Although most of the detected mutations were present in both the microdissected slides and the supernatant fluid samples, the latter showed additional mutations that support greater sensitivity to detect relevant DNA damage. The clonality of individual marker mutations was greater in supernatant fluid than in microdissected cells. Cytocentrifugation supernatant contains adequate levels of amplifiable DNA for mutation detection and characterization. The discovery of additional detectable mutations at higher clonality indicated that the supernatant fluid could be enriched for tumor DNA. The authors concluded that the results of this study suggest how supernatant fluid can be used as a source of molecular information and can become a powerful addition to standard cytological evaluation. Mutational profiling of DNA in normally discarded supernatant fluid can help resolve occasional diagnostic problems and can serve as a useful adjunct tool for cytopathologists when microscopic examination has not provided a conclusive diagnosis or when a sample is suboptimal.

Malhotra et al (2014) sought to better understand the supportive role that MP DNA from microdissected cytology slides and supernatants can play in diagnosing malignancy in FNAB and biliary brush specimens from patients with pancreatobiliary masses. Cytology results were examined in a total of 30 patients with associated surgical (n=10) or clinical (n=20) outcomes; MP DNA from microdissected cytology slides and discarded supernatant fluid was analyzed in 26 patients with atypical, negative, or indeterminate cytology. Cytology correctly diagnosed aggressive disease in 4 patients. Cytological diagnoses for the remaining 26 were as follows: 16 negative (9 false negative), 9 atypical, 1 undetermined. MP correctly determined aggressive disease in 1 case of false negative cytology and confirmed a diagnosis of negative cytology in 7 of 7 cases of non-aggressive disease. Of the 9 cases of atypical cytology, MP correctly diagnosed 7 as positive and 1 as negative for aggressive disease. A sample indeterminate by cytology was correctly diagnosed as non-aggressive by PM. When first-line malignant (positive) cytology results were combined with positive second-line PM results, 12/21 cases of aggressive disease were identified, compared to 4/21 cases identified with positive cytology alone. The authors concluded that when first-line cytology results were uncertain (atypical), questionable (negative), or impossible (nondiagnostic/indeterminate), MP provided additional information about the presence of aggressive disease. When used in conjunction with first-line cytology, MP increased the detection of aggressive disease without compromising specificity in patients who were difficult to diagnose with cytology alone.

The authors stated that this study had several drawbacks, including a small sample size that limited their ability to estimate the diagnostic yield of PM in pancreatic masses and associated biliary strictures. Although MP allowed these investigators to detect additional cases of aggressive disease, even when cytology and MP results were combined into an overall diagnosis, 9 cases of malignancy were missed. These false negative results were likely due to a combination of the less than perfect sensitivity of both tests, as well as sampling limitations related to FNA and brushing techniques. These researchers noted that despite these limitations, these promising findings provide support for future large-scale studies, with the addition of supernatant analysis providing an opportunity to overcome some of these limitations.

Gonda et al (2017) stated that it is a challenge to detect malignancy in biliary strictures. Various sampling methods are available to increase diagnostic throughput, but require more time and procedural expertise. These investigators evaluated the combined accuracy of fluorescent in situ hybridization (FISH) and PCR-based DNA MP of specimens collected using standard brush techniques. These investigators conducted a prospective study of 107 consecutive patients treated for biliary stricture by endoscopic retrograde cholangiopancreatography from June 2012 to June 2014. They performed routine cytology and FISH analysis on cells collected using standard brushing techniques and analyzed supernatants for mutations points in KRAS and LOH mutations in tumor suppressor genes at 10 loci (MP analysis was performed at Interpace Diagnostics). Stenoses were considered non-malignant based on repeated analysis of images or laboratory test results 12 months after the procedure. Malignant strictures were identified based on subsequent biopsies or cytologic analyses, pathologic analyzes of specimens collected during surgery, or death from biliary malignancy. These investigators determined the sensitivity and specificity with which FISH and MP analyzes detected malignancies using the exact binomial test. The final analysis included 100 patients; 41% had biliary neoplasms. Cytological analysis identified patients with neoplasms with a sensitivity of 32% and a specificity of 100%. Adding FISH or MP results to cytology results increased detection sensitivity to 51% (p < 0.01) without reducing specificity. The combination of cytology, MP, and FISH analysis detected malignancies with a sensitivity of 73% (p < 0.001); FISH identified 9 of the 28 malignancies missed by cytological analysis and MP identified 8 additional malignancies; FISH and MP together identified 17 of the 28 malignancies not detected by cytological analysis. The authors concluded that these findings support the use of FISH and MP tests based on LOH and KRAS tumor suppressor gene PCR in the evaluation of indeterminate or cytology-negative biliary strictures; The MP allowed greater diagnostic performance of each individual brush, since the cell-free supernatant that is normally discarded and contains DNA can be analyzed. Based on these findings, these investigators suggested the use of FISH or MP as a second-line diagnostic modality to first-line cytology. They stated that MP can be better prioritized in low cellularity scenarios. Any negative or indeterminate case by 2 test modalities should be submitted to a 3rd one to increase the probability of detection of possible malignancy. To do this, the supernatant fluid that is normally discarded must be saved for MP testing during preparation of cells for cytology by standard cytology cytocentrifugation. These researchers stated that further studies may help to better understand the reflex order of sequential testing and the impact of this reflex on health economics.

The authors stated that this study had several drawbacks that may have affected the generalized conclusions. In their cases, a slightly higher rate of benign stenosis was observed than in previous series. There were also relatively few patients with primary sclerosing cholangitis (PCC) included in this study. Previous studies have shown that there is a significant rate of aneuploidy associated with the premalignant lesions seen in primary sclerosing cholangitis. Because of this, the specificity of FISH for malignancy was expected to be lower in a cohort of patients with PSC than the authors reported in their cohort. Little was known about the detection of KRAS mutations in the progression of CEP to cholangiocarcinoma. However, based on this study cohort and previous studies, it is likely that these findings are not generalizable to the CEP population.

Khosravi et al (2018) stated that indeterminate cytology occurs in a significant number of patients with solid pancreatobiliary injury who undergo EUS-FNA or endoscopic retrograde cholangiopancreatography (ERCP) and may incur costly additional tests and inappropriate surgical interventions. Mutation profiling improves diagnostic accuracy and performance, but the impact on clinical management is uncertain. These investigators determined the performance of PM in clinical practice and its impact on the management of solid pancreatobiliary patients with indeterminate cytology. Solid pancreatobiliary patients with non-diagnostic, benign, atypical or suspicious cytology and who had a previous PM test were included. Mutation profiling examined the KRAS mutation and an associated tumor suppressor gene with a loss-of-heterozygosity mutation panel covering 10 genomic loci. Two sonographers made management recommendations without and after with the PM results, indicating their level of confidence. Mutation profiling improved diagnostic accuracy in 232 patients with indeterminate cytology. Among patients with non-diagnostic cytology, the low-risk PM provided high specificity and negative predictive value (NPV) for absence of malignancy, whereas the high-risk PM identified malignancies that would otherwise go unnoticed. The mutation profile increased physician confidence in treatment recommendations and resulted in more conservative treatment in 10% of patients. The mutation profile increased the rate of benign disease in patients recommended for conservative treatment (84% to 92%, p < 0.05) and the rate of malignant disease in patients recommended for aggressive treatment (53% to 71%, p < 0.05). 0.05). The authors concluded that PM improved diagnostic accuracy and significantly affected management decisions. Low-risk PM outcomes have increased recommendations for conservative treatment and increased the rate of benign outcomes in these patients, helping to avoid unnecessary aggressive interventions and improving patient outcomes. These investigators stated that their study was limited by its retrospective nature. Furthermore, they noted that while high-risk PM results could help confirm the presence of malignancy in cases where cytology indicated a high suspicion of malignancy, low-risk results could not effectively exclude the possibility of malignancy in these cases. .

Kushnir et al (2019) noted that routine cytology of biliary stricture brushings obtained during ERCP has suboptimal sensitivity for malignancy. These investigators compared the individual and combined ability of cytology, FISH analysis, and PCR-based MP to detect malignancy in standard biliary brushings. They conducted a prospective study of patients undergoing ERCP using histology or 1-year follow-up to determine patient outcomes; MP was performed on free DNA from biliary swab samples using normally discarded supernatant fluid. MP examined KRAS point mutations and LOH mutations associated with the tumor suppressor gene at 10 genomic loci; FISH examined specific chromosome gains or losses. A total of 101 patients were included in the final analysis and 69% had malignancy. Cytology had a sensitivity of 26% and a specificity of 100% for malignancy. The use of FISH or MP in combination with cytology increased sensitivity to 44% and 56%, respectively. The combination of the 3 tests (cytology, FISH and MP) showed the highest sensitivity for malignancy (66%). There was no difference in the specificity of the cytology, FISH, or MP tests when examined alone or in combination; MP improved the diagnostic yield of each procedure from 22% to 100%; FISH improved performance by 90%; MP detected 21 malignancies in addition to those identified by cytology; FISH detected an additional 13. The combination of FISH and MP detected an additional 28 malignancies. The authors concluded that MP and FISH are complementary molecular tests that can significantly enhance the detection of biliary malignancies when used in combination with routine cytology of standard biliary brushing specimens.

Gene expression profiles for cancer of unknown primary origin

Carcinoma of unknown primary (CUP) is a biopsy-proven metastatic solid tumor without an identified primary tumor and represents approximately 2% to 4% of all cancers. The diagnosis of CUP is made after inconclusive results of standard tests (eg, biopsy, immunochemistry and other blood tests, chest X-ray, and fecal occult blood test). The absence of a known primary tumor presents challenges for selecting appropriate treatment strategies. As a result, patients have a poor prognosis and less than 25% survive 1 year after diagnosis. A variety of tissue biopsy testing techniques are currently used to determine the source of CUP, including immunochemistry; Histological examination of specimens stained with eosin and hematoxylin and electron microscopy. These techniques definitively identify the type of carcinoma in less than 20% to 30% of CUPs.

Gene expression profiling is a technique used to identify the genetic makeup of a tissue sample by characterizing the mRNA patterns transcribed or "expressed" by its DNA. Specific patterns of gene expression, reflected in unique mRNA configurations, are associated with different types of tumors. By comparing the gene expression profile (GEP) of an unknown tumor with profiles of known primary cancers ("reference" profiles), it may be possible to determine the type of tumor from which the CUP originated.

In July 2008, the FDA gave marketing approval for the Pathwork Tissue of Origin test (Pathwork Diagnostics, Sunnyvale, CA), a gene expression profiling test that uses microarray processing to determine the type of cancer cells present in a tumor. of unknown origin. The test uses the PathChip (Affymetrix Inc., Santa Clara, CA), a custom gene expression matrix, to measure the expression of 1,668 sets of probes to quantify the similarity of tumor samples to 15 types of common malignancies, including: bladder, breast, colorectal, gastric, germ cell, hepatocellular, renal, non-small cell lung, non-Hodgkin's lymphoma, melanoma, ovary, pancreas, prostate, soft tissue sarcoma and thyroid. The degree of correspondence between the GEP of the tissue sample and a reference profile is quantified and expressed as a probability-based score.

A multicenter clinical validation study reported comparisons of GEP-based diagnoses of 477 stored tissue samples of undifferentiated and poorly differentiated metastases versus diagnoses based on standard-of-care pathology. Comparison of GEP-based versus pathology-based diagnoses yielded 89% agreement and concurrency was greater than 92% for 8 of 15 primary tumor types. The overall accuracy of the test was approximately 95% and 98% for positive and negative determinations, respectively (Monzon et al, 2007).

Gene expression profiling is a promising technology in the management of CUP; however, there is insufficient evidence of its clinical utility compared to that achieved by expert pathologists using current practice standards. The National Institute for Clinical Excellence (NICE, 2010) guideline project on metastatic malignancy of unknown origin recommends against the use of gene expression profiling (eg, Pathwork TOT, CupPrint, TherosCancerTypeID, miRview Mets) to identify primary tumors in patients with CUP. The guideline explained that there is currently no evidence that gene expression-based profiling improves management or changes outcomes for patients with CUP. The National Comprehensive Cancer Network (NCCN, 2010) guidelines on occult primary state that while gene expression profiling looks promising, "prospective clinical trials are needed to confirm whether this approach can be used to select treatment options that would improve prognosis." of patients with occult primary cancers".

An Andalusian Agency for Health Technology Assessment (AETSA, 2012) evaluation of microRNAs as a diagnostic tool for lung cancer found only two studies that evaluated the analytical validity of miRview in patients with non-small cell lung cancer. The miRNA sensitivity for carcinoma detection was between 96% and 100% and the specificity between 90% and 100%. The area under the ROC curve was close to unity and the positive and negative likelihood ratios showed high diagnostic accuracy (9.6 and 0.04, respectively). The review indicated that although the quality of the studies was moderate to high, the sensitivity of the diagnostic test may be overestimated as it is a case-control design.

A technology assessment prepared for the Agency for Healthcare Research and Quality (Meleth et al, 2013) found that the clinical accuracy of PathworkDx, miRview, and CancerTypeID is similar, ranging from 85 to 88 percent, and that evidence that the tests help identify a tumor of unknown origin was moderate. The review concluded that we do not have sufficient evidence to assess the effect of testing on treatment decisions and outcomes. The review noted that most studies of these tests have been funded wholly or in part by the manufacturers of these tests, and that the most pressing need in the literature is for the clinical utility of the tests to be evaluated by research groups that do not have evidence of a conflict of interest. .

Monzon et al (2009) stated that malignancies found in unexpected locations or with poorly differentiated morphologies may present a significant challenge for determining the origin of the tissue. Current histological and imaging techniques fail to definitively identify the tissue of origin in a significant number of cases. The aim of this study was to validate an expression profile of 1550 predefined genes for this purpose. Four institutions processed 547 frozen specimens representing 15 tissues of origin using oligonucleotide microarrays that were used in this study. Half of the samples were metastatic tumors and the rest were poorly differentiated and undifferentiated primary cancers chosen to resemble those presented as a clinical challenge. In this blinded multicentre validation study, the expression profile of 1550 genes was highly informative in tissue determination. The study found an overall sensitivity (positive percent agreement with the reference diagnosis) of 87.8% (95% CI 84.7% to 90.4%) and an overall specificity (negative percent agreement with the reference diagnosis) of 99 .4% (95% CI 98.3% to 99.9%). Performance within the metastatic tumor subgroup (n = 258) was slightly lower than that of the poorly differentiated and undifferentiated primary tumor subgroup, 84.5% and 90.7%, respectively (p = 0.04). Differences between individual laboratories were not statistically significant. The authors concluded that this study represents the first multicenter validation of an adequately sized gene expression profile for determination of tissue origin restricted to poorly differentiated and undifferentiated primary cancers and metastatic tumors. These results indicate that this profile should be a valuable addition or alternative to currently available diagnostic methods for the evaluation of uncertain primary cancers.

Monzon and Koen (2010) stated that tumors of uncertain or unknown origin are estimated in 3% to 5% of all cases of metastatic cancer. Patients with these types of tumors have worse outcomes compared to patients in whom a primary tumor is identified. New molecular tests are available to identify molecular signatures of a tissue of origin. The authors review the literature on existing molecular approaches to the diagnosis of metastatic tumors of uncertain origin and discuss the current status and future developments in this area. Published peer-reviewed literature, information available from medical organizations (NCCN), and other publicly available information from providers and/or manufacturers of tissue-sourced tests were used in this review. The authors concluded that molecular tests for determining the tissue of origin in metastatic tumors are available and have the potential to significantly impact patient management. However, available validation data indicate that not all tests have performance characteristics suitable for clinical use. Pathologists and oncologists should carefully evaluate the claims of accuracy and clinical usefulness for tissue-sourced tests before using the test results in patient care. The personalized medicine revolution includes the use of molecular tools to identify/confirm the site of origin of metastatic tumors and, in the future, this strategy could also be used to determine specific therapeutic approaches.

Anderson and Weiss (2010) noted that pathologists use various IHC dye panels to identify the local tissue of origin of metastatic tumors, particularly poorly differentiated or undifferentiated cancers of unknown or uncertain origin. Although clinicians believe that immunostains are very helpful in determining the likely primary site among 3 or more possibilities, convincing objective evidence has not been presented. This meta-analysis reviews the objective evidence supporting this practice and summarizes reported performance in 5 studies published between 1993 and 2007. A literature search was performed to identify IHC performance studies published since 1990 that were blinded, included more than 3 types of fabrics and used more than 50 copies. The 5 studies found in this search were separated into 2 subgroups for analysis: those that included only metastatic tumors (n = 368 specimens) and the combined studies that combined primary tumors and metastases (n = 289 specimens). The meta-analysis found that IHCs provided correct tissue identification for 82.3% (95% CI 77.4% to 86.3%) of the combined primary and metastatic samples and 65.6% (95% CI 77.4 % to 86.3%) of the combined primary and metastatic samples. 95%: 60.1% to 70.7%) of metastatic cancers. This difference is clinically and statistically significant. The authors concluded that this literature review confirms that there is still an unmet medical need for identification of the primary site of metastatic tumors. It establishes minimum performance requirements for any new diagnostic test intended to assist the pathologist and oncologist in determining tissue origin.

GeneSearch BLN

The presence of breast tumor cells in the axillary lymph nodes is a key prognostic indicator in breast cancer. During surgery to remove tumors from the breast, patients usually undergo a biopsy of the sentinel (i.e., the first) lymph node that receives lymph fluid from the breast. Currently, excised sentinel lymph nodes are evaluated postoperatively by hematoxylin and eosin (H&E) histology fixed in formalin and embedded in paraffin and IHC. The GeneSearch™ Breast Lymph Node (BLN) assay (Veridex, LLC, Warren, NJ) is a new method to examine harvested sentinel lymph nodes for metastases and can provide information during surgery within 30 to 40 minutes after delivery. removed. , potentially avoiding a second operation for some patients. The GeneSearch BLN Assay received FDA premarketing approval on July 16, 2007 as a qualitative in vitro diagnostic test for the rapid detection of metastases greater than 0.2 mm in nodal tissue removed from sentinel lymph node biopsies of patients with breast cancer. breast. The GeneSearch BLN assay uses real-time reverse transcriptase polymerase chain reaction (RT-PCR) to detect the gene expression markers mammaglobin (MG) and cytokeratin 10 (IC19), abundant in breast tissue but rare in lymph node cells . In the clinical trial conducted by Veridex, which was submitted to the FDA, the sensitivity of the GeneSearch BLN assay was reported to be 87.6% and the specificity to be 94.2% (Julian et al, 2008). According to the product label, "The GeneSearch™ Breast Lymph Node Assay (BLN) may be used in conjunction with a sentinel lymph node biopsy for a patient who has been counseled on the use of this test and has been informed of its performance False- positive results may be associated with increased morbidity False-negative and inconclusive test results may be associated with late axillary lymph node dissection .

Blumencranz et al (2007) compared the GeneSearch BLN assay with the results of conventional histological evaluation of 416 patients at 11 clinical centers and reported that the GeneSearch BLN assay detected 98% of metastases greater than 2 mm in size and 57% of metastases smaller than 0.2 mm. millimeter False positives were reported in 4% of cases. However, there were several limitations of this study, including a lack of description of patient recruitment, inadequate descriptions of several analyzes performed, substantial variations in test performance between sites, and an ad hoc comparison of the study with other intraoperative techniques.

Viale et al (2008) analyzed 293 lymph nodes from 293 patients using the GeneSearch BLN assay. Using histopathology as the gold standard, the authors reported that the BLN assay correctly identified 51 of 52 macrometastatics and 5 of 20 micrometastatic sentinel lymph nodes (SLNs), with a sensitivity of 98.1% for detecting metastases larger than 2 mm. 94.7% for metastases larger than 1 mm and 77.8% for metastases larger than 0.2 mm. Overall concordance with histopathology was 90.8%, with specificity of 95.0%, positive predictive value of 83.6% and negative predictive value of 92.9%. When results were evaluated according to the occurrence of additional non-NS metastases in patients with histologically positive NS, the assay was positive in 33 (91.7%) of the 36 patients with additional metastases and in 22 (66.6%) of the the 33 patients without significant progressive involvement. The authors concluded that the sensitivity of the GeneSearch BLN assay is comparable to that of histopathological examination of the entire SLN using serial 1.5 to 2 mm slices.

Although treatment for metastases larger than 2.0 mm is widely accepted as beneficial, clinical studies have not yet provided data for a consensus on the benefit of treatment based on very small breast cancer metastases (between 0.2 and 2 mm). .0mm) in SLN. False-positive results may be associated with increased morbidity, usually due to the effects of axillary lymph node dissection surgery. Patients undergoing axillary lymph node dissection (ALND) have significantly higher rates of increased swelling in the upper arm and forearm (lymphedema), pain, numbness, and stiffness of motion in the shoulder compared to patients undergoing sentinel lymph node dissection ( SLND) alone. ). False-negative and inconclusive test results may be associated with delayed axillary lymph node dissection. To date, clinical studies are inconclusive regarding the benefit of treatment based on findings of breast cancer micrometastases in SLNs. Preliminary data suggest that the GeneSearch BLN assay has high specificity and moderate sensitivity when only macrometastases are included in the analysis. The clinical significance of micrometastases is still debated in the literature, so the significance of the failure of the GeneSearch BLN assay to function adequately in detecting micrometastases is unknown.

A systematic review of evidence conducted by the BlueCross BlueShield Association Center for Technology Assessment (BCBSA, 2007) determined that the use of the GeneSearch BLN assay to detect sentinel lymph node metastases in early-stage breast cancer does not meet the criteria for ECT. The review stated: "There are a number of operational issues that add difficulty to using the GeneSearch assay, including the need for fresh samples (rather than placing them in formalin for permanent fixation), the learning curve involved in reducing both the percentage of samples invalid results (from about 15% initially to 4-8% for more experienced technicians) and time to test compared to alternative intraoperative techniques (taking less than 15 minutes)." Furthermore, the review indicated that "The GeneSearch assay also provides less information for staging than other intraoperative procedures because it cannot distinguish between micro and macro metastases. It also cannot indicate the location of the metastasis (inside or outside the nodule). To Therefore, postoperative histology is required in all cases.It is less crucial when performing histology on frozen sections, as pathologists can assess the size of the metastasis and its location from this test, although distortion is possible. In summary, the available data are inadequate to assess the clinical usefulness of the GeneSearch assay compared to postoperative histology alone or alternative intraoperative tests such as impression cytology and frozen section histology. may require greater specificity to avoid unnecessary ALND and its sequelae, while GeneSearch design emphasizes za the sensitivity."

A report from the Adelaide Health Technology Assessment indicated that if the GeneSearch BLN assay is to play a role in reducing mortality in patients with breast cancer, it will be through more accurate diagnosis of breast cancer metastasis during SNB (Ellery, et al. al., 2010). However, the report noted that, to date, there is no data to indicate whether the SNB itself reduces the death rate among patients with breast cancer. Therefore, it is unclear whether the GeneSearch BLN study would have any indirect effect on breast cancer mortality until further research on SNB is completed.

Therefore, there is not enough evidence to conclude about the effectiveness of the GeneSearch BLN assay. The FDA requires the manufacturer to perform two post-approval studies. The primary objective of the first study is to estimate the positive predictive agreement between the GeneSearch BLN assay and histology as routinely practiced, and the objectives of the second clinical study are

determine the test turnaround time from the time of removing the node to reporting the test result to the surgeon, and
determine whether or not the test result was received in time for an intraoperative decision, and
collect data against other surgical procedures during sentinel lymph node dissection/breast surgery to determine whether test response time resulted in longer surgical time.

test BT

Provista Life Sciences (Phoenix, AZ) has developed a laboratory test called the Biomarker Translation Test, or BT Test, which is a test score based on the analysis of multiple protein biomarkers (i.e., IL-2, -6, -8 , - 12, TNFa, EGF, FGF, HGF, VEGF) and the medical profile of a person's breast cancer risk factors. It is intended to be used as an adjunct test in conjunction with other breast cancer screening modalities, however there are no published studies on the effectiveness of this test.

Bcl-2

Bcl-2 (B-cell CLL/lymphoma 2; BCL2) is a proto-oncogene whose protein product, bcl-2, suppresses programmed cell death (apoptosis), resulting in prolonged cell survival without increasing cell proliferation. Dysregulation of programmed cell death mechanisms plays a role in cancer pathogenesis and progression, as well as tumor responses to therapeutic interventions. Many members of the Bcl-2 family of apoptosis-related genes have been found to be differentially expressed in various malignancies (Reed, 1997).

Salgia (2008) reviewed the evidence for detection of Bcl-2 in lung cancer. The author noted that Bcl-2 overexpression has been reported in 22 to 56% of lung cancers with higher expression in squamous cell carcinoma compared to adenocarcinoma histology. However, the author concluded that the association of Bcl-2 expression and prognosis in non-small cell lung cancer is unclear. Several reports have shown that Bcl-2 positive lung cancers are associated with a superior prognosis compared to those that are Bcl-2 negative. However, other studies failed to demonstrate any impact on survival with bcl-2 positivity, while overexpression was also associated with a worse outcome. A meta-analysis that included 28 studies that examined the prognostic influence of Bcl-2 in non-small cell lung cancer concluded that Bcl-2 overexpression was associated with a significantly better prognosis on surgical resection (hazard ratio 0.5, CI 95% 0.39-0.65).

Compton (2008) recently reviewed the evidence on the Bcl-2 oncogene and other tumor markers in colon cancer. Compton explained that Bcl-2 is a gene related to cell apoptosis/suicide. Bcl-2 overexpression leading to inhibition of cell death signaling has been observed as a relatively early event in the development of colorectal cancer. The author concluded that the independent influence of the Bcl-2 oncogene on prognosis has not yet been proven, explaining that variability in study methodology, conflicting results from multiple studies examining the same factor, and the prevalence of multiple small studies lacking multivariate data Statistically Robust Evidence All analyzes contribute to the lack of conclusive data. Compton concluded that before the Bcl-2 oncogene and some other tumor markers can be incorporated into clinically meaningful prognostic stratification systems, "further studies using multivariate analysis, well-characterized patient populations, current and reproducible methodology, and standardized reagents are needed." ". .

CD31

Compton (2008) reviewed the evidence for intratumoral microvessel density (MVD) and antibodies to CD31 in colorectal cancer. The author explained that intratumoral VMD is a reflection of tumor-induced angiogenesis. Microvessel density was independently associated with shorter survival in some but not all studies. A meta-analysis of all studies linking MVD expression to prognosis concluded that at least part of the variability can be explained by different methods of assessing MVD. The author observed that there was a significant inverse correlation between immunohistochemical expression and survival when MVD was evaluated using antibodies against CD31 or CD34, but not against factor VIII. The author concluded, however, that there is a need to assess VMD in large studies of prognostic factors using multivariate analysis; however, standard guidelines for staining, evaluating, and interpreting MVDs are lacking.

In a review, Hayes (2008) reviewed the evidence to assess angiogenesis factors in breast cancer. The author noted that, in a previous report, MVD count (as indicated by IHC staining for endothelial cells as factor VIII-related antigen or CD31) was a statistically significant independent predictor of overall and disease-free survival. breast cancer negative and lymph node positive. However, the author noted that subsequent data are conflicting, with some studies confirming and others refuting the initial findings. The author stated that "as with many other studies of tumor markers, the assessment of angiogenesis is complicated by technical variation, reader inconsistency, and potential interaction with therapy."

Burgdorf (2006) reviewed the use of CD31 in acquired progressive lymphangioma. The author stated that special staining techniques reveal that the cells are variable CD31-positive, but that the staining patterns are too variable to be diagnostically meaningful.

Some authorities have stated that CD31 staining may be useful in diagnosing angiosarcomas (Schwartz, 2008; Carsiand Sim, 2008; Fernandezand Schwartz, 2007; McMainsand Gourin, 2007). CD31 immunostaining can help confirm that the tumor originates in blood vessels.

top2a

Topoisonmerase II alpha is a protein encoded by the TOP2A gene and is proposed as a predictive and prognostic marker for breast cancer. It is also proposed as an aid in predicting response to anthracycline therapy in breast cancer. Two types of tests are available for topoisomerase II alpha: topoisomerase II alpha protein expression test by immunohistochemistry (IHC); and FISH TOP2A gene amplification test (eg, TOP2A FISH pharmDx assay).

The topoisomerase II alpha (TOP2A) gene is co-localized with the HER-2 oncogene at chromosomal location 17q12-q21 and is either amplified or deleted (with the same frequency) in the vast majority of HER-amplified primary breast tumors. 2 and also in tumors without HER-2 amplification. Recent experimental trials, as well as numerous large multicenter trials, suggest that amplification (and/or deletion) of TOP2A may explain both sensitivity and resistance to commonly used cytotoxic drugs (eg, anthracyclines), depending on the specific genetic defect in TOP2A . place. An analysis of TOP2A aberrations in the Danish Breast Cancer Cooperative Group 89D trial (Nielsen, et al., 2008) suggested a differential benefit of adjuvant chemotherapy in patients with primary breast cancer, favoring epirubicin treatment in patients with amplifications and perhaps deletions of TOP2A; however, the authors concluded that "additional studies are needed to clarify the exact significance of TOP2A deletions in outcome, but deletions have been shown to be associated with a very poor prognosis."

The National Comprehensive Cancer Network (NCCN, 2008) guidance on breast cancer does not address the use of the TOP2A test. The American Society of Clinical Oncology (2016) guidelines state: “The clinician should not use TOP2A gene amplification or TOP2A protein expression by IHC to guide selection of adjuvant chemotherapy: This is a moderately strong recommendation. based on high-quality evidence. The guidelines also advise against the use of TOP2A gene coamplification to guide the selection of adjuvant chemotherapy.

TSP-1

Ghoneim et al (2008) explained that thrombospondin-1 (TSP-1) is a member of a family of five structurally related extracellular glycoproteins that play important roles in cell-matrix and cell-cell interactions. Due to its multifunctional nature and its ability to bind to a variety of cell surface receptors and matrix proteins, TSP-1 has been identified as a potential regulator of angiogenesis and tumor progression. Data collected by Secord, et al. (2007) suggested that elevated THBS-1 levels may be an independent predictor of worse progression-free survival and overall survival in women with advanced-stage epithelial ovarian cancer. However, a phase II clinical trial (García, et al., 2008) of bevacizumab and low-dose oral metronomic cyclophosphamide in recurrent ovarian cancer reported that TSP-1 levels were not associated with clinical outcome.

mdr1

In a review of multidrug resistance in acute leukemia, List and Spier (1992) explained that the mdr1 gene or its glycoprotein product, P-glycoprotein, is detected with high frequency in secondary acute myelogenous leukemia (AML) and in subgroups with high risk of developing acute leukemia. lymphoblastic leukemia. Investigations of the regulation of mdr1 in normal hematopoietic elements have shown a pattern that corresponds to its regulation in acute leukemia, explaining the linkage of mdr1 with specific cellular phenotypes. Therapeutic trials are currently underway to test the ability of various MDR reversal agents to restore responsiveness to chemotherapy in high-risk acute leukemias.

In a phase III multicenter randomized trial to determine whether quinine would improve survival in adult patients with de novo AML, Soary et al (2003) reported that neither the mdr1 gene nor P-glycoprotein expression influenced clinical outcome.

A phase I/II study of the MDR modulator Valspodar (PSC 833, Novartis Pharma) combined with daunorubicin and cytarabine in patients with refractory primary relapsing acute myelogenous leukemia (Gruber et al, 2003) reported no obvious response to P-glycoprotein improvement of treatment results.

MRP-1

Motility-related protein (MRP-1) is a glycoprotein with an identical sequence to CD9, a white blood cell differentiation antigen. The level of expression of MRP-1/CD9 has been found in research studies to inhibit cell motility and low expression of MRP-1/CD9 may be associated with the metastatic potential of breast cancer (Miyake et al, 1995). CD9 immunoexpression is also being investigated as a potential new predictor of tumor behavior in patients with head and neck squamous cell carcinoma (Mhawech et al, 2004) as well as other tumors (e.g., urothelial carcinoma of the bladder, cancer colon cancer, lung cancer). Cancer); however, prospective studies are needed to determine the clinical role of MRP-1/CD9 expression in tumors.

FLAP

The National Comprehensive Cancer Network guideline on occult primary tumors lists placental alkaline phosphatase (PLAP) as a useful marker to help identify seminoma and non-seminoma germ cell tumors in unknown primary (NCCN, 2009).

MPO

Myeloperoxidase (MPO), a blood protein, is the main component of the azurophilic granules of neutrophils. The myeloperoxidase assay has been used to distinguish between immature cells in acute myeloblastic leukemia (positively staining cells) and those in acute lymphoblastic leukemia (negatively staining cells). The National Comprehensive Cancer Network guidelines for acute myelogenous leukemia (AML) include analysis of MPO in the classification of AML (NCCN, 2008).

Matsuo et al (2003) examined the prognostic factor of the percentage of MPO-positive blast cells for AML. Cytochemical analysis of MPO was performed in 491 patients enrolled in the Japan Adult Leukemia Study Group (study AML92). Patients were divided into two groups using the percentage of MPO-positive blasts (high [> or = 50%] and low [< 50%]). Complete remission rates were 85.4% in the first and 64.1% in the second (p = 0.001). OS and DFS were significantly better in the high MPO group (48.3 vs. 18.7% for OS and 36.3 vs. 20.1% for DFS, p < 0.001, respectively). Multivariate analysis showed that both karyotype and percentage of MPO-positive blasts were equally important prognostic factors. The high MPO group still had better survival even when restricted to the intermediate chromosomal risk group or patients with normal karyotype (p < 0.001). The OS of patients with normal karyotypes in the high MPO group was almost equal to that of the favorable chromosomal risk group. The authors concluded that the percentage of MPO-positive blast cells is a simple and highly significant prognostic factor for patients with AML and especially useful for stratifying patients with normal karyotypes.

DCP

The most commonly used marker for hepatocellular carcinoma (HCC) is the level of AFP. Des-gamma-carboxy prothrombin (DCP) (also known as "prothrombin produced by absence of vitamin K or II antagonism" [PIVKA II]) has also shown promise in the diagnosis of HCC (Toyoda et al, 2006; Ikoma et al, 2002; Nomura et al, 1996; Liebman et al, 1984). In a series of 76 patients with HCC, this marker was elevated in 69 patients with a mean serum concentration of 900 mcg/L. Much lower mean values were seen in patients with chronic active hepatitis, liver metastatic disease, and normal subjects (10 and 42 mcg/L and undetectable, respectively) (Liebman et al, 1984). Elevations in DCP serum levels are less frequent in tumors less than 3 cm in size (Nakamura et al, 2006; Weitzand Liebman, 1993;). Aoyagi et al (1996) as well as Weitzand Liebman (1993) reported that abnormal prothrombin levels do not correlate well with serum AFP.

Toyoda et al. (2006) measured AFP, culinaris lens agglutinin A-reactive fraction AFP (AFP-L3) and PCD to assess tumor progression and prognosis in patients with HCC (n = 685) at the time of the study, at initial diagnosis. Positivity was determined for AFP > 20 ng/dL, AFP-L3 > 10% of total AFP, and/or DCP > 40 mAU/mL. In addition, tumor markers were measured after CHC treatment. Of the 685 patients, 337 (55.8%) were AFP positive, 206 (34.1%) AFP-L3 positive, and 371 (54.2%) DCP positive. In a comparison of patients positive for only 1 tumor marker, patients positive for AFP-L3 alone had a greater number of tumors, whereas patients positive for DCP alone had larger tumors and a higher prevalence of portal vein invasion. When patients were compared based on the number of tumor markers present, the number of markers present clearly reflected the extent of HCC and patient outcomes. The number of markers present significantly decreased after treatment. The authors concluded that AFP-L3 and DCP tumor markers appeared to represent different features of tumor progression in patients with HCC and that the number of tumor markers present could be useful for assessing tumor progression, predicting patient outcome, and effectiveness of treatment. .

The National Comprehensive Cancer Network's guidance on HCC (NCCN, 2008) does not include DCP measurement among surveillance testing options for HCC. Per NCCN guidelines, proposed surveillance for early detection of HCC among high-risk populations (eg, patients infected with chronic hepatitis C virus) includes liver ultrasound every 3 to 6 months and assessment of alkaline phosphatase, albumin and AFP. The guidelines stated: "It remains unclear whether early detection of hepatocellular cancer with routine screening improves the percentage of patients detected with disease at a potentially curative stage, but patients infected with high-risk chronic hepatitis C virus should be considered for continual disease recurrence." until these issues are resolved. The level of the protein des-gamma-carboxy-prothrombin induced by the absence of vitamin K (PIVKA-II) is also increased in many patients with hepatocellular carcinoma. However, as with AFP, PIVKA-II can be elevated in patients with chronic hepatitis". In addition, according to Sherman (2008), DCP has not been adequately studied as a HCC detection test and cannot be recommended right now.

NMP66

Researchers at Matritech (Newton, MA) detected the presence of nuclear matrix protein (NMP) in the blood of women with early-stage breast cancer, which is absent in the blood of healthy women as well as those with fibroadenoma. NMP66 was selected as a marker for further development and clinical trials of a test for use in detecting and monitoring women with breast cancer or at risk of breast cancer have begun (Wright and McGechan, 2003). However, there are no published studies on the effectiveness of NMP66 tests at this time.

HERmark

The HERmark breast cancer assay (bioscience monogram) is used to help determine prognosis and therapeutic options for metastatic breast cancer (Raman, et al., 2013). Clinical practice guidelines recommend determination of HER2 status in patients with all types of invasive breast cancer, but caution that current methods of HER2 testing, such as central immunohistochemistry and fluorescence in situ hybridization testing, may be inaccurate in about 20% of cases. According to the HERmark website, their method accurately quantifies total HER2 protein and HER2 homodimer levels in formalin-fixed, paraffin-embedded tissue sections, and fluorescence in situ hybridization has excelled in determining outcomes for patients with breast cancer metastatic.

The HERmark test has been proposed for several indications, including its use to predict response to trastuzumab in the treatment of metastatic breast cancer. Monogram, makers of the HERmark test, claims that the test can provide a more accurate and quantitative measurement of the HER2 gene than IHC and fluorescent in situ hybridization (FISH) tests. HERmark provides a quantitative measure of total HER2 protein and HER2 homodimer levels, whereas conventional methods are an indirect measure of the HER2 gene, the manufacturer claims. The HERmark test will be offered as a CLIA-validated assay through Monogram's CAP-certified clinical laboratory. Other proposed indications for HERmark include determining breast cancer prognosis and predicting treatment outcomes in cancers other than breast cancer (eg, ovarian, prostate, head and neck, etc.). There are no current recommendations from leading medical professional organizations for the use of the HERmark test for breast cancer.

Yardley et al (2015) compared quantitative HER2 expression using the HERmark breast cancer assay (HERmark) with routine HER2 testing using immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH) and correlated the HER2 results with the overall survival (OS) of breast cancer patients in a multicenter collaborative study of biomarkers (CBS). Two hundred and thirty-two formalin-fixed, paraffin-embedded breast cancer tissues and local laboratory HER2 test results were provided by 11 CBS centers. The HERmark assay and the HER2 IHC repeat test at the core laboratory were performed retrospectively in a blinded manner. HER2 results were obtained by all test methods in 192 cases. HERmark produced a continuum of total HER2 expression (H2T) ranging from 0.3 to 403 RF/mm2 (approximately 3 logs). The distribution of H2T levels was significantly (P<0.0001) correlated with all routine HER2 test results. The concordance of positive and negative values (doubtful cases excluded) between HERmark and routine HER2 testing was 84% for local IHC, 96% for central IHC, 85% for local FISH and 84% for local HER2 status. OS analysis revealed a significant correlation of shorter OS with HER2 positivity by local IHC (HR=2.6, P=0.016), central IHC (HR=3.2, P=0.015) and HERmark (HR=5 .1, P<0.0001) in this cohort of patients, most of whom did not receive HER2-directed therapy. The low OS discordant curve (HER2 positive but low H2T, 10% of all cases) aligned with the concordant negative (HER2 negative and low H2T, HR = 1.9, P = 0.444), but showed significantly more OS longer than concordant positive (HER2 positive and high H2T, HR=0.31, P=0.024). In contrast, the discordant high OS curve (HER2 negative, but high H2T, 9% of all cases) aligned with positive concordant (HR=0.41, P=0.105), but showed significantly shorter OS than negative concordant . (HR=41, p<0.0001).

MDM2

Noon et al (2010) stated that renal cell carcinoma (RCC) is the most common type of renal cancer and follows an unpredictable disease course. These investigators reviewed 2 critical genes associated with disease progression, p53 and murine double minute 2 (MDM2), and provided a comprehensive overview and critical review of the literature on these genes in RCC. Information was compiled by searching the PubMed database for articles published or electronically published through April 1, 2009. Search terms included renal cancer, renal cell carcinoma, p53, and MDM2. Full articles and any supplementary data were reviewed; and, where appropriate, references were checked for additional material. All studies that described the assessment of p53 and/or MDM2 in renal cancer were included. The authors concluded that increased p53 expression, but not p53 mutation, is associated with reduced overall survival/more rapid disease progression in RCC. There was also evidence that MDM2 up-regulation is associated with decreased disease-specific survival. Two features of RCC stood out as unusual and will require further investigation:

increased p53 expression is closely related to increased MDM2 expression; It is
patients with tumors that show increased expression of p53 and MDM2 may have the worst overall survival.

As there was no evidence to support the conclusion that p53 mutation is associated with poorer survival, it seemed clear that increased p53 expression in RCC occurs independently of mutation. The authors state that further investigation of the mechanisms that lead to increased p53/MDM2 expression in CRC may lead to a better prognosis and identification of new therapeutic interventions.

OVA1

OVA1 is a blood test used to aid in the evaluation of pelvic masses for likelihood of malignancy prior to surgery. OVA1 measures five biomarkers: apolipoprotein A1 (Apo A-1), beta-2 microglobulin (B2M), prealbumin CA-125 and transferrin. The results of these measurements are applied to an algorithm, resulting in a numerical score.

The OVA1 test (Vermillion Inc. and Quest Diagnostics) is a serum test designed to help clinicians determine whether a woman is at risk for a malignant pelvic mass prior to biopsy or exploratory surgery, when the physician's independent clinical and radiologic evaluation does not indicate malignancy (Mundy, et al., 2010). The OVA1 test uses an in vitro diagnostic multivariate index (IVDMIA) that combines the results of five immunoassays to produce a numerical score that indicates a woman's likelihood of malignancy. The OVA1 test is intended to help clinicians assess whether a pelvic mass is benign or malignant to help determine whether to refer a woman to a gynecologic oncologist for surgery. The OVA1 test is approved by the FDA for use in women who meet the following criteria: age 18 years or older, ovarian adnexal mass for which surgery is planned, and not yet referred to an oncologist. The intended use of the OVA1 test is as an aid to better assess the likelihood of malignancy present when the physician's independent clinical and radiological evaluation does not indicate malignancy. According to the product label, the OVA1 test is not intended to be a standalone screening or diagnostic assay. There is a lack of evidence in published and peer-reviewed medical literature on the OVA1 test.

Ueland et al (2011) attempted to compare the effectiveness of medical assessment with the OVA1 multivariable index assay in identifying high-risk ovarian tumors. The multivariate index study was evaluated in women scheduled for ovarian tumor surgery in a prospective multi-institutional study involving 27 primary care and specialty care centers across the United States. Preoperative serum was collected and the results of the multivariable index assay, physician assessment, and CA 125 were correlated with surgical pathology. Each physician documented the physician's assessment prior to surgery. CA 125 cutoffs were chosen according to the American College of Obstetricians and Gynecologists reference guidelines. The study enrolled 590 women, with 524 evaluable for multivariate index and CA 125 testing, and 516 for medical evaluation. Fifty-three percent were enrolled by non-gynecologic oncologists. There were 161 malignant neoplasms and 363 benign ovarian tumors. Physician assessment plus multivariate index assay correctly identified malignancies missed by physician assessment in 70% of non-gynecologic oncologists and 95% of gynecologic oncologists. The multivariate index assay also detected 76% of the malignancies that CA 125 missed. Physician assessment plus multivariate index assay identified 86% of malignancies missed by CA 125, including all advanced cancers. The investigators stated that the performance of the multivariate index assay was consistent across early and late stage cancers.

Ware Miller et al (2011) attempted to estimate the performance of the ACOG reference guidelines for pelvic mass with the OVA1 multivariable index assay. A multi-institutional prospective study included 27 primary and specialty care settings across the United States. College guidelines were evaluated in women scheduled for ovarian mass surgery. Clinical criteria and blood for biomarkers were collected before surgery. A standard CA 125-II assay was used and the value was applied to the multivariate index assay algorithm and CA 125 analysis. Study results were correlated with surgical pathology. Of 590 women enrolled with an ovarian mass on pelvic imaging, 516 were evaluable. There were 161 malignancies (45 premenopausal and 116 postmenopausal). The College's reference criteria had modest sensitivity in detecting malignancy. Replacing CA 125 with the multivariate index assay increased sensitivity (77-94%) and negative predictive value (87-93%) while decreasing specificity (68-35%) and positive predictive value (52-40% ). Similar trends were observed for premenopausal women and early-stage disease.

Bristow et al. (2013) attempted to validate the effectiveness of a multivariate index assay in identifying ovarian malignancy compared with clinical assessment and CA125-II, among women undergoing surgery for an adnexal mass after enrollment by healthcare professionals. non-gynecological oncology. A prospective multi-institutional study enrolled female patients scheduled for surgery for an adnexal mass from 27 non-gynecologic oncology practices. Preoperative serum samples and physician assessment of ovarian cancer risk were correlated with final surgical pathology. A total of 494 subjects were evaluated for the multivariate index assay, CA125-II, and clinical impression. In total, 92 patients (18.6%) had pelvic neoplasia. Primary ovarian cancer was diagnosed in 65 patients (13.2%) and 43.1% had stage I FIGO disease. For all ovarian malignancies, the sensitivity of the multivariate index assay was 95.7% (95% CI = 89.3-98.3) when combined with clinical impression. The multivariate index assay correctly predicted ovarian malignancy in 91.4% (95% CI = 77.6-97.0) of cases of early-stage disease, compared with 65.7% (95% CI = 49. 2-79.2) for CA125-II. The multivariate index assay correctly identified 83.3% of malignancies that were missed by clinical impression and 70.8% of cases that were missed by CA125-II. The multivariate index assay was superior in predicting the absence of ovarian malignancy, with a negative predictive value of 98.1% (95% CI = 95.2-99.2). Both clinical impression and CA125-II were more accurate in identifying benign diseases. The multivariate index assay correctly predicted benign pathology in 204 patients (50.7%, 95% CI = 45.9-55.6) when combined with clinical impression.

Longoria et al (2014) sought to analyze the effectiveness of the OVA1 Multivariate Index Assay (MIA) in identifying early-stage ovarian malignancy compared to clinical assessment, CA 125-II, and modified guidelines from the American Congress of Obstetricians and Gynecologists. (ACOG) among women undergoing surgery for an adnexal mass. Patients were recruited in 2 prospective multi-institutional studies involving 44 sites. All women underwent preoperative imaging and biomarker analysis. Preoperative biomarker values, physician assessment of ovarian cancer risk, and modified ACOG guideline risk stratification correlated with surgical pathology. A total of 1016 patients were evaluated for MIA, CA 125-II, and clinical evaluation. Overall, 86 patients (8.5%) had stage I/II primary ovarian cancer, with 70.9% having stage I disease and 29.1% having stage II disease. For all early-stage ovarian malignancies, MIA combined with clinical assessment had significantly higher sensitivity (95.3%; 95% confidence interval [CI], 88.6-98.2) compared with clinical assessment isolated (68.6%; 95% CI, 58.2-77.4), CA 125-II (62.8%; 95% CI, 52.2-72.3), and modified ACOG guidelines (76, 7%; 95% CI, 66.8-84.4) (p < 0.0001). Among 515 premenopausal patients, the sensitivity for early-stage ovarian cancer was 89.3% (95% CI, 72.8-96.3) for MIA combined with clinical assessment, 60.7% (95% CI, 72.8-96.3). 95%, 42.4-76.4) for clinical evaluation only, 35.7% (95% CI, 20.7-54.2) for CA 125-II, and 78.6% (95% CI, 20, 7-54.2) to 60.5-89.8) for modified ACOG guidelines. Early-stage ovarian cancer in postmenopausal patients was correctly detected in 98.3% (95% CI, 90.9-99.7) of cases by MIA combined with clinical assessment, compared to 72.4% (95% CI, 59.8-82.2) for clinical evaluation only. 75.9% (95% CI, 63.5-85.0) for CA 125-II and 75.9% (95% CI, 63.5-85.0) for modified ACOG guidelines.

Bristow et al (2013) evaluated the impact on referral patterns of using the OVA1 multivariate index assay, CA125, the modified American College of Obstetricians and Gynecologists referral guidelines, and clinical evaluation among patients undergoing surgery for an adnexal mass after evaluation by non-gynecological oncologists. . In total, 770 patients were enrolled by non-gynecological oncologists from 2 related prospective multi-institutional studies and retrospectively analyzed. All patients had preoperative imaging and biomarker analysis. The subset of patients enrolled by non-gynecological oncologists was analyzed for projected referral patterns and sensitivity for malignancy based on the Multivariate Index Assay (MIA), CA125, modified American College of Obstetricians and Gynecologists (ACOG) guidelines, and clinical evaluation compared with the real . practice. The prevalence of malignancy was 21.3% (n = 164). In clinical practice, 462/770 patients (60.0%) were referred to a gynecological oncologist for surgery. Classification based on predicted CA125 remission of 157/770 patients (20.4%) with a sensitivity of 68.3% (95% confidence interval [CI], 60.8-74.9). Triage based on the modified ACOG guidelines would have resulted in referral of 256/770 patients (33.2%) with a sensitivity of 79.3% (95% CI, 72.4-84.8). Clinical assessment predicted referral in 184/763 patients (24.1%) with a sensitivity of 73.2% (95% CI, 65.9-79.4). Risk stratification using a multivariate index assay would have resulted in referral of 429/770 (55.7%) patients, with a sensitivity of 90.2% (95% CI, 84.7-93.9). MIA demonstrated statistically significantly higher sensitivity (P < 0.0001) and lower specificity (P < 0.0001) for detecting malignancy compared with clinical assessment, CA125 and modified ACOG guidelines.

Goodrich et al (2014) investigated the relationship between imaging and the multivariable index (MIA) assay in predicting the likelihood of ovarian malignancy before surgery. Subjects were recruited in 2 prospective multi-institutional studies involving 44 sites in the United States. The women underwent ovarian imaging, biomarker analysis, and surgery for an adnexal mass. Ovarian tumors were classified as high risk for solid or papillary morphological condition on imaging. Biomarkers and imaging results were correlated with surgical findings. Of the 1110 women who enrolled with an adnexal mass on imaging, 1024 cases were evaluable. There were 255 malignant and 769 benign tumors. High-risk findings were present in 46% of 1232 imaging exams and 61% of 1024 MIA exams. The risk of malignancy increased with increasing MIA scores; Likewise, the probability of malignancy was higher for high-risk compared to low-risk images. Sensitivity and specificity for predicting malignancy were 98% (95% CI, 92-99) and 31% (95% CI, 27-34) for ultrasound or MIA; 68% (95% CI, 58-77) and 75% (95% CI, 72-78) for ultrasound and MIA, respectively. For computed tomography or MIA, the sensitivity was 97% (95% CI, 92-99) and the specificity was 22% (95% CI, 16-28); sensitivity and specificity for CT and MIA were 71% (95% CI, 62-79) and 70% (95% CI, 63-76). Only 1.6% of ovarian tumors were malignant when both tests indicated low risk.

A review by the BlueCross BlueShield Association Center for Technology Assessment (BCBSA, 2013) stated, "Evidence for an effect of OVA1... in concluding that this results in better health outcomes. The clinical trials performed in the studies are not well characterized. Although OVA1 improves sensitivity, specificity decreases so much that most patients test positive."

A technology review by the ECRI Institute (2015) concluded that the evidence for OVA1 consists of cross-sectional studies of diagnostic accuracy. This evidence, as reported in the article abstracts, is unclear whether the use of OVA1 improves patient-centered outcomes because none of the studies reported the direct impact of this evidence on survival or quality of life. The main reason for using these tests is to select the type of surgeon who will perform the primary surgery.

Stewart et al (2016) reported on a survey of primary care physicians on how often they referred patients diagnosed with ovarian cancer to gynecologic oncologists and found that a total of 84% of primary care physicians (87% of primary care physicians family/general, 81% of internists and OB/GYNs) said they always refer patients to gynecologic oncologists for treatment. Common reasons for not always referring were patient preference or a lack of gynecological oncologists in the practice area. A total of 23% of primary care physicians had heard of the OVA1 test, which helps determine whether referral to a gynecological oncologist is necessary. The authors note that although referral rates reported here are high, it is unclear whether patients with ovarian cancer actually seek out gynecologic oncologists for treatment.

Eskander et al (2016) performed a retrospective chart review of patients who received OVA1. Twenty-two OB/GYNs were recruited from a variety of practices and hospitals across the United States. A total of 136 patients with high-risk study outcomes were evaluated, of whom 122 underwent surgery to remove an adnexal mass. Prior to surgery, 98 (80%) patients were referred to a gynecological oncologist, and another 11 (9%) had a gynecological oncologist available if intraoperative findings warranted. Primary ovarian cancer was found in 65 (53%) patients, and gynecological oncologists performed 61 (94%) of the initial surgeries in these patients. Similar results were found in premenopausal and postmenopausal patients.

Forde et al (2016) performed an economic analysis model to assess the clinical and cost implications of adopting OVA1 in clinical practice versus modified reference guidelines of ACOG and CA-125 alone, over a lifetime. , from the perspective of the public payer. Clinical parameters used to characterize patients' disease status, quality of life, and treatment decisions were estimated using results from published studies; costs have been approximated using reimbursement rates from the CMS rate tables. Model endpoints included overall survival (OS), costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER). The break-even point was set at $50,000 per QALY. A one-way sensitivity analysis was performed to assess the uncertainty of the individual parameters included in the analysis. All costs are reported in 2014 US dollars. The use of OVA1 was cost-effective, resulting in fewer reoperations and pretreatment CT scans. Overall, OVA1 resulted in an ICER of $35,094/QALY earned. OVA1 also saved costs and increased QALY compared to using CA-125 alone with an ICER of $12,189/QALY gained. One-way sensitivity analysis showed that ICER was most affected by the following parameters:

OVA1 sensitivity;
mACOG sensitivity; It is
Percentage of patients, not referred to a gynecologic oncologist, who were correctly diagnosed with advanced epithelial ovarian cancer (EOC).

The authors concluded that OVA1 is a more economical classification strategy than mACOG or CA-125. The percentage of women with ovarian cancer referred to gynecologic oncologists is expected to increase, which has been shown to improve clinical outcomes. Limitations include the use of assumptions when published data were not available and the use of multiple sources for survival data.

Urban et al (2017) reported that the addition of a patient-reported symptoms (SI) index, which objectively captures subjective symptoms, improved the sensitivity of the OVA1 multivariate index (MIA) assay. The researchers conducted a prospective study of patients seen at a tertiary medical center. After consent, patients completed an SI and preoperative serum was collected for an OVA1 multivariable index assay. SI and OVA1 results were correlated with operative findings and surgical pathology. Of the 218 patients enrolled, 124 (56.9%) had benign disease and 94 (43.1%) had borderline tumors or carcinomas. Sixty-six patients had primary cancer of the ovary or fallopian tubes. The median age of patients included in this study was 54 years (interquartile range, 44-63 years), of which 148 (67.9%) were postmenopausal. More than one-third (36.3%) of patients with benign masses were accurately identified as low-risk by MIA and SI. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the SI for primary ovarian cancer was 87.9% (95% CI, 77.9-93.7%). , 70.2% (95% CI, 61.6% -77.5%), 61.1% (95% CI, 51.0-70.2%) and 91.6% (95% CI, 84 .3%-95.7%), respectively. The sensitivity, specificity, PPV, and NPV of CA125 was 75.4% (95% CI, 63.7%-84.2%), 85.7% (95% CI, 78.3%-90.9% ), 74.2% (62.6-82.3%) and 86.4% (95% CI, 79.1-91.5%), respectively. MIA sensitivity, specificity, PPV, and NPV were 93.9% (95% CI, 85.4%-97.6%), 55.6 (95% CI, 46.9%-64.1%), 53 0.0% (95% CI 44.0-61.8% and 94.5% (95% CI, 94.5-100%), respectively. The overall sensitivity for the MIA plus SI combination was 100 %. (66/66; 95% CI, 94.5%-100%), and the specificity was 36.3% (45/124; 95% CI, 28.4%-45.0%), with PPV of 45.5% (37.6% to 53.6%) and 100% NPV (95% CI, 92.1%-100%).Limitations of this study noted by the authors include the small sample size and the high prevalence of ovarian malignancies in this population which was largely from a tertiary care center. It should also be noted that the sensitivity and negative predictive value of the SIplus CA 125 was 96.9% (95% CI 89.5%-99, 2%) and 97.3% (95% CI 90.5%-99.2%), which exceeded that of MIA alone but was slightly lower than that of MIA plus SI.

The National Comprehensive Cancer Network's ovarian cancer guidelines (2016) note that the Society for Gynecologic Oncology (SGO), the FDA, and the Mayo Clinic have stated that the OVA1 test should not be used as a screening tool to detect cancer. The NCCN explains that OVA1 attempts to classify adenixal masses as benign or malignant preoperatively and suggests that an experienced gynecological oncologist may assess patients to determine who should undergo surgery and who may undergo surgery in the community. "Based on data documenting improved survival, NCCN guideline panel members recommended that all patients undergo surgery by an experienced gynecological oncologist (category 1 recommendation)."

The American College of Obstetricians and Gynecologists (ACOG, 2016) guidelines on the management of adnexal masses state that the OVA1 multivariable index assay demonstrated greater sensitivity and negative predictive value compared to clinical impression and CA 125 alone. The guidelines state that serum biomarker panels [OVA1 and ROMA] can be used as an alternative to CA 125 only to determine the need for referral or consultation with a gynecologic oncologist when an adnexal mass requires surgery. The guidelines state that trials that have evaluated the predictive value of these panels show potential for improving specificity; "[Y]however, comparative research has yet to define the best testing approach."

The ACOG guidelines (2016) state that, primarily based on consensus and expert opinion (Level C), "Serum biomarker panels can be used as an alternative to the CA 125 level alone to determine the need for referral. " or consult a gynecologist. oncologist when an adnexal mass requires surgery." The guidelines state that, based on "limited or inconsistent" evidence (Level B), consultation or referral to a gynecologic oncologist is recommended for women with an adnexal mass who are attending to one or more of the following criteria: of the following criteria;

Postmenopausal women with an elevated CA 125 level, ultrasonographic findings suggestive of malignancy, ascites, nodular or fixed pelvic mass, or evidence of abdominal or distant metastases;
premenosause with very high CA 125 level, sonographic findings suggestive of malignancy, ascites, nodular or fixed pelvic mass, or evidence of abdominal or distant metastases;
Pre- or postmenopausal women with high scores on a formal risk assessment test, such as the multivariable ratio assay, malignancy risk index, or ovarian malignancy risk algorithm, or one of the International ultrasound-based scoring systems Ovarian Tumor Analysis Group.

The UK National Institutes of Health Research Health Technology Assessment Program commissioned an assessment (Westwood, et al., 2016) comparing the Malignancy Risk Index (RMI) with alternative risk scores for ovarian cancer, including Overa/OVA2 (Vermillion), as well as the ROMA score, Simple Rules Ultrasound Classification System (IOTA), Assessment of Different Neoplasms in Attachment Model (ADNEX) (IOTA group). The assessment is expected to be completed in 2017.

Dunton et al (2019) stated that, based on evidence that African American (AA) women have lower CA125 values than Caucasian (C) women, these investigators examined whether this disparity would affect breast cancer detection. index. test (MIA). Sera from 2 prospective trials of 1029 specimens (274 malignancies [250 C/24 AA]) were tested for CA125 and MIA results. Clinical performance was calculated. MIA sensitivity in Caucasian women was 93.2%, 74.4% for CA125 at the ACOG-approved cut-off level of 200 U/mL, and 80.4% using the Dearking 2007 cut-off of 67 U/mL. In women with AA, MIA sensitivity was 79.2%, 33.3% for CA125 at ACOG-approved cutoff levels, and 62.5% at Dearking's 2007 cutoff of 67 U/mL. The authors concluded that these findings supported that CA125 in AA women with adnexal masses had a lower sensitivity than MIA regardless of the cutoff value. The implementation of MIA in the evaluation of adnexal masses should increase the sensitivity of detection of malignancy compared to CA125, especially in women with AA.

These investigators stated that, as the number of patients with AA in the studies that formed the analysis database for this investigation was quite small, they presented data on all malignancies rather than dividing them according to histological subtype. During their exploratory analysis, these researchers performed these calculations, but due to the small sample size, they found that the confidence intervals (CIs) were too wide to draw firm conclusions. These investigators are developing research opportunities to add more AA women with primary ovarian malignancies to their sample pools to confirm the results found in this analysis. Furthermore, the authors stated that over the next 10 years, this research could be a stepping stone to closing the survival gap between Caucasian women and AA women when it comes to ovarian cancer. Increased use of a more sensitive test like the MIA in minority women and clinical awareness of ingrained medical practice shortcomings like the CA125 could potentially increase early detection, which is critical to improving survival.

In a retrospective study, Dunton et al (2020) examined the use of the multivariate index assay (MIA OVA1) by gynecologists and determined referral practices and surgical decision-making for women with adnexal masses and low-risk MIA OVA1 scores. Information about patients who received an OVA1 test was collected from 22 gynecological offices through a chart review. Referral patterns of patients with low-risk OVA1 results were examined prior to their first surgical intervention. The chart reviews came from a variety of clinical and hospital settings representing major US geographic regions. A total of 282 independent patient charts were reviewed. Low-risk outcomes were found in 146 patients (52%). Surgery was performed in 82 (56%) patients with low-risk scores. The referral rate for specialist care was 21% (17/82) for low-risk OVA1 patients. A total of 3 low-malignant potential tumors were identified in low-risk patients without cases of invasive malignancy; 86% of surgeries performed on low-risk OVA1 patients were minimally invasive. In 44% of low-risk OVA1 patients, no surgical intervention was performed. The authors concluded that a high proportion of patients with low-risk OVA1 were not referred to a gynecological oncologist before surgery, which indicates that gynecologists can use MIA OVA1 together with the clinical and radiographic hallazgos to adequately retain patients for Your attention. This practice is safe and can save costs, with implications for patient satisfaction. These investigators stated that these findings may help catalyze the development of prospective studies to further examine the role of OVA1 in the clinical decision-making process and to aid in the collection of long-term data that can be used to examine outcomes. of the decisions. performed in low- and high-risk OVA1 trials.

The authors stated that this study had several drawbacks. As this was a retrospective study, it may be of limited use in causal inference because it was not possible to identify all confounders that affect referral and surgical planning. Physicians who chose to participate in the study were more likely to have a positive opinion of the test and used it to guide referral. Furthermore, as clinicians could select patients for review, there was the potential for selection or recall bias for cases that best fit the clinical ideal for clinical application in both high- and low-risk settings. For example, in a previous study (Eskander et al, 2016), these investigators showed that the malignancy rate in this population was 64%, much higher than previous studies of intended-use populations (Bristow et al, 2013; Ueland et al. al , 2011).

Dunton et al (2021) noted that ovarian cancer is the deadliest gynecological cancer and a screening test to aid in early detection is not recommended. Cancer antigen 125 (CA125) is a serum biomarker commonly used by clinicians to assess preoperative cancer risk, but it underperforms in premenopausal women, early-stage malignancies, and various histologic subtypes. OVA1 is a multivariate index assay that combines CA125 and 4 other serum proteins to assess the malignancy risk of an adnexal mass. These investigators examined the performance of OVA1 in a cohort of patients with low-risk CA125 serum values. They analyzed data from patients from previous collections (n = 2305, prevalence = 4.5%) in whom CA125 levels were at or below 67 units/milliliter (U/ml) for premenopausal women and 35 U/ml. ml for postmenopausal women. These investigators compared the performance of OVA1 with CA125 in ranking the risk of malignancy in this cohort, including sensitivity, specificity, PPV, and NPV. The overall sensitivity of OVA1 in patients with low-risk serum CA125 was 59%, with a false positive rate of 30%. OVA1 detected more than 50% of ovarian malignancies in premenopausal women despite a low-risk serum CA125. OVA1 also correctly identified 63% of early-stage cancers that CA125 missed. The most common epithelial ovarian cancer subtypes in the studied population were mucinous (25%) and serous (23%) carcinomas. Despite a low-risk CA125, OVA1 successfully detected 83% of serous, 58% of mucinous, and 50% of clear cell ovarian cancers. The authors concluded that, as an independent test, CA125 missed a significant number of ovarian malignancies that OVA1 could detect. This was particularly important for premenopausal women and early-stage cancers, which have much better long-term survival than advanced-stage malignancies. These investigators stated that the use of OVA1 in the context of a normal serum CA125 could help identify ovarian tumors at risk for referral to a gynecological oncologist, potentially improving OS.

The authors stated that a disadvantage of this study was the retrospective nature of the data analysis, which was performed after merging several study databases. Furthermore, the percentage of early-stage ovarian cancer in this study (70%) was twice that expected in the general population, suggesting possible sampling bias. However, this shift towards early-stage cancers allowed for a more robust assessment of test performance in this cohort.

ColonSentry

The ColonSentry test (GeneNews, Toronto, Canada) measures the expression of seven genes, which serve as biomarkers to detect colorectal cancer. Interpretation of the status of these seven biomarkers is intended to help clinicians identify patients who are currently at increased risk. According to the manufacturer, people tested at increased risk of colorectal cancer should consider having a colonoscopy. People assessed as having a current reduced risk of colorectal cancer should talk to their doctor about further screening, including having ColonSentry repeated at regular intervals. There is a lack of evidence in peer-reviewed published medical literature on the effectiveness of colonosenteric screening for colorectal cancer. No current evidence-based guidance from professional medical organizations or public health agencies recommends ColonSentry for colorectal cancer screening.

Px prostate

Prostate Px (Aureon) uses tissue from a biopsy of a patient with prostate cancer to provide an assessment of disease severity and recurrence. Clinical data is integrated with an analysis of each patient's cancer using tissue histology and molecular biomarkers, such as androgen receptor, associated with disease progression. Although the manufacturer claims that Prostate Px results can be used in decision-making, evidence of the clinical usefulness of this test for modifying patient care in ways that improve clinical outcomes is lacking.

postoperative px

Post-Op Px is a prognostic test that uses a proprietary systems pathological approach to analyze prostatectomy tissue by combining cellular, molecular, and clinical information to provide a complete and more accurate picture of the individual risk of prostate cancer recurrence for each patient. . (Aureon, 2010). Donovan et al (2011) evaluated the performance of a systems-based risk assessment tool with defined standard risk groups and the 10-year postoperative nomogram in predicting disease progression. The systems model was found to be more accurate than standard risk groups in both predicting significant disease progression (p < 0.001) and predicting PsA recurrence (p < 0.001). However, this study has not been replicated in the peer-reviewed literature.

EGFR

A BlueCross BlueShield Association Technology Evaluation Center evaluation (2010) concluded that analysis of the epidermal growth factor receptor (EGFR) mutation of tumor cells to predict response to erlotinib (Tarceva) in cancer patients. non-small cell (NSCLC) meets the Blue Cross and Blue Shield Association's Technology Assessment Center (TEC) Criteria requirements. In addition, the National Comprehensive Cancer Network (NCCN, 2010) guidelines recommend EGFR testing for the following histological subtypes of NSCLC:

adenocarcinoma,
big cell and
NSCLC not otherwise specified.

Epidermal growth factor receptor testing is not recommended for squamous cell carcinoma.

The Alberta Provincial Thoracic Tumor Team clinical practice guideline on "Stage IV Non-Small Cell Lung Cancer" (2011) stated that "first-line monotherapy with factor receptor tyrosine kinase inhibitor is recommended". Eligible patients with advanced NSCLC and adenocarcinoma histology who are being considered for first-line therapy with gefitinib should be tested for EGFR mutations, regardless of gender, ethnicity, and smoking status.

The NCCN Non-Small Cell Lung Cancer Guidelines (2015) state that EGFR and ALK testing should be performed as part of multiplex/next generation sequencing. The NCCN NSCLC guideline panel "strongly supports broader molecular profiling with the goal of identifying rare mutations for which effective drugs may already be available or adequately advising patients about the availability of clinical trials." of patients with NSCLC".

Gao et al (2012) stated that gefiinib and erlotinib are 2 similar selective reversible small molecule epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), which have been approved for second- or third-line indication in patients previously treated. patients with advanced NSCLC. The results of comparing EGFR-TKI with standard platinum-based dual chemotherapy as first-line treatment in patients with advanced NSCLC with activated EGFR mutation are still controversial. A meta-analysis was performed to derive a more accurate estimate of these regimens. Finally, 6 eligible studies involving 1021 patients were identified. Patients receiving EGFR-TKI as first-line therapy had a significantly longer PFS than patients treated with chemotherapy [median PFS was 9.5 vs 5.9 months; FC = 0.37; 95% CI 0.27 to 0.52; p < 0.001]. The overall response rate (ORR) of EGFR-TKI was 66.60%, while the overall response rate (ORR) of the chemotherapy regimen was 30.62%, which was also a statistically significant favor for EGFR- TKI [relative risk (RR) = 5.68; 95% CI: 3.17 to 10.18; p < 0.001]. OS was numerically greater in patients receiving EGFR-TKI than in patients receiving chemotherapy, although the difference did not reach statistical significance (median OS was 30.5 vs 23.6 months; HR = 0.94, 95% CI 0.77 to 1.15, p = 0.57 ). Compared with first-line chemotherapy, EGFR-TKI treatment achieved statistically significantly longer PFS, higher ORR, and numerically longer OS in patients with advanced NSCLC harboring activated EGFR mutations, therefore it should be the first choice in patients with Previously untreated NSCLC with activated EGFR mutation.

The American Society of Clinical Oncology (2016) guidelines state, "The clinician should not use epidermal growth factor receptor/HER1 expression by IHC to guide selection of adjuvant chemotherapy" in breast cancer.

CEACAM-7

Messick et al (2010) evaluated the expression of carcinoembryonic antigen cell adhesion molecule 7 (CEACAM-7) in rectal cancer as a predictor of recurrence. A single institution colorectal cancer database and a frozen tissue biobank for patients with rectal cancer were consulted. CEACAM-7 messenger RNA (mRNA) expression from normal rectal mucosa and rectal cancer was analyzed by real-time quantitative polymerase chain reaction (PCR). Differences in expression level between normal tissue, disease-free survivors and those who developed recurrence were analyzed. A total of 84 patients were included in the study, 37 patients with non-recurrent disease (mean follow-up 170 months), 29 patients with recurrent disease and 18 patients with stage IV disease. CEACAM-7 expression decreased 21-fold in rectal cancers compared to normal mucosa (p = 0.002). CEACAM-7 expression levels were relatively decreased in tumors that developed recurrence compared to those that did not, significantly for stage II patients (14-fold relative decrease, p = 0.002). For stages I-III, disease-free survival segregates were based on relative CEACAM-7 expression titers (p = 0.036), specifically for stage II (p = 0.018). The authors concluded that CEACAM-7 expression is significantly decreased in rectal cancer. Expression differences between long-term survivors and those with recurrent disease introduce a potential tumor marker to define a subset of patients who benefit most from adjuvant therapy. Furthermore, they stated that additional studies and validations are needed before CEACAM-7 can be applied in clinical settings.

CFL1

Castro et al (2010) evaluated the potential value of the cofilin gene (CFL1) (major member of the invasion/metastasis pathway) as a prognostic and predictive biomarker of NSCLC. Tumor tissue microarray meta-analysis was applied to examine CFL1 expression in archival lung cancer samples from 111 patients, and its clinicopathological significance was investigated. The robustness of the finding was validated using another independent dataset. Finally, the authors tested the role of CFL1 levels on tumor invasiveness and drug resistance in vitro using 6 human NSCLC cell lines with different baseline degrees of CFL1 gene expression. Cofilin levels in biopsies discriminated between good and poor prognosis in early tumor stages (IA, IB and IIA/B), where high levels of CFL1 correlate with lower overall survival rate (p < 0.0001). Biomarker performance was further analyzed using IHC, risk index (p < 0.001) and receiver operating characteristic curve (area = 0.787; p < 0.001). Elevated levels of CFL1 mRNA and protein content correlate positively with cell invasiveness (as determined by the Matrigel invasion chamber system) and resistance (2-fold increase in 50% growth-inhibiting drug dose) against a list of 22 alkylating agents. Hierarchical clustering analysis of the CFL1 gene network was similarly robust for patients with stratified NSCLC. The authors concluded that these findings indicate that the CFL1 gene and its network of functional genes can be used as prognostic biomarkers for NSCLC and can also guide chemotherapeutic interventions. Furthermore, large-scale prospective randomized clinical trials are needed to establish the role of CFL1 as a prognostic marker and drug resistance for NSCLC.

Early CDT-Lung

HeEarlyThe CDT-Lung test (Oncinmune, De Soto, KS) measures antibodies against 6 tumor associated antigens: p53, NY-ESO-1, CAGE, GBU4-5, Annexin 1 and SOX2. Elevation of any of the immunomarkers in the panel above a predetermined cutoff suggests that a tumor may be present. The test is designed to be used in conjunction with diagnostic imaging. High-risk individuals with a positive resultEarlyCDT-Lung would have additional tests such as a CT scan, or the test would be used as a follow-up test for indeterminate pulmonary nodules identified by CT scan.

Boyle et al (2011) reported the sensitivity and specificity of a panel of autoantibodies to 6 tumor-associated antigens (p53, NY-ESO-1, CAGE, GBU4-5, annexin 1 and SOX2) in patients with lung cancer. Three groups of patients with newly diagnosed lung cancer were identified: group 1 (n = 145), group 2 (n = 241) and group 3 (n = 269). Patients were individually matched for sex, age, and smoking history with a control subject with no history of malignancy. Serum samples were obtained after diagnosis but before any cancer treatment. Autoantibody levels were measured against a panel of 6 tumor-related antigens (p53, NY-ESO-1, CAGE, GBU4-5, Annexin 1 and SOX2). Assay sensitivity was tested against demographic variables and cancer type/stage. The autoantibody panel demonstrated sensitivity/specificity of 36%/91%, 39%/89% and 37%/90% in groups 1, 2 and 3, respectively, with good reproducibility. There was no significant difference between the different stages of lung cancer, indicating that the included antigens covered the different types of lung cancer well. The authors concluded that the assay confirms the value of an autoantibody panel as a diagnostic tool and offers a potential system to monitor patients at high risk for lung cancer.

There is insufficient evidence for the effectiveness ofEarlyCDT-Lung as a screening test for the early detection of lung cancer. No major professional organization unequivocally recommends lung cancer screening. The USPSTF (2004) concluded that the current evidence was insufficient to recommend for or against lung cancer screening. It remains unclear whether early detection of lung cancer will translate into a mortality benefit.

ecad

Deeb et al (2004) stated that E-cadherin (E-cad) and the epidermal growth factor receptor (EGFR) are important mediators of cell adhesion and the signaling pathway. Theyr reported the results of a study aiming to evaluate its expression in lung adenocarcinoma (AdC) and squamous cell carcinoma (SCC) and its association with clinical-pathological variables. Two or three cores of 130 resectable lung cancers (stages I-IIIA) were placed in three blocks using a Beecher system. Marker expression and co-expression were analyzed in relation to clinical-pathological variables (age, gender, smoking status, performance status, weight loss, histology, grade, stage and lymph node involvement) and patient survival. For E-cad, 65 cases (55%) were positive (+), 53 (45%) were negative (-); and for EGRF, 43 cases (34%) were (+) and 83 (66%) were (-). There was no significant association between E-cad or EGFR and any of the clinicopathological variables, except for an association between EGFR(+) and SCC histological type. Both negative and cytoplasmic E-cad staining correlated with shorter patient survival with P = 0.008 and 0.002, respectively. EGFR expression did not correlate with patient survival, but patients with the E-cad(-)/EGFR(+) phenotype had worse survival than those with the E-cad(+)/EGFR(-) (P = 0.026). The authors concluded that pulmonary AdC and SCC can be stratified on the basis of E-cad and EGFR expression and that E-cad(-)/EGFR(+) expression has a worse disease outcome.

EML4-ALK

Yoshida et al (2011) report that a subset of lung cancers harbor a gene fusion EML4-ALK (Echinoderma Lymphoma Kinase-Associated Lymphoma 4-Anaplastic Protein) and examined 15 polymerase chain reaction lung adenocarcinomas. with EML4- proven by reverse transcriptase ALK fusion transcripts and 30 ALK negative cases. Positive rearrangement signals (splits or isolated 3' signals) were identified in 13 to 78% (mean ± SD, 41 ± 19%) of tumor cells in the ALK-positive cohort and in 0 to 15% (mean ± SD, 6% ± 4%) of cells in the ALK negative cohort. Sensitivity was 93% and specificity 100%. The only false-negative tumor that had only 13% CISH-positive cells showed predominantly (76%) isolated 5' signals unaccompanied by 3' signals. FISH showed very similar signal profiles and the results were fully consistent with CISH. The authors stated that they successfully introduced CISH to diagnose EML4-ALK positive lung adenocarcinoma. This method allows simultaneous visualization of tumor genetics and cytomorphology and facilitates molecular evaluation and can be applied in clinical practice to detect lung cancer that may respond to ALK inhibitors.

Ellis et al (2011) conducted a systematic review and consensus meeting of Canadian lung cancer oncologists and pathologists to make recommendations on the use of biomarkers in NSCLC. Articles were peer-reviewed by oncologists and pathologists to determine eligibility for inclusion. Ten oncologists and pathologists reviewed and summarized the literature in a meeting attended by 37 people. Findings included that there is some evidence that histology is prognostic for survival, as well as evidence from several randomized controlled trials to recommend the following: histological subtype predicts treatment efficacy and toxicity of some agents. Immunohistochemical testing should be performed on NSCLC specimens that cannot be accurately classified with conventional H&E staining. Since EGFR mutations predict benefit from tyrosine kinase inhibitors, NSCLC diagnostic specimens should be routinely tested for activation of EGFR mutations. Clinical data on K-RAS mutations are inconsistent, so testing is not recommended. There is not enough evidence to recommend other biomarker tests. To date, no biomarker reliably predicts increased efficacy of anti-VEGF therapy. The authors concluded that routine screening for EML4/ALK mutations is not currently recommended, although emerging data suggest that it may be useful in the near future.

MUC4

Shanmugan et al. (2010) stated that mucin 4 (MUC4) is abnormally expressed in colorectal adenocarcinomas (CRCs), but its prognostic value is unknown. Archival tissue specimens collected from 132 patients with CRC who underwent surgical resection without pre- or post-surgical treatment were evaluated for MUC4 expression using a mouse monoclonal antibody and horseradish peroxidase. MUC4 expression levels have been correlated with clinicopathological characteristics and patient survival. Survival was estimated using univariate Kaplan-Meier and multivariate Cox regression methods. In both normal colonic epithelium and RCCs, MUC4 staining was localized primarily to the cytoplasm. The optimal immunostain cutoff value (greater than or equal to 75% positive cells and an immunostain score greater than or equal to 2.0), which was derived by the bootstrap method, was used to classify CRCs into high-risk groups. expression (33 of 132 patients; 25%) or low expression (99 of 132 patients; 75%). Patients with early-stage tumors (stages I and II) with high MUC4 expression had lower disease-specific survival (log rank; p = 0.007) than patients with low expression. Patients with advanced stage RCC (stages III and IV) showed no such difference (log rank; p = 0.108). Separately generated multivariate regression models for patients with early-stage and advanced-stage RCC confirmed that increased MUC4 expression was an independent predictor of poor prognosis only for patients with early-stage RCC (HR 3, 77, 95% CI: 1. 46). at 9.73). The authors stated that, after validating these findings in larger prospective and retrospective studies, a staged analysis could establish the usefulness of MUC4 as a prognostic molecular marker of early-stage CRC.

ProOnc TumorSourceDx

The ProOnc TumorSourceDx test is designed to identify the tissue or source of a metastatic tumor. It identifies 25 possible classes of tissue origin corresponding to 17 different tissues and organs. It only requires 48 microRNAs to identify tissue of origin based on microRNA expression levels. However, there is not enough evidence about their clinical value as tumor markers.

CLIMATE

Coccoand Associates (2010) examined serum amyloid A (SAA) expression in endometrioid endometrial carcinoma and evaluated its potential as a serum biomarker. SAA gene and protein expression levels were evaluated in endometrial endometrial carcinoma and normal endometrial tissues by real-time PCR, IHC and flow cytometry. The concentration of SAA in 194 serum samples from 50 healthy women, 42 women with benign disease, and 102 patients, including 49 grade 1, 38 grade 2, and 15 grade 3 endometrial carcinoma, were also studied using a sensitive immunoassay. microspheres. SAA gene expression levels were significantly higher in endometrial endometrial carcinoma compared to normal endometrial tissues (mean real-time PCR copy number = 182 vs 1.9; p = 0.001). IHC revealed diffuse cytoplasmic staining of SAA protein in poorly differentiated endometrial carcinoma tissues. High intracellular levels of SAA were identified in primary endometrial carcinoma cell lines evaluated by flow cytometry, and SAA was actively secreted in vitro. SAA concentrations (microg/mL) had a median of 6.0 in normal healthy women and 6.0 in patients with benign disease (p = 0.92). In contrast, serum SAA values from patients with endometrial endometrial carcinoma had a median of 23.7, significantly higher than those in the healthy group (p = 0.001) and the benign group (p = 0.001). Patients with G3 endometrial endometrial carcinoma were found to have significantly higher SAA concentrations than G1/G2 patients. The authors concluded that SAA is not only a protein secreted by the liver, but also a product of endometrial endometrial carcinoma cells. SAA is actively expressed and secreted by G3 endometrial carcinoma and is present in high concentration in the serum of patients with endometrial endometrial carcinoma. SAA may represent a new biomarker for endometrial endometrial carcinoma to monitor disease recurrence and response to therapy. They stated that additional studies are needed to validate these findings.

Caris Target Now / Caris Molecular Profiling Service

Molecular Intelligence Services (formerly known as Target Now Molecular Profiling Test) uses a cross-platform profiling approach that includes gene sequencing (NGS and Sanger), protein expression analysis (immunohistochemistry), and DNA copy number analysis. genes (chromogenic hybridization or fluorescence in situ [FISH] ). The test has been used to examine tumor samples for underlying molecular changes that could shed light on different and potentially overlapping treatment options for people with these types of tumors.

According to the manufacturer, Caris Life Sciences' molecular profiling test, Caris Target Now, examines genetic and molecular changes unique to a patient's tumor so that treatment options can be matched to the molecular profiling of the tumor. The manufacturer states that the Caris Target Now test is performed after the diagnosis of cancer has been established and the patient has exhausted standard therapies or if there are doubts in therapeutic management. Using tumor samples obtained from a biopsy, the tumor is examined to identify biomarkers that may influence therapy. Using this information, Caris Target Now aims to provide information on the drugs most likely to produce a positive response. The manufacturer claims that Caris Target Now can be used with any type of solid cancer, including lung cancer, breast cancer and prostate cancer.

There is insufficient evidence to support the use of Caris Target Now Molecular Profiling. One study (Von Hoff et al, 2010) compared the progression-free survival (PFS) of patients with refractory metastatic cancers using a treatment regimen selected by Caris Target Now Molecular. PFS patient tumor profile for the most recent regimen on which the patient progressed. therapy selected for PFS in previous therapy) greater than or equal to 1.3. In 86 patients in whom molecular profiling was attempted, a molecular target was detected in 84 (98%). Sixty-six of the 84 patients were treated based on molecular profiling results. Eighteen (27%) of the 66 patients had a PFS score greater than equal to 1.3 (95% CI 17% to 38%). Therefore, the null hypothesis (that less than or equal to 15% of this patient population would have a PFS index greater than or equal to 1.3) was rejected. The authors concluded that, in 27% of patients, the molecular profiling approach resulted in longer PFS with a regimen suggested by molecular profiling than with the regimen on which the patient had just progressed. An accompanying editorial (Doroshow, 2010) noted that the study had several significant limitations, including uncertainty regarding time to progression (the primary endpoint of the study) and the lack of a randomized design for this study.

A report by the National Horizon Scanning Center (2013) indicated that the company stated that the tumor profiling service provided by Caris Life Sciences has been extensively modified with the addition of several new technologies. The new service is called Caris Life Sciences Molecular Intelligence Services. The NHSC stated that randomized clinical trials comparing the clinical outcomes of patients using molecular carismoprofiling with those receiving standard specialist care are needed to determine whether this testing service is effective and cost-effective.

Racemasa CoA (P504S) and HMWCK (34betaE12)

Kumaresan et al (2010) reviewed 1034 cases of morphologically difficult prostate cancer, which were divided into benign (585), malignant (399) and suspicious (50) and evaluated using CoA racemase (P504S) and HMWCK (34betaE12). Forty-nine suspected cases were resolved using both markers, while 1 case was resolved using additional CD68 support. The original diagnosis was changed in 15 of the 50 suspected cases from benign to malignant, one case from benign to high-grade PIN, and in one case from malignant to benign. The authors concluded that a combination of HMWCK and AMACR is valuable in combating morphologically suspicious cases and that, although the sensitivity and specificity of HMWCK and AMACR in this study were high, "it should be used with caution, due to all its dangers and limitations".

P504S

Murray et al (2010) studied P504S expressing circulating prostate cells as a marker of prostate cancer. The authors stated that PSA is the only biomarker routinely used in screening. This study aimed to develop a system to assess the presence of circulating prostate cells in men without a diagnosis of prostate cancer in relation to age, serum PSA levels and prostate biopsy, determining the co-expression of several markers, such as CD82. , HER-2 and matrix metalloproteinase 2 MMP-20. Results indicated that among 409 men screened for prostate cancer, 16.6% tested positive for circulating prostate cells. The authors concluded that studying circulating prostate cells with various markers could be a useful adjunctive screening test for prostate cancer in men with elevated PSA levels.

FLT3

FLT3 has been used to predict prognosis in acute myelogenous leukemia (Chin, et al, 2006). FLT3 mutations are common in AML and have been associated with poorer survival in cytogenetically normal children and young adults receiving intensive chemotherapy.

The NCCN task force issued a report in November 2011 that updated its position regarding molecular markers for diagnosis, prognosis, prediction, and complementary diagnostic markers (Febbo et al., 2011). As a result of these recommendations, the use of MGMT, IDH mutation and 1p/19qcode deletion for glioma is now established. Furthermore, the use of ALK gene fusion for non-small cell lung cancer has been established. Updated NCCN guidelines have not yet established the efficacy of ColoPrint, CIMP, LINE-1 hypomethylation, or immune cells for colon cancer. Likewise, the efficacy of the FLT3-TKD mutation, WT1 mutation, RUNX1 mutation, MLL-PTD, IDH1 mutation, IDH2 R172 mutation, and IDH2 codon 140 mutation for use in myelogenous leukemia has not been established.

ColoImpresión

ColoPrint (Agendia) is an 18-gene profile that classifies colon cancer at low risk or high risk of recurrence by measuring genes representative of the metastatic pathways of colon cancer metastasis that were selected for their predictive relationship with the likelihood of metastasis to the colon. distance in 5 years (Raman, et al., 2013). ColoPrint is indicated for stage II colon cancer and provides risk stratification for recurrence independent of clinical and pathological factors such as stage T4 and MSI status. ColoPrint determines if the patient is a candidate for chemotherapy. An NCCN Task Force report (NCCN, 2011) concluded that the effectiveness of ColoPrint has not been established.

DecisionDx-UM

The DecisionDx test is a gene expression profiler that determines the molecular signature of a patient's melanoma. The test results provide information about the risk of metastasis in the short term (5 years). Tumors with a Class 1 signature are associated with a good prognosis and low potential for dissemination (or metastasis), whereas tumors with a Class 2 signature have a high potential for dissemination.

Aaberg et al (2014) performed a chart review and cross-sectional survey of ophthalmologists who treat uveal melanoma to assess current clinical practices for uveal melanoma (UM) and the impact of molecular prognostic testing on treatment decisions. This study involved a chart review of all Medicare beneficiaries evaluated for UM gene expression profiling in 2012, conducted under a board-approved institutional review protocol. In addition, 109 ophthalmologists specializing in UM care were asked to participate in a 24-question survey in 2012; 72 were invited to participate in a 2014 23-question survey. The Medicare medical record review included 191 evaluable patients, 88 (46%) with documented medical treatment actions or institutional policies related to surveillance plans. Of these 88, all patients with a class 1 MU gene expression profile (GEP) were treated with low-intensity surveillance. All patients with UM GEP Class 2 were treated with high-intensity surveillance (P<0.0001 versus Class 1). There were 36 (19%) with referral information after initial diagnosis. Of these 36, the 23 Class 2 patients were referred to medical oncology; however, none of the 13 Class 1 patients were referred (P<0.0001 vs. Class 1). Only Class 2 patients were recommended for adjuvant treatment regimens. 2012 Survey: 50 respondents with an annual average of 35 new UM patients. Most respondents (82%) performed molecular analyzes of UM tumors after fine needle biopsy (FNAB); median: 15 PAAF per year; 2014 Survey: 35 respondents with an annual average of 30 new UM patients. Most offered molecular analysis of UM tumor samples for most patients. Patients with low metastatic risk (dysomy 3 or GEP Class 1) were generally assigned to less frequent (every 6 or 12 months) and less intensive clinic visits. Patients at high risk of metastases (monosomy 3 or GEP Class 2) were assigned to more frequent surveillance with liver imaging and liver function tests every 3 to 6 months. High-risk patients were considered more suitable for adjuvant treatment protocols.

Chappell et al (2012) reported a retrospective case series of gene expression profiles of patients with uveal melanoma to characterize the clinical spectrum of class 1 and class 2 uveal melanomas and their relationship to response to intraocular proton radiation. A total of 197 patients with uveal melanoma from a single institution were analyzed for pathology, clinical features, and response to radiotherapy. A total of 126 patients (64%) had class 1 tumors and 71 (36%) had class 2 tumors. Patients with class 2 tumors were older (mean: 64 years vs 57 years; P = 0.001), had thicker baseline mean ultrasound (7.4 mm vs 5.9 mm; P = 0.0007) and were more likely to have epithelioid. or mixed cells in cytopathology (66% vs 38%; P = 0.0004). Although mean ultrasound thicknesses before and after treatment were significantly different between class 1 and class 2 tumors, there was no difference in mean change in thickness 24 months after radiotherapy (mean difference: class 1 = -1, 64 mm, class 2 = -1.47; P = 0.47) or the overall rate of change in thickness (slope: P = 0.64). Class 2 tumors were more likely to metastasize and cause death than class 1 tumors (disease-specific survival [DSS]: P < 0.0001).

Worley et al (2007) compared a gene expression-based classifier with the standard genetic prognostic marker, monosomy 3, to predict metastasis in uveal melanoma. Gene expression profiling, fluorescent in situ hybridization (FISH) and array comparative genomic hybridization (aCGH) were performed on 67 primary uveal melanomas. Clinical and pathological prognostic factors were also evaluated. The researchers found that the molecular classifier based on gene expression assigned 27 tumors to class 1 (low risk) and 25 tumors to class 2 (high risk). According to Cox univariate proportional hazards, class 2 signature (P = 0.0001), advanced patient age (P = 0.01), and scleral invasion (P = 0.007) were the only variables significantly associated with metastasis. Only the class 2 signature was needed to optimize predictive accuracy in a multivariate Cox model. A less significant association with metastasis was observed for monosomy 3 detected by aCGH (P = 0.076) and FISH (P = 0.127). The sensitivity and specificity for the molecular classifier (84.6% and 92.9%, respectively) were superior to monosomy 3 detected by aCGH (58.3% and 85.7%, respectively) and FISH (50.0% and 72.7%, respectively). Positive and negative predictive values (91.7% and 86.7%, respectively) and positive and negative likelihood ratios (11.9 and 0.2, respectively) for the molecular classifier were also higher than for monosomy. 3.

In a prospective case series study, Corrêa and Augsburger (2016) sought to determine whether any conventional clinical prognostic factors for uveal melanoma metastasis retain prognostic significance in multivariate models incorporating the gene expression profile (GEP) class of tumor cells. The investigators conducted a single-institution study of PEG testing and other conventional prognostic factors for metastasis and metastatic death in 299 patients with posterior uveal melanoma evaluated by fine needle aspiration biopsy (FNAB) at or shortly before treatment. Univariate prognostic significance of all potential prognostic variables assessed (patient age, greatest basal linear tumor diameter [LBD], tumor thickness, intraocular tumor location, cytomorphologic melanoma subtype, and GEP class) was performed by comparing the curves Kaplan-Meier event rate and Cox univariate proportional hazards models. Multivariate prognostic significance of combinations of significant prognostic factors identified by univariate analysis was performed using ascending and descending Cox proportional hazards models. GEP class was the strongest prognostic factor for metastatic death in this series. However, tumor LBD, tumor thickness, and intraocular tumor location also proved to be significant individual prognostic factors in this study. In multivariate analysis, a 2-term model incorporating PEG class and largest basal diameter was associated with strong independent importance for each of the factors.

Correa and Augsburger (2014) sought to determine the relative suitability of paired aspirates of posterior uveal melanomas obtained by FNAB for cytopathology and PEG, and their prognostic significance in predicting death from metastases. patients with uveal melanoma sampled by FNAB in at least two tumor sites between September 2007 and December 2010. Cases were analyzed for suitability of aspirates obtained for cytopathological classification and GEP classification. Statistical strength of associations between variables and PEG class was calculated using the Chi-square test. Cumulative actuarial survival curves of subgroups of these patients based on their cytopathologic categories versus those assigned by PEG were calculated using the Kaplan-Meier method. The endpoint for this survival analysis was death from metastatic uveal melanoma. FNAB aspirates were insufficient for cytopathological classification in 34 of 159 cases (21.9%). In contrast, FNAB aspirates were insufficient for PEG classification in only one of the 159 cases (0.6%). This difference is statistically significant (P < 0.001). Six of 34 tumors (17.6%) that gave an aspirate insufficient for cytopathology were classified as GEP class 2, while 43 of 125 tumors (34.7%) that gave an aspirate sufficient for cytopathology were classified as GEP class 2. 14 of 49 patients with GEP class 2 tumor (28.6%), but only five of 109 patients with GEP class 1 tumor (5.6%) developed metastases. Fifteen of 125 patients (12%) whose tumors had insufficient aspirates for cytopathological classification, but only 4 of 34 patients (11.8%) whose tumors had insufficient aspirates for cytopathological classification developed metastases. The mean post-biopsy follow-up time for surviving patients in this series was 32.5 months. Cumulative 5-year actuarial probability of death from metastasis of 14.1% for those with insufficient aspirate for cytopathological classification vs. 22.4% for those with enough aspirate for cytopathological classification (log rank P = 0.68). In contrast, the 5-year cumulative actuarial probability of metastatic death was 8.0% for those with insufficient/unsatisfactory aspirate for GEP classification or GEP class 1 tumor, versus 45.0% for those with GEP class 2 tumor (log rank P = 0.005).

In a prospective study, Oniken et al (2012) evaluated the prognostic performance of the DecisionDx15 gene expression profile (GEP) analysis that assigns primary posterior uveal melanomas to prognostic subgroups: class 1 (low metastatic risk) and class 2 (high metastatic risk). ). A total of 459 patients with posterior uveal melanoma were recruited from 12 independent centers. Tumors were classified by GEP as class 1 or class 2. The first 260 samples were also tested for chromosome 3 status using a single nucleotide polymorphism assay. A net reclassification improvement analysis was performed to compare the prognostic accuracy of GEP with the seventh edition clinical Tumor-Node-Metastasis (TNM) classification and chromosome 3 status. The investigators found that the GEP trial successfully classified 446 of 459 cases (97.2%) . PEG was class 1 in 276 cases (61.9%) and class 2 in 170 cases (38.1%). The mean follow-up was 17.4 months (mean 18.0 months). Metastases were detected in 3 class 1 cases (1.1%) and 44 class 2 cases (25.9%) (log-rank test, P<10(-14)). Although there was an association between GEP class 2 and monosomy 3 (Fisher's exact test, P<0.0001), 54 of 260 tumors (20.8%) were discordant for GEP and chromosome 3 status, among which GEP demonstrated accuracy superior prognosis (log-rank test, P = 0.0001). Using multivariate Cox models, PEG class had a stronger independent association with metastasis than any other prognostic factor (P < 0.0001). Chromosome 3 status did not provide additional prognostic information that was independent of GEP (P = 0.2). At 3 years of follow-up, the net improvement of PEG reclassification over TNM classification was 0.43 (P = 0.001) and 0.38 (P = 0.004) on chromosome 3 status.

Klufas et al (2015) reported their experience with specific GEP tests for uveal melanoma (UM) in a series of cytopathologically confirmed choroidal metastatic tumors so that physicians can know that receiving a test result of class 1 or class 2 in melanoma it's possible. These investigators performed a retrospective review of all cytopathology and DecisionDx-UM GEP reports between January 2012 and December 2014 from intraoperative ANF biopsy of choroidal tumors undergoing brachytherapy. Four patients with cytopathology compatible with a non-melanoma primary tumor were identified. All 4 patients had a single unilateral choroidal tumor, which was treated with iodine-125 brachytherapy and underwent intraoperative ANF biopsy for cytopathology and UM-specific GEP testing for molecular prognosis. Testing the gene expression profile of the choroidal tumor in each patient revealed class 1A in 3 patients and class 2 in 1 patient. The authors concluded that the DecisionDx-UM GEP may be a useful test for molecular prognosis in patients with UM; however, class 1 and class 2 test results are possible in the setting of a non-melanoma malignancy. They recommended that cytopathology and/or other melanoma-specific tests be performed in all cases of suspected choroidal melanoma because PEG with this assay cannot rule out the diagnosis of choroidal melanoma.

Plasseuard et al (2016) attempted to assess the clinical validity and usefulness of DecisionDx-UM. As of March 2010, 70 patients were enrolled in an IRB-approved prospective multicenter study to document differences in patient management and clinical outcomes associated with low risk. High risk Class 1 and Class 2 results indicated by DecisionDx-UM tests. Thirty-seven patients in the prospective study were Class 1 and 33 were Class 2. Class 1 patients had a 3-year metastasis-free survival of 100% compared with 63% for Class 2 (log-rank test 𝑝 = 0.003) with a mean follow-up of 27.3. up to months in this interim analysis. Class 2 patients received significantly more intensity monitoring and more referrals to oncology/clinical studies compared to Class 1 patients (Fisher's exact test 𝑝 = 2.1 × 10 −13 and 𝑝 = 0.04, respectively) . The investigators concluded that the results of this study provide additional prospective evidence in an independent cohort of patients that Class 1 and Class 2 patients are treated according to the differential metastatic risk indicated by DecisionDx-UM.

In a review of ocular melanoma treatment, Blum et al (2016) commented on the potential use of DecisionDx, noting that while there is no clear survival benefit from early detection of metastatic disease, patients may benefit from eligibility for trials clinical and palliative therapy. with early detection.

The UK national guidelines on "uveal melanoma" (2015) recommended that these molecular diagnostic tests be performed as part of a research protocol.

Molecular diagnosis of thyroid cancer

Molecular markers associated with thyroid cancer have been proposed to help determine malignancy and guide surgical decisions for individuals with indeterminate cytopathology of fine needle aspiration (FNA) thyroid nodules.

Thyroid nodules are abnormal growths or lumps that develop on the thyroid gland. Although most are benign (non-cancerous), a small percentage are malignant (cancerous).9 To determine malignancy, fine needle aspiration (FNA) is used to obtain a sample (aspirate) of the nodule which is evaluated for cytopathology and sorted according to the results. Most are classified as benign (70 to 75%) and a small percentage as malignant (5% to 10%). Approximately 25% are classified as indeterminate (unable to determine a diagnosis) and require further evaluation, often including thyroid surgery and histopathologic evaluation of thyroid tissue. However, nearly 80% of indeterminate nodules are benign based on histopathology.

Thyroid gene expression screening tests and thyroid cancer mutation analysis of fine needle aspirates of thyroid nodules differ from genetic testing. Genetic testing, also known as germline mutation testing, analyzes an individual's DNA and can identify genetic mutations to determine hereditary disease risk. An individual's germline DNA is constant and identical in all types of body tissue. RNA activity is measured by gene expression analysis. It is dynamic and responds to cellular environmental signals. Mutation analysis of fine needle aspirates or tumor tissue determines DNA mutations that have been acquired over an individual's lifetime. These DNA changes are only present in the sampled tissue, are generally not representative of an individual's germline DNA, and are not heritable. For information about genetic testing (also known as germline mutation testing) for thyroid cancer (eg, multiple endocrine neoplasia [MEN]), seeCPB 0319 - Testing the proto-oncogenes RET.

The National Comprehensive Cancer Network (NCCN, 2014) Thyroid Carcinoma Guidelines state: "Molecular diagnostic tests to detect individual mutations (eg, BRAF, RET/PTC, RAS, PAX8/PPAR [activated receptors "Recognition approaches using Molecular classifiers may be useful in evaluating ANF samples that are indeterminate to aid in management decisions. The choice of precise molecular test depends on the cytology and the clinical question being asked." 2) UA/FLU The NCCN Panel recommends (category 2B) molecular diagnostic tests to evaluate ANF results that are suspicious for: 1) follicular or Hurthle cell neoplasms; or 2) AUS /FLUS (see Evaluation of Nodules in the NCCN Guidelines for Thyroid Carcinoma.) For the 2014 update, the NCCN panel revised the recommendation for molecular diagnostic tests of the Category 2A to Category 2B for undetermined FNA results based on a series of panel votes. The panel noted that molecular testing (gene expression classifier and single mutation analysis) was available at most NCCN member institutions (>75%). About 70% of panelists would recommend the use of a gene expression classifier in the evaluation of follicular lesions. The gene expression classifier measures the expression of at least 140 genes. BRAF mutation analysis was recommended by 50% of panellists in the evaluation of thyroid nodules (not restricted to follicular lesions). Furthermore, approximately 60% of tasters would recommend the BRAF test in the evaluation of follicular lesions. A minority of panelists expressed concern regarding the observation of follicular lesions because they were perceived as potentially pre-malignant lesions with very low but unknown malignant potential if not surgically removed (leading to recommendations for observation or surgical resection). in lesions classified as benign by molecular tests). Rather than proceeding with immediate surgical resection to obtain a definitive diagnosis for these intermediate cytological groups of ANF (follicular lesions), patients can be followed closely if the application of specific molecular diagnostic tests results in a predicted risk of malignancy comparable to that of the rate seen in cytologically benign thyroid FNABs (approximately < 5%). The NCCN guidelines state that it is important to note that the predictive value of molecular diagnostics can be significantly influenced by the pre-test probability of disease associated with the various FNA cytology groups. Furthermore, in cytologically indeterminate groups, the risk of malignancy from FNAB can vary widely between institutions. As the published studies focus mainly on adult patients with thyroid nodules, the diagnostic utility of molecular diagnosis in pediatric patients remains to be defined. Therefore, proper implementation of molecular diagnostics in clinical care requires an understanding of both the performance characteristics of the specific molecular test and their clinical significance across a range of pre-test disease probabilities.

To support the use of a gene classifier, the NCCN guidelines reference validation studies of the Afirma Thyroid FNA assay (Alexander et al, 2012; Chudova et al, 2010; Kloos, et al, 2013; McIver et al, 2014 ) and Thyroseq ( Nikiforov et al, 2009; Ohori et al, 2010; Nikiforov et al, 2011). These studies demonstrate that this molecular diagnosis meets the NCCN malignancy prediction threshold of 5% or less (ie, 95% negative predictive value), allowing clinicians to look for an indeterminate thyroid nodule rather than surgery.

The American Thyroid Association guidelines (2015) state that, "If molecular testing is being considered, patients should be counseled about the potential benefits and limitations of the test and about possible uncertainties in long-term clinical and therapeutic implications." .This is a strong recommendation based on low-quality evidence.The guidelines state that the largest studies of preoperative molecular markers in patients with indeterminate FNAC cytology evaluated, respectively, a panel of seven genes for mutations and gene rearrangements ( BRAF , RAS, RET /PTC, PAX8/PPARc), a gene expression classifier (167 GEC; mRNA expression of 167 genes) and galectin-3 immunohistochemistry (cell blocks). assessment. The guidelines state that "currently there is no single molecular test. Indeterminate cytology results and long-term outcome data are needed to demonstrate clinical utility."

The American Association of Clinical Endocrinologists guidelines (Gharib et al, 2016) state that molecular testing should be considered to complement, not replace, cytological evaluation, where results are expected to influence clinical management. As a general rule, it is not recommended in nodules with established benign or malignant cytological features. The guidelines recommend considering screening for BRAF and RET/PTC mutations and possibly PAX8/PPARG and RAS if such screening is available. The guidelines state that, due to insufficient evidence and limited follow-up, they do not recommend either for or against the use of gene expression classifiers (GECs) for cytologically indeterminate nodules.

The Thyroid Gene Expression Classifier (GEC) (e.g., Affirma Gene Expression Classifier)is a messenger ribonucleic acid (mRNA) gene expression assay that analyzes samples of thyroid ANF to classify indeterminate nodules using an algorithm owner. Thyroid GEC analyzes the mRNA expression of 167 genets. The thyroid GEC is described as an "exclusion" test because a negative (ie, benign) result rules out the presence of cancer.

(Video) Thyroid Cancer (Papillary, Follicular, Medullary & Anaplastic) | Symptoms, Diagnosis, Treatment

Thyroid malignancy grading tests (eg, Affirma MTC, Affirma BRAF) are intended to test for thyroid nodules that have been classified as "malignant" or "suspicious" by cytopathology or as "suspicious" by GEC. The Afirma MTC was developed to identify the presence of medullary thyroid cancer (MTC), while the Afirma BRAF was developed to determine the presence of the BRAF V600E mutation. Both are started automatically in the lab, if ordered by a doctor, after a thyroid nodule has been classified as abnormal. This process is known as reflex testing.

Thyroid cancer-targeted mutational analysis of thyroid FNA samples has also been proposed to detect individual gene mutations associated with thyroid cancer and include BRAF V600E, RAS (HRAS, KRAS, NRAS), RET/PTC, PAX8/PPARgamma, PIK3CA .

On May 4, 2018, the FDA approved Tafinlar (dabrafenib) and Mekinist (trametinib), together, for the treatment of anaplastic thyroid cancer (ATC) that cannot be removed by surgery or has spread to other parts of the body ( metastatic), and has an abnormal gene type, BRAF V600E (braf V600E mutation positive).

Thyroid cancer mutation panel using next-generation sequencing (NGS) (eg, ThyroSeq) analyzes deoxyribonucleic acid (DNA) and RNA in thyroid FNA samples to determine the presence of genetic mutations associated with thyroid cancer thyroid.

Quest Diagnostics offers a panel of molecular tests designed to help clinicians determine whether a thyroid gland is cancerous and requires surgical removal. The test includes the panel of seven gene mutations and rearrangements addressed by the American Thyroid Association for the clinical management of indeterminate thyroid biopsies. According to Quest Diagnostics, the Quest Diagnostics Thyroid Cancer Mutation Panel helps detect cancer in thyroid biopsies that are indeterminate for cancer based on current cytology testing methods. Approximately 15% to 20% of these biopsies, which are obtained by fine needle aspiration (FNA), yield indeterminate results. An unclear result may increase the risk that a doctor, out of an abundance of caution, will biopsy additional tissue with a larger needle or surgically remove part or all of a suspected thyroid thyroid that is later diagnosed as healthy. Approximately 300,000 FNA thyroid biopsy procedures are performed annually in the United States. The panel identifies mutations in the gamma molecular markers BRAF V600E, RAS, RET/PTC and PAX8/PPAR, which are associated with papillary and follicular thyroid cancer, two common forms of the disease. The manufacturer states that the American Thyroid Association's practice guidelines recommend that clinicians consider these markers as an aid in the clinical management of patients with indeterminate biopsy test results. Results of a Quest Diagnostics study found that 90 of 149 ANF samples, or about 60%, had mutations for one or more of the four markers assessed by the new panel (Reitz, et al., 2014). The study authors stated that the presence of all four markers was generally mutually exclusive, suggesting potential value in a hierarchical screening strategy for samples with limited tissue. According to the American Cancer Society, about two out of 10 tests may need to be repeated because the sample doesn't contain enough cells for the test.

The Thyroid Cancer Mutational Panel (eg, ThyGenX [formerly miRInform], Thyroid Cancer Mutational Panel) screens thyroid FNA samples for genetic mutations associated with thyroid cancer (eg, BRAF V600E, RAS [HRAS, KRAS, NRAS], RET/PTC, PAX8 /PPARgamma, PIK3CA). Mutational panels are described as "regulatory" tests because a positive result indicates that a nodule is at high risk for malignancy; therefore it identifies, or rules, cancer. However, because these mutations generally occur infrequently in thyroid cancer, a negative result does not rule out cancer.

Thyroid microRNA (miRNA) GEC (eg, RosettaGX Reveal, ThyraMIR) measures microRNA expression levels to classify presumed thyroid nodules with indeterminate ANF cytopathology. miRNA GEC can be offered alone or in combination with a thyroid cancer mutation panel (ie ThyraMIR and ThyGenX) to purportedly improve specificity and sensitivity test results.

ThyraMIR Thyroid miRNA Sorter is a PCR-based microRNA (miRNA) gene expression sorter that examines the expression levels of 10 miRNA genes in biopsy FNA: miR-29-1-5p, miR-31-5p, miR- 138-1-3p, miR-139-5p, miR-146b-5p, miR-155. miR-204-5p, miR-222-3p, miR-375 and miR-551b-3p. It is performed after a negative ThyGenX result for all mutations or when the mutations detected are not fully indicative of malignancy (i.e. ThyGenX results favor a benign nodule but cancer may still be present). The test is used on the same FNA cytology specimen. The ThyraMIR test reports a positive or negative qualitative result depending on gene expression levels.

A study combining the seven-gene mutation test (ThyGenX) with the expression of a set of 10 miRNA genes (ThyraMIR) in preoperative FNAB samples from 109 patients with indeterminate cytology, showed a sensitivity of 89%, a specificity of 85%. a VPP of 73% and a VPP of 94%. % NPV over this group with a malignancy prevalence of 32% (Labourier, et al., 2015). Laborier et al. (2015) reported that testing with ThyGenX and ThyraMIR for DNA, mRNA, and miRNA can accurately classify benign and malignant thyroid nodules, increase diagnostic yield, and further improve risk-based preoperative management of benign thyroid nodules with cytology. Laborier et al. (2015) tested surgical and preoperative ANF specimens (n = 638) for 17 validated genetic alterations using the miRInform Thyroid Assay (ThyGenX) and with a 10 miRNA gene expression classifier (ThyraMIR) yielding positive (malignant) or negative (benign). . A cross-sectional study of thyroid nodules with atypia of undetermined importance/follicular lesion of undetermined importance (AUS/FLUS) or follicular neoplasia/suspicious cytology of follicular neoplasia (FN/SFN) (n = 109) was performed in 12 endocrinology centers around the world. the country. the United States. Qualitative molecular results were compared with surgical histopathology to determine the diagnostic performance and clinical effect of the model. Mutations were detected in 69% of nodules with malignant outcome. Among mutation-negative samples, the miRNA test correctly identified 64% of malignant cases and 98% of benign cases. The diagnostic sensitivity and specificity of the combined algorithm were 89% (95% confidence interval [CI], 73-97%) and 85% (95% CI, 75-92%), respectively. With a cancer prevalence of 32%, 61% of molecular findings were benign with a negative predictive value of 94% (95% CI, 85-98%). Regardless of variations in cancer prevalence, the test increased the yield of true benign results by 65% over mRNA-based gene expression classification and decreased the rate of preventable diagnostic surgeries by 69%.

In a news article about testing for indeterminate thyroid nodules, Tucker (2015) reviewed data from Labourier, et al. (20150 plus additional abstracts on ThyGenX/ThyraMIR presented at the 2015 Annual Scientific and Clinical Meeting of the American Association of Clinical Endocrinologists, the article quoted AACE Immediate Past President R. Mack Harrell, MD, warning that "further validation is needed in real world settings with higher numbers for this new platform, as well as other "next generation" molecular tests such as ThyroSeq... Much of the predictive value of these tests depends on what you get. benchmark, the effectiveness of the test is completely different from starting with a practice that is getting every thyroid nodule in town, so it must be tested in a real community practice setting with lots of patients before I can be sure exactly how it will work .

Lithwick-Yanai et al (2017) attempted to develop an assay, the RosettaGX Reveal, that could classify indeterminate thyroid nodules as benign or suspicious using routinely prepared fine needle aspiration cytology (FNAC) smears. A training set of 375 FNA smears was used to develop the microRNA-based assay, which was validated using a retrospective, multicenter, masked cohort of 201 smears. The final diagnosis of the validation samples was determined based on the corresponding surgical samples, reviewed by the collaborating institute pathologist and two independent pathologists. Validation samples were from adult patients (≥18 years) with a nodule size >0.5 cm and a final diagnosis confirmed by at least one of two blinded independent pathologists. The developed assay differentiates benign from malignant thyroid nodules using quantitative RT-PCR. Test performance on the 189 samples that passed quality control: negative predictive value: 91% (95% CI: 84% to 96%); sensitivity: 85% (CI 74% to 93%); specificity: 72% (CI 63% to 79%). Performance for cases where all three pathologists agreed on the final diagnosis (n=150): negative predictive value: 99% (94% to 100% CI); sensitivity: 98% (CI 87% to 100%); specificity: 78% (CI 69% to 85%). The authors concluded that this assay using microRNA expression in cytology smears distinguishes benign from malignant thyroid nodules using a single ANF-stained smear and does not require fresh tissue or special collection and transport conditions. The authors stated that this assay offers a valuable tool for the preoperative screening of thyroid specimens with indeterminate cytology. Limitations of this study include its small size and large number of post hoc exclusions to create a pool on which all three pathologists agreed.

Benjamin et al (2016) reported the analytical validation of the RosettaGX Reveal assay. More than 800 FNA slides were analyzed, including slides stained with Romanowsky and Pap smears. The following assay characteristics were examined: intranode concordance, effect of staining type, acceptable RNA dips, performance on low thyroid cell numbers, effect of time since sampling and analytical sensitivity, specificity, and reproducibility. The authors reported that the assay can be performed on FNA slides where only 1% of the cells are thyroid epithelial cells or where only 5 ng of RNA has been extracted. Samples composed entirely of blood failed quality control and were not classified. The type of stain did not affect performance. All slides were stored at room temperature. However, the time elapsed between sampling and processing the FNA did not affect assay performance. There was a high level of interlaboratory agreement (96%) and agreement for slides created from the same FNAB pass was 93%. The authors concluded that the microRNA-based assay was resistant to various physical processing conditions and different sample characteristics. The authors concluded that, due to the performance, robustness, and use of routinely prepared FNA slides, the assay has the potential to provide valuable assistance to clinicians in the diagnosis of thyroid nodules.

Bhatia et al (2015) observed that FNA cytology, being the mainstay for the diagnosis of thyroid nodules, does not provide definitive results in a subset of patients. The use of molecular marker tests has been described as a useful aid in differentiating thyroid nodules that have an indeterminate cytodiagnosis. Molecular tests such as the Afirm gene classifier, mutation assay, and immunohistochemical markers have been increasingly used to further increase accuracy and delay unnecessary surgery for benign thyroid nodules. However, in light of current literature, their emerging roles in clinical practice are limited due to financial and technical constraints. However, their synergistic implementation can predict the risk of malignancy and provide an accurate diagnosis. This review discussed the clinical utility of various molecular tests performed on indeterminate FNA nodules to avoid diagnostic thyroidectomies and justifies the need for future multi-institutional studies.

Cibas and Ali (2017) noted that the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) established a standardized, category-based reporting system for fine needle aspiration (FNA) thyroid specimens. The 2017 revision reaffirmed that every thyroid ANF report should begin with 1 of 6 diagnostic categories, whose names have remained unchanged since they were first introduced:

Non-diagnostic or unsatisfactory
Benigno
Atypia of undetermined significance (AUS) or follicular lesion of undetermined significance (FLUS)
Follicular neoplasm or suspected follicular neoplasm;
suspicion of malignancy
Malignant.

There is a choice of 2 different names for some of the categories. A laboratory should choose the one it prefers and use it exclusively for that category. Synonymous terms (e.g. AUS and FLUS) should not be used to denote 2 different interpretations. Each category has an implicit cancer risk ranging from 0-3% for the "benign" category to nearly 100% for the "malignant" category, and in the 2017 review, the malignancy risks were updated based on new data (post -2010). . Based on their risk associations, each category is linked to up-to-date, evidence-based clinical management recommendations. The recent reclassification of some thyroid neoplasms as non-invasive follicular thyroid neoplasm with nuclear papilla-like features (NIFTP) has implications for malignancy risk, and this is taken into account with regard to diagnostic criteria and optional notes. These notes can be useful to help guide surgical management.

Roma

The Ovarian Malignancy Risk Algorithm (ROMA) is an FDA-approved blood test to aid in the evaluation of pelvic masses for likelihood of malignancy prior to surgery. ROMA measures human epididymal protein 4 (HE4) and CA-125. These measurements are applied to an algorithm, combined with menopausal status, to calculate a numerical score.

The BCBS TEC review of "Multi-Analyte Testing for Assessment of Adnexal Masses" (2013) concluded that ROMA does not meet the TEC criteria. He noted that "evidence for the effect of... ROMA and effects on health outcomes is indirect and based on studies of diagnostic performance of tests in patients undergoing surgery for adnexal masses. Although studies show improvements in sensitivity and worsening specificity with using the tests in conjunction with clinical assessment, there are problems in concluding that this results in better health outcomes. .Underlying these questions is some uncertainty as to the benefit of initial treatment by a gynecological oncologist beyond the need for reoperation in some cases."

The National Comprehensive Cancer Network (NCCN, 2016) ovarian cancer guidelines state that "specific biomarkers (serum HE4 and CA-125) have been suggested in conjunction with an algorithm (Ovarian Malignancy Risk Algorithm [ROMA]) may be useful to determine whether a pelvic mass is malignant or benign. The FDA has approved the use of HE4 and CA-125 to estimate the risk of ovarian cancer in women with a pelvic mass. The NCCN Panel currently does not recommend the use of these biomarkers to determine the status of an undiagnosed pelvic mass."

The American College of Obstetricians and Gynecologists (ACOG, 2017) guidelines on the management of adnexal masses state that ROMA includes HE4, which was found to be more sensitive and specific than CA 125 for the evaluation of adnexal masses. The guidelines state that serum biomarker panels [OVA1 and ROMA] can be used as an alternative to CA 125 only to determine the need for referral or consultation with a gynecologic oncologist when an adnexal mass requires surgery. The guidelines state that trials that have evaluated the predictive value of these panels show potential for improving specificity; "[Y]however, comparative research has yet to define the best testing approach."

The UK National Institutes of Health Research Health Technology Assessment Program commissioned an assessment (Westwood, et al., 2016) comparing the Malignancy Risk Index (RMI) with alternative risk scores for ovarian cancer, including ROMA score, as well as Overa /OVA2 (Vermillion), Simple Rules Ultrasonic Classification System (IOTA), Different Adnexal Neoplasms Assessment Model (ADNEX) (IOTA group). The assessment is expected to be completed in 2017.

ERCC1

Yu et al. (2012) stated that excision repair cross-complement group 1 (ERCC1) plays an essential role in DNA repair and has been associated with resistance to platinum-based anticancer drugs among patients with advanced NSCLC. These investigators examined whether ERCC1 gene variants Asn118Asn and C8092A are associated with response to platinum-based chemotherapy treatment. They performed a meta-analysis using 10 eligible cohort studies (including 11 datasets) with a total of 1252 NSCLC patients to summarize existing data on the association between ERCC1 Asn118Asn and C8092A polymorphisms and response to platinum regimens. The odds ratio or risk ratio with a 95% CI was calculated to estimate the correlation. These investigators found that neither the ERCC1 C8092A polymorphism nor the Asn118Asn variant is associated with a different response to platinum-based treatment among patients with advanced NSCLC. Furthermore, these two genetic variants are unrelated to treatment response in Caucasian or Asian patients. The authors concluded that the findings of this meta-analysis indicated that the ERCC1 Asn118Asn and C8092A polymorphisms may not be good prognostic biomarkers for platinum-based chemotherapy in patients with stage III-IV NSCLC.

Wang et al (2012) performed a meta-analysis using 20 eligible studies to examine ERCC1, GST, TS, and MTHFR polymorphisms in predicting clinical outcomes (response rate, OS, and toxicity) in patients with gastric cancer. (GC) treated with platinum/5-Fu-based chemotherapy. The association was measured by fixed/random effect odds ratios (ORs) or risk ratios (HRs) combined with their 95% CIs, depending on study heterogeneity. Statistical analysis was performed using the STATA 9.0 software package. No significant association was found between response rate and genetic polymorphism in TS, MTHFR, ERCC1, GSTM1 and GSTP1. However, the response rate was higher in the GSTT1 (+) genotype compared to the GSTT1 (-) genotype (T-/T+: OR = 0.67, 95% CI 0.47 to 0.97). Regarding long-term outcomes, these investigators observed a significant increase in OS in TS 3R/3R [(2R2R+2R3R)/3R3R: HR = 1.29, 95% CI 1.02 to 1.64] and GSTP1 GG /GA [(GG+ AG)/AA : HR = 0.51, 95% CI: 0.39 to 0.67] genotypes. Furthermore, a significant association between toxicity and genetic polymorphism in TS, MTHFR and GSTP1 was demonstrated in the included studies. The authors concluded that ERCC1, GST, TS, and MTHFR polymorphisms were strongly associated with clinical outcomes in patients with GC treated with platinum/5-Fu-based chemotherapy. Furthermore, they state that studies with large sample sizes using the multivariate analysis method may help us to provide more persuasive data about the supposed association in the future.

In a meta-analysis, Gong and colleagues (2012) examined whether RRM1 expression is associated with clinical outcome of the gemcitabine-containing regimen in advanced NSCLC. An electronic search was performed using PubMed, Medline, EMBASE, Cochrane Library, and CNKI databases from inception through May 2011. A systemic review of studies on the association between RRM1 expression in advanced NSCLC and clinical outcome of regimen containing gemcitabine. done. Pooled odds ratios (ORs) for response rate, weighted median survival, and time to progression were calculated using Revman 5.0 software. The search strategy identified 18 eligible studies (n = 1243). The response rate to the regimen containing gemcitabine was significantly higher in patients with low/negative RRM1 (OR = 0.31, 95% CI 0.21 to 0.45, p < 0.00001). Patients with low/RRM1 negative non-small cell lung cancer who were treated with a regimen containing gemicitabine survived an additional 3.94 months (95% CI 2.15 to 5.73, p < 0.001) and had a longer longer to progression of 2.64 months (95% CI 0.39 to 4.89, p = 0.02) than those with high/positive RRM1. The authors concluded that low/negative expression of RRM1 in advanced NSCLC was associated with a higher response rate to the gemcitabine-containing regimen and better prognosis. Furthermore, they stated that large randomized phase III trials are needed to identify whether detection of RRM1 is clinically valuable for predicting prognosis and sensitivity to the gemcitabine-containing regimen in advanced NSCLC.

Friboulet et al (2013) stated that the ERCC1 protein is a potential prognostic biomarker of the effectiveness of cisplatin-based chemotherapy in NSCLC. Although several ongoing studies are evaluating the expression level of ERCC1, no consensus has been reached regarding an evaluation method.

Besse et al (2013) observed that somatic expression levels of ERCC1 and ribonucleotide reductase M1 (RRM1) were widely explored as markers of DNA repair capacity in tumor cells. Although low expression of ERCC1 and/or RRM1 is generally associated with platinum sensitivity, results published in retrospective and prospective studies are not always consistent. These investigators examined the function of these 2 biomarkers, as well as the tools available for their assessment and the associated technical issues. Their prognostic and predictive values were summarized and considered in terms of customizing systemic therapy according to biomarker expression levels (ERCC1 and RRM1). The authors discussed why the use of both markers should be restricted to clinical research at this time.

GSTP1

Prostate cancer is the most common cancer in men and the second leading cause of cancer-related deaths in the US. Cancer. Several studies have shown that hypermethylation of the promoter regions of the GST-P1 and APC genes occurs significantly more frequently in prostate cancer samples than in benign prostate conditions. Hypermethylation of the promoter regions of the GST-P1 and APC genes may help predict prostate cancer (Raman, et al., 2013).

Trock et al (2012) observed that hypermethylation of genes such as glutathione-S-transferase P1 (GSTP1) and adenomatous polyposis coli (APC) occurs with high frequency in prostate tumor tissue, but is much less common in prostate cancer. however, the potential value of gene methylation biomarkers as an adjunct to biopsy histopathology has been poorly studied. When measured in histologically benign prostate biopsy tissue, hypermethylation of the APC gene was found to have a high negative predictive value (NPV) and high sensitivity. It was found that hypermethylation of GSTP1 has a lower yield than APC. These investigators evaluated the performance of DNA methylation biomarkers in the context of repeat biopsy in men with an initially negative prostate biopsy but a high index of suspicion for undetected prostate cancer. They prospectively evaluated 86 men with histologically negative initial prostate biopsy and high-risk features. All men underwent a repeat biopsy guided by 12-core ultrasound. DNA methylation of GSTP1 and APC was determined using tissue from the initially negative biopsy and compared with histology from the repeat biopsy. The primary end point was the relative NPV of APC compared to GSTP1 and its 95% CI. At repeat biopsy, 21/86 (24%) of the men had prostate cancer. Below-threshold APC and GSTP1 methylation rates (which predict no cancer) produced a NPV of 0.96 and 0.80, respectively. The relative NPV was 1.2 (95% CI 1.06 to 1.36), indicating that APC has a significantly higher NPV. Methylation ratios above the threshold produced a sensitivity of 0.95 for APC and 0.43 for GSTP1. Combining both methylation markers produced similar performance to APC alone. APC methylation patterns were consistent with a possible field effect or early onset in carcinogenesis. The authors concluded that APC methylation provided a very high NPV with a low false negative rate, in the first prospective study to evaluate the performance of DNA methylation markers in a clinical cohort of men undergoing repeat biopsy. They stated that the potential of APC methylation to reduce unnecessary repeat biopsies justifies validation in a larger prospective cohort.

In a systematic review and meta-analysis, Yu and colleagues (2013) examined the association between the GSTP1 Ile105Val polymorphism and prostate cancer (CaP) in different models of inheritance. A total of 13 eligible studies were pooled in this meta-analysis. There was a significant association between the GSTP1 Ile158Val and variant PCa genotypes for the Ile/Ile versus Val/Val comparison [odds ratio (OR) = 0.705; I 2 = 63.7%; 95% CI: 0.508 to 0.977], comparison Ile/Val versus Val/Val (OR = 0.736; I 2= 8.0%; 95% CI: 0.613 to 0.883) and dominant model (OR = 0.712; I 2= 45 .5%; 95% CI: 0.555 to 0.913). However, no associations were detected for other genetic models. In the analysis stratified by ethnicity, significant associations were also found between the GSTP1 Ile105Val polymorphism and the risk of CaP among Caucasians (comparison Ile/Ile versus Val/Val OR = 0.818, I 2 = 0.0%, 95% CI: 0.681 to 0.982; Ile/ Comparison Val versus Val/Val OR = 0.779, I 2 = 0.0%, 95% CI: 0.651 to 0.933 and dominant model OR = 0.794, I 2= 0.0%, 95% CI: 0.670 to 0.941), while no associations were found for other genetic models. However, no associations were found in Asians and African Americans for all genetic models when stratified by ethnicity. The authors concluded that the findings of this meta-analysis indicated that the GSTP1 Ile105Val polymorphisms contributed to PCa susceptibility. However, they stated that a study with a larger sample size is needed to better assess the gene-environment interaction in GSTP1 Ile105Val polymorphisms and PCa risk.

An evaluation by the Swedish Office for Health Technology Assessment (SBU, 2011) concluded that there is insufficient scientific evidence to determine the diagnostic accuracy of the me-GSTP1 urine test.

CEACAM6

An UpToDate review of “Breast Cancer Testing” (Fletcher, 2013) DOES NOT mention the use of carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6). Furthermore, the NCCN Clinical Practice Guideline on "Breast Cancer" (Version 2.2013) DOES NOT mention the use of carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6).

DCIS Recurrence Score

An UpToDate review of "Ductal carcinoma in situ: treatment and prognosis" (Collins et al, 2013) states that "A DCIS recurrence score was developed using a multigene assay, and the prospective evaluation of this assay was performed using tumors from 327 patients who participated in the aforementioned study E5194. In a preliminary analysis, patients were stratified by recurrence score into three groups that were associated with the following risks of an ipsilateral breast event (DCIS or invasive breast cancer) or invasive breast cancer:

Low (less than 39) -- 12 and 5%, respectively
Intermediate (39 to 54) -- 25 and 9%, respectively
High (greater than or equal to 55) -- 27 and 19%, respectively

These results suggest that the DCIS score can help select patients who should undergo adjuvant radiation. However, further validation of these results is needed before the multigene assay can become part of clinical practice. It is also worth noting that a 12% risk of an ipsilateral breast event at 10 years in the lowest risk category may not be low enough to warrant routine omission of post-excision RT."

MiPRS

MyPRS Plus (Signal Genetics) analyzes all nearly 25,000 genes in a patient's genome to determine the gene expression profile associated with their condition (Raman, et al., 2013). In the case of myeloma, the gene expression profile is composed of the 70 most relevant genes that help predict the patient's evolution. MyPRS helps patients and physicians determine the best treatment for patients with myeloma.

The NCCN Multiple Myeloma (MM) Clinical Practice Guideline (Version 2.2013) stated that "greater understanding of the molecular subtypes of MM is emerging from the application of high-throughput genomic tools such as gene expression profiling." information currently available on new treatment approaches, most patients with MM can now look forward to long-term disease control. powerful and rapid tool with the potential to provide additional prognostic value to better define risk stratification, assist in therapeutic decisions and inform the design and development of new drugs. Onostics should be used to direct patient management. The NCCN guideline does not include a specific recommendation for the use of the MyPRS test in risk stratification or prognostic determination in the clinical management of patients with MM.

Oxnard et al (2013) stated that the identification of oncogenic driver mutations underlying susceptibility to EGFR and anaplastic lymphoma kinase tyrosine kinase inhibitors generated great interest in identifying additional target oncogenes in NSCLC. A number of new potentially oncogenic genetic alterations have been characterized in recent years, including BRAF mutations, HER2 insertions, phosphatidylinositol-4,5-bisphosphonate 3-kinase, catalytic subunit alpha polypeptide (PIK3CA) gene mutations, growth factor receptor 1 fibroblasts (FGFR1 ). ), amplifications, discoidin receptor domain 2 (DDR2) mutations, ROS1 rearrangements, and RET rearrangements. These investigators discussed the techniques used to discover each of these candidate oncogenes, the prevalence of each in NSCLC, preclinical data supporting their role in lung cancer, and data on small molecular inhibitors in development.

SNA

Janku et al (2013) noted that despite the development of new therapies, metastatic colorectal cancer (mCRC) remains largely an incurable disease. Approximately 2-6% of colorectal cancers harbor NRAS mutations. The anti-VEGF antibody bevacizumab is the backbone of most therapeutic regimens; however, there are no known biomarkers that predict its activity. These investigators reported 2 cases of mCRC with a Q61K NRAS mutation that had a favorable response to bevacizumab and the histone deacetylase inhibitor valproic acid. In contrast, none of the 10 patients with wild-type NRAS or unknown NRAS status and KRAS mutation (NRAS and KRAS mutations are mutually exclusive) responded to the same regimen. The authors concluded that these results suggest that the NRAS mutation deserves further investigation as a potential biomarker that predicts the efficacy of bevacizumab-based treatment.

The EGAPP EWG (2013) found insufficient evidence to recommend for or against testing for mutations inSNAand/or loss of PTEN or AKT protein expression. The level of certainty of this evidence was low. In the absence of supporting evidence and considering other contextual issues, the EWG advises against using these tests to guide decisions about initiating anti-EGFR therapy with cetuximab or panitumumab, unless more evidence supports better clinical outcomes.

Phosphatidylinositol-4,5-bisphosphonate 3-kinase, polypeptide catalytic subunit alpha gene (PIK3CA)

The EGAPP EWG (2013) found insufficient evidence to recommend for or against testing for mutations inPIK3CA. The level of certainty of this evidence was low. In the absence of supporting evidence and considering other contextual issues, the EWG advises against using these tests to guide decisions about initiating anti-EGFR therapy with cetuximab or panitumumab, unless more evidence supports better clinical outcomes.

The American Society of Clinical Oncology guidelines (Sepulveda, et al., 2017) stated, "There is insufficient evidence to recommend PIK3CA mutational analysis of colorectal carcinoma tissue for therapy selection outside of a clinical trial (Type: no recommendation; Strength of evidence: insufficient, benefit/harm balance unknown; Quality of evidence: insufficient)."

Cyclin D1 and FADD

Cyclin D1 is used to diagnose mantle cell lymphoma and predict disease recurrence (Chin, et al., 2006). D-type cyclins are predominantly expressed in the G1 phase of the cell cycle. Cyclin D1 expression pattern has been extensively studied in certain types of cancer, including lymphoma and non-small cell lung cancer. Approximately 30% of breast carcinomas are positive for cyclin D1. Cyclin D1 overexpression is now a well-established criterion for diagnosing mantle cell lymphoma, a malignant non-Hodgkin lymphoma characterized by a single translocation of the t-chromosome(11;14).

Rasamny et al (2012) stated that cyclin D1 and FADD (Fas-associated protein with death domain) regulate the cell cycle and apoptosis, respectively, and are located on chromosome 11q13, which is frequently amplified in squamous cell carcinoma of head and neck (HNSCC). ). This study evaluated these proteins as predictors of clinical outcomes for HNSCC. A total of 222 patients with upper aerodigestive HNSCC were included in this study. Patients with strongly cyclin D1 and FADD-positive tumors had reduced OS (p = 0.003 and p < 0.001), disease-specific survival (DSS; p = 0.039 and p < 0.001) and DFS (p = 0.026 and p < 0.001) survival, respectively. Together, the 2 markers effectively stratified OS (p < 0.001), DSS (p < 0.001) and DFS (p = 0.002). Strong FADD staining correlated with increased alcohol consumption and varied significantly with primary tumor site: 56% of hypopharyngeal tumors expressed high levels of FADD, but only 7% of glottic tumors. Using Cox regression analysis, FADD and N stage were independently significant predictors of DSS and DFS, whereas cyclin D1, FADD and N stage were independently significant for OS. The authors concluded that cyclin D1 and FADD may be useful as predictors of long-term outcomes for patients with HNSCC. Furthermore, they stated that further studies are needed to determine whether these proteins predict response to different treatment approaches or help select patients for multimodal therapy.

You are talking

Prolaris (Myriad Genetics, Salt Lake City, UT) uses archived tumor specimens as a source of mRNA, reverse transcriptase polymerase chain reaction amplification, and a low-density RTPCR array platform. Prolaris is used to quantify the expression levels of 31 cell cycle progression (CCP) genes and 15 internal genes to generate a CCP score. An evaluation by the Center for Technology Assessment (TEC) of the BlueCross BlueShield Association concluded that direct evidence is insufficient to establish the analytical validity, clinical validity, or clinical utility of Prolaristest. The BlueCross BlueShield Association review (BCBSA, 2015) stated, "Published evidence is sparse and insufficient to draw conclusions about the analytical validity, clinical validity, or clinical utility of Prolaris...in patients under an active surveillance program."

An assessment by the Adelaide Health Technology Assessment (Ellery, et al., 2014) found that there is currently uncertainty about the clinical usefulness of Prolaris. Citing a study by Shore, et al. (2014) showing that only a small percentage of urologists would definitely change treatment based on the study results, "There appears to be some doubt about the use of technology in clinical practice and it appears that changes in clinical management based on the prognostic information provided by these genetic testing is unlikely. Therefore, HealthPACT recommends that no further research be conducted on their behalf at this time."

The NCCN Prostate Cancer Guidelines (2015) state: "The Prolaris assay produces a cell cycle progression (CCP) score from RNA expression levels of 31 genes involved in CCP... For example, Prolaris was successful in 93% of radical prostatectomy specimens and 70% of diagnostic prostate biopsy specimens The Prolaris CCP score has been shown to be predictive when applied in retrospective prospective designs for biochemical recurrence or metastases after radical prostatectomy, for survival when men were seen after diagnosis with transurethral resection of the prostate or diagnostic needle biopsy and for biochemical recurrence and survival after external beam radiotherapy... Prolaris changed treatment recommendations in 32% to 65% of cases and may improve adherence to recommended treatment...Both [Prolaris and Oncotype DX Prostate] Molecular biomarker tests were developed with broad support, guidance and participation industry and have been marketed under the FDA's strict regulatory pathway for biomarkers. Its clinical utility awaits evaluation in prospective randomized clinical trials, which are unlikely to occur. The market and comparative efficacy research may be the only way for these and similar trials to gain their rightful place for better risk stratification for men with clinically localized prostate cancer.”

An American Society of Clinical Oncology guideline on active surveillance for prostate cancer (Chen, et al., 2016) stated that "[t]he use of ancillary tests in addition to DRE, PSA, and biopsy to enhance patient screening or as part of monitoring in an AS regimen remains under investigation, although there is the potential for genomic testing to use biopsy tissue to predict which patients are more, not less, likely to have disease progression and cancer-specific mortality, and multiparametric MRI ( mpMRI) to guide biopsies to find clinically more aggressive disease, prospective validation of these tests is needed to assess their impact on patient outcomes such as survival. Selective use of these ancillary tests may be appropriate in patients with clinical findings and/or discordant pathologies."

A review by the National Institute for Health and Care Excellence (NICE, 2016) noted that most of the evidence relevant to Prolarisis for the clinical validity and evidence for the prognostic value of Prolaris is based solely on retrospective analyzes of archival material. No studies have examined the prospective use of Prolaris on patient outcomes.

The American Urological Association Localized Prostate Cancer Guidelines (Sanda et al, 2017) state, based on expert opinion, that among most patients with low-risk localized prostate cancer, Prolaris and other biomarkers. tissues has not shown a clear role in selecting candidates for active surveillance. The guidelines also state that tissue-based genomic biomarkers are not required for follow-up. The guidelines state that the Prolaris trial has not been validated as a substantial benefit in the active surveillance population.

The National Comprehensive Cancer Network's clinical practice guideline on "Prostate Cancer" (version 4.2019) recommends coverage of "Decipher", "Oncotype DX Prostate", "Prolaris", and "ProMark" (Category 2A). Prolaris: for post-biopsy per NCCN, very low, low, and intermediate risk patients with a life expectancy greater than 10 years who have not received treatment for prostate cancer and are candidates for active surveillance or definitive therapy.

Oncotype Dx Prostate

The Oncotype DX test for prostate cancer (Genomic Health) is a genomic test that determines the risk of cancer before starting treatment (Raman, et al., 2013). The test predicts whether the cancer is likely to be low-risk and contained in the prostate, or higher-risk and more likely to grow and spread. With this information, the patient and their doctor can choose the most appropriate treatment option. For example, a lower-risk prostate cancer with more favorable pathology, which may not need invasive treatment and can be safely treated through close and careful monitoring, a treatment approach called active surveillance. This genomic test measures biology through the expression of 17 genes in several important biological pathways in prostate cancer that can predict prostate cancer aggressiveness, providing an individualized risk assessment.

The Oncotype Dx Prostate Cancer Assay (Genomic Health, Redwood City, CA) is used to quantify the expression levels of 12 cancer-related genes and 5 reference genes to generate a prostate genomic score (GPS). In the final analysis, the cell cycle progression (CCP) score (median 1.03, interquartile range 0.41 to 1.74) and GPS (range 0 to 100) are combined in proprietary algorithms with clinical risk criteria ( PSA, Gleason grade, tumor stage) to generate new risk categories (i.e., reclassification) designed to reflect the biological sluggishness or aggressiveness of individual lesions and thereby inform treatment decisions.

Klein et al (2014) sought to identify and validate a biopsy-based gene expression signature that predicts clinical recurrence, death from prostate cancer (PCa), and adverse pathology. Gene expression was quantified by reverse transcription-polymerase chain reaction for three studies: a prostatectomy discovery study (n=441), a biopsy study (n=167), and a prospectively designed independent clinical validation study. (n=395)-test retrospectively collected needle biopsies from contemporary (1997-2011) low-to-intermediate clinical risk patients who were candidates for active surveillance (AS). The main outcome measures that defined aggressive PCa were clinical recurrence, death from PCa, and adverse pathology at prostatectomy. Cox proportional hazards regression models were used to assess the association between gene expression and time to event endpoints. The results of the prostatectomy and biopsy studies were used to develop and block a signature based on the expression of multiple genes, called the Genomic Prostate Score (GPS); in the validation study, logistic regression was used to test the association between GPS and pathologic stage and grade at prostatectomy. Decision curve analysis and risk profiles were used in conjunction with clinical and pathological features to assess clinical utility. Of the 732 candidate genes analyzed, 288 (39%) predict clinical recurrence despite heterogeneity and multifocality, and 198 (27%) predict aggressive disease after adjustment for prostate-specific antigen, Gleason score, and clinical stage. Further analysis identified 17 genes representing multiple biological pathways that were combined in the GPS algorithm. In the validation study, GPS predicted high grade (odds ratio [OR] per 20 GPS units: 2.3; 95% confidence interval [CI], 1.5-3.7; p<0.001) and high grade (OR per 20 GPS units: 1.9; 95% CI, 1.3-3.0; p=0.003) in surgical pathology. GPS predicted high-grade and/or advanced-stage disease after controlling for established clinical factors (p<0.005) as an OR of 2.1 (95% CI, 1.4-3.2) when adjusting for the prostate cancer risk assessment. A limitation of the validation study was the inclusion of men with low-volume, intermediate-risk CaP (Gleason score 3+4), for whom some providers would not consider AS.

Cullen et al (2015) used a racially diverse cohort of men (20% African American [AA]) to assess the association of clinically validated 17-gene prostate genomic score (GPS) with recurrence after radical prostatectomy and prostatectomy. AP) in surgery Biopsies from 431 National Comprehensive Cancer Network (NCCN) men treated for very low-, low-, or intermediate-risk PCa between 1990 and 2011 at two US military medical centers were analyzed to validate the association between GPS and biochemistry relapse (BCR) and confirm the association with PA. Metastatic recurrence (MR) was also evaluated. Cox proportional hazards models were used for BCR and MR, and logistic regression was used for AP. Review of core pathology was performed by a uropathologist. BP was defined as a primary pattern of Gleason 4 or any pattern 5 and/or pT3 disease. GPS results (scale: 0-100) were obtained in 402 cases (93%); 62 men (15%) had BCR, 5 developed metastases and 163 had PA. The median follow-up was 5.2 years. GPS predicted time for BCR in univariate analysis (hazard ratio per 20 GPS units [HR/20 units]: 2.9; p<0.001) and after adjustment for NCCN risk group (HR/20 units: 2.7; p<0.001). GPS also predicted time to metastasis (HR/20 units: 3.8; p=0.032), although the event rate was low (n=5). GPS was strongly associated with BP (odds ratio per 20 GPS units: 3.3; p<0.001), adjusted for the NCCN risk group. In AA and Caucasian men, the median GPS was 30.3 for both, the distributions of the GPS scores were similar, and the GPS predicted the outcome similarly.

Dall'Era et al (2015) performed a retrospective chart review to assess the impact of incorporating the Oncotype DX Genomic Prostate Score on treatment recommendations and decisions for men with newly diagnosed low-risk prostate cancer in community urology practices. A total of 24 urologists who ordered the Oncotype DX prostate cancer test shortly after launch (May 2013) were invited to participate in the study. Clinicopathological data, prostate genomic score results, and treatment-related information were retrieved from medical records. Data were also collected for a pre-Genomic Prostate Score baseline group diagnosed between May 2012 and April 2013. Descriptive analyzes were performed to assess the proportion of men for whom active surveillance was recommended and used before and after the availability of the Genomic Prostate Score. In total, 15 physicians contributing 211 patients (Genomic Prostate Score group 124, reference group 87) participated in the chart review. Patients in the prostate genomic scoring and reference groups were at comparable risk based on traditional clinical pathology, with 82% having very low or low risk NCCN® disease. With the Genomic Prostate Score, the recommended relative increase in inactive surveillance was 22% (baseline 50% and Genomic Prostate Score 61%, absolute increase 11%) and the relative increase in the use of active surveillance was 56% ( baseline 43% and Prostate Genomic Score 67%, absolute increase 24%). Treatment recommendations for active surveillance were directionally consistent with the study-reported risk.

Badani et al (2015) conducted a prospective study to assess the impact of incorporating the Oncotype Dx GPS on treatment recommendations in 3 high-volume urology practices. Men with newly diagnosed prostate cancer who met specific NCCN criteria were prospectively included in the study. The biopsy tissue was analyzed. Urologists indicated treatment recommendations in questionnaires administered before and after GPS. The primary objectives of the study were to assess any changes in treatment modality and/or treatment intensity after GPS. A total of 158 men were included in the analysis, including 35, 71, and 52 in very low, low, and low intermediate risk NCCN. Biorisk predicted by GPS differed from clinical NCCN risk in only 61 men (39%). Overall, 18% of recommendations between active surveillance and immediate treatment changed after GPS. The relative increase in active surveillance recommendations was 24% (absolute change from 41% to 51%). In 41 of 158 men (26%), modality and/or intensity recommendations changed after GPS, including 25, 14, and 2 in which the recommendation intensity was decreased, increased, and equivocal, respectively. All changes were directionally consistent with GPS. The NCCN low-risk group had the greatest change in absolute recommendation after GPS (37%). In 17 of 57 men (30%), the initial recommendation for radical prostatectomy was changed to active surveillance after GPS. Urologists were more confident and found that the incorporation of GPS was helpful in 85% and 79% of cases, respectively, even when biological risk confirmed the clinical risk category.

Brand et al (2016) performed a patient-specific meta-analysis (MA) of two independent clinical validation studies of a 17-gene biopsy-based genomic assay (Oncotype Dx Prostate) as a predictor of favorable pathology in radical prostatectomy. A patient-specific MA was performed on data from 2 studies (732 patients) using the prostate genomic score (GPS; scale 0-100) in conjunction with the prostate cancer risk assessment score (CAPRA) or the National Comprehensive Cancer Network (NCCN) risk group as predictors of favorable pathology probability (LFP). Risk profile curves associating GPS with LFP were generated by CAPRA score and NCCN risk group. Decision curves and receiver operating characteristic curves were calculated using patient-specific MA risk estimates. Patient-specific MA-generated risk profiles ensure more accurate LFP estimates with narrower confidence intervals than any single study. The researchers claimed that GPS added significant predictive value to each clinical classifier. A model using GPS and CAPRA provided the greatest risk discrimination. In decision curve analysis, a greater net benefit was shown for combining GPS with each clinical classifier compared to the classifier alone. The area under the receiver operating characteristic improved from 0.68 to 0.73 when adding GPS to CAPRA and from 0.64 to 0.70 when adding GPS to the NCCN risk group. The proportion of patients with LFP >80% increased from 11% with the NCCN risk group alone to 23% with GPS with NCCN. The use of GPS with CAPRA identified the highest proportion (31%) of patients with LFP > 80%.

In a prospective study, Albala et al (2016) evaluated the clinical utility and economic impact of Oncotype DX Prostate GPS in patients with low-risk prostate cancer. insurance company. The insurer calculated cost data for the first 180 days after diagnosis (including the cost of the diagnostic biopsy) and provided the average cost of care per patient from its analysis, comparing standards of management and costs from an unanalyzed baseline population. with a similar population. Prospective population tested by GPS. The study's primary endpoint was the net percent difference in prospective treatment decisions using the Oncotype DX Prostate GPS compared to reference standards of care without using the GPS. Of the 71 men in the baseline group who were very low-risk and NCCN low-risk, 27 (38%) were treated with AS, 25 (35%) had RP, 18 (25%) were treated with IMRT, and 1 (1%) underwent cryoablation of the entire gland. Of 51 very low and low risk NCCN patients evaluated with GPS, 30 (59%) were treated with AS, 13 (25%) had PR, 6 (12%) were treated with IMRT, 1 (2% seized with multimodal therapy (IMRT and brachytherapy) and 1 (2%) opted for focal cryoablation. The use of SA was 21% higher in the very low-risk and low-risk prospective cohort of men evaluated with GPS compared to the reference cohort of men in the same risk group The PR rate was 10% lower and the IMRT rate was 14% lower in the prospective cohort of very low and low risk men when GPS was incorporated into treatment decisions compared to the baseline cohort of men in the same-risk group. Of the 29 intermediate-risk NCCN patients not tested with GPS, 5 (17.2%) were treated with AS, 12 (41.4%) with RP, 11 (37.9%) with IMRT and 1 (3.4%) with CyberKnife Radiosurgery. NCCN assessed with GPS, no patients chose AS, 14 (48%) chose PR, 11 (38%) chose RT, 1 (3%) chose brachytherapy, and 3 (10%) chose multimodal treatment. AS utilization decreased and PR increased slightly in patients in the NCCN favorable intermediate risk group after GPS use. IMRT use was unchanged between the baseline and prospective groups. Comparing payer costs in the first 180 days after diagnosis for the entire NCCN at-risk population (n = 80), there was a mean aggregate cost of $1023 per patient, including GPS cost at $4520 (total net addition of $81,855 for the entire GPS-analyzed population). When comparing payer costs in the first 180 days after diagnosis for the very-low-risk and low-risk NCCN populations, there was an average per-patient savings of $2286, including the cost of the GPS.

An evaluation by the BlueCross BlueShield Technology Evaluation Center (TEC, 2014) concluded that direct evidence is insufficient to establish the analytical validity, clinical validity or clinical utility of Oncotype Dx Prostate.

The BlueCross BlueShield Technology Evaluation Center review of "Gene Expression Analysis for Treatment of Prostate Cancer" (BCBSA, 2015) concluded that "There is insufficient evidence to determine whether...the Oncotype Dx Prostate Test improves the results of above, neither the Prolaris nor the Oncotype Dx Prostate matrix-based gene expression test meets the TEC criteria." The review stated: Published evidence is sparse and insufficient to draw conclusions about . . . clinical validity or usefulness of Oncotype Dx Prostate in patients under active surveillance program".

An Adelaide Health Technology Assessment review (Ellery, et al., 2014) concluded that "there is uncertainty about the clinical usefulness" of the Oncotype Dx Prostate and Prolaris tests, "even taking into account the highest level of evidence available". The evaluation indicated that it remains to be seen whether the gene expression of the single gene panels involved is robust for heterogeneous sampling of prostate tissue at the time of biopsy. In addition, there is concern about the need for previously fixed tissue for histological analysis. The review noted that this is the most obvious reason for the relatively high number of patients for whom a valid test result could not be obtained.

The European Association of Urology Prostate Cancer Guidelines (2015) states that genomics in tissue sampling looks "promising" but "more study data will be needed before these markers can be used in standard clinical practice".

The NCCN Prostate Cancer Guidelines (NCCN, 2016) state, "These listed molecular tests were developed with broad industry support, guidance, and participation, and were marketed under the FDA's loosest regulatory pathway for biomarkers." utility requires prospective randomized clinical trials, which are unlikely to be performed, the panel believes that men with clinically localized disease may consider using tumor-based molecular assays at this time. Future comparative efficacy research may allow these and similar trials to gain additional evidence about their usefulness for better risk stratification of men with prostate cancer."

More recently, the NCCN Prostate Cancer Guidelines (NCCN, 2019) state, "These molecular biomarker tests were developed with broad industry support, guidance, and participation, and were marketed under the least regulatory path." , randomized clinical trials, which are unlikely to be conducted, the panel believes that men with low-intermediate or favorable disease risk may consider using Decipher, Oncotype Dx Prostate, Prolaris, or ProMark during initial risk stratification... Further Research Efficacy comparisons may allow these and similar trials to gain additional evidence about their usefulness for better risk stratification of men with prostate cancer.

The clinical practice guidelines of the American Urological Association (AUA)/American Society for Radiation Oncology (ASTRO)/Society for Urologic Oncology (SUO) and the American Society of Clinical Oncology (ASCO) state that among men with lower-grade prostate cancer risk, tissue genomic-based genomic biomarkers have not shown a clear role in selecting candidates for active surveillance (Bekelman, et al., 2018; Sanda, et al., 2018a; Sanda, et al., 2018b).

Prostate cancer guidelines from the European Association of Urology (EAU), the European Society of Radiotherapy and Oncology (ESTRO), the European Society of Urogenital Radiology (ESUR), and the International Society of Geriatric Oncology (SIOG) (Mottet, et al. al., 2018) state that Oncotype Dx Prostate has been shown in prospective studies to provide prognostic information in men with clinically localized prostate cancer, in addition to conventional clinicopathologic parameters, including pathologic grade and PSA level. "Results from prospective multicenter studies are awaited before a recommendation on its routine application can be made."

The American Urologic Association Localized Prostate Cancer Guidelines (Sanda, et al., 2017) state, based on expert opinion, that among most patients with low-risk localized prostate cancer, Oncotype Dx Prostate and other biomarkers Tissue-based genomics has not shown a clear role in selecting candidates for active surveillance. The guidelines also state that tissue-based genomic biomarkers are not required for follow-up. The guidelines state that the Oncotype Dx Prostatetest has not been validated as a substantial benefit in the active surveillance population.

In a multi-institutional prospective study, Eure et al (2017) studied the impact of genomic testing on shared decision-making for clinically low-risk men with CaP. Patients with clinically low-risk PCa were enrolled in this study of a validated tissue-based reverse transcription polymerase chain reaction assay of 17 genes (Genomic Prostate Score [GPS]). These investigators reported the results of the first 297 patients enrolled in the study with valid trial results for 17 genes and decision change data. Primary outcomes were shared decision on initial management and persistence in active surveillance (AS) one year after diagnosis; SA utilization and persistence were compared using similar outcomes in a group of patients who did not undergo genomic testing (initial cohort). Secondary objectives included the perceived usefulness of the study and patient decision conflict before and after the study; 1-year results were available in 258 patients. The alternation between initial recommendation and shared decision occurred in 23% of patients. SA utilization was higher in the GPS-tested cohort than in the untested reference cohort (62% vs. 40%). The proportion of men who selected and persisted in AS at 1 year was 55% and 34% in the GPS and reference cohorts, respectively. Doctors reported that GPS was helpful in 90% of cases. Mean decision-making conflict scores decreased in patients after the GPS test. The authors concluded that patients who received the GPS test were more likely to select and persist in AS for initial treatment compared to a matched control group. They stated that these data indicated that GPS helps guide shared decisions in clinically low-risk PCa.

The authors stated that this study had several drawbacks. This study was based on an interim analysis of the first 297 patients enrolled in a large prospective multicenter trial (n= 1200) and therefore should be considered preliminary. There were marked differences between participating practices regarding initial use of SA; this can significantly affect exchange rates. No definitive surgical pathology was available in patients who opted for AS. The high initial decision rates for AS in the GPS-tested cohort may be due in part to a Hawthorne effect; providers may have overused SA earlier in this study, which focused on a test of SA decision making. The actual rate of AEs in routine practice may be lower than reported in the study. They stated that despite these limitations, this analysis highlighted the potential benefits of incorporating genomic testing into individualized risk estimation and shared decision-making for patients with CaP in a real-world setting.

Steven and colleagues (2017) stated that many men with low-risk prostate cancer (PCa) receive definitive treatment, despite recommendations reported by 2 large randomized trials promoting active surveillance (AS). These investigators conducted a retrospective cohort study using the Optum (Eden Prairie, MN) research database of electronic health records (EHR) and administrative claims data to assess AS use in patients evaluated with a prostate score. . 17-gene genomics (GPS; Genomic Health, Redwood City, CA) and/or magnetic resonance imaging (MRI) of the prostate. Unidentified records of health plans enrolled from June 2013 to June 2016 that had more than or equal to 1 PCa record (n = 291,876) were extracted. Inclusion criteria included age 18 years or older, new diagnosis, American Urological Association low-risk CaP (stage T1 to T2a, PSA less than or equal to 10 ng/mL, Gleason score = 6), and clinical activity. at least 12 months before and after diagnosis. Data included baseline characteristics, use of GPS and/or MRI testing, and definitive procedures. GPS or MRI testing was performed in 17% of men (GPS, n = 375, 4%; MRI, n = 1174, 13%). Use of AS ranged from a minimum of 43% for men who underwent MRI only to 89% for men assessed by GPS who did not undergo MRI (p < 0.001). At 6-month follow-up, AS use was 31.0% higher (95% CI 27.6 to 34.5%, p < 0.001) for men who received only the GPS test compared to men who did not. submitted to the GPS test or MRI; the difference was 30.5% at the 12-month follow-up. The authors concluded that, in a large US pay system, GPS testing was associated with significantly greater SA use at 6 and 12 months compared with men who had MRI scans only or who had no MRI scans, GPS. or MRI.

The authors stated that "Several limitations were inherent in the design of a study with an EHR and complaints database. First, some patients followed up to 6 months did not have follow-up data at 12 months, which may be why patients sought care elsewhere, a safe switch or small mortality risk between This definition likely captured most patients with AS, but may have allowed for some misclassification (some selected patients may not have undergone AS per the guidelines). The GPS test may have had vastly different perspectives regarding the adoption of emerging medical technologies compared to those who did not request the test, and therefore may not be representative of the field at large."

Van Den Eeden et al (2018) stated that a reverse transcription polymerase chain reaction assay based on biopsy of 17 genes, which provides a prostate genomic score (GPS scale from 0 to 100), has been validated as an independent predictor of adverse pathology and biochemical recurrence after radical prostatectomy (RP) in men with low- and intermediate-risk PCa. In a retrospective study, these investigators evaluated GPS as a predictor of CaP metastasis and CaP-specific death (PCD) in a large cohort of men with localized CaP and long-term follow-up. A stratified cohort sampling design was conducted in a cohort of men treated with PR in Kaiser Permanente Northern California; RNA from archival diagnostic biopsies was analyzed to generate GPS results. SE investigators assessed the association between GPS and time to metastasis and PCD in prespecified single and multivariate statistical analyses, based on Cox proportional hazards models accounting for sampling weights. The final study population consisted of 279 men with low-, intermediate-, and high-risk PCA between 1995 and 2010 (mean follow-up 9.8 years) and included 64 PCDs and 79 metastases. Valid GPS results were obtained for 259 (93%). In univariate analysis, GPS was strongly associated with time to PCD, hazard ratio (HR)/20 GPS units = 3.23 (95% CI 1.84 to 5.65, p < 0.001) and time to metastasis, HR /20 units = 2.75 (95% CI: 1.63 to 4.63; p < 0.001). The association between GPS and both endpoints remained significant after adjusting for NCCN, AUA, and Prostate Cancer Risk Assessment (CAPRA) risks (p < 0.001). No patient with low- or intermediate-risk disease and GPS less than 20 developed metastases or PCD (n = 31). In the analysis of the operating characteristics of the PCD receiver over 10 years, GPS improved the c-statistic from 0.78 (CAPRA only) to 0.84 (GPS + CAPRA; p < 0.001). The authors concluded that GPS is a strong independent predictor of long-term outcomes in clinically localized PCa in men treated with PR and may improve risk stratification for men with newly diagnosed disease.

The authors noted that it is important to note that all patients in the cohort received definitive treatment with RP, and therefore a limitation of the study was that it did not assess outcomes in patients treated with AS or radiotherapy (RT). Another disadvantage of the study was that the patients were treated during an era when definitive treatment was the standard of care with little uptake of active surveillance.

Canfield et al (2018) stated that many men with low-risk PCa receive definitive treatment despite recommendations that were reported by 2 large randomized trials encouraging AD. These investigators conducted a retrospective cohort study using the Optum (Eden Prairie, MN) research database of electronic health records and administrative data to evaluate AS use in patients evaluated with a 17-gene GPS assay (Genomic Health, Redwood City, CA) and/or magnetic resonance imaging (MRI) of the prostate. Unidentified records of health plans enrolled from June 2013 to June 2016 that had more than or equal to 1 PCa record (n = 291,876) were extracted. Inclusion criteria included age 18 years or older, new diagnosis, low-risk AUA PCa (stage T1 to T2a, prostate-specific antigen less than or equal to 10 ng/mL, Gleason score 5 6) and clinical activity for at least 18 years old, at least 12 months before and after diagnosis. Data included baseline characteristics, use of GPS and/or MRI testing, and definitive procedures. GPS or MRI testing was performed in 17% of men (GPS, n = 375, 4%; MRI, n = 1174, 13%). Use of AS ranged from a minimum of 43% for men who underwent MRI only to 89% for men assessed by GPS who did not undergo MRI (p < 0.001). At 6-month follow-up, AS use was 31.0% higher (95% CI 27.6 to 34.5%, p < 0.001) for men who received only the GPS test compared to men who did not. submitted to the GPS test or MRI; the difference was 30.5% at the 12-month follow-up. The authors concluded that, in a large US pay system, GPS testing was associated with significantly greater SA use at 6 and 12 months compared with men who had MRI scans only or who had no MRI scans, GPS. or MRI.

The authors noted that several limitations were inherent in a study design with a claims database and EHR. First, some patients followed up to 6 months did not have follow-up data at 12 months, which could be because patients sought care elsewhere, a change in insurance, or a small risk of mortality between 6 and 12 months. Second, as there is no single CPT code for AS, these investigators defined it as no definitive therapy during the observation period. This definition likely captured the majority of patients with AS, but may have allowed for some misclassifications (some selected patients may not actually have had AS according to the guidelines). Finally, physicians who ordered GPS testing may have vastly different perspectives regarding the adoption of emerging medical technologies compared to those who did not order testing, and therefore may not be representative of the broader field.

Lynch et al (2018) compared CaP management strategies at 6 Veterans Affairs Medical Centers (VAMC) before and after the introduction of the Oncotype DX Genomic Prostate Score (GPS) assay. These investigators reviewed the records of patients diagnosed with PCa between 2013 and 2014 to identify patterns of management in an untested cohort. From 2015 to 2016, these patients received GPS testing in a prospective study. The 6-month post-biopsy charts for both cohorts were reviewed to compare the treatment received in the untested and tested cohorts. Men newly diagnosed with very low, low, and selected intermediate-risk PCa NCCN were eligible for this study. Patient characteristics were generally similar in the untested and tested cohorts. Use of active surveillance (AS) was 12% higher in the tested cohort compared to the untested cohort. In men younger than 60 years, SA use in screened men was 33% higher than in unscreened men; SA in tested men was higher in all NCCN risk groups and races, particularly in low-risk men (72% vs. 90% for untested and tested, respectively). Tested veterinarians exposed to Agent Orange (AO) received less SA than non-tested veterinarians. Tested unexposed veterinarians received 19% more SA than unexposed veterinarians. Median GPS results did not differ significantly as a factor of race or exposure to OC. The authors concluded that men who underwent GPS testing are more likely to use AS within one year of diagnosis, regardless of age, race, and NCCN risk group. Median GPS was similar between racial groups and OA exposure groups, suggesting similar biology between these groups. They stated that the GPS assay can be a useful tool to refine PCa risk assessment and increase AE rates among patients at low clinical and biological risk, which is in line with guideline-based care. Furthermore, they stated that future studies showing persistence in AE and long-term outcomes should be considered to further support the usefulness of the GPS assay.

This study has several disadvantages. The untested cohort included a significantly higher proportion of intermediate-risk patients. While this could be interpreted to mean that greater SA use in the tested cohort was driven by lower baseline risk, within-group changes indicated that there was greater SA use in the tested patients, regardless of risk group. this shift was most pronounced for the NCCN low-risk category; but it was present in patients at very low and intermediate risk for NCCN. The authors considered AS any patient who did not receive definitive treatment within 6 months after the biopsy. Therefore, some patients classified as receiving AS may simply be delaying planned definitive treatment. Non-evaluated patients were seen at the VA within 2 years prior to enrollment of evaluated patients. Practice patterns are evolving towards higher rates of AS in all practice settings, and part of this change may be related to time trends. However, these researchers believed that it was unlikely that the differences between the groups in this study were achieved over a period of 1 to 2 years, particularly because the institutions and providers were the same and the reference rate of AS already was very tall.

Hu et al (2018) noted that tissue-based GECs can help with management decisions in patients with newly diagnosed PCa. These investigators evaluated the current use of GEC tests and determined how test results were associated with management of the primary disease. In this observational study, patients diagnosed with localized CaP were screened through the Michigan Urologic Surgery Improvement Collaborative Registry. The use and outcomes of 3 GECs (Decipher Prostate Biopsy, Oncotype DX Prostate and Prolaris) were collected prospectively. Practice patterns, predictors of GEC use, and the effect of GEC outcomes on disease management were examined. Of the 3966 newly diagnosed patients, 747 (18.8%) underwent the GEC test. The rate of GEC use in individual practices ranged from 0% to 93%, and patients who underwent the GEC test were more likely to have a lower PSA level, lower Gleason score, lower clinical T stage, and less positive nuclei (all p < 0.05). Among patients with a favorable clinical risk for cancer, the rate of AS differed significantly between patients with an above-threshold GEC result (46.2%), those with a below-threshold GEC result (75.9%) and those who did not undergo GEC (57.9%; p < 0.001 for comparison of the 3 groups). This resulted in an estimate that for every 9 men at favorable cancer risk who undergo the GEC test, 1 additional patient may have their disease initially controlled with SA. In multivariate analysis, patients with favorable-risk PCa who were classified as low-risk for GEC were more likely to be treated with SA than those without evidence (OR, 1.84; p = 0.006). The authors concluded that there is great variability in use at the practice level and in the GEC tests ordered in patients with newly diagnosed localized PCa. In patients with favorable clinical risk of cancer, the GEC test significantly increased AS use. These investigators stated that further follow-up will help to examine whether incorporating the GEC test into initial patient care favorably affects clinical outcomes.

Kornberg et al (2019) stated that the OncotypeDx GPS (Genomic Prostate Score) is a 17-gene RNA expression assay intended to help guide treatment decisions in men diagnosed with CaP. The PI-RADS (Prostate Imaging Reporting and Data System) version 2 was developed to standardize the risk stratification of lesions identified on multiparametric MRI of the prostate. These investigators examined whether these tests are associated with an increased risk of biopsy improvement in men with AS. They identified all patients with AS at the University of California-San Francisco who had low/intermediate-risk PCa (PSA 20 ng/mL or less and clinical stage T1/T2) and disease with a Gleason score 6 who had undergone multiple biopsies and had an available GPS and/or underwent a multiparameter prostate MRI with an available PI-RADS version 2 score. The main outcome of the study was biopsy improvement, defined as an increase in the Gleason score from 3 + 3 to 3 + 4 or more, which was analyzed using Cox proportional hazards regression. Of the men, 140 had only GPS test results, 169 had only a PI-RADS version 2 score, and 131 had both data. Each 5-unit increase in GPS was associated with an increased risk of biopsy improvement (HR 1.28, 95% CI 1.19 to 1.39, p < 0.01). PI-RADS scores of 5 vs 1-2 (HR 4.38, 95% CI 2.36 to 8.16, p < 0.01) and 4 vs 1-2 (HR 2.62, 95% CI 1 .45 to 4.76, p < 0.01) were also associated with an increased risk of high-grade biopsy. In the subanalysis of patients with GPS and PI-RADS version 2 scores, GPS was associated with improved biopsy, adding value to clinical covariates (partial likelihood ratio p = 0.01). The authors concluded that a higher GPS or PI-RADS version 2 score of 4 or 5 was associated with a greater risk of biopsy improvement.

Eggener et al (2019) validated the Oncotype DX Genomic Prostate Score (GPS) biopsy-based gene expression assay of 17 genes as a predictor of adverse pathology (AP, Gleason score [pGS] greater than or equal to 4+3 and/ or greater than or equal to pT3) in a prospectively recruited cohort. Between July 2014 and September 2015, a total of 1200 men with very low, low, and intermediate risk prostate cancer (CaP) were enrolled in a multi-institutional prospective study of the GPS study. The subgroup undergoing immediate radical prostatectomy (RP) after GPS testing was included in a prespecified subanalysis of GPS biopsy and its association with surgical PA in PR using logistic regression and receiver operation characteristic curves. The effect of GPS tests on physicians' and patients' attitudes towards decision making was assessed with the Decision Conflict Scale. A total of 114 patients (treated by 59 physicians from 19 sites) chose PR and 40 (35%) had PA; The GPS result was a significant predictor of BP (odds ratio per 20 GPS units [OR/20 units]: 2.2, 95% CI: 1.2 to 4.1, p=0.008) in the univariate analysis and remained significant after adjustment for biopsy Gleason score, clinical T-stage, and logPSA (OR/20 units: 1.9, 95% CI: 1.0 to 3.8, p=0.04) or NCCN risk group (OR /20 units: 2.0, 95% CI: 1.1 to 3.7, p = 0.02). The mean pre-GPS decision-making conflict scale score was 27 (95% CI 24 to 31), which improved significantly after the GPS test to 14 (95% CI 11 to 17) (p < 0.001). The authors concluded that in this multi-institutional real-world study, the GPS assay was prospectively confirmed as an independent predictor of BP at surgery; The GPS test was associated with a reduction in patient decision conflict.

The authors stated that the data presented were not without limitations; RP was performed by 59 surgeons from 19 centers and providers did not receive training on how to consistently approach PCa decision-making. In addition, biopsy and prostatectomy specimens were evaluated locally without central review. Although the lack of standardization across sites has introduced the potential for site-specific variation and interobserver variability in classification/staging, the fact that these results were derived from diverse practices may increase the external validity and relevance of these findings. . test in practice. Although there were only 40 AP events, there was a strong and significant association between GPS and the AP result and it was unlikely that additional data points would have substantially modified the result. This study did not include patients treated with initial AS or radiotherapy; Evaluations of the GPS test in men treated with initial AS or radiotherapy are ongoing. These investigators stated that, despite these limitations, this prospective analysis highlighted the added value of the GPS test for predicting PA in a contemporary AS-eligible PCa cohort. The added potential of genomic testing to inform management decisions may help to increase the pool of men who are eligible and appropriate for AS, while also identifying men with more aggressive disease who may consider definitive treatment.

An accompanying editorial asked "how do we know that similar improvements in decision conflict could not have been achieved through the use of free and publicly available decision aids?" (Singhal, et al., 2019). The editorialists noted that the authors acknowledged that this study did not include men who opted for active surveillance. "While men who choose treatment are expected to experience some level of relief after simply making that decision, the more pertinent question may be how GPS affects patients who seek a less definitive and more anxiety-associated approach, such as surveillance. Editorialists noted that the study reported the odds ratio and overall variation of GPS scores, but noted that it is difficult to understand the clinical significance of the odds ratio without a clear illustration of GPS distribution. of the research are not adequate to discriminate between subjects who experience and do not experience the outcome at the individual level. The editorialists also noted that since tests such as the Oncotype Dx Prostate are at issue that report a continuum of risk, it is the lack of a single, clear threshold that can approve or disprove the projected result. The editorialist suggested that future studies report va threshold values with very high specificity and sensitivity observed in the studied population. "Clinical usefulness in this setting will continue to be challenged by the need to identify such thresholds for safe decision-making at the individual level." The editorialists concluded, "Further studies will help clarify the optimal clinical scenarios for implementing this trial and others in this rapidly evolving field."

The National Comprehensive Cancer Network's clinical practice guideline on "Prostate Cancer" (version 4.2019) recommends coverage of "Decipher", "Oncotype DX Prostate", "Prolaris", and "ProMark" (Category 2A). Oncotype DX Prostate: for post-biopsy based very low-risk, low-risk, and favorable intermediate-risk NCCN patients with a life expectancy greater than 10 years who have not received treatment for prostate cancer and are candidates for active surveillance or therapy definitive.

Oncotype DX AR-V7 core detection assay for men with metastatic castration-resistant prostate cancer

The Oncotype DX Nuclear Screening Test with Nucleus-Located Androgen Receptor 7 (AR-V7) Splice Variant is a liquid biopsy test based on circulating tumor cells that detects CTC patients who have nuclear expression of the truncated AR protein -V7 and targets aims to provide information that may help guide treatment selection in patients with metastatic castration-resistant prostate cancer (mCRPC). Detection of androgen receptor splicing mRNA variant 7 (AR-V7) in CTCs from men with advanced prostate cancer has been found to be associated with resistance to enzalutamide and abiraterone.

Antonarakis et al (2014) used a quantitative reverse transcriptase polymerase chain reaction assay to assess AR-V7 in circulating tumor cells from prospectively recruited patients with metastatic castration-resistant prostate cancer initiating therapy with enzalutamide or abiraterone. Investigators examined the associations between AR-V7 status (positive vs. negative) and rates of prostate-specific antigen (PSA) response (the primary end point), progression-free PSA (progression-free survival from PSA), the absence of clinical signs or radiographic progression. survival and overall survival. A total of 31 patients treated with enzalutamide and 31 patients treated with abiraterone were included, of whom 39% and 19%, respectively, had detectable AR-V7 in circulating tumor cells. Among men who received enzalutamide, AR-V7-positive patients had lower PSA response rates than AR-V7-negative patients (0% vs. 53%, P = 0.004) and progression-free survival of Shorter PSA (median, 1.4 months vs. 6.0). months; P<0.001), clinical or radiographic progression-free survival (median, 2.1 months vs. 6.1 months; P<0.001), and overall survival (median, 5.5 months vs. not achieved; P=0.002). Similarly, among men who received abiraterone, AR-V7-positive patients had lower PSA response rates than AR-V7-negative patients (0% vs. 68%, P = 0.004) and longer survival free. shorter PSA progression (median, 1.3 months vs. not achieved; P<0.001), clinical or radiographic progression-free survival (median, 2.3 months vs. not achieved; P<0.001), and overall survival (median, 10.6 months vs. not reached, P=0.006). The association between AR-V7 detection and therapeutic resistance was maintained after adjusting for full androgen receptor mRNA expression. The investigators concluded that the detection of AR-V7 in circulating tumor cells from patients with castration-resistant prostate cancer may be associated with resistance to enzalutamide and abiraterone. They stated that these findings require large-scale prospective validation. An accompanying editorial (Nelson, et al., 2014) agreed that "the small number of patients in the study by Antonarakis and colleagues requires validation. The proprietary combination of antibodies used to capture circulating tumor cells is a potential limitation. If other methods of isolating prostate cancer cells would give similar results."

Antonarakis et al (2015) investigated whether AR-V7-positive patients would maintain responsiveness to taxane-based chemotherapy and whether AR-V7 status would have a differential impact in men treated with taxane compared to men treated with enzalutamide or abiraterone. The investigators screened CTCs for AR-V7 mRNA using a reverse transcription polymerase chain reaction assay. From January 2013 to July 2014, researchers prospectively recruited patients with metastatic CRPC starting taxane-based chemotherapy (docetaxel or cabazitaxel) at a single academic institution. Their prespecified statistical design called for a sample size of 36 taxane-treated men. Investigators evaluated the associations between AR-V7 status and rates of prostate-specific antigen (PSA) response, PSA progression-free survival (PSA PFS), and clinical and/or radiographic progression-free survival (PFS). After incorporating updated data from the previous study of 62 patients treated with enzalutamide or abiraterone, they also investigated the interaction between AR-V7 status (positive or negative) and type of treatment (taxane versus enzalutamide or abiraterone). Of the 37 taxane-treated patients enrolled, 17 (46%) had detectable AR-V7 on CTC. PSA responses were achieved in AR-V7 positive and AR-V7 negative men (41% vs. 65%; P = 0.19). Likewise, PSA PFS (hazard ratio [HR], 1.7, 95% CI, 0.6-5.0; P = 0.32) and PFS (HR, 2.7, 95% CI, 0 .8-8.8; P = 0.11) were comparable in AR -V7 positive and AR-V7 negative. A significant interaction was observed between AR-V7 status and type of treatment (P < 0.001). Clinical outcomes were superior with taxanes compared with enzalutamide or abiraterone therapy in AR-V7-positive men, while outcomes did not differ by treatment type in AR-V7-negative men. In AR-V7 positive patients, PSA responses were greater in men treated with taxane than in men treated with enzalutamide or abiraterone (41% vs. 0%; P < 0.001), and PSA PFS and PFS were significantly longer in taxane-treated men (HR, 0.19 [95% CI, 0.07-0.52] for PSA PFS, P = 0.001; HR, 0.21 [95% CI, 0.07-0.59] for PFS, P = 0.003). The investigators concluded that detection of AR-V7 in CTCs from men with metastatic CRPC was not associated with primary resistance to taxane-based chemotherapy. In AR-V7 positive men, taxanes appear to be more effective than enzalutamide or abiraterone therapy, whereas in AR-V7 negative men, taxanes and enzalutamide or abiraterone may have comparable efficacy. The investigators suggested that detection of AR-V7 based on circulating tumor cells may serve as a treatment selection biomarker in CRPC. An accompanying editorial (Taplin and Balk, 2015) noted that "an obvious implication of these AR-V7 studies is that men with AR-V7-positive CRPC, particularly if they were also previously treated with abiraterone or enzalutamide, are more likely to respond to taxanes than alternative RA-targeted therapy, but this needs to be confirmed in further studies using validated assays." They noted that although the difference in response to taxane-based chemotherapy was not statistically significant, the confidence intervals were wide and there was a 24% difference (AR-V7 positive men were less likely to respond) and further study would be needed. . larger trial to assess accurately. quantify any difference.

Luo (2016) stated that prostate cancer cells demonstrate marked "addiction" to androgen receptor (AR) signaling at all stages of disease progression. As such, suppression of AR signaling remains the therapeutic target in the systemic treatment of prostate cancer. Several molecular changes arise in patients treated with RA-targeted therapies. These molecular alterations may indicate the emergence of resistance to treatment and may be the target of the development of new agents for prostate cancer. The presence of functional androgen receptor splice variants may represent a possible explanation for resistance to abiraterone and enzalutamide, new agents targeting the AR developed to treat mCRPC. Over the past 8 years, many androgen receptor splicing variants have been identified and characterized. Among these, the splice variant of androgen receptor 7 (AR-V7) has been extensively investigated. In AR-V7, the entire COOH-terminal linker-binding domain of the canonical AR is truncated and replaced with a 16-amino acid variant-specific peptide. Functionally, AR-V7 is able to mediate constitutive nuclear localization and androgen receptor signaling in the absence of androgen or in the presence of enzalutamide. Methods have been developed to detect AR-V7 in clinical samples of mCRPC. The author concluded that AR-V7 can be reliably measured in tissues and CTCs derived from mCRPC patients, allowing for cross-sectional and longitudinal clinical correlative studies; Current evidence derived from studies focused on the detection of AR-V7 in mCRPC supports its potential clinical utility as a treatment selection marker.

Ciccarese and colleagues (2016) noted that RA plays a key role in progression to mCRPC. Despite recent progress in treating persistent RA activity with state-of-the-art hormone therapies (abiraterone acetate and enzalutamide), resistance to these agents limits therapeutic efficacy for many patients. Various explanations for the response and/or resistance to abiraterone acetate and enzalutamide are emerging, but the growing interest is focused on the importance of AR splicing variants (AR-V) and, in particular, AR-V7. Growing evidence has highlighted the concept that the variant expression could be used as a potential predictive biomarker and therapeutic target in advanced prostate cancer. The authors concluded that understanding the mechanisms of resistance or sensitivity to treatment may help to achieve more effective management of mCRPC, increasing clinical outcomes and representing a promising and attractive area of prostate cancer research.

In a cross-sectional cohort study, Scher and colleagues (2016) determined whether pretherapy nuclear AR-V7 protein expression and localization in CTCs is a treatment-specific marker for response and outcome among antiretroviral inhibitors. androgen receptor signaling (ARS) and taxanes. . A total of 265 men with progressive mCRPC who underwent a change in treatment were considered; 86 were excluded because they were not starting ARS or taxane therapy; and 18 were excluded due to processing time limitations, leaving 161 patients for analysis. Between December 2012 and March 2015, blood was collected and processed from patients with progressive mCRPC immediately prior to new line systemic therapy. Patients were followed for up to 3 years. The main outcome measures were (PSA response, time to receive therapy, radiographic PFS (rPFS) and OS. Overall, of 193 blood samples collected prospectively from 161 men with mCRPC, 191 were evaluable (128 ARS pre -inhibitor and 63 pre-taxane) AR-V7 positive CTCs were found in 34 samples (18%), including 3% of the first-line, 18% of the second-line, and 31% of the third-line or greater line of samples. positives before ARS inhibition had resistance after therapy PSA changes (PTPC), shorter rPFS, shorter time on therapy, and shorter OS than those without AR-V7 positive CTCs. Overall, resistant PTPCs were seen in 65 of 112 samples (58%) with no detectable AR-V7 positive CTCs before ARS inhibition There were statistically significant differences in OS but not in PTPC, time on treatment or time on treatment rPFS for patients with or without positive CTC s for AR-V7 before treatment treated with a taxane A multivariable model adjusted for baseline factors associated with survival showed superior OS with taxanes over ARS inhibitors when AR-V7 positive CTCs were detected before therapy (HR, 0.24; 95% CI 0.10 to 0.57; p = 0.035). The authors concluded that these findings validated nuclear CTC expression of the AR-V7 protein in men with mCRPC as a treatment-specific biomarker associated with superior survival on taxane therapy versus ARS-directed therapy in a practice setting. Furthermore, they stated that continued examination of this biomarker in prospective studies will further contribute to clinical utility. Furthermore, they stated that, given the magnitude of understratification and the specificity of the nuclear-specific AR-V7 protein test result in CTCs, a diagnostic-level test that informs the selection of ARS inhibitors or taxanes has the potential to significantly improve results, allowing patients to receive treatments to which they are most likely to respond, avoiding the toxic effects and costs associated with ineffective treatment. These investigators stated that prospective studies are underway to validate these findings and further elucidate clinical utility. An accompanying editorial (Montgomery and Plymate, 2016) noted that the assay used in this study may be less sensitive and specific than the assay used in the studies previously described by Antonorakis, et al. The editorialist noted that there was a smaller proportion of CTC AR-V7 positive patients on each line of therapy using the protein-based assay used by Sher, et al. compared to the mRNA-based assay used by Antonorakis, et al. Although the patient groups were not completely identical, this difference in detection may have reflected the greater sensitivity provided by the polymerase chain reaction (PCR) used by Antonorakis versus the antibody detection systems used by Sher et al. The editorialist also noted that the issues of antibody versus PCR specificity are clearly different. The editorialist cited studies showing that AR-V7 antibodies can react with non-prostatic tissue and cell lines in which AR-V7 cannot be identified.

In a cross-sectional cohort study, Scher and colleagues (2017) assessed whether expanding the positivity criteria to include nuclear and cytoplasmic (“nuclear-agnostic”) AR-V7 localization identifies more patients who would benefit from a taxane. on an androgen receptor signaling inhibitor (ARSi), such as abiraterone acetate, apalutamide, and enzalutamide. Between December 2012 and March 2015, a total of 193 pre-therapy blood samples, 191 of which were evaluable, were collected and processed from 161 unique mCRPC patients prior to starting a new line of systemic therapy for disease progression at Memorial Sloan Kettering Cancer Center. We explored the association between two AR-V7 scoring criteria, post-therapy PTPC and OS after treatment with ARSI or taxane. One criterion required specific nuclear localization of AR-V7, and the other required a signal from AR-V7, but was independent of protein localization in CTC. Correlation of AR-V7 status with PTPC and OS was investigated. Relationships with survival were analyzed by Cox multivariate regression and log-rank analysis. A total of 34 (18%) specimens tested positive for AR-V7 using specific nuclear criteria and 56 (29%) tested positive for AR-V7 using agnostic nuclear criteria. After treatment with ARSI, none of the 16 specific nuclear AR-V7 positive samples and 6 of 32 (19%) nuclear agnostic AR-V7 positive samples had a PTPC greater than or equal to 50% at 12 weeks. The most important baseline factor influencing OS was the interaction between the presence of core-specific AR-V7 positive CTCs and treatment with a taxane (HR 0.24, 95% CI 0.078 to 0.79; p = 0.019 ). This interaction was not significant when nuclear agnostic criteria were used. The authors stated that the results highlighted a major limitation of mRNA-based approaches in CTCs: the inability to determine whether the AR-V7 message was translated into protein and, if so, whether the protein is present in the nucleus, where it is located. It is known to function as an oncogenic driver of tumor growth. A prospective clinical trial testing the predictive ability of pooled AR-V7 CTC mRNA and specific nuclear AR-V7 protein in pooled samples is currently underway. They concluded that to reliably inform treatment selection using an AR-V7 protein biomarker in CTCs, specific nuclear localization is required. In an accompanying editorial, Lamb, et al. (2017) noted that a critique of the study by Sher, et al. is that "nuclear" or "agnostic" status can be assigned based on a single CTC. "We await further data to confirm whether a single cell can indeed reflect an individual's predominant disease burden. Furthermore, it will be important to establish the biological explanation for these findings."

Bernemann et al (2017) found that a subset of patients may benefit from abiraterone and/or enzalutamide despite the detection of AR-V7 splicing variants in their CTCs. The researchers evaluated response in a cohort of 21 patients with PCR-positive AR-V7 castration-resistant prostate cancer who received therapy with abiraterone or orenzalutamide. Investigators detected a subgroup of six AR-V7 positive patients who showed benefit with either abiraterone or enzalutamide. Progression-free survival ranged from 26 days (censored) to 188 days. Four patients had a PSA decrease > 50%. Looking at past therapies, the researchers noted that only one of the six patients had received state-of-the-art ADT prior to CTC collection. As a result, investigators concluded that AR-V7 status on CTCs cannot fully predict non-response to next-generation ADT, and AR-V7-positive patients should not be routinely denied treatment with abiraterone or enzalutamide, especially since Effective alternative treatment options are still limited.

Scher et al (2018) examined whether a validated assay for AR-V7 protein in circulating tumor cells could determine the differential OS between mCRPC patients treated with taxanes versus ARSi. This blinded correlative study, conducted from 31 December 2012 to 1 September 2016, included 142 patients with histologically confirmed mCRPC who were treated at Memorial Sloan Kettering Cancer Centre, Royal Marsden or London Science Centre. London Health. Blood samples were obtained prior to administration of ARS inhibitors or taxanes as second-line or main systemic therapy for progressive mCRPC. The main outcome measures were OS after treatment with ARSI or taxane relative to pre-therapy AR-V7 status. Among the 142 patients in the study (mean [SD] age 69.5 [9.6] years), 70 were designated as high risk by conventional prognostic factors. In this high-risk group, RA-V7-positive patients treated with taxanes had a higher OS than those treated with ARSI (median OS, 14.3 versus 7.3 months; HR, 0.62; 95% CI: 0 .28 to 1.39, p=0.25). AR-V7 negative patients treated with ARSI had a higher OS than those treated with taxanes (median OS, 19.8 vs. 12.8 months; HR, 1.67; 95% CI: 1.00 to 2. 81; p = 0.05). The authors concluded that the findings of this study suggested that the nucleus-located AR-V7 protein in circulating tumor cells could identify patients who may live longer with taxane-based chemotherapy versus ARSI treatment. Furthermore, these investigators stated that a limitation of this study was that patients were not prospectively randomized to treatment based on biomarker results, which was addressed in part by using risk scores in the analysis to mitigate confounding between treatment and risk. underlying patient for which latent. , the included features may not capture the unknown imbalances.

An editorial accompanying the study by Sher et al. He pointed out a number of remaining challenges. He stated that intrapatient heterogeneity with contemporaneous ARV7 positive and ARV7 negative CTCs is a problem; follow-up tissue studies should explore AR-V7 intermetastatic and intrametastatic positivity, as AR-V7 positive patients may still have a subset of the sensitive cell population. The editorialists also stated that confidence in false-negative rates will require simultaneous assessment of AR-V7 CTC positivity in men with AR-V7 positive tumor biopsies. Such studies must take into account the fact that many patients with CRPC, especially if they have not received chemotherapy, do not have CTCs in their blood, which is a cause of false-negative results. The editorialists stated that it is also necessary to determine whether the AR-V7 assay is prognostic or predictive. Studies should correlate treatment response with assay positivity, not just survival data, to ensure that the assay is not simply a prognostic biomarker. The editorialists noted that AR-V7 positivity in the Sher et al. study was associated with higher levels of LDH, alkaline phosphatase, and PSA, suggesting a greater burden of disease in the taxane group. This finding indicates that AR-V7 positivity by this assay may be more prognostic, associated with disease burden, than predictive. Finally, this assay used by Sher, et al. is an antibody against cryptic exon 3 of AR-V7 that has been reported to be non-specific, increasing the risk of false-positive results. This concern can be alleviated by comparing this AR-V7 CTC protein assay used by Sher et al. with the CTC AR-V7 mRNA assay used in other studies.

The NCCN Clinical Practice Guideline on Prostate Cancer (2019) stated that: "These clinical experiences suggest that AR-V7 assays are promising predictors of aberaterone and enzalutamine resistance. In addition, the prevalence of AR-V7 positivity is only 3% in patients prior to treatment with enzalutamide, abiraterone, and taxanes, therefore the panel believes that AR-V7 screening would not be helpful in informing treatment decisions in the naive setting. On the other hand, the prevalence of positivity of AR-V7 is higher after progression with abiraterone or enzalutamide (19-39%), but data have already shown that abiraterone/enzalutamide cross-therapy is rarely effective and taxanes are more effective in this setting. Use of the AR-V7 test may be considered to help guide therapy selection in the setting of post-abiraterone/ensalutamide metastatic CRPC."

Armstrong et al (2019) noted that AR-V7 results in a truncated receptor, leading to ligand-independent constitutive activation that is not inhibited by antiandrogenic therapies including abiraterone or enzalutamide. Since previous reports have suggested that the detection of circulating AR-V7 tumor cells (CTCs) is a poor prognostic indicator for the clinical efficacy of secondary hormone therapies, these investigators conducted a prospective multicenter validation study. PROPHECY is a prospective, blinded, multicenter study of men with high-risk metastatic castration-resistant prostate cancer (mCRPC) starting treatment with abiraterone acetate or enzalutamide. The primary objective was to validate the prognostic significance of baseline CTC AR-V7 based on radiographic or clinical PFS using the Johns Hopkins University Modified CTC AR-V7 mRNA Assay and the Johns Hopkins nuclear-specific AR-V7 protein. Epic Sciences CTC . rehearsal; OS and PSA responses were secondary endpoints. These investigators recruited 118 men with mCRPC who were starting treatment with abiraterone or enzalutamide; Detection of AR-V7 using the Johns Hopkins and Epic AR-V7 assays was independently associated with shorter PFS (HR, 1.9 [95% CI, 1.1 to 3.3; p = 0.032] and 2.4 [95% CI: 1.1 to 5.1, p = 0.020], respectively) and OS (HR, 4.2 [95% CI: 2.1 to 8.5] and 3.5 [95% CI: 1.6 to 8.1], respectively) after adjusting for the number of CTCs and clinical prognostic factors. Men with positive mCRPC AR-V7 had fewer confirmed PSA responses (0% to 11%) or soft tissue responses (0% to 6%). The percent agreement observed between the 2 AR-V7 assays was 82%. The authors concluded that detection of AR-V7 on CTCs by 2 blood tests was independently associated with shorter PFS and OS with abiraterone or enzalutamide, and these men with mCRPC should receive alternative treatments.

Boeriggter et al (2019) noted that the study by Armstrong, et al. It has some important limitations. First, the number of AR-v7 positive patients was relatively small and alternative treatments were not tested. Second, there were discrepancies in positive AR-v7 results between the two tests. Eleven patients tested positive for AR-v7 using the Epic Sciences Test Platform (antibodies), while 28 patients tested positive for AR-v7 using AdnaTest (PCR test). "Taken together, detection of AR-v7 is a promising predictive biomarker. Detection of AR-v7 in CTCs can predict response to treatment with enzalutamide and abiraterone, and it appears that taxane treatment should be preferred for these patients." Clinical trials are needed to validate the AdnaTest platform and Epic Sciences to confirm its clinical utility for patients with mCRPC."

Sciarra et al (2019) sought to critically analyze: frequency of AR-V7 expression in cases of metastatic castration-resistant prostate cancer (mCRPC), impact of AR-V7 expression on abiraterone, enzalutamide and taxane therapy. The authors searched the Cochrane Library and Medline databases for literature from the past 10 years. The authors critically evaluated the level of evidence according to the European Association of Urology (EAU) guidelines. Twelve clinical trials were selected. The authors said that determination of AR-V7 in peripheral blood using mRNA from circulating tumor cells appears to be the preferred method. At baseline, the median percentage of cases with AR-V7 positivity was 18.3% (range 17.8-28.8%). All mCRPC data submitted to enzalutamide or abiraterone reported significantly lower (P <0.05) clinical progression-free survival (CPFS) and overall survival (OS) for V7+ AR than RA -V7- (CPFS risk ratio [HR ]: 2.3; 95% CI 1.1-4.9; OS HR: 3.0; 95% CI 1.4-6.3). In mCRPC cases undergoing chemotherapy, the data are not homogeneous and some studies have not shown an association between CPFS or OS and AR-V7 status (OS HR 1.6; 95% CI 0.6-4.4; P = 0.40). The authors concluded that the suggestion is that taxane therapy is more effective than abiraterone or enzalutamide for men with CRPC AR-V7+. In contrast, the authors found that clinical outcomes did not appear to differ significantly depending on the type of therapy used among V7-RA cases.

prostavision

ProstaVysion (Bostwick Labs) is a prognostic genetic panel for prostate cancer (Raman, et al., 2013). This test examines two main mechanisms of prostate carcinogenesis: the fusion/translocation of the ERG gene and the loss of the PTEN tumor suppressor gene. This test is a fabric-based panel. By examining these two markers, ProstaVysion can provide a molecular analysis of prostate cancer aggressiveness and the patient's long-term prognosis. ERG gene fusions are found in 40% of primary prostate cancers and are associated with a more aggressive phenotype. PTEN deletion occurs both in localized prostate cancers and in 60% of metastases.

PAM50 and Prosigna

The PAM50 intrinsic breast cancer classifier (University of Utah) examines 50 genes and classifies breast cancer into four subtypes (Raman, et al., 2013). Each subtype responds differently to standard therapies, and knowing the subtype allows clinicians to tailor treatment for each patient. The PAM50 assay can help shape prognosis and predict the benefit of adjuvant tamoxifen and was found to be superior to immunohistochemistry.

A review by the National Institute for Health Research (Ward, et al., 2013) concluded that the evidence for PAM50 is limited. The report concluded that "the evidence base for PAM50 is still relatively immature".

An international task force (Azim, et al, 2013) found insufficient evidence for the analytical and clinical validity of the PAM50. They found insufficient evidence for the clinical utility of PAM50 or other breast cancer genomic tests they evaluated.

A report by the Belgian Center for Health Knowledge (KCE) (San Miguel, et al., 2015) found that the evidence for the PAM50 is limited to studies that support the prognostic ability (clinical validity) of the test. Most evidence is found in patients with positive lymph nodes. The KCE found insufficient evidence on the impact of PAM50 on clinical management (clinical usefulness).

Prosigna is intended to be used as a prognostic indicator in conjunction with other clinicopathologic factors for 10-year distant recurrence-free survival in postmenopausal women who are hormone receptor (HR) positive, lymph node negative/stage I or II, or lymph node positive .lymphatic breast cancer positive (1 to 3 lymph nodes positive)/stage II to be treated with adjuvant endocrine therapy alone. The assay measures the expression profiles of genes included in the PAM50 gene signature, as well as 8 housekeeping genes (for normalization), 6 positive controls, and 8 negative controls.

The BlueCross BlueShield Association (2015) concluded that the use of Prosigna to determine the risk of recurrence in women with early-stage breast cancer does not meet criteria for ECT. The evidence is insufficient to allow conclusions about health outcomes. Assay performance characteristics of the commercially available version of the test indicate high reproducibility.

A report on medical technology innovation from the National Institute for Health and Clinical Excellence (NICE, 2015) noted that none of the women tested in the clinical validation studies (citing Gnant, et al. (2014), Sestak, et al (2015) and Dowsett et al (2013) received chemotherapy as part of their initial treatment. As a result, the prognostic value of the ProsignaROR score in a chemotherapy-treated population is unknown. These clinical validation studies Sestak et al (2015) pooled previously analyzed data by Dowsett et al (2013) and Gnant et al (2014) Dowset et al (2013) and Sestak (2015) used the clinical treatment score as a comparator instead of the online tools Adjuvant! Online and PREDICT, or NPI, which are standard practice in the UK. Similarly, Gnant et al. (2014) used a combined score of clinicopathological parameters as a comparator for Prosigna. The NICE stated that such scores are always incomplete because they may not include r all parameters used by physicians in other healthcare systems to assist in clinical decision-making. The NICE report also noted that all included studies received financial support or revealed conflicting interests from the manufacturer, and this presents the potential for bias in reporting the results.

The American Society of Clinical Oncology (2016) guidelines state: "If a patient has ER/PgR-positive, HER2-negative (node negative) breast cancer, the clinician may use the PAM50 (Prosigna Breast Cancer Prognostic Gene Signature Assay; NanoString Technologies, Seattle, WA), along with other clinicopathologic variables, to guide decisions about adjuvant systemic therapy." This is a strong recommendation based on high-quality evidence. The guidelines do not recommend the use of PAM50 to guide decisions about adjuvant systemic therapy in patients with ER/PgR positive and HER2 negative (node positive) breast cancer. The guidelines also advise against the use of PAM 50 in HER2-positive breast cancer and TN breast cancer. The guidelines recommended not using the PAM50 to guide decisions about extended endocrine therapy for patients with ER/PgR-positive, HER-2-negative (node negative) breast cancer who have received 5 years of endocrine therapy with no evidence of recurrence.

The Cancer Care Ontario guidelines (Chang, et al., 2016) state: "While no study represents a gold standard, Oncotype DX is supported by the widest range of evidence for prognosis and prediction of chemotherapy benefit, while Prosigna and EndoPredict have evidence validity based on providing some of the same or similar clinical information."

Oncotype Dx colon

Oncotype Dx Colon has been promoted for use in colorectal cancer. However, evidence is lacking to establish the clinical usefulness of this test in colorectal cancer.

The results of the Quick, Simple and Reliable Study (QUASAR) were published by Gray et al (2011). The purpose of the QUASAR study was to develop quantitative gene expression assays to assess the risk of recurrence and the benefits of chemotherapy in patients with stage II colon cancer. Recurrence score (RS) and treatment score (TS) were calculated from the gene expression levels of 13 cancer-related genes and five reference genes. Study results showed that the risk of recurrence was significantly associated with RS (95% confidence interval [CI]: 1.11 to 1.74; p = 0.004). The 3-year recurrence risks were 12%, 18%, and 22% for the predefined low, intermediate, and high recurrence risk groups, respectively. Continued RS was associated with risk of recurrence (p = 0.006), but there was no trend towards greater benefit of chemotherapy with higher ST (p = 0.95). The continuous 12-gene RS was validated in a prospective study to assess the risk of recurrence in patients with stage II colon cancer after surgery. RS has also been found to provide prognostic value complementing T stage and mismatch repair.

Yamanaka et al (2016) evaluated the 12-gene recurrence score assay for stage II and III colon cancer without chemotherapy to reveal the natural course of recurrence risk in stage III disease. A cohort sampling design was used. From 1487 consecutive patients with stage II to III disease who underwent surgery alone, 630 patients were sampled for inclusion with a 1:2 ratio of recurrence to no recurrence. Sampling was stratified by stage (II v III). The assay was performed on formalin-fixed, paraffin-embedded primary cancer tissue. The association of the recurrence score result with the recurrence-free interval (RFI) was assessed using Cox-weighted proportional hazards regression. Overall, 597 of 630 patients were analyzable: 247 patients had stage II colon cancer and 350 had stage III colon cancer. Ongoing recurrence score was significantly associated with RFI after adjusting for disease stage (hazard ratio for a 25-unit increase in recurrence score, 2.05; 95% CI, 1.47 to 2.86 ; P < 0.001). Regarding the pre-specified subgroups, defined by low (<30), intermediate (30 to 40) and high (≥41) risk groups of the Recurrence Score, patients with stage II disease in the high-risk group were at increased risk . 5-year recurrence rate similar to patients with stage IIIA to IIIB disease in the low-risk group (19% vs. 20%), while patients with stage IIIA to IIIB disease in the high-risk group had a similar recurrence risk to that of patients with stage IIIC disease in the low-risk group (approximately 38%).

The authors concluded that this study provides the first validation of the 12-gene Recurrence Score assay in stage III colon cancer without chemotherapy and has shown the heterogeneity of recurrence risks in stage III and II colon cancer.

The NCCN Clinical Practice Guideline on "Colon Cancer" (Version 2.2015) states that there are insufficient data to recommend the use of multigene assays (eg, the Oncotype DX colon cancer assay) to determine therapy.

A review prepared by the Agency for Healthcare Research and Quality (Meleth, 2014) stated: "For CRC, the evidence does not adequately support the added prognostic value for Oncotype DX Colon. The evidence does not support the added prognostic value or we found no studies with RoB low enough to support a conclusion about prognostic value.

The Institut national d'excellence en santé et services sociaux (INESSS) (Boily, et al., 2016) reviewed data on Oncotype Dx Colon and noted that Oncotype Dx Colon has prognostic value in stage II colon cancer, but currently does not. it is used. paid in Quebec.

Decipher

The Decipher test appears to be an RNA biomarker "assay" for prostate cancer. Decipher does this by measuring the expression levels of 22 RNA biomarkers involved in several biological pathways across the genome that are associated with aggressive prostate cancer.

Studies of the Decipher genetic test have evaluated its correlation with tumor characteristics (Den, et al., 2016; Klein, et al., 2016) and have reported the use of this panel of genes to predict biochemical recurrence, metastatic progression, and specific disease. survival after radical prostatectomy with or without external beam radiotherapy (Ehro, et al., 2013; Den, et al., 2013; Cooperberg, et al., 2015; Ross, et al., 2014; Klein, et al., 2015; Karnes, et al., 2013; Den, et al., 2014; Den, et al., 2015; Lee, et al., 2016; Klein, et al., 2016; Glass, et al., 2016; Freedland, et al., 2016; Ross, et al., 2016). The impact of Decipher was evaluated in a clinical utility study in which 21 uro-oncologists were presented with 24 patient cases (12 potential candidates for adjuvant therapy and 12 for salvage external beam radiotherapy) and asked for treatment recommendations with and without genetic test information. (Badani, et al., 2013). The recommendation changed in 43% of the adjuvant cases and 53% in the salvage setting, suggesting a potentially significant impact on treatment decisions after radical prostatectomy.

Michalopoulos et al (2014) reported that the Decipher genomic classifier was useful in the clinic when used as part of risk stratification when recommending adjuvant radiation for patients with high-risk pathology. In that study, 43% of patients switched to observation based on information from the Decipher genomic classifier after radical prostatectomy. The authors stated that this study had several drawbacks. First, treatment recommendations were submitted via an electronic CDI rather than tracking the actual treatment administered by chart review. Therefore, treatment recommendations may vary from actual standards of practice. Second, these investigators were unable to assess the patient's influence on the treatment decision-making process. Third, the association between genomic classifier (GC) test results and treatment recommendations was determined using the 'first users' of the test. Therefore, treatment recommendations may not be indicative of physician decision-making among other physicians in the field; for example, those who were hesitant or reluctant to modify their practice patterns, or those who were unaware of the availability of the GC test. It should be noted, however, that the participants were community physicians and not those with academic/research center consultations, and treatment strategies may deviate from standard practice. Ultimately, influencing practice management decisions on patient outcomes would initially involve inferences based on "chain of evidence" and related decision analytic principles commonly used by the highly respected USPSTF and recently endorsed in editorial discussion. Validity testing of a biomarker test for colon cancer screening. A consensus is emerging that long-term prospective studies in diverse settings will leverage generalizable knowledge to inform best practices for such technologies. This approach and other evidence-building recommendations were recently recommended by a diverse and independent group of researchers, insurers, and policymakers addressing the challenges of realizing the promise of tumor biology research. Studies in other settings, funded by various independent sources (eg, NIH), would be appropriate and warranted.

Nguyen et al (2015) examined how the results of the Decipher study changed radiotherapists' and urologists' recommendations for adjuvant treatment of 10 patients after RP for prostate cancer. Using only clinical information, observation was recommended in 42% of decisions made by urologists versus 23% by radiotherapists (p < 0.0001). GC test results altered 35% and 45% of treatment recommendations made by radiotherapists and urologists, respectively. Badani et al. (2015) reported a study in which 51 urologists provided treatment recommendations for patients with high-risk prostate cancer with and without Decipher GC test results. Each urologist was asked to provide treatment recommendations in 10 cases randomly selected from a pool of 100 medical records. Without knowledge of the results of the Decipher GC test, observation was recommended for 57% (n = 303), adjuvant radiotherapy (ART) for 36% (n = 193), and other treatments for 7% (n = 34) of patients. Overall, 31% (95% CI: 27-35%) of treatment recommendations changed with knowledge of the Decipher GC test results. However, the long-term impact of these management changes is unknown (Bostrom et al, 2015).

Spratt et al (2017) conducted an individual patient-level meta-analysis on the performance of the Decipher genomic classifier in high-risk men after prostatectomy in predicting the development of metastatic disease. We searched MEDLINE, EMBASE, and the Decipher genomic resource information database for reports published between 2011 and 2016 of men treated for prostatectomy evaluating the benefit of the Decipher trial. Multivariate Cox proportional hazards models adjusted for individual patient data were performed; Meta-analyses were performed pooling study-specific hazard ratios (HRs) using random-effects models. The degree of heterogeneity between studies was determined using the I(2) test. Five studies (975 patients in total and 855 patients with individual patient data) were eligible for analysis, with a median follow-up of 8 years. Of the total cohort, 60.9%, 22.6% and 16.5% of the patients were classified by Decipher as low, intermediate and high risk, respectively. The 10-year cumulative incidence metastasis rates were 5.5%, 15.0%, and 26.7% (p < 0.001), respectively, for the three risk classifications. Combining the Decipher-specific HRs of the five studies resulted in a HR of 1.52 (95% CI, 1.39 to 1.67; I(2) = 0%) per 0.1 unit. In multivariate analysis of individual patient data, adjusting for clinicopathological variables, Decipher remained a statistically significant predictor of metastasis (HR, 1.30; 95% CI, 1.14 to 1.47; P < 0.001) by 0, 1 units. The C-score for 10-year distant metastases in the clinical model alone was 0.76; this increased to 0.81 with the inclusion of Decipher. The authors concluded that the Decipher test can improve the prognosis of patients after prostatectomy. The authors stated that further studies are needed on how best to incorporate genomic testing into clinical decision-making and subsequent treatment recommendations.

Dalela et al (2017) aimed to develop and internally validate a risk stratification tool that incorporates the Decipher score, along with routinely available clinicopathological resources, to identify patients who would most benefit from postoperative adjuvant radiotherapy. Patient and methods Our cohort included 512 patients with prostate cancer treated with radical prostatectomy at one of the four academic centers in the EE. UU. between 1990 and 2010. All patients had disease ≥ pT3a, positive surgical margins or pathological invasion of lymph nodes. Cox multivariate regression analysis tested the relationship between available predictors (including the Decipher score) and clinical recurrence (CR), which was then used to develop a new risk stratification tool. Overall, 21.9% of patients received adjuvant radiotherapy. The median follow-up in censored patients was 8.3 years. The 10-year CR rate was 4.9% vs. 17.4% in patients treated with adjuvant radiotherapy versus baseline observation (p < 0.001). Pathologic stage T3b/T4, Gleason score 8 to 10, lymph node invasion, and Decipher score > 0.6 were independent predictors of CR (all P < 0.01). The cumulative number of risk factors was 0, 1, 2, and 3 to 4 in 46.5%, 28.9%, 17.2%, and 7.4% of patients, respectively. Adjuvant radiotherapy was associated with a decrease in CR rate in patients with two or more risk factors (10-year CR rate 10.1% on ART vs. 42.1% at baseline; P = 0.012) , but not in those with less than two risk factors (P = 0.18). The researchers concluded that, using the new model to indicate adjuvant therapy, radiotherapy could reduce overtreatment, reduce unnecessary adverse effects, and reduce the risk of CR in the subgroup of patients (approximately 25% of all patients with aggressive pathological disease in our cohort) who benefit from this therapy.

In a review of genomic predictors of prostate cancer outcome, Bostrom et al (2015) noted that the Decipher test, as well as other gene panels (Prolaris, Oncotype DX Genomic Prostate Score) was evaluated for prognostic value. The future will tell whether this additional information is considered sufficient by patients with urologic and prostate cancer to change practice (Bostrom, et al., 2015; Nguyen, et al., 2015). Bostrom, and others. (2015) commented, "While clinical studies have suggested potential benefits with these tests, actual clinical use and long-term data are needed to assess the added value."

The American Urological Association Localized Prostate Cancer Guidelines (Sanda, et al., 2017) state, based on expert opinion, that among most patients with low-risk localized prostate cancer, Decipher and other genomic-based biomarkers in tissues have not shown a clear role in selecting candidates for active surveillance. The guidelines also state that tissue-based genomic biomarkers are not required for follow-up. The guidelines state that the Decipher test has not been validated as a substantial benefit in the active surveillance population.

Furthermore, the NCCN Clinical Practice Guideline on "Prostate Cancer" (version 4.2018) states that "these molecular biomarker tests were developed with broad industry support, guidance, and participation and were commercialized under the regulatory pathway." standard for biomarkers. Although full evaluation of its clinical usefulness requires prospective randomized controlled trials, which are unlikely to be performed, the Panel believes that men with low-to-favorable intermediate disease may consider using Decipher, Oncotype DX Prostate, Prolaris, or ProMark during early phase stratification. of risk. In addition, Decipher may be considered during the study for persistence or recurrence of PsA in radical prostatectomy (category 2B). Future comparative efficacy research may allow these and similar tests to gain additional evidence about their usefulness for better stratification of men with prostate cancer.

Klein et al (2016) evaluated the ability of the Decipher genomic classifier to predict metastases from the analysis of tumor tissue samples for prostate needle biopsy diagnosis. A total of 57 patients were identified with available biopsy specimens from a cohort of 169 men treated with radical prostatectomy (RP) in a previously reported Decipher validation study at the Cleveland Clinic. A multivariate Cox proportional hazards model and a survival C-index were used to evaluate the performance of Decipher. With a median follow-up of 8 years, 8 patients metastasized and 3 died of prostate cancer. The Decipher plus NCCN model had an improved C-score of 0.88 (95% CI: 0.77 to 0.96) compared to the NCCN alone (C-score 0.75, 95% CI: 0.64 to 0.96). 0.87). In multivariate analysis (MVA), Decipher was the only significant predictor of metastasis when adjusted for age, preoperative PSA, and biopsy Gleason score (Decipher HR per 10% increment 1.72, 95% CI: 1.07 to 2.81, p = 0.02) . The authors concluded that the Decipher biopsy predicted the risk of metastasis 10 years after radical prostatectomy. These investigators stated that, although further validation in larger cohorts is needed, preoperative knowledge of biopsy-derived risk of Decipher may indicate the need for multimodal therapy and help define patient expectations about therapeutic burden.

Nguyen et al (2017a) examined the ability of a biopsy-based 22-marker genomic classifier (GC) to predict distant metastases after radiation and a median of 6 months of androgen deprivation therapy (ADT). These investigators studied 100 patients with intermediate-risk (55%) and high-risk (45%) prostate cancer who received definitive radiation plus a median of 6 months of ADT (range 3 to 39 months) from 2001 to 2013 in only one center and had biopsy tissue available. Six to ten 4-micron sections of the needle biopsy core with the highest Gleason score and percentage of tumor involvement were macrodissected for RNA extraction; GC scores (range 0.04 to 0.92) were determined. The study's primary endpoint was time to distant metastasis. The mean follow-up was 5.1 years. There were 18 metastases during the study period. In univariate analysis (UVA), each 0.1 unit increase in GC score was significantly associated with time to distant metastasis (HR: 1.40 (1.10 to 1.84), p = 0.006) and remained significant after adjustment for clinical variables in MVA (adjusted HR: 1.36 (1.04 to 1.83), p = 0.024). The c index for 5-year distant metastasis was 0.45 (95% CI 0.27 to 0.64) for the prostate cancer risk assessment score, 0.63 (0.40 to 0.78) for the NCCN risk groups and 0.76 (0.57 to 0.89) for the GC score. Using prespecified GC risk categories, the cumulative incidence of metastases for GC>0.6 reached 20% at 5 years post-radiation (p = 0.02). The authors believe that this was the first demonstration of the ability of the biopsy-based GC score to predict distant metastases after definitive radiation and ADT for intermediate and high-risk prostate cancer. Patients with the highest risk of GC (GC greater than 0.6) had high rates of metastases despite multimodal therapy, suggesting that they may be potential candidates for treatment intensification and/or enrollment in clinical trials of new therapies for GC.

The authors stated that a limitation of this study was the size of the dataset and duration of follow-up, and therefore further studies are needed to validate these findings in larger datasets with longer follow-up. Furthermore, the hypothesis that patients with very low CO scores less than or equal to 0.2 might skip ADT requires further testing in previously treated cohorts, as well as prospective studies, which are being planned. Finally, while the GC score was prognostic for distant metastases, it did not have a significant association with biochemical recurrence. This may reflect the fact that the test was originally developed to specifically predict distant metastases, and generally only a minority of biochemical recurrences will result in distant metastases. Another consideration was that none of the patients in this study received multiparametric MRI, and it has been previously demonstrated that MRI could add prognostic information to clinical variables through superior staging or by detecting potentially high-grade lesions. These researchers stated that future studies should assess how GC adds prognostic value when multiparametric MRI was also performed, although studies with cell cycle progression scoring suggest that MRI and genomic information capture different types of information.

Nguyen et al (2017b) evaluated the ability of Decipher biopsy to predict metastasis and prostate cancer-specific mortality (PCSM) primarily in intermediate-to-high-risk patients treated with PR or radiotherapy (RT). A total of 235 patients treated with PR (n = 105) or RT ± androgen deprivation therapy (n = 130) with available genomic expression profiles generated from diagnostic biopsy specimens from 7 medical centers were included in this study. Samples were taken from the highest grade core and the Decipher was calculated based on a gated random forest model. Metastasis and PCSM were the primary and secondary study outcomes, respectively. Cox analysis and c-index were used to evaluate Decipher's performance. With a median follow-up of 6 years among censored patients, 34 patients developed metastases and 11 died of prostate cancer. In MVA, Decipher biopsy remained a significant predictor of metastasis (HR: 1.37 per 10% increase in score, 95% CI]: 1.06 to 1.78, p = 0.018) after adjusting for variables clinics. To predict metastasis 5 years after biopsy, the prostate cancer risk assessment score had a c-index of 0.60 (95% CI 0.50 to 0.69), while the prostate cancer risk plus biopsy Decipher had a c index of 0.71 (95% CI: 0.60 to 0.82); The NCCN risk group had a c-score of 0.66 (95% CI: 0.53 to 0.77), while the NCCN plus Decipher biopsy had a c-score of 0.74 (95% CI: 0 .66 to 0.82). Biopsy Decipher was a significant predictor of PCSM (HR: 1.57 per 10% increase in score, 95% CI: 1.03 to 2.48, p = 0.037), with a 5-year PCSM rate of 0% , 0% and 9.4% for Low, Intermediate and High Decryption respectively. The authors concluded that these findings suggest that patients classified as high risk for Decipher are at very high risk of distant metastasis (21%) and PCSM (9.4%) at 5 years and therefore may rationally undergo therapy. multimodal or enroll in clinical trials targeting men with the highest-risk disease.

These investigators stated that the size of the cohort in this study was limited by access to biopsy tissue from the community and referral health centers; 93% of the missing cohort had unavailable or inadequate tissue and 7.4% failed RNA extraction. Of the 909 patients eligible for this study, only 235 had biopsy tissue available at the institution where the PR or RT ± ADT was performed. A larger cohort size with longer follow-up would have strengthened this study and could have generated more PCSM events to allow for an MVA of PCSM predictors rather than just a univariate analysis. Running multivariate models on a relatively small number of events can also lead to validity issues; therefore, these researchers adjusted the Cox models using an adaptation of Firth's penalized approach, designed to minimize bias in this scenario. Another limitation of this study was that most patients were at intermediate or high risk for NCCN, and the authors were unable to draw any conclusions about Decipher among low-risk patients, who represented only 10% of patients in the study. This information would be helpful in guiding decisions about treatment versus active surveillance. Finally, research is ongoing to determine the concordance between Decipher scores derived from biopsy and prostatectomy samples, which have been reported in previous small studies to be 64%, 75%, and 86%.

Gore et al (2017) stated that PCa patients and their providers face uncertainty when considering ART or salvage radiotherapy (SRT) after undergoing radical prostatectomy. These investigators prospectively evaluated the impact of the Decipher test, which predicts the risk of metastasis after radical prostatectomy, on decision making about ART and SRT. A total of 150 patients considering ART and 115 considering SRT were included. Vendors submitted a management recommendation prior to processing the Decipher test and again upon receipt of test results. Patients completed validated surveys on CaP-specific decision-making efficacy and CaP-related anxiety. Prior to the Decipher trial, observation was recommended for 89% of patients considering ART and 58% of patients considering SRT. After the Decipher study, 18% (95% CI 12% to 25%) of treatment recommendations changed in the ART arm, including 31% among high-risk patients; and 32% (95% CI 24% to 42%) of management recommendations changed in the rescue arm, including 56% among high-risk patients. Decision Conflict Scale (DCS) scores were better after viewing the results of the Decipher test (ART arm: medium DCS before Decipher, 25 and after Decipher, 19 [p < 0.001]; SRT arm: medium DCS before from Decipher, 27 and after Decipher, 23 [p < 0.001]). PCa's specific anxiety changed after the Decipher test; fear of PCa recurrence in the ART arm (p = 0.02) and PCa-specific anxiety in the SRT arm (p = 0.05) significantly decreased among low-risk patients. Decipher results reported a 5% increase in the probability of metastasis at 5 years associated with the decision to continue ART (OR, 1.48, 95% CI 1.19 to 1.85) and SRT (OR, 1.41 , 95% CI 1.09 to 1.81) in multivariate logistic regression analysis. The authors concluded that knowledge of Decipher test results was associated with treatment decision making and improved decision effectiveness among men with PCa considering ART and SRT. These investigators stated that the Decipher test has the potential to be an important aid in clinical decision-making in men with adverse pathology or PSA elevation after undergoing radical prostatectomy for CaP.

The authors stated that this study had several drawbacks. First, they provided interim data on treatment recommendations, which may not correlate with the actual treatment received. The final analysis of the current study will identify treatments received within 12 months of the Decipher trial. Second, patients were their own controls; these investigators did not include a group not exposed to the Decipher tests. Patients who were given more time to consider their clinical and pathological characteristics may see decisive changes in efficacy parallel to the current study findings. Third, patients in the SRT arm had heterogeneous time since prostatectomy, which may influence treatment recommendations regardless of Decipher test results. Finally, to our knowledge, no genomic test to date has been validated against a control to show that use of the test improved PCa-specific outcomes.

Spratt et al (2018) noted that it is a clinical challenge to integrate genomic classifier results reporting a numerical risk of recurrence into treatment recommendations for localized prostate cancer, which are based on risk group structure. These investigators have developed a new clinicogenomic risk grouping system that can be easily incorporated into treatment regimens for localized prostate cancer. 2 multicenter cohorts (n = 991) were used for training and validation of clinical-genomic risk groups and 2 additional cohorts (n = 5937) for reclassification analyses. Competitive risk analysis was used to estimate the risk of distant metastasis. Time-dependent indices were constructed to compare clinical-pathological risk models with clinical-genomic risk groups. With a median follow-up of 8 years for patients in the training cohort, the 10-year distant metastasis rates for low, favorable-intermediate, poor-intermediate, and high-risk NCCN were 7.3%, 9.2%, 38 .0% and 39.5%. , respectively. In contrast, the 3-level clinicogenomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low, intermediate, and high risk, respectively, which were consistent across validation. . cohort (0%, 25.9% and 55.2%, respectively). C scores for the clinical-genomic risk clustering system (0.84, 95% CI: 0.61 to 0.93) improved over the NCCN (0.73, 95% CI: 0.93). 60 to 0.86) and prostate cancer risk assessment (0.74; 95% CI 0.65 to 0.84), and 30% of patients using low/intermediate/high NCCN would be reclassified by the new 3 levels and 67% of patients would be reclassified from NCCN level 6 (very low to very high risk) by the new 6-tier system. The authors concluded that a commercially available genomic classifier in combination with standard clinicopathological variables could generate an easy-to-use clinicogenomic risk grouping that more accurately identifies patients at low, intermediate, and high risk of metastases and can be easily incorporated. in current guidelines to better stratify patient risk.

The authors noted that they did not include a separate NCCN very high risk category in their model for several reasons. First, although these men have poor oncologic outcomes, there is a lack of consensus for the definition of high-risk disease and therefore it was not included in the 2017 American Urological Association/American Society of Urology guidelines. Radiation Oncology/Society of Urologic Oncology. . Second, only 1.5% of their training cohort were at very high risk for NCCN. In contrast, 25.7% of their training cohort was at high clinical-genomic risk, which has significantly worse outcomes than the NCCN high-risk group, and therefore these investigators identified a much larger group, large numbers of patients with very bad results. Lastly, a potential source of bias that was present in this retrospective cohort was that the samples tested were typically over 10 years old. Therefore, it was possible that samples with a higher tumor burden were more likely to be tested successfully. This may explain why event rates were generally higher than comparable clinical trial series. This was in contrast to normal clinical use fabric, which has a high pass rate even for very low risk NCCN patients. Given the constant migration of stages and degrees, it was a challenge to simultaneously have modern patients who also had long-term results. For example, 12-year results were recently reported by Radiation Therapy Oncology Group (RTOG) 9601, a study that began more than 20 years ago. These investigators stated that, despite these drawbacks, it will be important for continued validation of their clinical-genomic risk system.

Berlin et al (2019) stated that the NCCN recently endorsed the stratification of intermediate-risk prostate cancer (IR-PCa) into favorable and unfavorable subgroups and recommended adding ADT to RT for unfavorable IR-PCa. Recently, more accurate prediction was demonstrated by integrating a 22-feature CG into the NCCN stratification system. These investigators tested the usefulness of GC to better identify patients with IR-PCa who are sufficiently treated with RT alone. They identified a new cohort composed of 121 patients with IR-PCa treated with up-dose image-guided RT (78 Gy in 39 fractions) without ADT. GC scores were derived from tumor samples in diagnostic biopsies; MVA, including NCCN subscore and GC scores, were performed for biochemical failure (PSA nadir + 2 ng/mL) and metastasis occurrence. By the NCCN subclassification, 33 (27.3%) and 87 (71.9%) men were classified as having favorable and unfavorable RI-PCa, respectively (1 case not classifiable). GC scores were high in 3 favorable IR-PCa and low in 60 unfavorable IR-PCa. Higher GC scores, but not NCCN risk subgroups, were associated with biochemical relapse (HR, 1.36; 95% CI, 1.09 to 1.71] per 10% increase; p = 0.007) and metastasis (HR , 2.05; 95% CI, 1.24 to 4.24; p = 0.004). GC predicted biochemical failure at 5 years (area under the curve [AUC], 0.78; 95% CI 0.59 to 0.91), and the NCCN + GC combinatorial model significantly outperformed the NCCN model alone in predicting early metastasis early (AUC for 5-year metastasis 0.89 vs. 0.86 [GC alone] vs. 0.54 [NCCN alone]). The authors demonstrated the accuracy of GC in predicting disease recurrence in IR-PCa treated only with image-guided RT with increasing dose. The authors concluded that these findings highlight the need to evaluate this SLN in a prospective clinical trial examining the role of ADT-RT in IR-PCa subgroups defined by clinicogenomics.

The authors stated that this study had several drawbacks. First, it can be argued that this study was insufficient due to the modest sample size and, consequently, few metastatic events. However, this cohort of patients was identified from a prospective registry with strict criteria of inclusion of diagnostic biopsy tissue suitable for review and sampling for central pathology, omission of ADT concomitant with RT, and contemporary RT dose intensity and technique. . of 78 Gy in 39 fractions administered by IGRT. This reflected real-life clinical practice, and the fact that OC was robust in prognosing these patients was compelling evidence for its routine clinical implementation in men with IR-PCa treated with RT. Next, although these investigators acknowledge that the addition of ADT to RT for unfavorable IR-PCa disease has been considered standard practice by several institutions, it remains debatable whether the reported benefits of the combinatorial approach hold in the context of increased dose. Currently, the European Organization for Research and Treatment of Cancer Phase III clinical trial 22991 provides the main specific evidence in support of this clinical enigma. However, it should be noted that the study cohort also consisted of 25% of NCCN-defined high-risk patients, treatment regimens with smaller dose escalations (i.e., 70 Gy or 74 Gy in more than 75% of cases ) and treatment administered without imaging guidance; all elements could partially explain the poor results in the EBRT control group alone. Thus, at the time of the present study, the authors' practice for the clinical management of IR-PCa remained largely unchanged, and the low rates of biochemical relapse and observed metastatic events supported this approach. For example, the 5-year biochemical relapse-free rates in this series were 94% and 88% for the favorable and unfavorable subgroups, respectively, mirroring the 87% reported in the DE-RT arm of the Radiation Therapy Oncology study. Group 0126 in predominantly favorable IR-PCa. However, the authors could not completely exclude the presence of selection bias in this cohort; indeed, in recent years, their practice has increasingly adopted the combination of DE-IGRT and short-acting ADT, especially in IR-PCa with unfavorable indices and/or other aggressive features such as intraductal and cribriform subpathologies. Finally, although these investigators demonstrated the potential usefulness of the GC test in identifying an unfavorable subgroup of men who are likely to require treatment escalation beyond DE-IGRT, this study was not positioned to determine the efficacy of ADT and DE-IGRT. to overcome the adverse prognosis of patients with a high risk score for GC.

Kim et al (2019) stated that many men diagnosed with prostate cancer are candidates for active surveillance (AS). However, AS may be associated with an increased risk of disease progression and metastasis due to delay in therapy. Genomic classifiers, eg Decipher, may allow better risk stratification of newly diagnosed prostate cancers for AS. Decipher was initially evaluated in a prospective cohort of prostatectomies to explore correlation with clinically significant biologics, and then evaluated in diagnostic biopsies from a retrospective multicenter cohort of 266 men with very low/low risk and favorable risk prostate cancer. NCCN. Decipher and Cancer of the Prostate Risk Assessment (CAPRA) were compared as predictors of adverse pathology (AP), so there is universal agreement that patients with long life expectancy are not suitable candidates for AS (primary pattern 4 or 5). , advanced stage site [pT3b or greater] or lymph node involvement). Failure to read prostatectomies was significantly associated with adverse pathological features (p < 0.001). Decoding of 266 diagnostic biopsies (64.7% NCCN-very low/low and 35.3% favorable-intermediate) was an independent predictor of BP (OR 1.29 per 10% increase, 95% CI: 1. 03 to 1.61, p-value 0.025) when adjusting for CAPRA. CAPRA AUC was 0.57 (95% CI 0.47 to 0.68). Adding Decipher to CAPRA increased the AUC to 0.65 (95% CI 0.58 to 0.70). The negative predictive value (NPV), which determines the degree of confidence in the absence of AP for patients, was 91% (95% CI: 87 to 94%) and 96% (95% CI: 90 to 99%) for o Decipher thresholds of 0.45 and 0.2, respectively. Using a threshold of 0.2, Decipher was a significant predictor of AP when adjusted for CAPRA (p-value 0.016). The authors concluded that the Decipher test could be applied to prostate biopsies from patients at intermediate-very-low/low and favorable-intermediate NCCN risk to predict the absence of adverse pathology. These patients are expected to be good candidates for AS.

The authors stated that this study did not have long-term follow-up to consider survival outcomes and the sample size and low number of events did not allow Decipher to be evaluated in individual NCCN risk groups (e.g. only intermediate favorable) . These investigators stated that an ongoing multi-institutional study of favorable intermediate-risk patients aims to address this limitation.

The National Comprehensive Cancer Network's clinical practice guideline on "Prostate Cancer" (version 4.2019) recommends coverage of "Decipher", "Oncotype DX Prostate", "Prolaris", and "ProMark" (Category 2A). Very-low-risk NCCN patients with a life expectancy greater than 10 years who have not received treatment for prostate cancer and are candidates for active surveillance or definitive therapy.

miRNA for prostate cancer

Maugeri-Sacca et al (2013) stated that prostate cancer is one of the most common causes of cancer-related death. The management of patients with prostate cancer has become increasingly complex, which is why there is a need to identify and validate prognostic and predictive biomarkers. Growing evidence indicates that microRNAs play a crucial role in the pathobiology of neoplastic diseases. Dysregulation of myRNoma cells in prostate cancer has been associated with multiple tumor-promoting activities, including aberrant activation of growth signals, anti-apoptotic effects, prometastatic mechanisms, androgen receptor pathway disruption, and cancer regulation. stem cell phenotype. With the elucidation of the molecular mechanisms controlled by microRNAs, investigations have been carried out in an attempt to explore these molecules in the clinical setting. Furthermore, the multifaceted biological activity of microRNAs makes them an attractive candidate as an anticancer agent. This review summarized current knowledge about microRNA dysregulation in prostate cancer and the rationale behind their exploration as cancer biomarkers and treatments.

Yu and Xia (2013) reviewed novel microRNA biomarkers in prostate cancer. The literature on microRNAs and prostate cancer cited in this review was obtained primarily from PubMed published in English between 2004 and 2012. Original articles on the new role of microRNAs in prostate cancer were selected. MicroRNAs play important roles in prostate cancer, such as cell differentiation, proliferation, apoptosis, and invasion. Especially, microRNAs correlate with epithelial-mesenchymal transition (EMT) of prostate cancer cells, cancer stem cells (CSCs), drug sensitivity, cancer microenvironment, energy metabolism, androgen independence transformation, and diagnostic prediction. The authors concluded that microRNAs are involved in several aspects of prostate cancer biology. Furthermore, they state that the role of microRNA in the initiation and development of prostate cancer deserves further study.

Chiam et al (2014) observed that epigenome alterations are characteristic of almost all human malignancies and include alterations in DNA methylation, histones and microRNA (miRNA) modifications. However, what induces these epigenetic changes in cancer is largely unknown, and their role in the mechanism of prostate tumorigenesis is just beginning to be evaluated. Identifying the epigenetic modifications involved in the development and progression of prostate cancer will not only identify new therapeutic targets, but also prognostic and diagnostic markers. This review focused on the use of epigenetic modifications as biomarkers for prostate cancer.

Furthermore, the National Comprehensive Cancer Network's clinical practice guideline on "Prostate Cancer" (version 1.2014) does not mention the use of the RNA/microRNA biomarker as a management tool.

Galectina-3

There is emerging evidence for galectin-3 in the pathogenesis and progression of prostate cancer. However, there is not enough evidence of its impact on detection, diagnosis or treatment. The National Comprehensive Cancer Network's clinical practice guideline on "Prostate Cancer" (version 1.2015), as well as its Compendium of Biomarkers, do not have recommendations for galectin-3 in prostate cancer.

The National Comprehensive Cancer Network clinical practice guidelines on "bone cancer" (version 1.2020) and "myelodysplastic syndromes" (version 1.2020) do not mention galectin-3 as a management tool.

MLH1 promoter methylation

Metcalf et al (2014) stated that colorectal cancer (CRC) exhibiting high microsatellite instability (MSI-H) may be caused by germline mutations in mismatch repair (MMR) genes or transcriptional silencing, not inherited from the MLH1 promoter. A correlation has been reported between MLH1 promoter methylation, specifically the 'C' region, and BRAF V600E status in CRC studies. Germline MMR mutations also greatly increase the risk of endometrial cancer (EC), but a systematic review has not been performed to determine whether these tumor markers can be useful predictors of MMR mutation status in patients with EC. Endometrial cancer cohorts that met the review's inclusion criteria included 2675 tumors from 20 studies for BRAF V600E and 447 tumors from 11 studies for the MLH1 methylation test. BRAF V600E mutations were reported in 4/2675 (0.1%) endometrial tumors of unknown MMR mutation status, and there were 7/823 (0.9%) full sequence variants in exon 11 and 27/1012 (2.7 %) in exon 15 Promoter No MLH1 methylation was observed in tumors from 32 MLH1 mutation carriers, nor in 13 MSH2 or MSH6 mutation carriers. MMR-mutation negative individuals with MLH1 tumor and loss of PMS2 IHC had MLH1 methylation in 48/51 (94%) of the tumors. These investigators also detailed specific examples that show the importance of the MLH1 promoter region, assay design, and quantification of methylation. The authors concluded that this review showed that BRAF mutations occur so rarely in endometrial tumors that they can be ruled out as a useful marker for predicting negative MMR mutation status, and further studies of endometrial cohorts of MMR mutation status are warranted. of MLH1 tumor promoter methylation as a marker of germline negative MMR mutation status in patients with CE.

In addition, UpToDate reviews of "Endometrial Carcinoma: Clinical Features and Diagnosis" (Chen ad Berek, 2015) and "Endometrial Carcinoma Overview" (Plaxe and Mundt, 2015), as well as the NCCN Clinical Practice Guideline on "Uterine neoplasms" (Version 2.2015). ) do not mention MLH1 promoter methylation tests.

p16

p16 is a tumor suppressor gene that regulates cell growth and proliferation by acting as an inhibitor of cyclin-dependent kinase 4 (CDK4) (Chen, et al. 2006). This test determines whether a patient has a mutation in the p16 gene, indicating a predisposition to melanoma and pancreatic cancer.

Chung et al (2014) observed that although p16 protein expression, a surrogate marker for oncogenic human papillomavirus (HPV) infection, is recognized as a prognostic marker in oropharyngeal squamous cell carcinoma (OPSCC), its prevalence and importance have not been well established. in cancer of the oral cavity, hypopharynx, or larynx, collectively referred to as non-OPSCC, where HPV infection is less common than in the oropharynx. P16 expression and high-risk HPV status in non-OPSCCs from RTOG studies 0129, 0234, and 0522 were determined by immunohistochemistry (IHC) and in situ hybridization (ISH). Hazard ratios for Cox models were expressed as positive or negative, stratified by trial, and adjusted for clinical features. p16 expression was positive in 14.1% (12/85), 24.2% (23/95) and 19.0% (27/142) and HPV ISH was positive in 6.5% (6 of 93), 14.6% (15 of 103), and 6.9% (7 of 101) non-OPSCC from RTOG studies 0129, 0234, and 0522, respectively. The hazard ratios for p16 expression were 0.63 (95% CI: 0.42 to 0.95; p = 0.03) and 0.56 (95% CI: 0.35 to 0.95). 0.89, p = 0.01) for PFS and OS, respectively. When comparing OPSCC and non-OPSCC, p16-positive OPSCC patients have better PFS and OS than non-OPSCC p16-positive patients, but p16-negative and non-OPSCC OPSCC patients have similar outcomes. The authors concluded that, similar to outcomes in OPSCC patients, non-OPSCC p16-negative patients have worse outcomes than non-OPSCC p16-positive patients, and HPV may also play a role in outcome in a subset of patients. However, these investigators stated that further development of a p16 IHC scoring system in non-OPSCC and improvement of HPV detection methods is needed before wide application in the clinical setting; they noted that further research using multimodal testing in non-OPSCCs and the development of more accurate HPV tests is warranted.

MUC5AC

Ruzzenente et al (2014) stated that mucin 5AC (MUC5AC) is a glycoprotein found in different epithelial cancers, including biliary tract cancer (BTC). These investigators examined the role of MUC5AC as a serum marker for BTC and its prognostic value after surgery with curative intent. From January 2007 to July 2012, a quantitative evaluation of serum MUC5AC with enzyme-linked immunoassay was performed on a total of 88 subjects. Clinical and biochemical data (including CEA and Ca 19-9) from 49 BTC patients were compared with a control population including 23 patients with benign biliary disease (BBD) and 16 healthy control subjects (HCS). Serum MUC5AC was higher in patients with BTC (mean 17.93 ± 10.39 ng/ml) compared to BBD (mean 5.95 ± 5.39 ng/ml; p < 0.01) and HCS ( average of 2.74 ± 1.35 ng/ml) (p < 0.01 ). Multivariate analysis showed that MUC5AC was related to the presence of BTC compared to Ca 19-9 and CEA: p < 0.01, p = 0.080 and p = 0.463, respectively. In the BTC group, serum MUC5AC greater than or equal to 14 ng/mL was associated with lymph node metastases (p = 0.050) and American Joint Committee on Cancer/International Union for Cancer Control stage IVb disease (p = 0.047). Furthermore, in patients undergoing surgery with curative intent, serum MUC5AC greater than or equal to 14 ng/mL was associated with a worse prognosis compared to patients with lower levels, with 3-year survival rates of 21.5% and 59.3%, respectively (p = 0.039). The authors concluded that MUC5AC could be proposed as a new serum marker for BTC. In addition, quantitative assessment of serum MUC5AC may be related to tumor stage and long-term survival in patients with BTC undergoing surgery with curative intent.

The authors stated that "limitations of this study include lack of data on serum MUC5AC levels in patients with obstructive jaundice and with premalignant biliary lesions such as hepatolithiasis, sclerosing cholangitis, and choledochal cysts... MUC5AC also in patients with obstructive jaundice and with premalignant lesions... Our data need to be confirmed by well-designed large-scale prospective studies."

Furthermore, the NCCN clinical practice guideline on "Hepatobiliary Cancers" (Version 2.2015) does not mention mucin 5AC (MUC5AC) as a management tool.

Tp53

In a pilot study, Erickson et al (2014) examined whether tumor cells could be detected in the vagina of women with serous ovarian cancer via TP53 analysis of DNA samples collected by placing a vaginal swab. Women undergoing surgery for a pelvic mass were identified at the gynecological oncology clinic. A vaginal tampon was inserted before surgery, which was removed in the operating room. Cells were isolated and DNA was extracted from cells trapped in the plug and from the primary tumor. In patients with serous carcinoma, DNA was interrogated for TP53 mutations using a method capable of detecting rare mutant alleles in a mixture of mutant and wild-type DNA. A total of 33 patients were included; Eight patients with advanced serous ovarian cancer were included for analysis; and 3 had previous tubal ligation. TP53 mutations were identified in all 8 tumor samples. DNA analysis of the plugs revealed mutations in 3 of the 5 patients with intact tubes (60% sensitivity) and in none of the 3 patients with tubal ligation. In the 3 participants with a mutation detected in the plug sample, the tumor and vaginal DNA contained the exact same TP53 mutation. The DNA fraction derived from exfoliated tumor cells ranged from 0.01% to 0.07%. The authors concluded that, in this pilot study, tumor-derived DNA was detected in the vaginas of 60% of ovarian cancer patients with intact fallopian tubes. They stated that, with further development, this approach could hold promise for early detection of this deadly disease. They stated that for this method to be clinically useful, several factors must be considered: This approach must be able to adequately detect early stages of the disease to provide sufficient time for effective intervention. In this sense, one of the disadvantages of this study was that all samples were obtained from patients with advanced stage cancer. Another limitation was that these investigators did not sequence DNA from tampons from patients with benign diseases. Therefore, the specificity could not be calculated. These researchers stated that larger studies are needed to further validate this method and identify a more accurate detection rate.

In an editorial accompanying the aforementioned study, Mulch (2014) stated that "In terms of clinical utility, the sensitivity of this test could be around 60% in patients with intact tubes and clinically evident cancer, but we don't know what will be." in patients with less advanced disease... However, the barrier to detecting ovarian cancer is the fact that the prevalence of the disease is so low in the general population that any screening test must have low sensitivity and specificity. realistic... This technology is very promising...The technology represented here has the potential to do what other screening tests cannot...We must be careful not to endorse it until its usefulness is fully validated."

Furthermore, the NCCN clinical practice guideline on "ovarian cancer" (version 3.2014) does not mention TP53 mutation analysis as a management tool.

Zhang et al (2015) summarized the potential diagnostic value of 5 serum tumor markers in esophageal cancer. These investigators systematically searched PubMed, Embase, China National Knowledge Infrastructure (CNKI), and China Biomedical Database (CBM), up to February 28, 2013, with no language restriction. Study quality was assessed using the QUADAS (Study Quality Assessment of Diagnostic Accuracy). Positive Likelihood Ratio (PLR) and Negative Likelihood Ratio (NLR) were pooled separately and compared with measures of overall accuracy using symmetric diagnostic odds ratios (DORs) and summary receiver operating characteristics (SROC) curves. Of the 4391 initially identified studies, 44 eligible studies including 5 tumor markers met the inclusion criteria for meta-analysis, whereas meta-analysis could not be performed for another 12 tumor markers. Approximately 79.55% (35/44) of the included studies were of relatively high quality (QUADA score greater than or equal to 7). The summary estimates of PLR, NLR and DOR for diagnosing CD were as follows: CEA, 5.94/0.76/9.26; Cyfra21-1 (a cytokeratin 19 fragment), 12.110.59/22.27; p53 antibody, 6.71/0.75/9.60; squamous cell carcinoma antigen (SCC-Ag), 7.66/0.68/12.41; and vascular endothelial growth factor C (VEGF-C), 0.74/0.37/8.12. The estimated SROC curves showed that the performance of the 5 tumor markers was reasonable. The authors concluded that current evidence suggests that CEA, Cyfra21-1, p53, SCC-Ag, and VEGF-C have potential diagnostic value for esophageal carcinoma.

Ki67

There is a strong correlation between proliferation rate and clinical outcome in various tumor types, and measurement of cell proliferative activity is an important prognostic marker (Chen, et al., 2006). This marker correlates with S phase by flow cytometry.

There is not enough evidence for Ki67. The NCCN Breast Cancer Guidelines (2015) state: "Measurement of the nuclear antigen, Ki-67 by IHC, provides an estimate of tumor cells in the proliferative phase (phases G1, G2, and M) of the cell cycle. Studies have demonstrated the prognostic value of Ki-67 as a biomarker and its usefulness in predicting response and clinical outcome of additional interventions. However, these data require additional analytical and clinical validation. In addition, standardization of tissue handling and processing is needed to improve the reliability and value of Ki-67 testing At present, there is no conclusive evidence that Ki-67 alone, especially early Ki-67 as an individual biomarker, helps to select the type of endocrine therapy for an individual patient. Therefore, the NCCN Breast Cancer Panel does not currently recommend Ki-67 screening."

The p16/KI-67 Dual Stain Test (CINtec PLUS) aims to detect viral-induced oncogenic molecular changes in the cell through immunocytochemical dual staining of the p16 tumor suppressor gene^TINTA4aand the Ki-67 proliferation marker, and thus improve the screening of women with equivocal cytology results (Kisser, et al., 2014). The Ludwig Boltzmann Institute performed a systematic review of studies evaluating the usefulness of the p16/Ki-67 dual stain test in screening for equivocal results or mild to moderate dysplasia in cervical cancer screening. Therefore, it was not possible to determine the clinical usefulness of the test (Kisser, et al., 2014). Consequently, inclusion of the test in the catalog of public health insurance benefits was not recommended.

The American Society of Clinical Oncology (2016) guidelines state, "The protein encoded by the IHC tagging index of the MKI67 gene should not be used to guide the choice of adjuvant chemotherapy." This is a moderate-strength recommendation based on intermediate-quality evidence.

a bloodbath

Mass spectrometry-based proteomic profiling (such as Veristrat) is a multivariate serum protein test that uses mass spectrometry and proprietary algorithms to analyze proteins in an individual's serum.

The NCCN Non-Small Cell Lung Cancer Guidelines (NCCN, 2015) recommend proteomic testing for patients with NSCLC and wild-type EGFR or unknown EGFR status. The guidelines state that a patient rated "poor" should not receive erlotinib as a second line. For support, the NCCN guidelines reference a study by Gregorc, et al. (2014), who reported that serum protein test status (Veristrat) predicts a differential benefit in overall survival for erlotinib versus chemotherapy in the second-line setting, and that patients classified as likely to have a poor outcome have better results with chemotherapy than with chemotherapy. erlotinib. From February 26, 2008 to April 11, 2012, patients (≥18 years of age) with histologically or cytologically confirmed second-line stage IIIB or IV non-small cell lung cancer were enrolled in 14 centers in Italy. Patients were stratified according to a minimization algorithm based on Eastern Cooperative Oncology Group performance status, smoking history, center, and masked pretreatment serum protein test score and were centrally randomized in a 1:1 ratio to receiving erlotinib (150 mg/day orally) or chemotherapy (pemetrexed 500 mg/m2 intravenously every 21 days or docetaxel 75 mg/m2 intravenously every 21 days). The proteomic test score was blinded to the patients and investigators who administered the treatments, and the treatment allocation was blinded to the investigators who generated the proteomic score. The primary endpoint was overall survival, and the primary hypothesis was a significant interaction between serum protein test score and treatment. Analyzes were performed on the per-protocol population. Investigators randomly assigned 142 patients to chemotherapy and 143 to erlotinib, and 129 (91%) and 134 (94%), respectively, were included in the per-protocol analysis. 88 (68%) patients in the chemotherapy group and 96 (72%) in the erlotinib group scored well on the proteomic test. Median overall survival was 9.0 months (95% CI: 6.8–10.9) in the chemotherapy group and 7.7 months (5.9–10.4) in the erlotinib group. The investigators observed a significant interaction between treatment and proteomic score (pinteration = 0.017 when adjusted for stratification factors; pinteration = 0.031 when not adjusted for stratification factors). Investigators found that patients with a poor proteomics test score had worse survival with erlotinib than with chemotherapy (hazard ratio 1.72 [95% CI: 1.08–2.74], p=0.022). There were no significant differences in overall survival between treatments for patients with a good proteomic test score (adjusted HR 1.06 [0.77–1.46], p=0.714). In the group of patients receiving chemotherapy, the most common Grade 3 or 4 toxic event was neutropenia (19 [15%] vs. one [<1%] in the erlotinib group), while skin toxicity (one [<1%] vs 22 [16%]) was more common in the erlotinib group.

Multiplex Tests for Myeloid Hematopathological Disorders

State-of-the-art multiplexing/sequencing tests can aid in the diagnosis of a variety of myeloid hematopathological disorders, particularly myelodysplastic syndrome (MDS). The International Consensus Working Group (ICWG) (Valent, et al., 2007) recommends that the minimum diagnostic criteria for MDS include: A) Prerequisite criteria, including stable cytopenia in one or more cell lineages and exclusion of others possible disorders as the main reason for dysplasia and/or cytopenia; B) MDS-related (critical) criteria, including significant dysplasia, blast counts of 5-19%, and/or MDS-specific cytogenetic abnormalities; and co-criteria for patients who attend A) but not B), including clear signs of a monoclonal population using molecular markers (such as DNA mutations) or flow cytometry, or markedly reduced colony formation. Furthermore, many of the genes have independent prognostic value in various myeloid malignancies including ASXL1, RUNX1, ETV6, EZH2, TP53 in multivariate analysis in MDS. Other critical illness genes such as DNMT3A, CBL, IDH2, IDH1, SRSF2, ZRSR2, NRAS, U2AF1, and SF3B1 have also been shown to be independent predictors of survival in MDS, as have ASXL1, SRSF2, CBL, and IDH2 in chronic myelomonocytic disease patients. (CMML), IDH1/2, EZH2, SRSF2, ASXL1 in primary myelofibrosis (PMF) and SETBP1 in atypical chronic myelogenous leukemia (aCML).

AnswerDx

The ResponseDX Panel: Colon® (Response Genetics) uses multi-gene testing, including KRAS mutation, BRAF mutation, ERCC1 expression, MSI, c-Met expression, EGFR expression, VEGFR2 expression, NRAS mutation, PIK3CA mutation, and thymidylate synthetase (Raman , and others, 2013). The test predicts disease prognosis and selects patients who may benefit from alternative therapies and assists in the selection of patients with metastatic colorectal cancer who may benefit from anti-EGFR monoclonal antibody therapies.

The ResponseDX:Lung® (Response Genetics) panel uses multigene assays, including ROS1 rearrangements, EGFR mutation, EML4-ALK rearrangement, ALK, ERCC1 expression, RRM1 expression, c-MET, TS expression, KRAS mutation, and PIK3CA mutation (Raman, et al., 2013) The test is used in patients with non-small cell lung cancer who are being considered for treatment with the tyrosine kinase inhibitor (TKI) Crizotinib.

The ResponseDX:Melanoma® (Response Genetics) panel uses tests for multiple genes, including BRAF mutation and NRAS mutation (Raman, et al., 2013). The test is performed on a formalin-fixed, paraffin-embedded (FFPE) biopsy specimen using fluorescent in situ hybridization (FISH) and polymerase chain reaction (PCR). The study is intended for patients with melanoma who are being considered for treatment with the tyrosine kinase inhibitor (TKI) and EGFR antagonists cetuximab and panitumumab.

ResponseDX: gastric panel® (Response Genetics) uses multigene assays, including HER2 gene amplification, ERCC1 expression, and thymidylate synthetase expression (Raman, et al., 2013). This is a PCR-based test performed on formalin-fixed, paraffin-embedded biopsy specimens. HER2 gene amplification is associated with greater disease recurrence and worse prognosis. ERCC1 expression predicts the best therapeutic combination of agents, including platinum, and selected patients who may benefit from platinum-based therapies. Thymidylate synthetase (TS) expression predicts the best therapeutic combination of agents, including pemetrexed or 5-FU, and selects patients who may benefit from pemetrexed-based therapies.

4K Score

The 4Kscore test measures blood plasma levels of four different prostate-derived kallikrein proteins [total PSA, free PSA, intact PSA, and human kallikrein2 (hK2)] and combines the results in an algorithm with age, DRE (nodules, no nodules) and previous biopsy. results. The result is supposed to be the specific probability that an individual will find high-grade prostate cancer with a Gleason score of 7 or higher on biopsy.

Parekh et al (2015) conducted the first prospective evaluation of the 4Kscore to predict Gleason ≥7 PCa in the US. Investigators prospectively recruited 1012 men scheduled for prostate biopsy, regardless of PSA level or clinical findings. , from 26 US urology centers between October 2013 and April 2014. The primary endpoint was Gleason ≥7 PCa on prostate biopsy. The area under the receiver operating characteristic curve, risk calibration and decision curve analysis (DCA) were determined, along with probability threshold comparisons to reduce the number of biopsies and their impact on late diagnosis. Gleason ≥7 CaP was found in 231 (23%) of 1012 patients. The investigators stated that the 4Kscore exhibited excellent calibration and demonstrated greater discrimination (area under the curve [AUC] 0.82) and net benefit compared to a modified model of Prostate Cancer Prevention Trial Calculator Risk Calculator 2.0 and standard of care (biopsy for all males) according to DCA. A possible 30-58% reduction in the number of biopsies with late diagnosis was identified in only 1.3-4.7% of cases of PCa Gleason ≥7, depending on the threshold used for biopsy. Pathological assessment was performed according to the standard of care at each site, without centralized review.

Stattin et al (2015) conducted a nested case-control study in a population-based cohort. PSA and three additional kallikreins (4KScore) were measured in cryopreserved blood from a population-based cohort in Västerbotten, Sweden. Of the 40,379 men who donated blood at ages 40, 50, and 60 between 1986 and 2009, 12,542 men were followed for more than 15 years. Of this cohort, the Swedish Cancer Registry identified 1423 incident cases of PCa, 235 with distant metastases. Most metastatic cases occurred in men with PSA in the top quartile aged 50 years (69%) or 60 years (74%), whereas the 20-year risk of metastasis for men with PSA below the median was low (≤0.6 % . The investigators reported that, among men with PSA > 2 ng/mL, a prespecified model based on four kallikrein markers significantly improved the prediction of metastasis compared with PSA alone. Approximately half of all men with PSA >2 ng/mL were defined as low-risk by this model and had a 15-year risk of metastasis of ≤1%. The authors concluded that, for 50-year-old men, screening should focus on those between 10% and 25% of PSA values, because most subsequent cases of distant metastases are in this range. Testing of four kallikrein markers in men with elevated PSA may help in biopsy decision-making.

Voigt et al (2014) performed a systematic review and meta-analysis to examine the aggregated results of published studies from the Kallikrein Panel. Results from the meta-analysis were used to model the effect of the Kallikrein Panel on healthcare costs. The authors reported that the meta-analysis demonstrates a statistically significant 8-10% improvement in predictive accuracy. The authors estimated that 48-56% of current prostate biopsies could be avoided and that the use of the kallikrein panel could generate annual US savings of approximately $1 billion.

Konety et al (2015) conducted a clinically useful study to assess the influence of the 4Kscore test on the decision to perform prostate biopsies in men referred to urologists for abnormal PSA and/or DRE results. The study population included 611 patients seen by 35 academic and community urologists across the United States. Urologists ordered the 4Kscore test as part of their evaluation of men referred for abnormal PSA and/or DRE test results. Patient outcomes were stratified into low risk (<7.5%), intermediate risk (7.5%-19.9%) and high risk (≥20%) of aggressive prostate cancer. Investigators reported that 4Kscore test results influenced biopsy decisions in 88.7% of men. Performing the 4Kscore test resulted in a 64.6% reduction in prostate biopsies in patients; the actual percentage of cases not biopsied was 94.0%, 52.9%, and 19.0% for men with low-, intermediate-, and high-risk 4Kscore results, respectively. A higher 4Kscore test was associated with a greater likelihood of having a prostate biopsy (P < 0.001). The researchers reported that among 171 patients who underwent a biopsy, the 4Kscore risk category was strongly associated with biopsy pathology.

Lin et al (2016) sought to assess the usefulness of the 4Kscore in predicting the presence of high-grade cancer in men on active surveillance. Plasma collected prior to the first and subsequent follow-up biopsies of 718 men prospectively enrolled in the multi-institutional Canary PASS study was evaluated. Biopsy data were split 2:1 into training and test sets. The investigators developed statistical models that included clinical information and the 4K panel or serum prostate-specific antigen (PSA). The outcome was reclassification to Gleason ≥7. Investigators used receiver operating characteristic curve (ROC) and area under the curve (AUC) analysis to assess discriminatory ability and decision curve analysis (DCA) to report net clinical benefit. Significant predictors for reclassification were 4Kpanel (odds ratio [OR] 1.54, 95% confidence interval [CI] 1.31-1.81) or PSA (OR 2.11, 95% CI 1.53 -2, 91), ≥20% positive cores (OR 2.10, 95% CI 1.33-3.32), two or more previous negative biopsies (OR 0.19, 95% CI 0.04-0.85), prostate volume (OR 0.47, 95% CI 0.31-0.70) and body mass index (OR 1.09, 95% CI 1.04-1.14). ROC curve analysis comparing 4K and baseline models indicated that the 4K panel improved accuracy in predicting reclassification (AUC 0.78 vs 0.74) at the first surveillance biopsy. Both models had comparable performance for predicting reclassification in subsequent biopsies (AUC 0.75 vs 0.76). In DCA, both models showed a greater net benefit compared to the biopsy-all and biopsy-no strategies. Limitations include the study's single-cohort nature and small numbers; results must be validated in another cohort before clinical use.

The National Comprehensive Cancer Network (NCCN, 2016) guidelines list the 4K score as preferred among several tests (i.e., percent free PSA and Prostate Health Index (PHI)) that may be considered for patients before biopsy and at various tests (ie, percent free PSA, PHI, PCA3, and ConfirmMDx) for those with a previous negative biopsy for men who are believed to have a clinically significant risk of prostate cancer. The NCCN guidelines state that the 4Kscore cannot be recommended over other tests (i.e., percent free PSA, Prostate Health Index (PHI). The NCCN guidelines explain that direct comparisons have been made in Europe for some of these tests, performed individually or in combinations in initial or repeat biopsy settings, but sample sizes were small and results varied. NCCN guidelines state that optimal order is unknown. individual patients, especially when results are conflicting. O panel states that it is important for patients and their urologists to understand, however, that a cut-off has not been set for the 4KScore.

The Memorial Sloan Kettering recommendations (Vickers, et al., 2016) state that in biopsy-naive men with PSA ≥3 ng/mL, prostate MRI is the strongest independent predictor of clinically significant prostate cancer. , but "as evidence continues to mount, we believe that prostate MRI may emerge as a valuable tool to reduce PCa overdiagnosis, likely in conjunction with newer biomarkers such as prostate health index, 4Kscore, and polymorphism panels single nucleotide.

A 2016 MolDx review of 4KScore concluded that "the intended use population has not been adequately validated; the 4Kscore model has continually changed; the model has been recurrently tested in potentially inappropriate patients (PSA > 10) and patients with inadequate biopsy specimens; it is not unclear how much hK2 and possibly intact PSA contribute to the model; the 4Kscore model/algorithm value is full of statistical hypotheses and not prospective results or agreement in a defined patient population likely to be considered for biopsy (eg: PSA 3- 10 ng/mL); follow-up of young men with unknown low-grade prostate cancer (not in AS) is assumed to be harmless; there is significant difficulty in equating the model used in the Swedish study with the currently proposed formula, and The incidence of prostate cancer diagnosis in patients at low risk by model/algorithm at 10 years is very concerning".

Anceschi et al (2019) stated that several alternative biomarkers to the standard prostate-specific antigen (PSA) have become available in recent years for the diagnosis of PCa. In a systematic review, these investigators examined current knowledge about alternative serum and urine biomarkers for diagnosing PCa. A Medline search was conducted, restricted to English-language articles published between December 2014 and June 2018, with the aim of updating previously published series on CaP biomarkers. Preferred reporting article criteria for systematic reviews and meta-analyses (PRISMA) were used to select studies with the lowest risk of bias. The emerging role and current controversies of alternative serum and urine biomarkers to standard PSA for the diagnosis, staging and prognostic evaluation of PCa, such as Prostate Health Index (PHI), PCA3, ConfirmMDx, glycosylation aberrant PSA, MiPS, miRNAs were presented critically in the current review. The authors concluded that although the use of various biomarkers has been recommended or questioned by different international guidelines, larger prospective randomized studies are still needed to validate their efficacy in detection, discrimination, prognosis and treatment efficacy of PCa. So far, only PHI and 4Kscore have shown clinical relevance to discriminate more aggressive CaPs. Furthermore, a new classification classification based on relevant molecular features is still needed for PCa risk stratification and personalized treatment.

Kim et al (2019) noted that prostate cancer (PCa) is the most common cancer diagnosed among men in the United States and the fifth most common cancer among men in Korea. Unfortunately, the early stages of CaP may not have any symptoms. Therefore, early detection is very important and clinicians treating voiding dysfunction should be aware of PCa. The traditional tests used for the early detection of PCa are the prostate-specific antigen (PSA) blood test and digital rectal examination (DRE). However, a high PSA level is not specific for CaP. Benign prostatic hyperplasia (BPH), prostatitis, urinary tract infection (UTI) and urinary retention can cause a high PSA level. Therefore, none of the tests show enough accuracy to be really useful in detecting PCa in men. Prostate biopsy is the only method that provides a definitive diagnosis of PCa; however, this test is invasive and uncomfortable. Recently, new biomarkers for detecting CaP have been proposed to improve the accuracy of the PSA test. These investigators summarized their knowledge of several new biomarkers, including PSA-associated biomarkers (the Prostate Health Index and 4Kscore), molecular biomarkers (PCA3, TMPRSS2:ERG fusion gene, and various miRNAs), and biomarkers associated with proteomics and the ways in which that they can improve the PCa detection rate. The authors concluded that, so far, many efforts have been made to predict early-stage PCa, such as PSA-associated markers, various molecular markers, miRNA markers, and protein markers. Unfortunately, follow-up validation studies are lacking for several reasons. Therefore, future CaP biomarker studies should focus on combinations of molecular biomarkers and clinical variables rather than biomarkers alone.

Marzouk et al (2019) stated that biomarkers and imaging technologies designed to improve PSA specificity have been developed in recent years. Widespread deployment of imaging technologies such as mp-MRI presents considerable logistical challenges. These investigators evaluated a biopsy strategy that uses selective mp-MRI as a follow-up biomarker test to improve detection of significant CaP. They developed a conceptual approach based on calculated risk from the 4Kscore using the results of the US prospective validation study, multiplied by the mp-MRI likelihood ratio of the PROMIS study. The primary outcome was Gleason grade greater than or equal to 7 (grade greater than or equal to 2 group) cancer on biopsy. Using decision curve analysis, the net benefit for this model was determined and compared using 4Kscore and mp-MRI independently at various biopsy thresholds. For a cut-off of 7.5% risk of high-grade disease, patients with less than 5% risk of a blood marker would not have a significant risk of CaP high enough on a positive mp-MRI to warrant a biopsy ; comparatively, patients with a risk greater than 23% would not have a low enough risk of a negative imaging study to forego biopsy. According to the 4Kscore validation study, 46% of men considered for a biopsy in the US have risks of 5% to 23%. Net profit was the highest for the combined strategy, followed only by 4Kscore. The authors concluded that selective mp-MRI in men with intermediate scores on a secondary blood test resulted in a more scalable biopsy strategy than mp-MRI for all men with elevated PSA. These investigators stated that prospective validation is needed to examine whether the predicted properties of the combined blood and imaging tests are empirically confirmed.

Falagario et al (2020) stated that the 2019 European Association of Urology guidelines recommend mpMRI for biopsy-naive patients with clinical suspicion of CaP and avoid biopsy in patients with negative mpMRI and low clinical suspicion. However, there is a lack of consensus on the ideal definition of low clinical suspicion. These investigators evaluated 266 biopsy-naive patients who underwent mp-MRI, 4Kscore testing, and prostate biopsy to define the best strategy to avoid unnecessary examinations and biopsies. The European Randomized Study of Prostate Cancer Screening (ERSPC-RC) risk calculator and PSA density (PSAd) were also considered. For men with Prostate Imaging-Reporting and Data System v2.0 (PI-RADS) 1⿿2 lesions, the highest negative predictive value (NPV) was observed for those at low or intermediate risk of 4Kscore (96.9% and 97, 1%). PSAd <0.10 ng/ml/cm3 (98.7%) and ERSPC-RC less than 2% (98.7%). For men with PI-RADS 3⿿5 lesions, the lowest positive predictive value (PPV) was observed for those with a low risk of 4Kscore (0%), PSAd less than 0.10 ng/mL/cm3 (13.2%) and ERSPC-RC less than 2% (12.3%). The best biopsy strategy was an initial 4Kscore followed by mp-MRI if the 4Kscore was greater than 7.5% and a subsequent biopsy if the mp-MRI was positive (PI-RADS 3⿿5) or the 4Kscore was ⿥18% . This would result in the loss of 2.7% (2/74) of clinically significant PCs (csPCs) and avoid 34.2% of biopsies. baseline mp-MRI followed by negative mp-MRI biopsy (PI-RADS 1⿿2) if 4Kscore was ⿥18% or PSAd was ⿥0.10 ng/mL/cm3 resulted in a similar percentage of csPC loss (2, 7% [2/74] and 1.3% [1/74]), but a slightly smaller number of biopsies were avoided (25.2% and 28.1%). Clinicians should consider clinical risk screening tools when ordering and interpreting MRI results to avoid unnecessary testing and misdiagnosis. The authors stated that performing the 4Kscore test in conjunction with mp-MRI for men with clinically suspected prostate cancer may help reduce unnecessary biopsies. These investigators stated that this study was limited by its small sample size and its retrospective nature; Prospective validation of these findings is required prior to their implementation in clinical practice.

An UpToDate review of "Prostate Cancer Screening" (Hoffman, 2021) states that "Referral for a urologic evaluation will not necessarily result in a prostate biopsy. The urologist may perform other tests (eg, free-to-total PSA ratio [f /T PSA ], PCA3, 4Kscore test, and/or MRI) to help determine the likelihood that the PSA is elevated due to prostate cancer, PSA monitoring may be done over time or a biopsy may be performed Considerations Relevant factors include the patient's health status, the clinical likelihood of harboring significant illness, and personal desires.

Confirm MDx

ConfirmMDx (MDxHealth, Irvine, CA) is an epigenetic assay that uses multiplex polymerase chain reaction (PCR) to measure DNA methylation of genetic regions associated with cancer. It is designed to distinguish prostate cancer patients who have a truly negative biopsy from those who may have occult cancer. The test reportedly helps urologists rule out unnecessary repeat biopsies in men without prostate cancer and helps rule out high-risk patients who may require repeat biopsies and possible treatment. However, there is inadequate evidence to support the clinical value of ConfirmMDx in patients with prostate cancer.

Stewart et al (2013) reported that ConfirmMDx, a methylation-specific multiplex quantitative polymerase chain reaction assay that determines the methylation status of GSTP1, APC, and RASSF1, was strongly associated with repeat biopsy outcome up to 30 months after initial negative biopsy in men with suspected prostate cancer. Cancer. Investigators blindly analyzed archived prostate needle biopsy tissue samples from 498 UK and Belgian subjects with histopathologically negative prostate biopsies, followed by positive (cases) or negative (controls) repeat biopsies at 30 months. The clinical performance of the panel of epigenetic markers was evaluated and cross-validated, emphasizing the negative predictive value. Multivariate logistic regression was used to assess all risk factors. Epigenetic testing performed on the first negative biopsies from this retrospective review cohort resulted in a negative predictive value of 90% (95% CI 87 to 93). In a multivariate model that corrects for patient age, prostate-specific antigen, digital rectal examination, and histopathology at first biopsy, epigenetic assay was a significant independent predictor of patient outcome (OR 3.17, 95% CI). %: 1.81 to 5.53). The researchers stated that the addition of this epigenetic assay could improve the prostate cancer diagnosis process and reduce unnecessary repeat biopsies.

Partin et al (2014) reported that the ConfirmMDx epigenetic assay was a significant independent predictor of prostate cancer detection in a repeat biopsy taken an average of 13 months after an initial negative result. Investigators evaluated archival cancer-negative prostate biopsy tissue samples from 350 subjects from a total of 5 urology centers across the United States. All individuals underwent a new biopsy within 24 months with a negative (controls) or positive (cases) histopathological result. Centralized blind pathological assessment of the 2nd series of biopsies was performed on all available individuals from each site. Biopsies were epigenetically profiled for GSTP1, APC, and RASSF1 against the ACTB reference gene using quantitative methylation-specific polymerase chain reaction. Predetermined analytical marker cutoffs were used to determine assay performance. Multivariate logistic regression was used to assess all risk factors. The epigenetic assay resulted in a negative predictive value of 88% (95% CI 85 to 91). In multivariate models corrected for age, prostate-specific antigen, digital rectal examination, histopathology at first biopsy, and race, the test was shown to be the most significant independent predictor of patient outcome (OR 2.69, 95% CI: 1.60 to 4.51). The researchers stated that adding this epigenetic assay to other known risk factors may help reduce unnecessary repeat prostate biopsies.

Wu and others. (2011) noted that PSA detection has low specificity. Assessment of methylation status in body fluids can complement PSA detection if the test has high specificity. The aim of this study was to perform a meta-analysis of the sensitivity and specificity for prostate cancer detection of glutathione-s-transferase-π (GSTP1) methylation in body fluids (plasma, serum, whole blood, urine, ejaculate and prostate) . secretions). These investigators performed a comprehensive literature search on Medline (PubMed). Studies were included if they met all 4 of the following criteria:

measurement of DNA methylation in body fluids;
a case-control or case-only design;
publication in an English magazine; It is
adult subjects.

Reviewers independently performed data extraction using a standardized protocol. Finally, a total of 22 studies were included in this document. The primer sequences and methylation method in each study were summarized and evaluated by meta-analysis. This article represented a unique interdisciplinary approach to molecular epidemiology. The pooled specificity of GSTP1 promoter methylation measured in plasma, serum, and urine samples from biopsy negative controls was 0.89 (95% CI 0.80 to 0.95). Stratified analyzes consistently showed high specificity across different sample types and methylation methods (includes primer sequence and location). The pooled sensitivity was 0.52 (95% CI 0.40 to 0.64). The authors concluded that the combined specificity of measures of GSTP1 promoter methylation in plasma, serum, and urine was excellent and much higher than the specificity of PSA. The sensitivity of GSTP1 was modest, not greater than that of PSA. They said these findings suggest that measuring GSTP1 promoter methylation in plasma, serum or urine samples may complement PSA detection for the diagnosis of prostate cancer.

Van Neste et al (2012a) from MDxHealth stated that PSA-targeted prostate cancer screening leads to a high rate of false-positives and unnecessary biopsy burden. Epigenetic biomarkers have proven to be useful, showing frequent and abundant inactivation of tumor suppressor genes through these mechanisms. An epigenetic multiplex PCR test for the diagnosis of prostate cancer may provide physicians with better tools to help their patients. Biomarkers such as GSTP1, APC and RASSF1 have been shown to be involved in prostate cancer, and the last two genes play a prominent role in the field effect. The epigenetic states of these genes can be used to assess the likelihood of the presence or absence of cancer. An initial test cohort of 30 prostate cancer positive and 12 cancer negative samples was used as the basis for the development and optimization of an epigenetic multiplex assay based on the GSTP1, APC and RASSF1 genes using methylation specific PCR (MSP) . The effect of prostate core needle biopsy specimen volume and age of formalin-fixed, paraffin-embedded (FFPE) specimens was evaluated in an independent follow-up cohort of 51 cancer-positive patients. Multiplexing affects copy number estimates consistently per run. Therefore, the methylation rates are changed compared to the respective singleplex assays, but the correlation with patient outcome remains equivalent. Additionally, tissue biopsy samples as small as 20 µm can be used to reliably detect methylation. The age of FFPE samples has a negative impact on DNA quality and quantity. The authors concluded that the developed multiplex assay appears functionally similar to the individual singleplex assays, with the benefit of lower tissue requirements, lower cost and lower signal variation. This assay can be applied to small biopsy specimens, up to 20 microns, which broadens the clinical applicability. Increasing the sample volume can compensate for the loss of DNA quality and quantity in older samples.

Van Neste et al (2012b) noted that prostate cancer is the most common cancer diagnosis in men and a leading cause of death. Improvements in disease management would have a significant impact and could be facilitated by the development of biomarkers, whether for diagnostic, prognostic or predictive purposes. The blood prostatic biomarker PSA has been part of clinical practice for more than 2 decades, although it is surrounded by controversy. While debates about public services are ongoing, alternatives need to be explored. Particularly with recent recommendations against routine PSA testing, the time has come to explore promising biomarkers to produce more efficient and accurate screening for prostate cancer detection and treatment. Epigenetic changes, more specifically DNA methylation, are among the most common changes in human cancer. These changes are associated with transcriptional silencing of genes, leading to altered cell biology. One gene in particular, GSTP1, has been extensively studied in prostate cancer. Therefore, a meta-analysis was performed to examine the role of this and other genes and possible contribution to prostate cancer control and detection refinement. More than 30 independent peer-reviewed studies have consistently reported high sensitivity and specificity of GSTP1 hypermethylation in tissue from prostatectomy or biopsy. The meta-analysis combined and compared these results. The authors concluded that detection of GSTP1 methylation may play an important role in the treatment of prostate cancer. The meta-analysis clearly confirmed a link between tissue DNA hypermethylation of this and other genes and prostate cancer. They stated that detection of DNA methylation in genes, including GSTP1, could play an important role in clinical practice.

Andres et al (2013) summarized the main advances in the field of epigenetics and specifically DNA methylation in the diagnosis of urological neoplasms. A review of the literature (PubMed, MEDLINE and COCHRANE) on the study of DNA methylation in urological neoplasms (prostate cancer, bladder cancer, kidney cancer and testicular cancer) was performed, considering all studies published up to January 2013 It is possible to determine the methylation status of many genes in tumor samples. When these were compared with healthy tissue samples, it was possible to define specific aberrant methylation patterns for each type of tumor. The study and definition of abnormal methylation patterns specific to each type of tumor is a potentially useful tool for the diagnosis, evaluation, prognosis and treatment of different forms of genitourinary cancer. Analysis of gene methylation in urine after urination or after prostate massage, urine, semen, wash plasma or fluid from prostate biopsies may allow early detection of bladder, prostate, kidney and testicular cancer. In each of the neoplasms, an epigenetic signature was identified that can be detected in DNA, obtained from very few or non-invasive samples, with potential in diagnosis and prognostic evaluation. Validation of these studies will confirm the accuracy, efficacy and reproducibility of the results available so far. Criteria have not yet been developed to determine whether a gene panel provides sufficient information in healthcare practice to guide an unambiguous diagnosis or therapeutic approach. More studies are needed to compare the sensitivity, specificity, positive and negative predictive values of the test in each case. There are also no multicentric studies that analyze the actual reproducibility of these results in a clinical setting. The authors concluded that the study of aberrant DNA methylation in biological samples from patients has enormous potential for early diagnosis and detection of genitourinary neoplasms. They stated that more studies are needed to define the set of genes that would signify unequivocal signatures of malignancy. This methodology also has potential in defining prognostic groups and potential response to different therapies.

Lin et al (2013) Prostate cancer is the second leading cause of cancer death among men worldwide, and not all men diagnosed with prostate cancer will die from the disease. Therefore, a critical challenge is to distinguish indolent prostate cancer from more advanced forms to guide appropriate treatment decisions. These investigators used enhanced reduced processing bisulfite sequencing, single base resolution, high genomic coverage DNA methylation method to profile seven localized prostate cancer samples, 7 matched benign prostate tissues, and 6 prostate cancer samples. prostate. They integrated these data with genome-wide and RNA DNA-seq data to comprehensively characterize prostate cancer methyloma, detect changes associated with disease progression, and identify new candidate prognostic biomarkers. Analyzes revealed the correlation of island-specific hypermethylation of cytosine guanine dinucleotides (CGI) with disease severity and the association of certain breakpoints (deletion, tandem duplications, and interchromosomal translocations) with DNA methylation. Furthermore, integrative analysis of methylation and single nucleotide polymorphisms (SNPs) discovered for the first time widespread allele-specific methylation (ASM) in prostate cancer. These researchers found that most DNA methylation changes occurred in the context of ASM, suggesting that variations in the epigenetic tumor landscape of individuals are mediated in part by genetic differences, which may affect ASM progression. Furthermore, they selected a panel of 13 CGIs demonstrating increased DNA methylation with disease progression and validated this panel in an independent cohort of 20 benign prostate tissues, 16 prostate cancer tissues, and 8 aggressive CRPC tissues. The authors concluded that these results warrant clinical evaluation in larger cohorts to help distinguish indolent prostate cancer from advanced disease.

Wojno and colleagues (2014) reported an observational study suggesting that ConfirmMDx may reduce biopsy rates in people with suspected prostate cancer. The investigators noted that the diagnosis of prostate cancer (PCa) depends on histological confirmation in biopsy core tissues. The biopsy procedure is invasive, puts the patient at risk for complications, and is subject to significant sampling errors. The authors stated that an epigenetic test using methylation-specific PCR to determine the epigenetic status of the PCa-associated GSTP1, APC, and RASSF1 genes has been clinically validated and is used in clinical practice to increase the negative predictive value (NPV) in men with no history of CaP compared to standard histopathology. The researchers stated that such information may help to avoid unnecessary repeat biopsies. The investigators postulated that the repeat biopsy rate may provide preliminary evidence of clinical utility regarding the potential impact of this study on the number of unnecessary repeat prostate biopsies performed in US urology offices. The investigators stated that the purpose of this preliminary study was to quantify the number of repeat prostate biopsy procedures to demonstrate a low rate of repeat biopsy in men with a negative histopathology history who received a negative epigenetic assay result for residual prostate tissue testing. . In this observational field study, practicing urologists used the ConfirmMDx for CaP to evaluate cancer-free men considered to be at risk for CaP. The authors stated that their test was previously validated in 2 blinded multicenter studies showing the superior NPV of the epigenetic test over standard histopathology for the detection of cancer in prostate biopsies. A total of 5 clinical urology practices that requested a minimum of 40 commercial epigenetic test requests for patients with previous negative biopsies for cancer in the last 18 months were contacted to assess their interest in participating in the study. Information from selected prostate screening and demographic parameters, as well as the incidence of repeat biopsy, specifically for patients with a negative test result, were collected and merged into a pooled database. All men from each of the 5 sites who tested negative on the test were included in the analysis. A total of 138 patients were identified at these urology clinics and included in the analysis. The mean age of the men was 63 years and the mean current PSA serum level was 4.7 ng/mL. Repeat biopsies were performed on 6 of the 138 (4.3%) men with negative epigenetic assay results in whom no evidence of cancer was found on histopathology. The authors concluded that a low rate of repeat prostate biopsies was observed in this study in the group of men with previous histopathologically negative biopsies who were considered at risk of harboring cancer. The data suggested that patients treated with ConfirmMDx for negative PCa results had a low rate of repeat prostate biopsies. Furthermore, they stated that these results warrant a large prospective controlled study to further evaluate the clinical utility of epigenetic testing in reducing the rate of unnecessary repeat biopsies.

The PASCUAL trial is a prospective, randomized, controlled study of 600 patients to screen the clinical usefulness of the ConfirmMDx assay in US urology practices for the treatment of patients with a previous histopathologically negative biopsy for cancer but with clinical risk factors suggesting the need to repeat the biopsy. (MD x Health, 2016). The study, which began in 2014, will compare the rate of repeat biopsies according to the standard of care with the rate of repeat biopsies in patients treated with the ConfirmMDx test results. Completion of the study is scheduled for 2017.

A review by Bostrom et al (2015) indicated that commercially available epigenetic ConfirmMDx may be valuable when considering repeat biopsies after negative initial prostate biopsies. The review concluded: "Many new genetic tests are newly available or are in advanced stages of clinical development, with potential applications in PCa decisions ranging from the need for repeat biopsy to initial treatment selection, decisions about secondary therapy, and selection of treatment for advanced disease. A greater understanding of the potential long-term benefits and limitations of these tests is important, and exactly how they should be used in clinical practice to optimize decision-making should be the subject of future prospective studies."

A review of the ConfirmMDX HealthPACT (Foerster et al, 2013) concluded: "Based on the available evidence base, detection of prostate cancer using DNA methylation assays appears to offer some benefit over existing diagnostic methods. The high NPVs and the observed low false-negative rates suggest that DNA methylation assays may provide a means of reducing the number of healthy men misdiagnosed and undergoing unnecessary biopsies, although high-quality comparative studies are needed before this can be done. actually determined overall patient survival."

Nguyen and colleagues (2015) stated that several biomarkers designed to improve prostate cancer risk stratification have been commercialized in recent years, while others are still being developed. In this review, these investigators focused on evidence supporting recently reported biomarkers, with a focus on gene expression signatures. Many recently developed biomarkers can improve traditional risk assessment at almost all stages of the disease. ConfirmMDx uses gene methylation patterns to improve detection of clinically significant cancer after a negative biopsy. Both Prolaris and the Oncotype DX Genomic Prostate Score may improve risk stratification after biopsy, especially among men eligible for active surveillance. Prolaris and the Decipher genomic classifier have been associated with the risk of adverse outcomes after prostatectomy, while Oncotype DX is being studied in this setting. Finally, recent reports of the association of androgen receptor-V7 in circulating tumor cells with resistance to enzalutamide and abiraterone raised the possibility of extending the use of genetic biomarkers to advanced disease. The authors concluded that, with the development of multiple prostate cancer gene expression panels, careful study and validation of these tests and integration into clinical practice will be essential to harness the potential of these tools.

The 2015 National Comprehensive Cancer Network Guidelines on Prostate Cancer Screening (NCCN, 2015) concluded on ConfirmMDx: "Despite the good NPV of this test, the panel noted that the study population included only patients who were selected for repeat biopsy. In addition, the panel noted that the NPV of the first negative biopsy alone is already in the range of 75% to 80% and questioned the true value added by the test results. Therefore, until prospective data for this test or data comparing this test with other tests available, the panel does not recommend their use."

National Comprehensive Cancer Network (NCCN, 2016) guidelines recommend ConfirmMDx non-preference among multiple markers (percent free PSA, PHI, 4KScore, PCA3, and ConfirmMDx) for men who intend to repeat the biopsy because the assay can identify people at increased risk of Prostate cancer diagnosis on repeat biopsy. The guidelines note that direct comparisons have been made for some of these tests, used independently or in combination, in initial biopsy or repat settings, but sample sizes were small and results varied. Therefore, the NCCN panel concluded that no single biomarker test can be recommended over any other at this time. The NCCN panel noted that the optimal order of testing and imaging of biomarkers is unknown; and it is still unclear how to interpret the results of various tests in individual patients, especially when results are conflicting.

A joint consensus statement from the American Urological Association and the Society for Abdominal Radiology (Rosenkrantz, et al., 2016) states, "Nonimaging markers (i.e., PSA-based measurements as well as PCA3) are likely to be helpful in selecting more patients with negative or low-suspicion MRI (PI-RADS score of 1 or 2, respectively) who may warrant routine biopsy despite MRI results. Ancillary markers given consistently strong independent effect of MRI-suspect score on Cancer detection in multivariate models. Further investigation to identify which of these markers best complements MRI findings in the and repeat biopsy setting."

Zhuang and Johnson (2016) observed that there has been progress in the application of genetic information for the treatment of diseases in the post-genomic era and that precision medicine is emerging in the treatment of prostate cancer. The Prostate Health Index, 4-Kallikrein (4K) score, PCA3, TMPRSS2-ERG, and Prostarix tests have the potential to refine prostate cancer screening alongside traditional prostate-specific antigen (PSA) tests. . The Confirm MDx and PCA3 tests have shown promise in identifying men who need a rebiopsy after a negative primary biopsy. Oncotype DX, Prolaris, the biopsy-based Decipher prostate cancer test, and ProMark can improve predictive risk stratification in addition to traditional Gleason scoring and tumor staging. Decipher and Prolaris may predict biochemical recurrence and metastasis after radical prostatectomy and possibly help identify patients who need adjuvant therapy. Androgen receptor 7 splicing variant appears effective in guiding the selection of second hormone manipulation with abiraterone or enzalutamide versus chemotherapy in the treatment of metastatic castration-resistant prostate cancer.

Van Neste et al (2016) noted that the diagnosis of prostate cancer (PCa) is challenging because efforts to achieve effective and timely treatment of men with significant cancer often result in overdiagnosis and repeated biopsies. The presence or absence of epigenetic aberrations, more specifically GSTP1, RASSF1 and APC DNA methylation in histopathologically negative prostate biopsies resulted in an increase in negative predictive value (NPV) of approximately 90% and therefore may lead to unnecessary reduction in repeat biopsies. These investigators examined whether in methylation-positive men, DNA methylation intensities could help identify those men who have high-grade CaP (Gleason score [GS] greater than or equal to 7), resulting in improved PPV. Two cohorts were matched, consisting of men with histopathologically negative index biopsies, followed by either a positive or negative repeat biopsy. EpiScore, a methylation intensity algorithm was developed in methylation-positive men, using the area under the curve (AUC) of the receptor operating characteristic (ROC) as a metric for performance. Next, a risk score combining the EpiScore with traditional clinical risk factors was developed to further improve the identification of high-grade cancer (GS greater than or equal to 7). Compared with other risk factors, detection of DNA methylation in histopathologically negative biopsies was the most significant and important predictor of high-grade cancer, resulting in a NPV of 96%. In methylation-positive men, the EpiScore was significantly higher for those with high-grade cancer detected on repeat biopsy compared with those with no cancer or low-grade cancer. The risk score resulted in further improvement in patient risk stratification and was a significantly better predictor compared to currently used metrics such as PSA and Risk Calculator (CR) from the Prostate Cancer Prevention Trial (PCPT). A decision curve analysis indicated high clinical utility for the risk score as a decision-making tool for repeat biopsy. The authors concluded that low levels of DNA methylation in PCa-negative biopsies resulted in a 96% NPV for high-grade cancer. The risk score, which comprises the intensity of DNA methylation and traditional clinical risk factors, improved the identification of men with high-grade cancer, minimizing unnecessary repeat biopsies. This risk score resulted in better patient risk stratification and significantly outperformed current risk prediction models such as PCPTRC and PSA. Risk scoring can help identify patients with histopathologically negative biopsies who harbor high-grade CaP.

Van Neste et al (2017) noted that early detection of aggressive CaP remains crucial for effective treatment of patients. However, PCa screening remains controversial due to the high rate of overdiagnosis and overtreatment. To better reconcile the two objectives, more effective methods are needed to assess the severity of the disease at the time of diagnosis. The relationship between DNA methylation and high-grade CaP was examined in a prospectively recruited cohort of 102 men who received standard 12-core prostate biopsies. EpiScore, an algorithm that quantifies the relative intensities of GSTP1, RASSF1, and APC DNA methylation in prostate biopsy tissue, was evaluated as a method to compensate for biopsy undersampling and improve risk stratification in diagnosis. DNA methylation intensities of GSTP1, RASSF1, and APC were higher in biopsy cores from men diagnosed with GS greater than or equal to 7 cancer compared to men diagnosed with GS 6 disease. This was confirmed by EpiScore, which was significantly higher for subjects with high-grade biopsies and higher NCCN risk categories (both p < 0.001). In patients with a diagnosis of OS greater than 7, elevated levels of DNA methylation were observed, not only in high-grade biopsy cores, but also in other cores with or without low-grade disease (p < 0.001). When EpiScore was combined with traditional clinical risk factors in a logistic regression model, the prediction of high blood glucose achieved an AUC of 0.82 (95% confidence interval [CI] 0.73 to 0.91) with EpiScore , digital rectal examination (DRE) and atypical histological examination . findings as major contributors. The authors concluded that in men diagnosed with CaP, DNA methylation profiling can detect subsampled high-risk CaP in prostate biopsy specimens via a field effect. Predictive accuracy increased when EpiScore was combined with other clinical risk factors. They stated that these EpiScore findings could aid in the detection of high-grade occult disease at the time of diagnosis, thereby improving the selection of candidates for active surveillance.

Kretschmer and colleagues (2017) stated that, in the era of personalized medicine and precision oncology, innovative genetic biomarkers are of emerging interest to fill the diagnostic and prognostic gap left by current clinicopathologic risk classifiers. These investigators summarized the evidence on prognostic and predictive genetic biomarkers currently in widespread clinical use in the initial diagnosis as well as after definitive treatment of prostate cancer. They briefly summarized the fundamentals of biomarker research studies and presented current data from the Progensa PC3 assay, the TMPRSS2:ERG gene fusion, ConfirmMDx, the Prolaris gene panel, the OncotypeDX prostate genomic score, and the Decipher classifier. The authors concluded that evidence on these genetic biomarkers has increased considerably recently; however, large, multicenter prospective clinical validation studies are still lacking. In addition, they stated that comparative studies are needed to investigate the prognostic value of several genetic biomarkers.

BioespeciFx

BioSpeciFx is an individualized molecular tumor profiling of a panel of tumor markers to establish a personalized molecular profile of a tumor to recommend treatment options. It is often ordered in combination with an in vitro chemosensitivity/chemoresistance assay or ChemoFx. To seeCPB 0245 - Tumor chemosensitivity assaysyCPB 0758 - Tumor chemoresistance assays. The combination of molecular profiling and in vitro drug response marker testing is sometimes referred to as comprehensive tumor profiling.

HeproDx-TM

Hepatocellular carcinoma (HCC) mRNA expression tests (eg, HeproDx-TM) reportedly incorporate levels of 161 genes, fresh hepatocellular carcinoma tumor tissue, AFP level, and an algorithm to report a risk classifier related to recurrence and metastasis of CHC.

network test

NETest is a multianalyte algorithm PCR-based genetic blood test that measures 51 transcripts of neuroendocrine tumor-specific genes in combination with molecular biomarkers that are believed to allow monitoring of neuroendocrine tumor gene activity levels.

Pęczkowska et al (2017) assessed whether NETEST has clinical utility as a diagnostic and prognostic marker. The researchers conducted a prospective cohort study. Subjects included well-differentiated aragangliomas and pheochromocytomas (PPGL) (n = 32), metastatic (n = 4); SDHx mutation (n = 25); 12 biochemically active, treated with lanreotide (n = 4). Age- and sex-matched controls and GEP-NET were compared. PPGL was NETest positive (100%). All exhibited higher scores than controls (55 ± 5% vs 8 ± 1%, P = 0.0001), similar to GEP-NETs (47 ± 5%). The area under the curve of the ROC analysis was 0.98 to differentiate PPGL/controls (cutoff point for normal: 26.7%). Mutation status was not directly related to NETest. Genetic and molecular clustering was associated (P < 0.04) with NETest scores. Metastatic (80±9%) and multicentric (64±9%) disease scored significantly (P<0.04) higher than localized disease (43±7%). Progressive disease (PD) had the highest scores (86 ± 2%) versus stable (SD, 41 ± 2%) (P < 0.0001). The area under the curve for PD of SD was 0.93 (PD cutoff: 53%). Proliferation, epigenetics, and somatostatin receptor gene expression were elevated (P < 0.03) in PD. Metabolic gene expression was reduced in SDHx mutations. Repeated NETest measurements defined clinical status in all 9 patients (6 SD and 3 PD). The amine measurement was not informative. Multivariate analysis identified NETest >53% as an independent prognostic factor.

(Video) Carcinoid Syndrome - causes, symptoms, diagnosis, treatment, pathology

Pavel et al (2016) evaluated NETest as a predictive and prognostic marker of the progression of gastroenteropancreatic neuroendocrine tumors. GEP-NETs (n = 34) followed for a mean of 4 years (2.2-5.4) were evaluated. WHO tumor grade/stage grade 1: n = 17, grade 2: n = 14, grade 3: n = 1 (for 2, no grade was available); 31 (91%) were in stage IV. Baseline and longitudinal images and blood biomarkers were available in all, and progression was defined according to standard clinical protocols (RECIST 1.0). NETest was measured by quantitative blood PCR and multianalytical algorithm analysis (disease activity scaled from 0 to 100% with activity risk cutoffs low <40% and high >80%)); chromogranin A (CgA) was measured by radioimmunoassay (normal <150 µg/l); Progression-free survival (PFS) was analyzed using Cox proportional hazards regression and Kaplan-Meier analysis. At baseline, 100% were NETest positive and CgA was elevated in 50%. The only baseline variable (Cox model) associated with PFS was the NETest (hazard ratio = 1.022, 95% confidence interval = 1.005-1.04; p < 0.012). Using Kaplan-Meier analyses, baseline NETest (>80%) was significantly (p = 0.01) associated with disease progression (median PFS 0.68 vs 2.78 years at levels <40%). NETest was more informative (96%) than CgA changes (<lower>></lower>25%) in consistently predicting disease changes (40%, p < 2 × 10-5, χ2 = 18) . NETest had a time point shift prior to the images (1.02 ± 0.15 years). Baseline NETest levels >40% in stable disease were 100% predictive of disease progression versus CgA (χ2 = 5, p < 0.03). NETest baseline values <40% accurately (100%) predicted stability over 5 years (p = 0.05, χ2 = 3.8 vs. CgA).

Bodei et al (2016) assessed the accuracy of circulating NET transcripts as a measure of PRRT efficacy and, in addition, to identify clusters of prognostic genes in pretreatment blood that could be interpolated with relevant clinical features to define a biological index for the tumor and a PRRT predictive efficacy ratio. Patients with TEN (n = 54), M: F 37:17, mean age 66, bronchial: n = 13, GEP-NET: n = 35, CUP: n = 6 were treated with PRRT based on (177)Lu ( cumulative activity: 6.5-27.8 GBq, median 18.5). At baseline: 47/54 low grade (G1/G2; typical/atypical bronchial), 31/49 (18) FDG positive and 39/54 progressive. Disease status was assessed using RECIST1.1. Transcripts were measured by real-time quantitative reverse transcription-PCR (qRT-PCR) and multi-analyte algorithmic analysis (NETest); CgA by enzyme-linked immunosorbent assay (ELISA). Gene cluster (GC) derivations: regulatory network, protein:protein interactome analysis. The disease control rate was 72%. The median PFS was not reached (follow-up: 1-33 months, median: 16). Only classification was associated with response (p < 0.01). At baseline, 94% of patients were NETest positive, while CgA was elevated in 59%. NETest accuracy (89%, χ(2) = 27.4; p = 1.2 × 10(-7)) correlated with treatment response, while CgA had 24% accuracy. Expression of the gene cluster (growth factor signalome and metabolome) had an AUC of 0.74±0.08 (z statistic=2.92, p<0.004) to predict response (76% accuracy). The combination with the classification achieved an AUC: 0.90 ± 0.07, regardless of tumor origin. Circulating transcripts were accurately (94%) correlated with PRRT responders (SD+PR+CR; 97%) vs. non-responders (91%).

Modlin et al (2016) examined whether a blood multianalyte neuroendocrine gene transcription assay (NETest) would define tumor debulking and therapeutic efficacy. A total of 35 GEP-NETs were evaluated in 2 groups. I: after surgery (R0, n = 15; residual, n = 12); II: non-surgical (n = 8: isolated gel-foam embolization [mild: n = 3]), transarterial chemoembolization (n = 2), and radiofrequency embolization (n = 3). Measurement (quantitative real-time polymerase chain reaction) and chromogranin A (CgA; enzyme-linked immunosorbent assay) were performed before operation and 1 month after treatment. The NETest score increased by 35 (100%) preoperatively; 14 (40%) showed increased CgA (χ(2) = 30, P < 2 × 10(-8)). Resection reduced the NETest from 80 ± 5% to 29 ± 5% (p < 0.0001). The decrease in CgA was insignificant (14.3 ± 1.6 U/L to 12.2 ± 1.7 U/L). NETest reductions correlated with tumor volume decrease (R(2) = 0.29, P = 0.03). Cytoreduction significantly reduced the NETest from 82 ± 3% to 41 ± 6%, p < 0.0001). CgA did not decrease (21.4 ± 5.5 U/L to 18.4 ± 10.1 U/L). Four (36%) of 11 R0s with increased NETest at 1 month developed positive images (sensitivity 100%, specificity 20%). One hundred percent (ablated group) were positive for transcription and imaging.

Modlin et al (2015) reported the sensitivity and selectivity of NETest in detecting tumors with reference to other benign and malignant gastrointestinal diseases. A total of 179 cases (gastrointestinal tumors: n=81; pancreatic disease: n=98) were prospectively collected and evaluated using NETest or chromogranin A (CgA) to determine metrics for detection of small bowel and pancreatic NETs. For intestinal carcinoids, the NETest accuracy was 93% (all NETs positive and 3 (12%) colorectal tumors were positive). CgA was positive in 80%, but 29% (n=7) of colorectal cancers were CgA positive. For pancreatic disease, the NETest accuracy was 94% (96% of NETs positive, 2 (6%) of intraductal papillary ucinous neoplasms (IPMNs) were positive). The accuracy of CgA was 56% (29% of pancreatic NETs were positive for CgA). Overall, NETest was significantly more sensitive than CgA for detecting small intestine (area under the curve 0.98 vs 0.75 P<0.0001) and pancreatic (0.94 vs 0.52, P <0.0001). NETest scores were high (P<0.05) in extensive disease and were more accurate (76-80%) than CgA levels (20-32%). The NETest multianalytical metrics met the performance criteria proposed by the National Institutes of Health for biomarkers, while the CgA measurement did not.

Modlin et al (2014) evaluated a PCR-based transcription signature 51 (NETest) and compared it with chromogranin A (CgA), pancreastatin (PST) and neurokinin A (NKA). The multigene signature was evaluated in two groups: i) a validation set of 40 NETs and controls and ii) a pool of prospectively collected NETs (n = 41, 61% small intestine, 50% metastatic, 44% currently treated) and 41 elderly ). sex-matched controls). Samples were analyzed using a two-step PCR protocol (51 marker genes) and ELISA for CgA, PST and NKA. Sensitivity comparisons included χ(2), nonparametric measures, ROC curves, and predictive feature importance analysis (PFAI). NETest identified 38 out of 41 NET. The performance metrics were: sensitivity 92.8%, specificity 92.8%, positive predictive value 92.8% and negative predictive value 92.8%. Single analyte ELISA metrics were: CgA 76, 59, 65, and 71%; PST 63, 56, 59 and 61% and NKA 39, 93, 84 and 60%. The AUCs (ROC analysis) were: NETest: 0.96±0.025, CgA: 0.67±0.06, PST 0.56±0.06, NKA: 0.66±0.06. NETest significantly outperformed single analyte tests (area differences: 0.284-0.403, Z statistic 4.85-5.9, P < 0.0001). PFAI analysis determined that NETest had the highest value (69%) at diagnosis (CgA (13%), PST (9%) and NKA (9%). Test data were consistent with the validation set ( NETest > 95% sensitivity and specificity, AUC = 0.98 vs. individual analytes: 59-67% sensitivity, AUC: 0.58-0.63).

Chen et al (2017) commented that NETEST and other new biomarkers are promising biomarkers in gastroenteropancreatic neuroendocrine tumors with potential clinical benefit, but more research is needed before their clinical application.

Pulmotype

A multi-antibody immunohistochemistry (IHC) assay (eg, Pulmotype) presumably aids in differentiating squamous cell carcinoma and adenocarcinoma histology from NSCLC. The assay uses tissue from a lung cancer biopsy to measure five biomarkers: cytokeratin 5/6 (CK5/6), mucin-1 (MUC-1), tripartite motif containing protein 29 (TRIM 29), adhesion molecule associated with carcinoembryonic cell antigen (CEACAM) and SLC7A5. The results of these measurements are applied to an algorithm, resulting in a class assignment.

NexCourse IHC4

AQUA Technology's NexCourse IHC4 is a proposed test to determine the risk of breast cancer recurrence by analyzing estrogen receptor (ER)/progesterone receptor (PR), HER2 and Ki67 expression. ER/PR testing is routinely performed in all people with invasive breast cancer using immunohistochemistry (IHC) to select those most likely to respond to hormone therapy.

EndoPredict

EndoPredict is a multigene assay that predicts the likelihood that women with estrogen receptor (ER) positive and human epidermal growth factor receptor 2 (HER2) negative breast cancer will develop metastases within 10 years of initial diagnosis (NICE, 2015). The test combines measures of gene expression (EndoPredict [EP] score) with nodal status and tumor size to generate a comprehensive risk score (EPclin score) that is used to identify tumor types that will not benefit from chemotherapy.

The American Society of Clinical Oncology guidelines state that, "If a patient has ER/PgR-positive, HER2-negative (node negative) breast cancer, the clinician may use the 12-gene risk score (EndoPredict; Sividon Diagnostics, K¨oln, Germany) to guide decisions about adjuvant systemic chemotherapy". This is a moderate-strength recommendation based on intermediate-quality evidence. against the use of Endopredictin in patients with HER2-positive breast cancer or TN breast cancer. had 5 years of endocrine therapy with no evidence of recurrence.

A review by the National Institute for Health and Care Excellence (NICE, 2015) found that published clinical evidence comes from 3 analytical validation studies and 5 clinical validation studies in which the test has generally been shown to be reproducible and have prognostic power. In 1 impact evaluation study, EndoPredict results were reported to change treatment decisions. A cost-effectiveness analysis found that using EndoPredict in combination with clinical regimens outside the UK was less expensive and more effective than risk stratifying clinical regimens alone.

Immunohistochemistry 4 (IHC4)

IHC4 measures levels of 4 key proteins (ER, PR, HER2 and Ki-67) as well as classic clinical and pathological variables (e.g. age, nodule status, tumor size and grade) and calculates a remote recurrence risk score using an algorithm (NICE, 2013). Quantitative ER, PR, and Ki-67 assessments are required for the IHC4 test. An online calculator for IHC4 is under development. The test uses paraffin-embedded specimens fixed in formalin.

The American Society of Clinical Oncology (2016) guidelines state: "If a patient has ER/PgR positive, HER2 negative (knot positive or negative) breast cancer, the clinician should not use immunohistochemistry 4 (IHC4) to guide decisions on adjuvant systemic chemotherapy". This is a moderate-strength recommendation based on intermediate-quality evidence. ASCO guidelines recommend the use of IHC4 to guide decisions about adjuvant systemic therapy for patients with HER2-positive breast cancer or TN breast cancer. IHC4 to guide decisions about extended endocrine therapy for patients with ER/PgR-positive, HER-2-negative (node negative) breast cancer who have received 5 years of endocrine therapy with no evidence of recurrence.

Tumor infiltrated lymphocytes

The American Society of Clinical Oncology (2016) guidelines state: "If a patient has ER/PgR-positive, HER2-negative breast cancer (node positive or nodule negative), the clinician should not use tumor-infiltrating lymphocytes to guide decisions in adjuvant systemic therapy". This is a strong recommendation based on insufficient evidence. Guidelines advise against using tumor-infiltrating lymphocytes to guide decisions about adjuvant systemic therapy in patients with HER2-positive breast cancer or TN.

Other markers to guide adjuvant therapy in breast cancer

Oncology (2016) recommends against duplication of CEP17, TIMP-1, FOXP3 and microtubule-associated Tau protein mRNA expression or mRNA expression by IHC to guide selection of adjuvant chemotherapy. ASCO guidelines also recommend against CYP2D6 and p27 polymorphisms to guide selection of endocrine therapy.

Active surveillance of prostate cancer in men with "favorable" intermediate-risk disease

Mulcahy (2016) stated that the NCCN is the first major organization in the US to have expanded the scope of prostate cancers that qualify for active surveillance to include men with "favorable" intermediate-risk disease. Active surveillance includes ongoing disease monitoring (with PSA tests, biopsies, and imaging tests) but does not include definitive treatment, such as surgery or radiation, or related harms, such as erectile dysfunction and incontinence. Previously, the NCCN and other organizations (eg, the American Urological Association [AUA]) recommended active surveillance only for very-low-risk and low-risk prostate cancers. However, the NCCN now recommends active surveillance for intermediate-risk prostate cancer with a Gleason score of 7 (3+4), considered favorable (grade 3 prostate cancer is predominant and accounts for at least 50% of tissue biopsied and grade 4 is secondary accounting for at least 5% but less than 50%)). Favorable intermediate risk also requires that less than 50% of a patient's biopsy cores are positive and that they have no more than 1 NCCN intermediate risk factor. These risk factors include a tumor stage of T2b to T2c and a PSA value of 10 to 20 ng/mL. Furthermore, the new NCCN recommendation does not include active surveillance for "unfavorable" intermediate-risk cancers, such as those with a Gleason score of 7 (4+3).

The Doctor. James Mohler of the NCCN Prostate Cancer Panel noted that the use of active surveillance in selected men with intermediate-risk prostate cancer has been ongoing for years at leading academic centers. However, the new NCCN recommendation is "somewhat more comprehensive than what many urologists have used to select these [intermediate-risk favorable] patients. Specifically, it is "more liberal" in terms of the percentage of positive biopsies it allows for." . and therefore will allow an even greater percentage of intermediate-risk patients to continue active surveillance." The recommendation is based on a radiation oncology study in which 1024 patients with intermediate-risk prostate cancer received radiotherapy with or without radiation therapy. androgen deprivation (Zumsteg et al, 2013). In that study, the authors analyzed recurrence-free survival and distant metastasis outcomes and concluded that intermediate-risk disease is "heterogeneous" with "favorable and unfavorable subsets" , groupings now used by the NCCN. The idea that active surveillance should be used in favorable intermediate-risk diseases was supported by Dr. Ann Raldow and colleagues at the Harvard Radiation Oncology Program. In their observational study, Raldow et al (2015) compared 3972 men with low-risk prostate cancer with 1608 men with favorable intermediate-risk prostate cancer, all treated for brachitis from 1997 to 2013. These researchers found that prostate cancer rates - cancer-specific mortality and all-cause mortality were similar in the 2 groups and concluded that these findings provided "evidence to support active surveillance as an approach for men with favorable intermediate-risk prostate cancer".

The Doctor. Mohler said the NCCN's Prostate Cancer Panel has been advocating for active surveillance for some time. In 2010, the NCCN was the first organization to recommend active surveillance as the sole initial therapy for many men. The use of active surveillance has increased in recent years in the US; Estimates range from 50% of men with low-risk prostate cancer in Michigan, where an insurance industry-funded awareness program is taking place, to 8% nationally. Medscape Medical News reported that another estimate of the practice's prevalence came from CaPSURE, a prostate cancer registry that has been collecting data on men seen at 47 primarily community-based clinical centers. An analysis of data collected between 2008 and 2013 showed that the main treatment for 38.4% of men with low-risk tumors was watchful waiting or active surveillance. Additionally, Dr. Stacy Loeb of New York University said that the United States has lagged behind certain European countries in adopting active surveillance. As of 2013, use of active surveillance in Sweden was 78% for men with very low-risk disease and 59% for men with low-risk disease.

CDX2 as a prognostic biomarker in colon cancer

Dalerbra and colleagues (2016) stated that identifying high-risk stage II colon cancers is critical for selecting patients who require adjuvant treatment after surgery. Microarray-based multigene expression signatures derived from stem cells and progenitor cells hold promise, but are difficult to use in clinical practice. These investigators used a novel bioinformatics approach to search for colonic epithelial differentiation biomarkers in gene expression matrices and then ranked candidate genes based on the availability of clinical-grade diagnostic assays. Using subgroup analyzes that included independent retrospective cohorts of patients with stage II or III colon cancer, the main candidate gene was tested for its association with DFS and a benefit of adjuvant chemotherapy. The CDX2 transcription factor ranked first in the screening test. One group of 87 out of 2115 tumor samples (4.1%) did not express CDX2. In the discovery dataset, which included 466 patients, the 5-year DFS rate was lower among the 32 patients (6.9%) with CDX2 negative colon cancer than among the 434 (93.1%) with colon cancer. of CDX2 positive colon (HR for disease recurrence, 3.44; 95% CI, 1.60 to 7.38; p = 0.002). In the validation dataset, which included 314 patients, the 5-year DFS rate was lower among the 38 (12.1%) patients with CDX2 negative colon cancer than among the 276 (87.9%). (HR, 2.42; 95% CI: 1.36 to 4.29; p = 0.003). In both groups, these findings were independent of age, sex, tumor stage and grade. Among patients with stage II cancer, the difference in 5-year DFS was significant in both the discovery dataset (49% among 15 patients with CDX2 negative tumors versus 87% among 191 patients with CDX2 positive tumors, p = 0.003) and in the validation dataset (51% among 15 patients with CDX2-negative tumors versus 80% among 106 patients with CDX2-positive tumors, p = 0.004). In a pooled database of all patient cohorts, the 5-year rate of DFS was higher among 23 patients with stage II CDX2-negative tumors who received adjuvant chemotherapy than among 25 who did not receive adjuvant chemotherapy (91% vs. .56%). %, p = 0.006). the authors concluded that the lack of CDX2 expression identified a subgroup of patients with high-risk stage II colon cancer who appeared to benefit from adjuvant chemotherapy. They stated that, due to the exploratory and retrospective design of the study, these findings need to be validated; advocated for these results to be confirmed in randomized clinical trials along with genomic DNA sequencing studies.

testes PDGFRB

The National Comprehensive Cancer Network Compendium of Biomarkers (2016) recommends the following for the PDGFRB test:

Myelodysplastic syndromes (MDS): Useful in some clinical situations: Screen patients with CML for 5q31-33 translocations and/or PDGFR beta gene rearrangements. (Category of evidence: 2A).

Non-melanoma skin cancers - Dermatofibrosarcoma protuberans (DFSP): Tumors lacking the t(17;22) translocation may not respond to imatinib. Molecular analysis of a tumor using cytogenetics may be useful prior to institution of imatinib therapy. (Category of evidence: 2A).

guardian360

The Guardant360 panel analyzes cell-free circulating tumor DNA (liquid biopsy) for 73 genes associated with a wide variety of solid tumors.

Considering that data on the influence of NGS based on hybrid capture (HC) on treatment is limited, Rozenblum, et al. (2017) investigated its impact on treatment decisions and clinical outcomes in a series of patients at a cancer center. This retrospective study included patients with advanced lung cancer who underwent HC-based NGS with broad gene panels between November 2011 and October 2015. HC-based NGS was performed on the recommendation of the treating physician, primarily on the basis of young age and in smoking. history. Standard molecular test results for EGFR mutations and ALK rearrangements were negative before HC-based NGS in 80.2% (81 of 101) and 70.3% (71 of 101) of patients, respectively. Initial HC-based NGS was performed in 15 patients because there was too little biopsy material. samples using a liquid biopsy approach with Guardant360 if the tissue sample had been depleted (n = 18). The study focused on genetic analyzes (GA) with potential clinical relevance. The baseline (level 1) analysis included GAs associated with US Food and Drug Administration-approved cancer therapies (including unapproved drugs) for all cancers. A subsequent (level 2) analysis included GAs with appropriate evidence-based targeted agents with anticonductive activity in lung cancer, as recommended by the National Comprehensive Cancer Network (NCCN) guidelines for NSCLC. GAs associated with experimental treatments were not included in the current analysis. Demographic and clinicopathological characteristics, treatment and outcome data were collected. A total of 101 patients (mean age 63 years [53% women, 45% never smokers, and 85% with adenocarcinoma]) were included. HC-based NGS was performed before and after the EGFR/ALK test was negative or inconclusive in 15% and 85% of patients, respectively. In 51.5% of patients, HC-based NGS was performed before first-line treatment and in 48.5% it was performed after treatment failure. HC-based NGS identified clinically actionable genomic alterations in 50% of patients, most often in EGFR (18%), Ret proto-oncogene (RET) (9%), ALK (8%), mesenchymal-epithelial transition factor (MET) tyrosine kinase receptor gene (6%) and erb-b2 receptor tyrosine kinase 2 (ERBB2) gene (5%). In 15 patients, it identified EGFR/ALK aberrations after previous negative standard test results. The treatment strategy was changed in 43 patients (42.6%). The overall response rate in these patients was 65% (14.7% complete response, 50% partial response). Median survival was not achieved. Immunotherapy was administered to 33 patients, most without an actionable factor, with a disease control rate of 32% and with an association with tumor mutational burden. The authors noted a number of limitations of this study, including its retrospective nature, its small sample size, and the fact that it was a single-center study. In addition, the high percentage of non-smokers, the preponderance of female patients, and the relatively young median age of the patient group represented a selection bias with a high pretest probability of the existence of a driver mutation. The authors note that the results of large prospective studies, such as the UK's National Lung Matrix Trial and the National Cancer Institute's Program for Molecular Analysis for Choice of Therapy, are eagerly awaited.

Kim et al (2017) reported an interim analysis of an open-label prospective clinical trial of ctDNA in patients with metastatic NSCLC, gastric cancer (GC) and other cancers. Investigators reported that somatic abnormalities were detected in 59 patients with GC (78%) and 25 patients (33%) had detectable abnormalities (ERBB2, n=11; MET, n=5; FGFR2, n=3; PIK3CA, n=5 ). 6). In the NSCLC, 62 patients (85%) had somatic abnormalities and 34 (47%) had target abnormalities (EGFR, n=29; ALK, n=2; RET, n=1; ERBB2, n=2). A subgroup of subjects (10 with GC and 17 with NSCLC) who had tissue to confirm the ctDNA findings were treated with targeted therapy. and 100%, respectively, in NSCLC. The authors note that this is the first prospective study to examine the clinical usefulness of comprehensive genomic ctDNA testing to guide selection of appropriate therapy. The authors stated that, as this study was not randomized, its main limitation is potential selection bias to recruit patients who are more likely to benefit. Furthermore, the cohort is heterogeneous, including patients on different lines of therapy and with multiple concomitant treatments, which limits the conclusions of this interim analysis. Not all patients with treatable conditions may receive combination therapy due to varying requirements of various parallel combination therapy substudy protocols, performance status, or loss to follow-up. The authors stated that the final analysis will help address the modest sample size of this interim analysis, as well as inform progression-free survival. The authors stated that future studies should examine the results of ctDNA-guided combination therapy in more racially diverse cohorts.

Noting that there is a dearth of data on concordance between circulating plasma cell-free tumor DNA (ctDNA) and tissue-based genomic tests, Villaflor, et al. (2016) reported a descriptive study of individuals with NSCLC undergoing ctDNA analysis using the Guardant360 next-generation sequencing assay at a single institution. The authors state that this study is the first clinical series of patients with NSCLC to evaluate the results of targeted therapies using a commercially available ctDNA assay. Of the 90 patients referred for ctDNA analysis as part of clinical care, 68 gave informed consent for inclusion in this study. 68 gene ctDNA panel. Of note, the 54-gene panel did not include ALK, RET, or ROS1 fusions. Tissue-based tests were performed on 44 subjects using 9 different test platforms. Demographic, clinicopathologic, and tissue and plasma-based test results for each subject were reviewed. , 17.7%) and other lung cancers (n = 1, 1.3%). More than 80% of patients had detectable ctDNA. Thirty-one patients had matching blood and tissue samples; the reason for the lack of tissue results for the remaining 37 patients was not routinely documented. In cases with detectable ctDNA and whole tissue analysis, EGFR activation was found in both tissue and blood in 5 paired samples and only in tissue in 2 samples (71% concordance). The time between biopsy and blood collection ranged from 0 days to 7 years, with a mean of 8.8 months and a median of 1.4 years between biopsy and blood collection. Investigators found no correlation between concordance and timing of blood draw versus tissue biopsy. A total of 9 subjects with matched tissue and blood samples had an EGFR driver mutation identified in either plasma or tissue (n=5), plasma only (n=1) or tissue only (n=3). Eight of these subjects were treated with either first-line or second-line erlotinib or afatinib. Two patients were still responding to therapy at the time of data analysis. Of the remaining 6 patients, the median progression-free survival was 11.5 months (range 7.5 months to 29 months; 95% CI 5.7 to 28.7). The investigators state that these data suggest that ctDNA analysis without biopsy is a viable first option when diagnostic tissue biopsy is insufficient for genotyping or at the time of progression when repeat invasive tissue biopsy is not possible/preferred. However, the authors noted that the numbers in this series are modest and more research is needed in larger prospective cohorts.

Thompson et al (2016) evaluated the feasibility of using circulating cell-free tumor DNA (ctDNA) NGS as an adjuvant or alternative to tissue NGS in a single-center observational study. A total of 112 plasma samples obtained from a consecutive study of 102 prospectively recruited patients with advanced NSCLC underwent ultra-deep sequencing of 68 or 70 genes and were compared to tissue samples where possible. Investigators detected 275 changes in 45 genes and at least one change in ctDNA in 86 of 102 patients (84%), with EGFR variants being the most common. The ctDNA NGS detected 50 driver and 12 resistance mutations, as well as mutations in 22 additional genes for which experimental therapies are available, including clinical trials. Although ctDNA NGS was completed in 102 consecutive patients, tissue sequencing was successful in only 50 patients (49%). The overall concordance for all variants covered and detected by both platforms was 60%. When wild-type calls, ie genes for which no variants were detected, are considered, the overall agreement was 97.5%. Processable EGFR mutations were detected in 24 tissues and 19 ctDNA samples, resulting in a 79% concordance, with a shorter time lag between tissue and blood draw associated with higher concordance (p = 0.038). ctDNA sequencing identified eight patients carrying a resistance mutation who developed progressive disease while receiving targeted therapy and for whom tissue sequencing was not possible.

Schwaederle et al (2016) collected plasma from 171 patients with various types of cancer and analyzed the plasma for ctDNA (54 genes and copy number variants (CNVs) in three genes (EGFR, ERBB2 and MET)). The most represented cancers were lung (23%), breast (23%) and glioblastoma (19%). Ninety-nine patients (58%) had at least one detectable abnormality, where actionability was defined as an abnormality that was the direct target or pathway component that could be targeted by at least one investigational or approved drug. judgment. The most frequent alterations were TP53 (29.8%), followed by EGFR (17.5%), MET (10.5%), PIK3CA (7%) and NOTCH1 (5.8%). In contrast, of the 222 healthy volunteers, only one had an aberration (TP53). Ninety patients with non-brain tumors had a noticeable aberration (65% of 138 patients; in 70% of patients with non-brain tumors with an abnormality, the abnormality was potentially actionable). Nine of 33 patients (27%) with glioblastoma had a condition (6/33 (18%) potentially actionable). In total, sixty-nine patients had potentially actionable abnormalities (40% of the total; 69.7% of patients (69/99) with abnormalities); 68 patients (40% of total; 69% of patients with alterations), for an FDA-approved drug. In summary, 65% of various cancers (as well as 27% of glioblastomas) had detectable ctDNA aberrations, most theoretically amenable to an approved agent. The authors noted a number of study limitations. First, this study included a limited number of patients in each histology. Second, the clinical note was not available because the database was not identified. Third, the definition of "actionable" and the level of evidence needed for such a determination is a matter of debate and is constantly evolving. Fourth, the use of tissue-based next-generation sequencing as a comparator to establish clinical utility was not accessible for this unidentified patient group. Finally, whether or not patients would have responded to these medications cannot be addressed in this study and will require further investigation.

Liang et al (2016) performed a retrospective review of 100 patients with high-risk stage 4 or 3 breast cancer. Of the 100 patients included in this study, 29 underwent tissue analysis during treatment. Only specific genomic alterations tested in both cell-free DNA (cfDNA) and tissue DNA were included in this analysis. Of the 29 patients with tissue analysis, 6 had no evidence of disease at the time of cfDNA analysis and were excluded from the comparative analysis of genomic alterations found between cfDNA and tissue DNA. A total of 55 single nucleotide variants (SNVs) and 4 copy number variants (CNVs) were evaluated for cfDNA and tissue DNA from the remaining 23 patients. The degree of agreement between genomic alterations found in tumor DNA (tDNA) and cfDNA was determined by Cohen's Kappa. Clinical disease progression was compared with the frequency of mutant alleles using a two-tailed Fisher's exact test. The presence of mutations and the frequency of mutant alleles were correlated with PFS using a Cox proportional hazards model and a log-rank test. The most common genomic alterations were TP53 and PIK3CA mutations and EGFR and ERBB2 amplification. PIK3CA mutation and ERBB2 amplification demonstrated strong agreement between tDNA and cfDNA (Cohen's Kappa = 0.64 and 0.77, respectively). TP53 mutation and EGFR amplification demonstrated poor agreement between tDNA and cfDNA (Cohen's kappa = 0.18 and 0.33, respectively). Directional changes in TP53 and PIK3CA mutant allele frequency were strongly associated with response to therapy (p = 0.002). The investigators stated that the presence of the TP53 mutation (p = 0.0004) and the frequency of the mutant PIK3CA allele [p = 0.01, HR 1.074 (95% CI 1.018 to 1.134)] were excellent predictors of PFS. The authors concluded that identifying selected cancer-specific genomic alterations from cfDNA could be a non-invasive way to monitor disease progression, predict PFS, and offer targeted therapy. They noted that this study was limited by its small sample size and the inherent nature of retrospective data collection of existing genomic information.

Aggarwal et al (2019) noted that the clinical implications of adding next-generation sequencing (NGS) of plasma-based circulating tumor DNA to tissue NGS for the detection of targeting mutations in non-small cell lung cancer (NSCLC ) have not been formally evaluated. In a prospective cohort study, these investigators examined whether the NGS plasma test was associated with better mutation detection and better delivery of personalized therapy in a real-world clinical setting. This study enrolled 323 patients with metastatic NSCLC who underwent plasma analysis as part of routine clinical management. Plasma NGS was performed using a commercial 73 gene platform. Patients were enrolled at University of Pennsylvania Hospital from April 1, 2016 to January 2, 2018. The database was locked for follow-up and analysis on January 2, 2018, with a median follow-up of 7 months (ranging from 1 to 21 months). The number of patients with detectable abnormalities detected with NGS plasma and tissue; the association between allelic fractions (AF) of mutations detected in tissue and plasma; and the association of the response rate with the plasma AF of the target mutations. Among 323 patients with NSCLC (60.1% female; mean age 65 years [range 33 to 93]), therapeutically specific mutations in EGFR, ALK, MET, BRCA1, ROS1, RET, ERBB2, or BRAF were detected in 113 (35.0%) overall; Plasma analysis was performed in 94 patients (29.1%) only at the discretion of the treating physician or the patient's preference. Among 94 patients with isolated plasma analysis, a therapeutically targetable mutation was detected in 31 (33.0%), eliminating the need for invasive biopsy. Among the remaining 229 patients who had concomitant tissue and plasma NGS or who were unable to have tissue NGS, a therapeutically targetable tissue-only mutation was detected in 47 patients (20.5%), while the addition of plasma testing increased this number to 82. (35.8%). %); 36 of 42 patients (85.7%) who received targeted therapy based on the plasma result achieved a complete response (CR) or partial response (PR) or stable disease (SD). Plasma-based targeting mutation AF did not correlate with Response Evaluation Criteria in Solid Tumors (RECIST) depth of response (r = -0.121; p = 0.45). The authors concluded that, given the ease of obtaining plasma-based genotyping and the success seen with this non-invasive approach, these findings justify the incorporation of plasma-based genotyping in the routine clinical management of patients with NSCLC.

The authors stated that this study had several drawbacks. This single-center study was conducted among physicians who were comfortable ordering and interpreting NGS plasma tests. This user bias was likely enriched for patients who had only plasma NGS and were likely to have specific mutations. A substantial proportion of patients were tested after progression to resistance mutations, which likely increased the frequency of patients with EGFR T790M. Furthermore, this study only considered the single-point plasma NGS test. The clinical utility of NGS-based longitudinal plasma monitoring is an area of active study in this group.

In an editorial accompanying the aforementioned study by Aggarwal et al (2019), Gyawali and West (2018) noted that "Leaving aside the question of whether and when NGS is appropriate, what the study by Aggarwal and colleagues shows for We cannot conclude from this work that plasma testing should obviate the need for tissue NGS in most patients, as 29% of patients with a therapeutically targetable mutation who underwent NGS testing both plasma and tissue had the mutation detected only in tissue, but the study convincingly demonstrates that plasma NGS can obviate the need for tissue NGS in patients in whom plasma tests demonstrate a mutation, given the response rate and disease control among patients who have had therapeutically specific mutations identified in plasma Relatively high rate of detection of molecular markers in plasma Plasma r also offers a robust option for challenging patients es for which fabric is not available. wanting to take These results, combined with patient satisfaction with the relative ease of providing blood rather than a solid tissue sample, suggest a clinical strategy of looking first for plasma NGS and then tissue NGS if plasma NGS cannot detect mutations relevant. Another driver of NGS plasma is the cost-effectiveness of liquid biopsy over tissue biopsy, as suggested in the International Association for the Study of Lung Cancer statement document5; however, there is still a lack of data to support this claim. In the context of the initial study of advanced non-squamous NSCLC, for which many therapeutically targeted mutations are potentially present, broader clinical use of NGS from one source or another seems reasonable, based on cost, time, and tissue efficiency. However, this may not be true in other settings where relevant targets are very limited, such as acquired resistance to T790Min in an older generation of EGFR TKI, or are extremely rare and/or not clinically relevant, such as in squamous NSCLC, resistance acquired to various other driver mutations and many other cancer patterns. In summary, the growing data now support the role of NGS plasma as a useful tool to supplement or even avoid the need for often scarce and difficult to obtain tissue for the NGS test, but this should not obviate the central question of whether the test NGS improves clinical outcomes and therefore whether it should be performed. Next-generation sequencing shouldn't be considered the right tool for every job... A shotgun approach might be appropriate if there's a good enough chance of hitting a target that is suspected to be there, but we don't know exactly where; however, there are more precise and accurate weapons if we have a better idea of where the true objective is. If not, and if there's little reason to expect a real target, then just having a shotgun handy shouldn't lead us to shoot blindly in the dark without realizing we can do unexpected damage."

Odegaard et al (2018) stated that liquid biopsies are powerful tools that allow non-invasive genotyping of advanced solid tumors; however, structured and comprehensive validation studies using validated orthogonal comparison methods are lacking. These investigators analytically and clinically validated a circulating cell-free tumor DNA sequencing assay for comprehensive tumor genotyping and demonstrated its clinical feasibility. Analytical validation was performed according to established principles and guidelines. The blood-to-blood clinical validation comprised a blind external comparison with digital PCR of clinical points in 222 consecutive clinical samples positive for biomarkers. Clinical blood-to-tissue validation included comparing digital sequencing calls with those documented in the medical records of 543 consecutive lung cancer patients. Clinical experience was reported from 10,593 consecutive clinical samples. Digital sequencing technology allowed detection of variants down to 0.02% to 0.04% of allelic fraction/2.12 copies with 95% detection limits less than or equal to 0.3%/2.24 to 2, 76 copies, maintaining a high specificity (positive predictive value (PPV) adjusted for prevalence) greater than 98%). Clinical validation using orthogonal plasma- and tissue-based clinical genotyping in more than 750 patients demonstrated high accuracy and specificity (percent positive agreement (PPA) and negative percent agreement (NPA) greater than 99% and PPV 92 to 100%). . Clinical use in 10,593 adult patients with advanced solid tumors demonstrated high feasibility (technical success rate greater than 99.6%) and clinical sensitivity (85.9%), with high action potential (16.7% with treatment options FDA-approved on the label; 72.0% with treatment or trial recommendations), particularly in non-small cell lung cancer, where 34.5% of patient samples contained a directly identifiable standard of care biomarker. The authors concluded that the high concordance with orthogonal plasma and orthogonal tissue-based genotyping methods corroborates the clinical accuracy of digital sequencing in all 4 types of detectable genomic alterations. The clinical applicability of digital sequencing is supported by high rates of technical success and biomarker discovery.

McCoach et al (2018) stated that patients with advanced NSCLC whose tumors harbor anaplastic lymphoma kinase (ALK) gene fusions benefit from treatment with ALK inhibitors (ALKi). Circulating cell-free tumor DNA (cfDNA) analysis may provide a non-invasive way to identify ALK fusions and actionable resistance mechanisms without invasive biopsy. The NSCLC Guardant360 unidentified case database (G360; Guardant Health) was consulted to identify 88 consecutive patients with 96 ALK fusions detected in plasma. The G360 is a clinical NGS cfDNA test that detects point mutations, selected copy number gains, fusions, insertions and deletions in plasma. Identified fusion partners included EML4 (85.4%), STRN (6%) and KCNQ, KLC1, KIF5B, PPM1B and TGF (a total of 8.3%); Forty-two ALK-positive patients had no history of targeted therapy (Cohort 1), and ALK tissue molecular testing was attempted in 21 (5 negative, 5 positive, and 11 insufficient tissues). Follow-up of 3 of 5 tissue-negative patients showed ALKi responses; Thirty-one patients with known or suspected ALKi progression (Cohort 2) were evaluated; 16 samples (53%) contained 1-3 ALK resistance mutations. In 13 patients, clinical status was unknown (Cohort 3) and no resistance mutations or pathways were identified. In 6 patients with known EGFR activating mutations, a progressive ALK fusion was identified (cohort 4; 4 STRN, 1 EML4; 1 STRN and EML4); 5 housed EGFR T790M. The authors concluded that in this cohort of cfDNA-detected ALK fusions, these investigators demonstrated that full-length cfDNA NGS provided a non-invasive means of detecting detectable changes and characterizing mechanisms of progressive resistance.

The authors stated that this study had several drawbacks. First, this was a retrospective analysis based on the clinical information provided in the sample submission. Therefore, full treatment history and clinical follow-up were not available (and cannot be verified) for all patients. This included patient demographic information, the type and duration of previous therapies, the modality of local tissue testing, and the results of previous molecular tests, both at diagnosis and progression rebiopsy. Additionally, there were limitations to the cfDNA platform, including the identification of multiple subclonal populations, which may not be clinically relevant for resistance. Furthermore, as G360 is a cfDNA clinical trial, only ALK fusion events that occurred with partners of known biological significance were reported. Finally, in this study, these investigators identified 6 patients in cohort 2 whose ALK fusion was not identified by cfDNA, but by the presence of the ALK resistance mutation. This reflected the complexity of fusion proteins and the fact that ALK has numerous fusion variants that can make it difficult to identify small fragments by cfDNA analysis. Furthermore, these investigators were unable to estimate the true false negative rate of cfDNA for ALK fusion detection, given the database search parameters.

Laufer-Geva et al (2018) stated that cfDNA NGS allows non-invasive genomic analysis of patients with NSCLC. While genomic changes detected in plasma (GA) have been shown to predict response to targeted therapy, evidence for durability of response is lacking or limited to small cohorts, as well as the impact of cfDNA NGS results on clinical decisions. This retrospective study of patients with stage IIIB/IV NSCLC between the years 2014 and 2017 in Israel used cfDNA NGS (Guardant360) to identify the target GA. These investigators consecutively evaluated 116 patients with NSCLC, 41.4% before first-line treatment (arm A), 34.5% after progression with chemotherapy or immunotherapy (arm B1), and 24.1% after progression with tyrosine kinase inhibitors. EGFR (group B2). Objective GAs were found in 31% of group A (15 of 48 patients), 32.5% of group B1 (13 of 40 patients) and 71% of group B2 (20 of 28 patients). Treatment decision was changed to targeted therapy in 23% (11 of 48 patients), 25% (10 of 40 patients) and 32% (9 of 28 patients), respectively (total cohort 26%; 30/116). The objective response rate (Response Evaluation Criteria in Solid Tumors [RECIST]) was 43% (12 of 28 patients), including 1 CR, PR in 39% (11 of 28 patients), SD in 32% (9 of 28 patients) and progressive disease in 25% (7 of 28 patients). The disease control rate was 75% during a median duration of 5 months of treatment. The authors concluded that comprehensive cfDNA testing affected clinical decisions in 1/4 to 1/3 of initial and subsequent lines of treatment in patients with advanced NSCLC. This retrospective study expanded on previous reports by showing that cfDNA-based responses were long-lasting and changed treatment decisions at initial presentation and progression.

The authors stated that limitations of this study included its retrospective nature, although disease control and response rate (DCR) were consistent with 2 previous prospective studies. Furthermore, more than 50% of patients in progression were on third-line treatment or higher, where response rates would be expected to be lower than published studies of second-line targeted therapies. The response rate, survival, and duration of treatment in this study population, which was enriched for plasma-positive patients with limited or failed tumor tissue genotyping, may introduce theoretical selection bias, as cfDNA is more likely to be positive in patients with aggressively growing metastases. while genotyping based on a single lesion biopsy is indifferent to whether the disease is indolent or aggressive. However, if true, the results of plasma-based treatment become more convincing rather than less so. However, the clinical outcomes reported in this study reflected the real-life impact. As patients were not randomized and cfDNA testing was ordered in those with the highest pre-test probability of mutation (women, non-smokers, etc.), the prevalence of GA here may be higher than in a cohort enrolled by randomization.

Lam and associates (2019) stated that routine molecular profiling of squamous cell carcinoma of the lung (LUSC) is not recommended by major guidelines because the prevalence of actionable abnormalities is believed to be low. The increased use of next-generation sequencing (NGS), particularly with cfDNA, facilitates a reassessment of this premise. These investigators retrospectively assessed the prevalence of actionable abnormalities in 2 separate LUSC cohorts with a total of 492 patients. A total of 410 consecutive patients with stage 3B or 4 LUSC were tested with an NGS cell-free DNA-specific assay, and 82 patients with LUSC of any stage were tested with an NGS tissue cancer panel. In the total cohort, 467 patients (94.9%) were diagnosed with LUSC and 25 patients (5.1%) had mixed histology with a squamous component. A total of 10.5% of the LUSC subgroup had therapeutically relevant somatic abnormalities including EGFR (2.8%), ALK/ROS1 (1.3%), BRAF (1.5%) and exon MET amplification or skipping 14 (5.1%); 16% of patients with mixed histology had an actionable change. In the LUSC subgroup, 3 evaluable patients were treated with therapy directed at an actionable condition; they all experienced a partial response. The authors concluded that in this large real-world LUSC cohort, they observed a clinically significant prevalence of actionable abnormalities. These investigators stated that further assessment of the genomic landscape is needed in this setting to identify potentially underappreciated treatment options.

Leighl and colleagues (2019) stated that complete and timely tissue genotyping is challenging, leading to a significant number of patients with newly diagnosed metastatic NSCLC (mNSCLC) with insufficient genotyping for all 8 genomic biomarkers recommended by professional guidelines. These investigators sought to demonstrate the non-inferiority of full-length cfDNA to clinician discretionary SOC tissue genotyping for identifying guideline-recommended biomarkers in patients with mNSCLC. Prospectively enrolled patients with previously untreated mNSCLC who underwent medical evaluation for SOC tissue genotyping submitted a pretreatment blood sample for complete cfDNA analysis (Guardant360). Among 282 patients, physician-critical SOC tissue genotyping identified a guideline-recommended biomarker in 60 patients versus 77 patients identified with cfDNA (21.3% versus 27.3%; p < 0.0001 for non-inferiority). In patients with positive tissue, the biomarker was identified alone (12/60) or concordant with the cfDNA (48/60), a clinical sensitivity of cfDNA of 80% for any biomarker recommended by the guideline. For FDA-cleared targets (EGFR, ALK, ROS1, BRAF), concordance was greater than 98.2% with a PPV of 100% for cfDNA versus tissue (34/34 patients positive for EGFR, ALK, or BRAF). Using cfDNA in addition to tissue increased detection by 48%, from 60 to 89 patients, including those with negative, untested, or insufficient tissue results. Mean response time for cfDNA was significantly faster than tissue (9 vs 15 days; p < 0.0001). Complete guideline genotyping was significantly more likely (268 versus 51; p < 0.0001). The authors concluded that a comprehensive, sensitive, and specific cfDNA test used in patients with newly diagnosed mNSCLC successfully identified guideline-recommended biomarkers at a rate at least as high as the SOC tissue test and produced these results significantly faster. larger proportion of the population. Furthermore, guideline-recommended biomarkers detected by cfDNA were invariably present in tissue when the tissue was successfully tested, reinforcing that cfDNA genotyping results can be used in clinical management in the same way as genotyping results. Tissue genotyping is currently used. Finally, when modeled together, these results suggested that initial biomarker screening using cfDNA rather than tissue ("blood first"), reserving tissue for PD-L1 IHC and reflex testing when cfDNA is negative for any known oncogenic mutation, improved the rate of discovery of biomarkers. , turnaround time, and increased the number of newly diagnosed mNSCLC patients receiving comprehensive biomarker testing according to the guidelines.

The authors stated that one of the main disadvantages of this study was that, although the cfDNA tests used a single platform, the genomic evaluation of the tissues was not standardized, but left to the discretion of the SOC physician, which included a variety of methodologies, including PCR , FISH, IHC and/or SNG. Since only 18% of patients underwent successful whole-tissue genomic profiling, many changes identified only in cfDNA were, in fact, the result of incomplete tissue genotyping due to choice of methodology and/or flaws in tissue testing, rather than analytical disagreement between tests. As part of the study design, providers were specifically instructed not to make any changes to their SOC tissue genotyping practices; however, these investigators could not rule out the possibility that receipt of a clinical cfDNA result could have influenced the decision to look for more tissue. genotyping in sequential test instances. Furthermore, these findings may not apply to other less sensitive or less complete cfDNA tests. They stated that while this limited certain comparisons, this design was central to the fundamental question addressed by this study, whether a well-validated cfDNA assay can match or even improve on SOC tissue methods.

An accompanying commentary (Meador and Oxnard, 2019) noted that the sensitivity of cfDNA genotyping may be low in patients with a lower metastatic burden, likely due to reduced plasma clearance of tumor DNA. Commentators asserted that this lack of sensitivity of cfDNA sequencing should be recognized as a major barrier to its application. "Ultimately, negative cfDNA sequencing may be better than no genotyping, but it is not sufficient to rule out the presence of mutations in the target driver, given the reduced sensitivity of these assays and the unknown rate of tumor excretion in any given patient."

Willis et al (2019) attempted to analytically validate the microsatellite instability test (MSI) with Guardant360 according to established guidelines and clinically validate it with 1145 cfDNA samples for which the MSI status of the tissue based on the MSI was available. The cfDNA-based MSI landscape in solid tumor types was investigated in a cohort of 28,459 clinical plasma samples. The clinical outcomes of 16 patients with cfDNA MSI-H gastric cancer treated with immunotherapy were evaluated. In evaluable patients, cfDNA tests accurately detected 87% (71/82) MSI-H tissue and 99.5% microsatellite stable tissue (863/867) for an overall accuracy of 98.4% (934/ 949) and a positive predictive value of 95% (71/75). The concordance of MSI cfDNA with tissue PCR and next generation sequencing was significantly higher than IHC. The prevalence of MSI cfDNA for major cancer types was consistent with that reported for tissues. Finally, robust clinical activity of immunotherapy treatment was observed in patients with MSI cfDNA positive advanced gastric cancer, with 63% (10/16) of patients achieving complete or partial remission with sustained clinical benefit. Limitations included the small number of subjects for whom clinical outcomes were evaluated.

The National Comprehensive Cancer Network Clinical Practice Guideline on "Non-Small Cell Lung Cancer" (Version 3.2019) states that "The Panel considers that circulating/cell-free tumor DNA testing should not be used in lieu of diagnosis Free DNA (cfDNA)/circulating tumor DNA tests have not been established to detect genetic alterations, there is a false-negative rate of up to 30%, and non-tumor related alterations (eg, clonal hematopoiesis of indeterminate cells). [CHIP]... However, cfDNA tests may be used in specific circumstances if the patient is not clinically fit for invasive tissue sampling, or if there is insufficient tissue for molecular analysis and the follow-up tissue-based analysis will be performed if an oncogenic factor is not identified Given the above caveats, careful consideration is needed to determine whether the cfDNA findings reflect a true th driver mutation or an unrelated discovery." Since Guardant360 includes a panel of 68 genes, and only about 5 of them are actionable. The clinical value of the full gene panel of Guardant360 has not been established.

Turner and colleagues (2020) stated that ctDNA testing could provide a current assessment of the genomic profile of advanced cancer, without the need for repeat tumor biopsy. In an open-platform, multicenter, multicohort Phase IIa study, these investigators examined the accuracy of ctDNA testing in advanced breast cancer and the ability of ctDNA testing to select patients for mutation-directed therapy. This study was carried out in 18 UK hospitals. Participants were women (18 years or older) with histologically confirmed advanced breast cancer and an ECOG performance status of 0 to 2. Patients had completed at least 1 previous line of treatment for advanced breast cancer or had relapsed within 12 months of neoadjuvant therapy. or adjuvant chemotherapy. Patients were recruited into 4 parallel matched treatment cohorts with identified ctDNA mutations: Cohort A comprised patients with ESR1 mutations (treated with 500 mg of extended-dose fulvestrant IM); cohort B included patients with HER2 mutations (treated with oral neratinib 240 mg, and if estrogen receptor positive with standard dose IM fulvestrant); Cohort C comprised patients with AKT1 mutations and estrogen receptor-positive cancer (treated with capivasertib 400 mg orally plus fulvestrant at standard dose IM); and Cohort D included patients with AKT1 mutations and estrogen receptor negative or PTEN mutation positive cancers (treated with capivasertib 480 mg orally). Each cohort had a confirmed ORR primary endpoint. For Cohort A, 13 or more responses out of 78 evaluable patients were required to infer activity and 3 or more out of 16 for Cohorts B, C, and D. Recruitment for all cohorts has been completed and long-term follow-up is in progress. Between December 21, 2016 and April 26, 2019, a total of 1051 patients were enrolled for the study, with ctDNA results available for 1034 patients. The concordance between ctDNA digital PCR and targeted sequencing was 96-99% (n = 800, kappa 0.89-0.93). The sensitivity of the ctDNA digital PCR test for mutations identified in tissue sequencing was 93% (95% CI 83 to 98) overall and 98% (87 to 100) with contemporaneous biopsies. In all cohorts, the combined median follow-up was 14.4 months (IQR 7.0 to 23.7). Cohorts B and C met or exceeded the target number of responses, with 5 (25% [95% CI: 9 to 49]) of 20 patients in Cohort B and 4 (22% [6 to 48]) of 18 patients. in cohort C having a response. Cohorts A and D did not meet the target number of responses, with 6 (8% [95% CI: 3 to 17]) of 74 in Cohort A and 2 (11% [1 to 33]) of 19 patients in Cohort A . cohort D having a response. The most common grade 3 to 4 AEs were elevated gamma-glutamyltransferase (13 [16%] of 80 patients; cohort A); diarrhea (4 [25%] of 20; cohort B); fatigue (4 [22%] of 18; cohort C); and rash (5 [26%] of 19; cohort D); There were 17 serious adverse reactions in 11 patients and there was 1 treatment-related death caused by grade 4 dyspnoea (in cohort C). The authors concluded that the ctDNA test provided accurate and rapid genotyping that allowed the selection of mutation-directed therapies for breast cancer patients with sufficient clinical validity for adoption in routine clinical practice. These investigators stated that these findings demonstrate clinically relevant activity of therapies targeted against rare HER2 and AKT1 mutations, confirming that these mutations can be targets for the treatment of breast cancer.

The authors stated that this study had several drawbacks. Including patients with relatively strong treatment experience may reduce targeted drug activity, especially in cohort A, and future ctDNA screening trials may benefit from more restrictive entry criteria. The study was designed to examine the therapies' activity against specific genomic events; however, it did not focus on PIK3CA mutations and, as a result, relatively few of the patients enrolled in the study responded to therapy (17 [1.6%] of 1051 patients). However, mutation-targeted therapy with alpelisib is now approved to target PIK3CA mutations, and this study has shown the clinical validity of using ctDNA for targeted therapy. Cohort D was designed as a basket cohort from the outset to examine capivasertib activity against different AKT pathway activating mutations. Only cohort D allowed entry of patients with previous tissue sequencing results, as it was anticipated that ctDNA testing alone might not recruit enough patients. Although these investigators identified low activity of capivasertib in PTEN mutated cancers when used as a single agent, AKT inhibition in combination with paclitaxel chemotherapy may be effective in PTEN mutated cancers. Capivasertib plus fulvestrant may be effective in endocrine-resistant estrogen receptor-positive breast cancer without mutation selection, as shown in the FAKTION trial. It was not possible to robustly compare MATCH Plasma with FAKTION as patients enrolled in MATCH Plasma had more prior treatment lines and AKT1 mutations were not tested and would be few in FAKTION.

In a single center retrospective study, Bustamante Alvarez et al (2020) reported their experience with cfDNA testing at the time of diagnosis and how this intervention could help to avoid further invasive interventions, how it could be used to determine the initiation of therapy and how variation in somatic disorder allele frequency affects response to subsequent treatment. This study included patients with advanced NSCLC who underwent plasma cfDNA analysis using the Guardant360 panel, which identifies somatic genomic alterations by massively parallel sequencing of target genes. A tissue panel of institutional clinical laboratory reproduction changes using FISH (for MET, RET, ROS1, and ALK) and NGS for selected genes was employed for tissue analysis. Actionable mutations are those with FDA-approved targeted therapies (EGFR, ALK, ROS, BRAF, NTRK fusions) or soon-to-be-approved therapies (RET fusions and MET amplifications; or MET exon 14 skipping mutation ). 163 blood samples from 143 patients were evaluated, 82 at diagnosis and 81 during disease progression. A total of 94 cases had tissue and cfDNA testing performed 12 weeks apart; 76 (81%) of the 94 cases were concordant, of which 22 cases were concordantly positive and 54 concordantly negative; 18 (19%) of the 94 cases were discordant, of which 11 had negative blood and tissue results, and 7 had negative blood and tissue results. The cfDNA test had a sensitivity of 67% (95% CI: 51% to 83%), specificity of 89% (95% CI: 81% to 97%), NPV of 83% (95% CI). %: from 74% to 92%) and PPV of 76% (95% CI: 60% to 91%); 19 (21%) of 82 cfDNA samples tested at diagnostics had identified processable mutations (4 EGFR exon 19 deletion, 2 EGFR exon 21 L858R, 2 EGFR L861Q, 1 L861R, 4 EML4-ALK fusion, 2 CD74-ROS1 fusion , 2 MET exon 14 skipping mutation, 2 KIF5B-RET fusion). Of the 82 patients with cfDNA testing performed at diagnosis, 8 patients (10%) started targeted therapy based on cfDNA results alone, with 6 patients experiencing RP, 1 patient CR, and 1 patient DS. The response rate for patients starting cfDNA-based targeted therapies alone at diagnosis was 88%. Variant allele frequency had no impact on response. The authors concluded that initiation of targeted therapy for advanced NSCLC was feasible based only on identifying actionable mutations by cfDNA testing in the 9% of cases where a tissue diagnosis could not be obtained. cfDNA identified actionable targets in 20% of samples sent at diagnosis. A considerable number of patients benefited from cfDNA testing at initial diagnosis because it identified actionable mutations that led to appropriate targeted therapies. These researchers stated that cfDNA test results are being incorporated and accepted into many clinical trials for patient enrollment, which also represents an opportunity to expand patient access to innovative treatments when it is difficult to obtain more tissue for testing. Progressive disease remains a challenge, and cfDNA test results may provide information about the mechanism of resistance to tyrosine kinase inhibitors that, in some cases, molecular tissue biopsy testing may not reflect. It should also be noted that the allele frequency of cfDNA variation does not predict the depth of response to targeted therapies.

In a retrospective data review, Dvir et al (2021) presented their real-world data on the use of liquid biopsies in the routine management of patients with NCSLC. These investigators performed a review of 279 consecutive patients with NSCLC in the community who underwent liquid biopsies between 2014 and 2019 as part of routine clinical management. Over a 5-year period, a total of 337 liquid biopsy specimens, taken from 279 patients, underwent NGS plasma testing. Median age at diagnosis was 73 years (range 36 to 93, SD 10.4), 141 (51%) were men and 138 (49%) were women. Most were white or Caucasian (80% vs. 8% Black/African American vs. 12% Mixed or Mixed Race) and had a history of smoking (79%). Excluding synonymous mutations and variants of unknown significance, 254 AAs were detected in 106 patients. Commonly detected AAs were EGFR (n = 127, 50%), KRAS (n = 61, 24%), BRAF (n = 24, 9.5%) and MET (n = 23, 9%). Tissue NGS detected actionable aberrations in 45 patients, with EGFR (n = 28, 57%) and KRAS (n = 10, 20%) being the most common actionable aberrations. Concordance between NGS plasma and tissue modalities was detected in 39 of 45 (86.7%) patients and was most often demonstrated in EGFR (n = 25) and KRAS (n = 11). In 44 of 106 (41.5%) patients in whom tissue NGS was not performed, further precision treatment was guided by actionable aberrations detected via liquid biopsy. The authors concluded that integrating liquid biopsy into the routine care of patients with NSCLC demonstrated detection of actionable aberrations in an additional 44 patients, resulting in a 42% increase in the detection rate of actionable aberrations when NGS was not performed. tissue. Furthermore, these researchers stated that more powerful studies are needed to examine whether there is an incremental benefit between tissue NGS and liquid biopsy; these findings cautiously suggest a role for the use of liquid biopsy as part of routine clinical management.

The authors stated that this study was limited by its retrospective nature. Information on tissue NGS and mortality was obtained by reviewing electronic medical records, which may be incomplete. Liquid biopsy information was obtained from the Guardant360 database. Detection bias may be introduced if providers choose to order liquid biopsies in selected cases based on specific characteristics (eg, insurance coverage and non-smoking status). The assessment of whether physicians had access to NGS genomic data and provided informed therapy to patients was not routinely documented, limiting understanding of physician decision-making with broad-based genomic sequencing results.

In a prospective study, Palmero et al (2021) examined comprehensive cfDNA NGS compared to SOC tissue-based testing to identify guideline-recommended changes in advanced NSCLC (aNSCLC). Previously untreated aNSCLC patients were analyzed using a well-validated NGS cfDNA panel and results were compared with SOC tissue tests. The primary endpoint was non-inferiority of cfDNA versus tissue analysis for the detection of 2 guideline recommended biomarkers (EGFR and ALK) and 6 additional processable biomarkers. Secondary analyzes included tissue biomarker discovery versus cfDNA, ORR, PFS for targeted therapy, and cfDNA PPV. The primary endpoint was achieved with cfDNA identifying actionable mutations in 46 patients versus 48 per tissue (p < 0.05). In total, 0/186 patients were genotyped for all 8 biomarkers with tissue, compared to 90.8% with cfDNA. Alterations in the target or KRAS were identified in 80.7% when cfDNA was used first versus 57.1% when tissue was used first; The PPV for EGFR detected with cfDNA was 100.0% (25/25); The ORR and PFS in patients who received targeted tissue-based or cfDNA therapy were similar to those previously reported. The authors concluded that this study confirmed a previous report that comprehensive cfDNA testing was not inferior to SOC tissue testing in detecting recommended biomarkers for NSCLC. Furthermore, first-line cfDNA-based therapy yielded results similar to tissue-based testing, demonstrating the clinical utility of comprehensive cfDNA genotyping as the initial genotyping modality in previously untreated NSCLC patients when tissue was insufficient or when neither all actionable biomarkers could be detected. evaluated quickly.

The authors stated that this study had several drawbacks. First, the lack of a standardized tissue-based testing algorithm precluded direct comparison of the total performance of cfDNA versus tissue tests. Clinicians used available tissue analysis in accordance with their institutional SOC, as this study was designed to specifically address the critical question of what impact the addition of comprehensive cfDNA-based testing might have on patient care in the real world and is not intended to to be a direct comparison of NGS and cfDNA tissue tests. In this study, only 2 patients underwent comprehensive NGS tissue testing. Second, although multicenter, this study was limited to Spain and may not reflect results in other patient populations or healthcare systems, although several studies with very similar results have been reported in the US and other parts of the world. Third, as this study was based on a primary endpoint of non-inferiority, only a small number of patients received therapy targeted at any single biomarker and was available for RECIST central response assessment. Fourth, these investigators did not examine the correlation of clinicopathologic features with cfDNA detection rates and blood tissue matching. This topic has already been addressed elsewhere and may be included in future studies. Finally, the findings of this study only apply to liquid biopsies that, like the current assay, perform comprehensive genomic profiling as defined by the MolDx program.

CancerIntercept

CancerIntercept (Pathway Genomics) is a liquid biopsy designed for use as a non-invasive screening test designed for early detection and monitoring of cancer. Cell-free DNA (cfDNA) in the blood is tested for the presence of circulating tumor DNA (ctDNA), detecting specific cancer-associated mutations using polymerase chain reaction (PCR) to amplify both mutant and wild-type DNA, followed by "mutant-specific enrichment and simultaneous removal of wild-type DNA using proprietary technology", after which "mutant DNA is sequenced on Illumina's state-of-the-art sequencing platform". The tests screen for the presence of 96 frequently occurring DNA mutation hotspots in nine cancer-causing genes (BRAF, CTNNB1, EGFR, FOXL2, GNAS, KRAS, NRAS, PIK3CA and TP53) that, when mutated, can cause cancer or contribute to for your progression. . These mutations are commonly associated with lung, breast, ovarian, colorectal, and melanoma cancers, and may occur less frequently in other cancers (such as pancreatic, head and neck, thyroid, stomach, and colon cancers). prostate). Clinical trials are underway to test the correlation of liquid biopsy results with the actual presence or absence of these mutations in the tumor itself.

The test is offered for two general indications: CancerIntercept Detect is a liquid biopsy designed to detect tumor DNA in high-risk individuals; The CancerIntercept Monitor is designed to monitor patients with previously diagnosed or active cancer.

Adequate clinical validation is lacking to justify the recommended use of CancerIntercept Detect for cancer detection in high-risk patients. There is a lack of evidence from clinical trials to show that CancerIntercept Detect results in earlier diagnosis or reduces cancer mortality.

Clinical trials are also examining the prognostic value of several mutations detected by the CancerIntercept Monitortest in terms of recurrence, survival and response to treatment.

Circulating cell-free nucleic acids in colorectal cancer

Toth and colleagues (2016) stated that screening methods for the most frequently diagnosed malignancy, CRC, have limitations. Circulating cell-free total DNA (cfDNA) analysis has become a potential screening test for CRC. Detection of epigenetic and genetic alterations of cfDNA, such as DNA methylation or mutations of DNA and related ribonucleic acids, may improve cancer detection based on specific CRC-specific patterns. These investigators summarized CRC-specific nucleic acid biomarkers measured in peripheral blood and their potential as screening markers. Detection of mutations in DNA has inadequate sensitivity; however, methylated DNA can be established with greater sensitivity from CRC plasma samples. Ribonucleic acid-based miRNA studies accounted for greater sensitivity for CRC compared to mRNA studies. Recently, cfDNA isolation has become an automated, highly reproducible, and high-throughput method. The authors concluded that, with potential automated diagnostic tools, a new approach for CRC detection, such as liquid biopsy, may become available.

Spindler (2017) noted that circulating DNA can be used for measuring cfDNA and for the detection and quantification of tumor-specific genetic alterations in peripheral blood, and the broad clinical potential of circulating DNA has attracted increasing attention in recent years. decade. Circulating DNA concentrations are high in metastatic RCC, and total cfDNA levels have strong prognostic value. Colorectal tumors are characterized by a high frequency of well-known clinically relevant genetic alterations, which are readily detected in cfDNA and have potential for adaptation of palliative therapy and for monitoring during treatment. These investigators reviewed current literature that specifically reported data on the potential utility of cfDNA and on tumor-specific mutations in metastatic RCC (mCRC). Methodological, biological, and clinical issues were discussed based on the most recent development in this specific setting, and eligible studies were identified through systematic literature searches of PubMed and Embase, as well as communications and conference papers. The literature on cfDNA in CRC is extensive and heterogeneous in terms of objectives, nomenclature, methods, cohorts, and clinical outcomes and, consequently, difficult to include in a single systematic search. However, available data underscore the great clinical value of measuring total cfDNA levels and tumor-specific mutations in the plasma of mCRC patients before and during systemic therapy. The authors concluded that this article compiled the most recent literature on various aspects of cfDNA in mCRC, including methodological, biological and clinical aspects, and discussed the great clinical potential in this specific setting, which needs to be validated in a carefully designed prospective cohort of statistically relevant studies. .

HMGB1 and RAGE in cutaneous malignancy

Tesarova and associates (2016) observed that activation of the receptor for advanced glycation end products (RAGE) due to its increased expression in cancer cells or stimulation by multiple ligands (AGE, high mobility group box-1 [HMGB1], proteins S100, etc.) may contribute to the proliferation, invasion of tumor cells and formation of distant metastases and also to the resistance of cancer to treatment. Therefore, RAGE ligands may become useful markers of disease severity and outcome and a potential therapeutic target. The authors concluded that a better understanding of the role of RAGE activation in different types of cancer may help define the role of RAGE/ligand antagonists as a promising cancer treatment.

Nguyen and colleagues (2017) stated that inflammation and the immune system play a role in the development and progression of melanoma, basal cell carcinoma (BCC) and SCC. The pro-inflammatory and tumor-promoting effects of HMGB1 and the RAGE protein were investigated in these skin malignancies. The clinical implication of these molecules is not fully described. The National Library of Medicine database was searched for articles addressing the clinical relevance of HMGB1 and RAGE in melanoma, BCC, and SCC. This systematic review included 9 articles, 6 summarizing RAGE in skin malignancies and 3 involving HMGB1. RAGE has been found to be up-regulated in SCC lesions as well as in melanoma. RAGE levels were higher in stage IV melanomas. Lower levels of soluble RAGE have been associated with poor OS in melanoma. Sporadic extracellular expression of HMGB1 was evident in BCC and SCC lesions, which can be released by necrotic tumor cells. HMGB1 was considered a prognostic marker in melanoma, and HMGB1 levels were elevated in patients who did not respond to treatment with ipilimumab. The authors concluded that HMGB1 and RAGE could serve as potential prognostic markers or therapeutic targets in the treatment of melanoma, BCC and SCC; however, more research is needed on the clinical utility of the HMGB1-RAGE axis in cutaneous malignancies.

Ki-67 in upper urinary tract carcinoma

Ley and associates (2015) noted that urothelial carcinomas of the upper urinary tract (UTUC) constitute 5% of urothelial malignancies. Prognostic biomarkers would allow for lower risk surgical approaches for less aggressive CUSI. One biomarker, Ki-67/mindbomb E3 ubiquitin protein ligase 1 (Ki-67/MIB-1), has shown promise in UTUC, but there have been conflicting findings regarding its prognostic role. This systematic review and meta-analysis examined the prognostic value of Ki-67/MIB-1 in UTUC in terms of UTUC-specific mortality rate, 5-year DFS, and 5-year OS (including DSS). A systematic review of the current literature produced 654 records. Finally, a total of 13 studies with 1030 patients were included in the meta-analysis; HRs with 95% CI were extracted or estimated. Individual HR estimates were combined into a pooled HR using a fixed effects model that summed the homogeneity of individual true HRs. Patients with Ki-67/MIB-1 overexpression had a significantly higher UTUC-specific mortality rate (pooled HR: 2.14, 95% CI: 1.73 to 2.64; p < 0.00001), Significantly reduced 5-year DFS (pooled HR: 2.27, 95% CI: 1.79 to 2.92; p < 0.00001) and significantly reduced 5-year OS (pooled HR = 1, 77, 95% CI 1.39 to 2.23 p < 0.00001). Significant heterogeneity was detected in the UTUC-specific mortality rate meta-analysis (I(2) = 63%) and the 5-year DFS meta-analysis (I(2) = 65%), but no significant heterogeneity was detected. the meta-analysis of OS at 5 years (I(2) = 0%). Egger's test showed that none of the results were influenced by publication bias (p > 0.05). The authors concluded that Ki-67/MIB-1 overexpression showed promise as a prognostic biomarker for patients with UTUC and requires further investigation.

Fan and colleagues (2016) stated that UTUC is a relatively uncommon but aggressive disease. The Ki-67 antigen is a classic marker of cell proliferation, but there is still controversy about the meaning and importance of Ki-67 in tumor progression. In this study, these investigators detected Ki-67 expression in UTUC patients for the first time using immunohistochemistry. They then quantitatively combined the results with those of the published literature in a meta-analysis after searching multiple databases. Immunohistochemistry results demonstrated that patients with muscle-invasive tumors (T2-T4) had higher expression of Ki-67 than those with non-muscle-invasive tumors (Tis-T1), suggesting that high expression of Ki-67 may be associated with the aggressive form of UTUC. Kaplan-Meier curves showed that patients with high expression of Ki-67 had significantly lower cancer-specific survival (CSS) and DFS. Furthermore, multivariate analysis suggested that Ki-67 expression was an independent prognostic factor for CSS (HR = 3196) and DFS (HR = 3517) in patients with UTUC. Next, a meta-analysis of the published literature was performed investigating Ki-67 expression and its effects on the prognosis of UTUC. After searching the PubMed, Medline, Embase, Cochrane Library, and Scopus databases, a total of 12 articles met the eligibility criteria for this review. Eligible studies included a total of 1740 patients with a mean of 82 patients per study (range 38 to 475). The pooled results showed that increased Ki-67 levels were associated with worse survival and disease progression, with a pooled HR estimate of 2081 and 2791, respectively. In the subgroup analysis, the pooled HR was statistically significant for CSS (HR = 2276), metastasis-free survival (HR = 3008) and DFS (HR = 6336). The authors concluded that high expression of Ki-67 was associated with poor survival in patients with UTUC, as well as a high risk of disease progression, although these findings should be interpreted with caution. They stated that properly designed large-scale prospective studies are needed to further confirm the prognostic value of Ki-67 in patients with UCUT.

Non-coding long RNA in gallbladder cancer and non-small cell lung cancer

Ricciuti and associates (2016) stated that recent advances in mosaic array and high-throughput analysis revealed that at least 87.3% of the human genome is actively transcribed, although less than 3% of the human genome encodes proteins. This unexpected truth suggests that most of the transcriptome is composed of non-coding RNA. Among them, high-resolution microarray analyzes and massively parallel sequencing have identified long non-coding RNAs (lncRNAs) as transcripts of non-coding proteins. lncRNAs are largely polyadenylated and are over 200 transcript nucleotides in length, involved in gene expression through epigenetic and transcriptional regulation, splicing, imprinting, and subcellular transport. Although the functions of lncRNAs are largely uncharacterized, accumulating data indicate that they are involved in fundamental biological functions. Rather, its deregulation has been increasingly recognized as contributing to the development and progression of various human malignancies, especially lung cancer, which represents the leading cause of cancer-related death worldwide. These investigators conducted a comprehensive review of the published literature focusing on the role and disruption of lncRNA in the biology of non-small cell lung cancer (NSCLC), also highlighting their value as biomarkers and potential therapeutic targets. lncRNAs are involved in the pathogenesis of NSCLC, modulating fundamental cellular processes such as proliferation, cell growth, apoptosis, migration, stem cell maintenance and epithelial-to-mesenchymal transition, and also serve as signaling transducers, decoy molecules and scaffolds. Furthermore, lncRNAs represent very promising biomarkers in patients with early-stage NSCLC and could become particularly useful in non-invasive screening protocols. lncRNAs can be used as predictive biomarkers for chemotherapy and the sensitivity of targeted therapies. Furthermore, selective targeting of oncogenic lncRNAs may provide a new therapeutic tool in the treatment of patients with NSCLC. The authors concluded that lncRNA disruption plays a critical role in the development and progression of NSCLC. They stated that these molecules also serve as diagnostic, prognostic and predictive biomarkers; the characterization of lncRNA genes and their mechanisms of action will enable the development of a more comprehensive clinical approach, with the ultimate goal of benefiting patients.

Xu and colleagues (2016) noted that lung cancer is the first most common cancer and the first leading cause of cancer-related death in China and worldwide. Due to the difficulty in early diagnosis and the occurrence of cancer metastases, the 5-year survival rate for lung cancer remains extremely low. Long non-coding RNAs, which lack the ability to encode proteins, have recently emerged as critical players in often-disregulated biological processes in a variety of cancers, including lung cancer. These researchers highlighted recent discoveries of lncRNAs in the pathogenesis of lung cancer. The authors concluded that although understanding lncRNAs in lung cancer initiation and progression is still in its infancy, there is no doubt that understanding lncRNA activities will undoubtedly ensure robust biomarkers and improve treatment options for lung cancer. .

Furthermore, the National Comprehensive Cancer Network clinical practice guidelines on "Non-Small Cell Lung Cancer" (Version 4.2017) do not mention the detection of long non-coding RNAs as a management tool.

Khandelwal and colleagues (2017) noted that gallbladder cancer (GBC) is the most common and aggressive form of bile duct carcinoma with an alarmingly low 5-year survival rate. Despite its high mortality rate, the mechanisms underlying the pathogenesis of GBC are not fully understood. Recently, based on a growing body of literature, long non-coding RNAs (lncRNAs) have emerged as key regulators of gene expression and appear to play vital roles in many human cancers. To date, several lncRNAs have been implicated in GBC, but their potential roles in GBC have not been systematically examined. These investigators discussed the emerging roles of lncRNAs in GBC and the pathways involved. Specifically, they observed that some lncRNAs have higher expression in tumor stages T1 and T2 compared to tumor stages T3 and T4 and that their dysregulation leads to changes in cell cycle progression and can cause increased cell proliferation or apoptosis. Furthermore, some lncRNAs control the epithelial-mesenchymal transition process, while others participate in the regulation of cancer-associated ERK/MAPK and Ras signaling pathways. These investigators also presented their potential usefulness in the diagnosis, prognosis and/or treatment of GBC. The authors concluded that the overall objective of this review was to stimulate interest in the role of lncRNAs in GBC, which may open new avenues in determining GBC pathogenesis and may lead to the development of new preventive and therapeutic strategies for GBC.

MUC1 in gastric cancer

Wang and colleagues (2016) stated that MUC1, a member of the mucin family, is expressed in tumors of various human organs and may function as an anti-adhesion molecule that inhibits cell-to-cell adhesion, thereby inducing tumor metastasis and serving as a a potential biomarker of tumor progression in early gastric cancer. However, its prognostic importance in gastric cancer is still disputed. These investigators performed a meta-analysis to assess the relationship between MUC1 expression and gastric cancer prognosis. A total of 10 eligible studies with 834 cases and 548 controls were included. MUC1-positive cases were highly positive in intestinal-type carcinomas (OR = 1.76, 95% CI 1.27 to 2.44, fixed effect p = 0.0008), higher rate of vascular invasion (OR = 1, 64, 95% CI 1.13 to 2.39, p = 0.009 fixed effects) and lymph node metastases (OR = 2.10, 95% CI 1.20 to 3.67, p = 0.01 random effect), as well as a lower 5-year survival rate (HR = 0.27, 95% CI 0.11 to 0.66, p = 0.004 random effect). However, the presence of MUC1 was not associated with gender, tumor size, histological differentiation and clinical stage. The authors concluded that MUC1 is a prognostic factor in gastric cancer, acting as a marker of poor prognosis in patients with gastric cancer; more clinical studies are needed to confirm the role of MUC1 in clinical practice.

Furthermore, the National Comprehensive Cancer Network clinical practice guidelines on "Gastric Cancer" (version 3.2016) do not mention the use of MUC1 as a biomarker.

Percepta Bronchial Genomic Classifier (Veracyte)

Silvestri et al (2015) stated that bronchoscopy is often non-diagnostic in patients with lung lesions suspicious for lung cancer. This usually results in additional invasive testing, although many lesions are benign. These researchers attempted to validate a bronchial airway gene expression classifier that could improve the diagnostic performance of bronchoscopy. Smokers or former smokers undergoing bronchoscopy for suspected lung cancer were enrolled at 28 centers in 2 prospective multicenter studies (AEGIS-1 and AEGIS-2). A gene expression classifier was measured in epithelial cells collected from the normal-appearing main bronchus to assess the likelihood of lung cancer. A total of 639 patients in AEGIS-1 (298 patients) and AEGIS-2 (341 patients) met the inclusion criteria. A total of 43% of bronchoscopy examinations were not diagnostic of lung cancer, and invasive procedures were performed after bronchoscopy in 35% of patients with benign lesions. In the AEGIS-1, the classifier had an area under the receiver operating characteristic curve (AUC) of 0.78 (95% confidence interval [CI] 0.73 to 0.83), a sensitivity of 88% ( 95% CI: 83 to 92) and a specificity of 47% (95% CI: 37 to 58). On the AEGIS-2, the classifier had an AUC of 0.74 (95% CI: 0.68 to 0.80), a sensitivity of 89% (95% CI: 84 to 92), and a specificity of 47% (95 % CI: 36 to 92). 59). The combination of classifier plus bronchoscopy showed a sensitivity of 96% (95% CI: 93 to 98) in AEGIS-1 and 98% (95% CI: 96 to 99) in AEGIS-2, regardless of lesion size and location. In 101 patients with an intermediate pretest probability of cancer, the negative predictive value of the classifier was 91% (95% CI: 75 to 98) among patients with a non-diagnostic bronchoscopy. The authors concluded that the gene expression classifier improved the diagnostic performance of bronchoscopy for detecting lung cancer. In intermediate-risk patients with non-diagnostic bronchoscopy, a negative classifier score supported a more conservative diagnostic approach.

The authors noted that there are several important limitations to this study:

Samples from 155 patients (11%) produced insufficient or low-quality RNA, precluding classifier measurement. However, similar rates of insufficient RNA quality or quantity have been observed with other gene expression tests that have been integrated into clinical practice, and sample quality can be improved by decreasing the time between sample collection and RNA isolation. Patients who were not included in the study for this reason did not appear to differ in terms of cancer prevalence or other clinical characteristics compared to the general study population; however, it cannot be determined whether the classifier performs similarly in this group;
Nine percent of patients were lost to follow-up and 5% did not have a definitive diagnosis established 12 months after bronchoscopy. This rate of loss to follow-up is not unexpected in an observational study in which an additional evaluation after bronchoscopic examination was not mandatory to be performed at the study site. Although the follow-up period was limited to 12 months, it is unlikely that they missed a substantial number of cancers that would have been found with an additional year of follow-up. Although guidelines suggest 24 months of surveillance, these recommendations are based on older studies of solitary pulmonary nodules discovered on chest radiography (not computed tomography [CT]). The high sensitivity of CT makes solid nodules stable in the first year unlikely to grow later; this is supported by lung cancer screening studies in which nodules stable for 1 year had a conversion rate to cancer of only 1 per 1000 during year 2;
the exclusion criteria in this study limit the generalizability of these findings among non-smokers and smokers with a history of lung cancer. It is unclear whether a similar lesion field exists in never-smokers or light smokers with lung cancer and whether the lesion field persists after tumor resection; more studies are needed to assess these issues;
these investigators considered bronchoscopy "diagnostic" only when the procedure led to a diagnosis of lung cancer. There were 49 bronchoscopic examinations in which a specific benign cause was identified, but 31 of the patients received additional invasive testing, including 4 patients who were ultimately diagnosed with lung cancer on subsequent lung biopsy; this suggests that cancer concern remained high despite the initial benign finding on bronchoscopy; It is
these investigators did not test the accuracy of a model incorporating the classifier in combination with clinical variables. Although risk prediction models for solitary pulmonary nodules have been developed, there are no validated models for patients undergoing diagnostic bronchoscopy, which includes patients with a wide range of findings, including larger lesions (i.e., larger than 3 cm), infiltrates or lymphadenopathy.

Therefore, most patients are selected for bronchoscopy based on the physician's qualitative assessment of the likelihood of lung cancer. The authors demonstrated that their classifier performed well in patients with intermediate likelihood of cancer evaluated by a clinician in a process that incorporated available clinical risk factors.

Whitney et al (2015) stated that the gene expression profile of cytologically normal bronchial airway epithelial cells had already been shown to be altered in patients with lung cancer. Although bronchoscopy is often used for diagnosing lung cancer, its sensitivity is imperfect, especially for suspicious small peripheral lesions. In this study, these investigators derived a gene expression classifier from bronchoscopic airway epithelial cells that detects the presence of cancer in smokers and former smokers undergoing bronchoscopy for suspected lung cancer and evaluated its sensitivity in detecting cancer. . They collected bronchial epithelial cells (BECs) from the main bronchus of 299 smokers or former smokers (223 cancer-positive and 76 cancer-free individuals) who underwent bronchoscopy for suspected lung cancer in a prospective multicenter study. RNA from these samples was run on gene expression microarrays to train a gene expression classifier. A logistic regression model was constructed to predict cancer status, and the finalized classifier was validated in an independent cohort from a previous study. These researchers found 232 genes whose expression levels in the bronchial airways were associated with lung cancer. They then constructed a classifier based on a combination of 17 cancer genes, gene expression predictors of smoking, smoking history and gender, as well as patient age. This classifier showed a ROC AUC curve of 0.78 (95% CI 0.70 to 0.86) in patients whose bronchoscopy did not lead to a diagnosis of lung cancer (n=134). In the validation cohort, the classifier had a similar AUC of 0.81 (95% CI: 0.73 to 0.88) in this same subgroup (n = 118). The classifier performed similarly across a range of mass sizes, histologies and cancer stages. The negative predictive value was 94% (95% CI 83 to 99%) in subjects without lung cancer detected by bronchoscopy. The authors concluded that they have developed a gene expression classifier measured in bronchial airway epithelial cells that is able to accurately identify lung cancer in smokers and former smokers who underwent bronchoscopy for suspected lung cancer. They stated that because of the classifier's high NPV, it could inform clinical decisions about the need for more invasive testing in patients whose bronchoscopy is non-diagnostic.

Ferguson et al (2016) noted that bronchoscopy is often used to evaluate suspicious lung lesions found on CT scans, but its sensitivity for detecting lung cancer is limited. Recently, a bronchial genomic classifier was validated to improve the sensitivity of bronchoscopy for detecting lung cancer, demonstrating high sensitivity and negative predictive value (NPV) in patients at intermediate risk (10 to 60%) of lung cancer with inconclusive bronchoscopy . These investigators examined whether a negative genomic classifier result classifying a patient as intermediate to low risk (less than 10%) for lung cancer would reduce the rate at which clinicians recommend more invasive testing among patients with inconclusive bronchoscopy. These investigators conducted a prospective, randomized, decision-impact research study evaluating pulmonologists' recommendations in patients diagnosed with lung cancer who had inconclusive bronchoscopy. Cases with pretest intermediate risk of lung cancer were selected from the AEGIS studies and randomly presented to pulmonologists with or without the patient's bronchial genome classifier result to determine how the classifier results affected medical decisions. A total of 202 physicians provided 1523 case reviews on 36 patients. Recommendations for invasive procedures were reduced from 57% with no classifier result to 18% with a negative classifier result (low risk) (p < 0.001). Recommendations for invasive procedures increased from 50% to 65% with a positive classification result (intermediate risk) (p < 0.001). When stratifying by final disease diagnosis, there was an overall reduction in recommendations for invasive procedures in patients with benign disease when classifier results were reported (54 to 41%, p < 0.001). For patients diagnosed with malignancy, there was an overall increase in recommendations for invasive procedures when classifier results were reported (50 to 64%, p = 0.003). The authors concluded that these findings suggested that a negative (low risk) bronchial genomic classifier reduces recommendations for invasive procedures after inconclusive bronchoscopy and that the classifier in general reduced recommendations for invasive procedures among patients diagnosed with benign disease. They stated that these results support the potential clinical utility of the classifier to improve the management of patients undergoing bronchoscopy for suspected lung cancer, reducing additional invasive procedures in the setting of benign disease.

Vachani et al (2016) stated that bronchoscopy is usually the initial diagnostic procedure performed in patients with lung lesions suggestive of lung cancer. A bronchial genomic classifier was previously validated to identify patients at low risk of lung cancer after inconclusive bronchoscopy. In this study, these researchers evaluated the potential of the classifier to reduce the use of invasive procedures in patients with suspected lung cancer. In 2 multicenter trials of patients undergoing bronchoscopy for suspected lung cancer, the classifier was measured in normal-appearing bronchial epithelial cells from a main bronchus. Among patients with low and intermediate pretest probability of cancer (n = 222), subsequent invasive procedures were identified after inconclusive bronchoscopy. Classifier capacity estimates were calculated to reduce unnecessary procedures. Of the 222 patients, 188 (85%) had inconclusive bronchoscopy and follow-up procedure data available for analysis; 77 (41%) patients underwent 99 additional invasive procedures, including surgical lung biopsy in 40 (52%) patients. Finally, benign and malignant diseases were diagnosed in 62 (81%) and 15 (19%) patients, respectively. Among those who underwent surgical biopsy, 20 (50%) were performed in patients with benign disease. Had the classifier been used to guide decision-making, procedures could have been avoided in 50% (21 of 42) of patients undergoing more invasive testing. Furthermore, among 35 patients with inconclusive index bronchoscopy who were diagnosed with lung cancer, the sensitivity of the classifier was 89%, with 4 (11%) patients having a false-negative classifier result. The authors concluded that invasive procedures after inconclusive bronchoscopy occur frequently, with the majority being performed in patients diagnosed with benign disease. They stated that the use of the genomic classifier as an adjunct to bronchoscopy may reduce the frequency and morbidity associated with these invasive procedures.

UpToDate Reviews on "Summary of Initial Evaluation, Diagnosis, and Staging of Patients with Suspected Lung Cancer" (Thomas and Gould, 2017a), "Modality Selection for Diagnosis and Staging of Patients with Suspected Non-Small Cell Lung Cancer" (Thomas and Gould, 2017b) and "Summary of Lung Cancer Screening, Treatment, and Prognosis" (Midthun, 2017) do not mention the use of genomic tests/classifiers.

Additionally, an UpToDate review of "Tissue Biopsy Procedures in Patients Suspected of Non-Small Cell Lung Cancer" (Thomas and Gould, 2017c) states that "Although obtaining samples of lavage or lavage fluids has been studied for genomic analysis as a potential diagnostic tool designed to improve the sensitivity of bronchoscopy for diagnosing lung cancer, further study is needed before it can be recommended for routine use."

Additionally, the National Comprehensive Cancer Network clinical practice guidelines on "Non-Small Cell Lung Cancer" (Version 4.2017) and "Small Cell Lung Cancer" (Version 2.2017) do not mention the use of genomic tests/classifiers.

Select MDx

SelectMDx is a reverse transcription PCR (RT-PCR) assay performed on post-DRE first urine specimens from patients with clinical risk factors for CaP who are being considered for biopsy. The test measures mRNA levels of the biomarkers DLX1 and HOXC6, using KLK3 expression as an internal reference, to aid in patient selection for prostate biopsy. Higher expression levels of DLX1 and HOXC6 mRNA are associated with an increased likelihood of high-grade prostate cancer (Gleason score (GS) greater than or equal to 7). SelectMDx provides the probability of detecting CaP on biopsy and the probability of high-grade versus low-grade disease with an AUC of 0.89 (95% CI 0.86 to 0.92).

Carlsson and Roobol (2017) provided an overview of the current state of evidence and highlighted recent advances in the assessment and diagnosis of clinically significant CaP, with a focus on biomarkers, risk calculators, and multiparameter MRI (mpMRI). In 2017, there are numerous options to improve early detection compared to a purely PSA-based approach. They all have strengths and drawbacks. In addition to repeating the PSA and performing a clinical study (DRE and estimating prostate volume), additional tests investigated in the initial biopsy setting are: % Free PSA, PHI, 4Kscore, SelectMDx, and Michigan Prostate Score and in the Repeat setting: % PSA free, PHI, 4Kscore, Prostate Cancer Antigen 3 and ConfirmMDx. Risk calculators are available for biopsy settings and incorporate clinical data with or without biomarkers; mpMRI is an important diagnostic aid. The authors concluded that there are several tests available that can help increase PSA specificity, in the context of initial and repeat biopsy; all agree on a small decrease in sensitivity for detecting high-grade cancer. They noted that profitability is crucial; and the way forward is a multivariate risk assessment based on readily available clinical data, potentially with the addition of PsA subforms, preferably at low cost; MRI in the pre-diagnosis setting holds promise, but it is not ready for "prime time".

Hendriks and colleagues (2017) noted that the diagnosis of PCa currently relies on abnormal serum PSA and/or DRE testing and histopathological evaluation of prostate biopsies. The main disadvantage of the PSA test is the lack of specificity for CaP. To improve early detection of CaP, more specific biomarkers are needed. In recent years, many new promising biomarkers have been identified; however, to date, only a few have made it into clinical practice. These investigators discussed new blood and urine biomarker models that hold promise for use in detecting, monitoring, and making treatment decisions for PCa. These include PHI, PCA3, 4-kallikrein panel (4K), serine transmembrane protease 2-ERG (TMPRSS2-ERG), ExoDx Prostate Intelliscore, SelectMDx and Mi-Prostate score. Only a few direct studies are available for these new biomarkers and/or fluid-based models. Blood PHI and urine PCA3 are 2 FDA-approved biomarkers for the diagnosis of CaP. These investigators also provided an overview of published studies comparing these 2 available biomarkers for the prediction of (i) PCa on prostate biopsy, (ii) pathologic features in radical prostatectomy specimens, and (iii) significant PCa in patients eligible for active surveillance. . Studies have shown opposite results comparing PHI with PCA3 for predicting PCa in initial and repeat prostate biopsy; PHI and PCA3 can predict pathological findings in radical prostatectomy specimens, such as tumor volume and Gleason score. Only PHI can predict seminal vesicle invasion and only PCA3 can predict multifocality. There is some evidence that PHI outperformed PCA3 in predicting significant PCa in an active surveillance population. The authors concluded that future research should focus on independent validation of promising fluid-based biomarkers/models and prospective comparison of biomarkers against each other.

Dijkstra and associates (2017) examined the cost-effectiveness of a new urinary biomarker-based risk score (SelectMDx; MDxHealth, Inc., Irvine, CA) to identify patients for TRUS-guided biopsy and compare it to the standard of current care. (SOC), using only PSA to select TRUS-guided biopsy. A decision tree and Markov model were developed to assess the cost-effectiveness of SelectMDx as a reflex test against SOC in men with a PSA level greater than 3 ng/mL. Transition probabilities, utilities, and costs were derived from the literature and expert opinion. Profitability was expressed in QALYs and care costs of both diagnostic strategies, simulating the evolution of patients over an 18-year time horizon. Deterministic sensitivity analyzes were performed to deal with uncertainty in assumptions. A diagnostic strategy involving SelectMDx with a cutoff chosen with a sensitivity of 95.7% for high-grade CaP resulted in a savings of €128 and a gain of 0.025 QALY per patient compared to the SOC strategy. Sensitivity analyzes showed that the disutility attributed to active surveillance had a high impact on the QALYs obtained and the disutility attributed to TRUS-guided biopsy only slightly influenced the model result. The authors concluded that, based on currently available evidence, reducing overdiagnosis and overtreatment due to the use of the SelectMDx test in men with PSA levels greater than 3 ng/mL may lead to a reduction in total costs per patient and a gain in QALYs.

Sari Motlagh et al (2022) compared SelectMDx and multiparametric MRI (mpMRI) as a diagnostic test to detect clinical prostate cancer (PCa) and high-grade PCa (HG) in men with suspected PCa. The authors systematically searched major web databases for studies comparing the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of SelectMDx and/or mpMRI that were published before September 30, 2021 A model that plotted sensitivity, specificity, PPV, NPV and likelihood ratio (LR) for detection of PCa and PCa-HG was applied to compare SelectMDx, mpMRI and combined strategies (both positive and one positive). or both positive). The authors found 7 studies comprising 1328 patients who underwent SelectMDx and mpMRI to detect CaP. For PCa detection, SelectMDx had a combined sensitivity of 81%, specificity of 69.8%, PPV of 64.7%, NPV of 85% and LR of +2.68 to -0.27, while mpMRI had a sensitivity combined 80.8% and a specificity of 73.4. %, VPP of 72.4%, VPN of 83.5% and LR from +3.03 to -0.26. The one or both positive strategy had the highest sensitivity (96.3%), NPV (95.7%) and the lowest -LR (0.06). While the two positive strategies had the highest specificity (80.9%), the VPP (76.5%) and +LR (3.68). In the scenario of PI-RADS 3 lesions not being biopsied in case of negative SelectMDx (n = 44), unnecessary biopsies would be reduced by 42% (44/105), while the risk of missing HG-PCa would be 9%. (4/44). The authors concluded that the performance of SelectMDx is comparable to that of mpMRI with respect to detection of PCa and HG-PCa. Furthermore, this biomarker may help to refine clinical decision-making about the need for biopsy in patients with suspected PCa.

An UpToDate review of "prostate biopsy" (Benway and Andriole, 2021) does not mention SelectMDx.

Furthermore, the NCCN clinical practice guideline on "Prostate Cancer" (version 1.2023) does not mention SelectMDx as a management tool.

Prostate ExoDx (IntelliScore)

ExoDx Prostate (IntelliScore) is a non-invasive urine-based liquid biopsy for PCa. It is used to identify high-grade prostate cancer (HGPCA) both at the time of biopsy and surgery. ExoDx Prostate is an exosomal RNA (exoRNA) based assay that can be used prior to initial biopsy as well as for sequential monitoring of disease progression in patients enrolled in active surveillance.

Di Meo and associates (2017) noted that there is a growing trend to explore the use of a minimally invasive "liquid biopsy" to identify biomarkers in various types of cancer, including urological malignancies. Multiple aspects of circulating cell-free DNA can be assessed, including cell-free DNA levels, integrity, methylation, and mutations. Other prospective liquid biopsy markers include circulating tumor cells, circulating RNAs (microRNA [miRNA], long non-coding RNA [lncRNA] and messenger RNA [mRNA]), cell-free proteins, peptides and exosomes that have also emerged as non-invasive cancer. biomarkers. These circulating molecules can be detected in various biological fluids, such as blood, urine, saliva and seminal plasma. Liquid biopsies hold great promise for personalized medicine due to their ability to provide multiple non-invasive global snapshots of primary and metastatic tumors. The authors noted that although this is a promising source of cancer biomarkers, few exosomal biomarkers have been implemented in clinical practice. This is due in part to the lack of accurate isolation and detection methods. They speculate that the development of sensitive capture platforms is likely to trigger the introduction of new exosomal biomarkers into the clinic in the near future.

Panigrahi and Deep (2017) found that African-American men in the US have higher PCa incidence and mortality rates compared to other races. In 2016 alone, nearly 30,000 cases of CaP were diagnosed in African American men, and 4,450 men died from CaP. The underlying reasons for this health disparity in CaP are complex and include social, economic, and biological determinants. To reduce or eliminate this health disparity, the biology of disease in African Americans must be better understood and then new diagnostic and prognostic biomarkers useful for timely and effective treatment decisions developed. Recently, there has been remarkable progress in understanding the role of exosomes (vesicles 30 to 150 nm in diameter) in cancer development and progression. Exosomes are loaded with a unique cargo, including proteins, nucleic acids, lipids and metabolites, which can predict the origin of cells. Therefore, circulating exosomes in cancer patients are being used as a type of biopsy to identify new biomarkers for early diagnosis, prognosis and therapy. The authors discussed the promising use of exosomes to identify unique race-related CaP biologics and discover new biomarkers for better diagnosis and prognosis of CaP, with the goal of reducing disparities in cancer health.

Foj and colleagues (2017) stated that miRNAs are small non-coding RNAs involved in the post-transcriptional regulation of many target genes. In this study, 5 miRNAs that were consistently dysregulated in CaP (miR-21, miR-141, miR-214, miR-375 and let-7c) were analyzed in urinary granules from 60 patients with CaP and 10 healthy subjects per month. . qRT-PCR. Furthermore, urinary exosomes were isolated by differential centrifugation and analyzed for these miRNAs. Significant up-regulation of miR-21, miR-141 and miR-375 was found when comparing CaP patients to healthy subjects in urinary pellets, while miR-214 was significantly down-regulated. Regarding urinary exosomes, miR-21 and miR-375 were also significantly increased in CaP, but no differences were found for miR-141. Significant differences were found for let-7c in PCa in urinary exosomes, whereas no difference was observed in urinary granules. A panel combining miR-21 and miR-375 is suggested as the best combination to distinguish PCa patients from healthy subjects, with an AUC of 0.872. Furthermore, the association of miRNAs with clinicopathological features was investigated. MiR-141 was significantly correlated with Gleason score in urinary granules and let-7c with clinical stage in urinary exosomes. Furthermore, miR-21, miR-141, and miR-214 were significantly dysregulated in intermediate/high-risk PCa versus healthy/low-risk subjects in urinary pellets. Significant differences were found between the two groups in urinary exosomes for miR-21, miR-375 and let-7c. The authors concluded that these findings suggest that analysis of miRNAs -especially miRNA-21 and miR-375- in urine may be useful as PCa biomarkers.

Yang et al. (2017) stated that exosomes are membrane-bound extracellular vesicles that are involved in intercellular communication and metastasis of tumor cells. In this study, flow field flow fractionation (FlFFF) was used to separate urinary exosomes by size, demonstrating a significant difference in exosome sizes between healthy controls and PCa patients. Fractions of exosomes of different sizes were collected for microscopic analysis during an FlFFF run and evaluated for exosome marker proteins by Western blot analysis. The results indicated that exosomes of different sizes originated from different cell types. The collected exosome fractions were further examined by ultra high performance nanoflow liquid chromatography, electrospray ionization, tandem mass spectrometry (nUPLC-ESI-MS/MS) for lipidomics analysis. A total of 162 lipids (out of 286 identified) were quantified using a selected reaction monitoring (SRM) method. The total amount of lipids increased 1.5 to 2 times in patients with CaP and the degree of increase was more significant in the smaller fractions (diameter less than 150 nm) than in the larger ones (diameter greater than 150 nm). neutral lipids such as diacylglycerol (DAG) and triacylglycerol (TAG) were decreased in all exosomes without size dependence. Furthermore, a dramatic increase was observed in 22:6/22:6-phosphatidylglycerol (PG) and a significant decrease in the (16:0, 16:0)- and (16:1, 18:1)-DAG species ( almost 5-fold) and highly abundant TAG species (more than 2.5-fold) were observed in PCa patients. The authors concluded that the findings of this study indicated that FlFFF can be employed for high-speed detection of urinary exosome sizes in patients with CaP, and lipidomic analysis of split exosomes has the potential to develop and distinguish CaP biomarkers.

Pan and colleagues (2017) stated that exosomes are small vesicular bodies released by a variety of cells. Exosomes contain miRNA, mRNA and proteins with the potential to regulate signaling pathways in recipient cells. Exosomes release nucleic acids and proteins to mediate communication between cancer cells and stromal cells. These researchers summarized recent progress in understanding the role of exosomes in CaP. PCa tumorigenesis, metastasis, and drug resistance are associated with exosome loads such as miRNA, lncRNA, and protein. In addition, PCa cells modulate surrounding stromal cells via exosomes. Affected stromal cells use exosomes to modulate the microenvironment and promote tumor growth and metastasis. Exosomes derived from PCa cells contribute to cancer chemoresistance. The lipid bilayer membrane of exosomes makes them promising vehicles for drugs and other therapeutic molecules targeting PCa. Furthermore, exosomes can be detected and isolated from various body fluids for the diagnosis of PCa. The authors concluded that the accumulating evidence confirms that exosomes are involved in PCa progression and metastasis. Many biological molecules are encapsulated in PCa exosomes, such as miRNA, lncRNA and proteins, and their expression levels differ from those of normal prostate cells. The easy isolation of exosomes from body fluids makes them potential PCa biomarkers. Furthermore, the lipid bilayer membrane of exosomes makes them promising vehicles for drugs and other therapeutic molecules to target PCa. In the near future, it is expected that the power of these nanometer-sized vesicles will be realized to further the clinical application of exosomes in the diagnosis and therapy of PCa.

Tutrone et al (2020) noted that the ExoDx Prostate Test (IntelliScore) (EPI) is a non-invasive risk assessment tool for high-grade prostate cancer (HGPC) screening that advises whether to proceed with prostate biopsy. These investigators examined the impact of EPI on the decision to perform a biopsy in a real clinical setting. They conducted a prospective, randomized, blinded, clinically useful, 2-arm study involving 1094 patients with 72 urologists from 24 urology practices. Patients were considered for prostate biopsy at enrollment based on standard clinical criteria. All patients had an EPI test; however, patients were randomized into EPI versus control arms, where only the EPI arm received the results for its biopsy decision. In the EPI arm (n = 458), 93 patients received negative EPI scores, of which 63% were recommended by the urologist to postpone the biopsy and 74% ended up postponing it. In contrast, 87% of patients with positive EPI scores were recommended for biopsy with a rate of adherence to the urologist's recommendation of 72%. This resulted in 30% more HGPCs being detected compared to the control group, and these investigators estimated that 49% fewer HGPCs were missed due to delays compared to standard of care (SOC). Overall, 68% of urologists reported that the EPI test influenced their biopsy decision. The main reason for not meeting the EPI results was the increase in PSA. The authors concluded that, to their knowledge, this was the first report of a study of the utility of CP biomarkers with a blinded control arm. The study demonstrated that the EPI test influenced the overall decision to delay or proceed with a biopsy and improved patient stratification.

The authors stated that this study had several drawbacks. There was a 5.7% failure rate in the EPI group (30 failed attempts out of 520 EPI patients). If these investigators included patients who were randomized to not receive the EPI test, the failure rate was 7.1%. The failed assay controls were representative of the assay's quality control procedures and reflected variations in the concentration of exosomes in urine. Although follow-up is ongoing, these investigators currently lack data assessing long-term outcomes among patients who deferred biopsy after EPI use or any health economic data. They anticipate that both aspects will be addressed in the coming year. Furthermore, despite the innovative study design, the large number of centers and urologists required simplified questionnaires, limiting comprehensive feedback assessment. The authors had a small number of patients (less than 5%) who underwent MRI before biopsy. A pre-biopsy MRI has the potential to help refine biopsy accuracy and provide additional information about EPI test performance. These investigators also did not use MRI-guided biopsies in this study, as they were not available to them in this real-world clinical setting. They stated that future studies could include a larger percentage of patients with available MRI data. (Conflict of interests PT, VT, MN and JS are Bio-Techne employees. TM and MJD are Bio-Techne consultants).

Furthermore, the National Comprehensive Cancer Network's clinical practice guideline on “Prostate Cancer” (Version 2.2021) still does not mention assaying urine exosome gene expression as a management tool.

Next generation sequencing and solid cancers

Forouzanfar and associates (2017) noted that esophageal squamous cell carcinoma is one of the deadliest cancers. Its metastatic properties portend a poor prognosis and a high recurrence rate. A more advanced method to identify new molecular biomarkers that predict disease prognosis may be whole exome sequencing (WES). These investigators reported the most effective genetic variants of the Notch signaling pathway in susceptibility of esophageal SCC to WES. These investigators screened 9 probands