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Performance of Gene Expression Profile Tests for Prognosis in Patients With Localized Cutaneous MelanomaA Systematic Review and Meta-analysis

Educational Objective
To examine the performance of commercially available gene expression profile (GEP) tests.
1 Credit CME
Key Points

Question  What is the performance of commercially available gene expression profile tests in predicting cutaneous melanoma outcomes in patients with stage I or stage II melanoma?

Findings  In this systematic review and meta-analysis of 7 studies including 1450 participants, gene expression profile test performance varied significantly by disease stage in external validation studies and was better at identifying recurrence in patients with stage II disease than in those with stage I disease. Studies were rated as having moderate to high risk of bias, and the quality of evidence was assessed as low to very low.

Meaning  In patients with clinically localized melanoma, there was variation in gene expression profile test performance by disease stage, suggesting limited potential for clinical utility for patients with stage I melanoma.

Abstract

Importance  The performance of prognostic gene expression profile (GEP) tests for cutaneous melanoma is poorly characterized.

Objective  To systematically assess the performance of commercially available GEP tests in patients with American Joint Committee on Cancer (AJCC) stage I or stage II disease.

Data Sources  For this systematic review and meta-analysis, comprehensive searches of PubMed/MEDLINE, Embase, and Web of Science were conducted on December 12, 2019, for English-language studies of humans without date restrictions.

Study Selection  Two reviewers identified GEP external validation studies of patients with localized melanoma. After exclusion criteria were applied, 7 studies (8%; 5 assessing DecisionDx-Melanoma and 2 assessing MelaGenix) were included.

Data Extraction and Synthesis  Data were extracted using an adaptation of the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS-PF). When feasible, meta-analysis using random-effects models was performed. Risk of bias and level of evidence were assessed with the Quality in Prognosis Studies tool and an adaptation of Grading of Recommendations Assessment, Development, and Evaluation.

Main Outcomes and Measures  Proportion of patients with or without melanoma recurrence correctly classified by the GEP test as being at high or low risk.

Results  In the 7 included studies, a total of 1450 study participants contributed data (age and sex unknown). The performance of both GEP tests varied by AJCC stage. Of patients tested with DecisionDx-Melanoma, 623 had stage I disease (6 true-positive [TP], 15 false-negative, 61 false-positive, and 541 true-negative [TN] results) and 212 had stage II disease (59 TP, 13 FN, 78 FP, and 62 TN results). Among patients with recurrence, DecisionDx-Melanoma correctly classified 29% with stage I disease and 82% with stage II disease. Among patients without recurrence, the test correctly classified 90% with stage I disease and 44% with stage II disease. Of patients tested with MelaGenix, 88 had stage I disease (7 TP, 15 FN, 15 FP, and 51 TN results) and 245 had stage II disease (59 TP, 19 FN, 95 FP, and 72 TN results). Among patients with recurrence, MelaGenix correctly classified 32% with stage I disease and 76% with stage II disease. Among patients without recurrence, the test correctly classified 77% with stage I disease and 43% with stage II disease.

Conclusions and Relevance  The prognostic ability of GEP tests among patients with localized melanoma varied by AJCC stage and appeared to be poor at correctly identifying recurrence in patients with stage I disease, suggesting limited potential for clinical utility in these patients.

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Article Information

Accepted for Publication: May 10, 2020.

Corresponding Author: Michael A. Marchetti, MD, Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, 530 E 74th St, New York, NY 10021 (marchetm@mskcc.org).

Published Online: July 29, 2020. doi:10.1001/jamadermatol.2020.1731

Author Contributions: Drs Marchetti and Bartlett contributed equally. Drs Marchetti and Bartlett had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Marchetti, Coit, Yu, Mancebo, Bartlett.

Acquisition, analysis, or interpretation of data: Marchetti, Dusza, Yu, McLean, Hu, Nanda, Matsoukas, Bartlett.

Drafting of the manuscript: Marchetti, Coit, Dusza, Yu, McLean, Bartlett.

Critical revision of the manuscript for important intellectual content: Marchetti, Dusza, Hu, Nanda, Matsoukas, Mancebo, Bartlett.

Statistical analysis: Marchetti, Dusza, McLean.

Administrative, technical, or material support: Marchetti, Coit, McLean, Hu, Matsoukas.

Supervision: Marchetti, Coit, Bartlett.

Conflict of Interest Disclosures: Dr Marchetti reported being a member of the Melanoma Prevention Working Group, which has drafted a consensus statement on the use of gene expression profile tests in cutaneous melanoma. No other disclosures were reported.

Funding/Support: This research was funded in part through the Memorial Sloan Kettering Cancer Center institutional National Institutes of Health/National Cancer Center Support Grant P30 CA008748 (Drs Marchetti, Coit, Dusza, Hu, and Bartlett and Mss Yu, Mclean, Nanda, and Matsoukas).

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

References
1.
Castle Biosciences. DecisionDx-Melanoma overview. 2019. Accessed March 18, 2020. https://castlebiosciences.com/products/decisiondx-melanoma/
2.
NeraCare GmbH. MelaGenix. 2019. Accessed June 19, 2020. https://www.melagenix.info/for-patients
3.
Amin  MB , Edge  S , Greene  F ,  et al, eds.  AJCC Cancer Staging Manual. 8th ed. Springer; 2017. doi:10.1007/978-3-319-40618-3
4.
Gastman  BR , Gerami  P , Kurley  SJ , Cook  RW , Leachman  S , Vetto  JT .  Identification of patients at risk of metastasis using a prognostic 31-gene expression profile in subpopulations of melanoma patients with favorable outcomes by standard criteria.   J Am Acad Dermatol. 2019;80(1):149-157.e4. doi:10.1016/j.jaad.2018.07.028PubMedGoogle ScholarCrossref
5.
Gastman  BR , Zager  JS , Messina  JL ,  et al.  Performance of a 31-gene expression profile test in cutaneous melanomas of the head and neck.   Head Neck. 2019;41(4):871-879. doi:10.1002/hed.25473 PubMedGoogle ScholarCrossref
6.
Gerami  P , Cook  RW , Russell  MC ,  et al.  Gene expression profiling for molecular staging of cutaneous melanoma in patients undergoing sentinel lymph node biopsy.   J Am Acad Dermatol. 2015;72(5):780-785.e3. doi:10.1016/j.jaad.2015.01.009PubMedGoogle ScholarCrossref
7.
Gerami  P , Cook  RW , Wilkinson  J ,  et al.  Development of a prognostic genetic signature to predict the metastatic risk associated with cutaneous melanoma.   Clin Cancer Res. 2015;21(1):175-183. doi:10.1158/1078-0432.CCR-13-3316 PubMedGoogle ScholarCrossref
8.
Brunner  G , Heinecke  A , Falk  TM ,  et al.  A prognostic gene signature expressed in primary cutaneous melanoma: synergism with conventional staging.   J Natl Cancer Inst Cancer Spectr. 2018;2(3):pky032. doi:10.1093/jncics/pky032 PubMedGoogle Scholar
9.
Vetto  JT , Hsueh  EC , Gastman  BR ,  et al.  Guidance of sentinel lymph node biopsy decisions in patients with T1-T2 melanoma using gene expression profiling.   Future Oncol. 2019;15(11):1207-1217. doi:10.2217/fon-2018-0912 PubMedGoogle ScholarCrossref
10.
Marchetti  MA , Bartlett  EK , Dusza  SW , Bichakjian  CK .  Use of a prognostic gene expression profile test for T1 cutaneous melanoma: will it help or harm patients?   J Am Acad Dermatol. 2019;80(6):e161-e162. doi:10.1016/j.jaad.2018.11.063 PubMedGoogle ScholarCrossref
11.
Maetzold  D . Castle Biosciences announces Medicare Coverage for the DecisionDx-Melanoma test in cutaneous melanoma. News release. Castle Biosciences Inc; October 18, 2018. Accessed May 18, 2020. https://skinmelanoma.com/castle-biosciences-announces-medicare-coverage-decisiondx-melanoma-test-cutaneous-melanoma/
12.
National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology—cutaneous melanoma. 2020. Accessed April 17, 2020. https://www.nccn.org/professionals/physician_gls/pdf/cutaneous_melanoma.pdf
13.
Riley  RD , Moons  KGM , Snell  KIE ,  et al.  A guide to systematic review and meta-analysis of prognostic factor studies.   BMJ. 2019;364:k4597. doi:10.1136/bmj.k4597 PubMedGoogle ScholarCrossref
14.
Moher  D , Liberati  A , Tetzlaff  J , Altman  DG . Preferred Reporting Items for Systematic Reviews and Meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264-269, w264.
15.
Marchetti  M , Bartlett  E , Dusza  S , Mclean  L , Yu  A , Matsoukas  K . Performance of gene expression profile-based tests for predicting clinical outcomes in localized cutaneous melanoma: a systematic review and meta-analysis: PROSPERO: CRD42019146778. Accessed November 20, 2019. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019146778
16.
Hayden  JA , van der Windt  DA , Cartwright  JL , Côté  P , Bombardier  C .  Assessing bias in studies of prognostic factors.   Ann Intern Med. 2013;158(4):280-286. doi:10.7326/0003-4819-158-4-201302190-00009 PubMedGoogle ScholarCrossref
17.
Cochrane Prognosis Methods Group. Tools. The Cochrane Collaboration. 2020. Accessed May 18, 2020. https://methods.cochrane.org/prognosis/tools
18.
Cochrane Reviews. Assessing risk of bias in included studies. 2020. Accessed April 17, 2020. https://handbook-5-1.cochrane.org/chapter_8/table_8_7_a_possible_approach_for_summary_assessments_of_the.htm
19.
Altman  DG , McShane  LM , Sauerbrei  W , Taube  SE .  Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): explanation and elaboration.   PLoS Med. 2012;9(5):e1001216. doi:10.1371/journal.pmed.1001216 PubMedGoogle Scholar
20.
Rector  TS , Taylor  BC , Wilt  TJ .  Chapter 12: systematic review of prognostic tests.   J Gen Intern Med. 2012;27(suppl 1):S94-S101. doi:10.1007/s11606-011-1899-y PubMedGoogle ScholarCrossref
21.
Cai  T , Pepe  MS , Zheng  Y , Lumley  T , Jenny  NS .  The sensitivity and specificity of markers for event times.   Biostatistics. 2006;7(2):182-197. doi:10.1093/biostatistics/kxi047 PubMedGoogle ScholarCrossref
22.
Tierney  JF , Stewart  LA , Ghersi  D , Burdett  S , Sydes  MR .  Practical methods for incorporating summary time-to-event data into meta-analysis.   Trials. 2007;8:16. doi:10.1186/1745-6215-8-16 PubMedGoogle ScholarCrossref
23.
Huguet  A , Hayden  JA , Stinson  J ,  et al.  Judging the quality of evidence in reviews of prognostic factor research: adapting the GRADE framework.   Syst Rev. 2013;2:71. doi:10.1186/2046-4053-2-71 PubMedGoogle ScholarCrossref
24.
Hayden  JA , Côté  P , Steenstra  IA , Bombardier  C ; QUIPS-LBP Working Group.  Identifying phases of investigation helps planning, appraising, and applying the results of explanatory prognosis studies.   J Clin Epidemiol. 2008;61(6):552-560. doi:10.1016/j.jclinepi.2007.08.005 PubMedGoogle ScholarCrossref
25.
Hsueh  EC , DeBloom  JR , Lee  J ,  et al.  Interim analysis of survival in a prospective, multi-center registry cohort of cutaneous melanoma tested with a prognostic 31-gene expression profile test.   J Hematol Oncol. 2017;10(1):152. doi:10.1186/s13045-017-0520-1PubMedGoogle ScholarCrossref
26.
Keller  J , Schwartz  TL , Lizalek  JM ,  et al.  Prospective validation of the prognostic 31-gene expression profiling test in primary cutaneous melanoma.   Cancer Med. 2019;8(5):2205-2212. doi:10.1002/cam4.2128 PubMedGoogle ScholarCrossref
27.
Zager  JS , Gastman  BR , Leachman  S ,  et al.  Performance of a prognostic 31-gene expression profile in an independent cohort of 523 cutaneous melanoma patients.   BMC Cancer. 2018;18(1):130. doi:10.1186/s12885-018-4016-3 PubMedGoogle ScholarCrossref
28.
Podlipnik  S , Carrera  C , Boada  A ,  et al.  Early outcome of a 31-gene expression profile test in 86 AJCC stage IB-II melanoma patients: a prospective multicentre cohort study.   J Eur Acad Dermatol Venereol. 2019;33(5):857-862. doi:10.1111/jdv.15454 PubMedGoogle ScholarCrossref
29.
Greenhaw  BN , Zitelli  JA , Brodland  DG .  Estimation of prognosis in invasive cutaneous melanoma: an independent study of the accuracy of a gene expression profile test.   Dermatol Surg. 2018;44(12):1494-1500. doi:10.1097/DSS.0000000000001588 PubMedGoogle ScholarCrossref
30.
Amaral  TMS , Hoffmann  MC , Sinnberg  T ,  et al.  Clinical validation of a prognostic 11-gene expression profiling score in prospectively collected FFPE tissue of patients with AJCC v8 stage II cutaneous melanoma.   Eur J Cancer. 2020;125:38-45. doi:10.1016/j.ejca.2019.10.027 PubMedGoogle ScholarCrossref
31.
Koelblinger  P , Levesque  MP , Kaufmann  C ,  et al.  A prognostic gene-signature based identification of high-risk thin melanomas.   J Clin Oncol. 2018;36(15)(suppl):e21575-e21575. doi:10.1200/JCO.2018.36.15_suppl.e21575 Google ScholarCrossref
32.
Faries  MB , Steen  S , Ye  X , Sim  M , Morton  DL .  Late recurrence in melanoma: clinical implications of lost dormancy.   J Am Coll Surg. 2013;217(1):27-34. doi:10.1016/j.jamcollsurg.2013.03.007 PubMedGoogle ScholarCrossref
33.
Green  AC , Baade  P , Coory  M , Aitken  JF , Smithers  M .  Population-based 20-year survival among people diagnosed with thin melanomas in Queensland, Australia.   J Clin Oncol. 2012;30(13):1462-1467. doi:10.1200/JCO.2011.38.8561 PubMedGoogle ScholarCrossref
34.
Hsueh  EC , DeBloom  JR , Cook  RW , McMasters  K .  Three-year survival outcomes in a prospective cohort evaluating a prognostic 31-gene expression profile (31-GEP) test for cutaneous melanoma (CM).   J Clin Oncol. 2019;37(15)(suppl):9519. doi:10.1200/JCO.2019.37.15_suppl.9519 Google ScholarCrossref
35.
Grooten  WJA , Tseli  E , Äng  BO ,  et al.  Elaborating on the assessment of the risk of bias in prognostic studies in pain rehabilitation using QUIPS-aspects of interrater agreement.   Diagn Progn Res. 2019;3:5. doi:10.1186/s41512-019-0050-0 PubMedGoogle ScholarCrossref
36.
Ioannidis  JP .  Why most discovered true associations are inflated.   Epidemiology. 2008;19(5):640-648. doi:10.1097/EDE.0b013e31818131e7 PubMedGoogle ScholarCrossref
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