Appropriate Use Criteria for the Integration of Diagnostic and Prognostic Gene Expression Profile Assays into the Management of Cutaneous Malignant Melanoma: An Expert Panel Consensus-Based Modified Delphi Process Assessment

Main Article Content

Brian Berman
Roger Ceilley
Clay Cockerell
Laura Ferris
Whitney A High
Mark Lebwohl
Mark S Nestor
Theodore Rosen
Graham H Litchman
Giselle Prado
Ryan M Svoboda
Darrell S Rigel


melanoma, genomics, gene expression profile, diagnosis, prognosis


Background: Despite the clinical availability and widespread usage of diagnostic and prognostic gene expression profiles (GEP) for the management of melanoma, no recommendations for Appropriate Use Criteria (AUC) exist to guide their integration into clinical practice.

Objective: To develop a set of consensus-based AUC recommendations for the use of GEP profiling technology in the diagnosis and management of melanoma in specifically-defined situations commonly encountered by the practicing dermatologist.

Methods: A systematic Medline literature search was performed to identify all existing evidence pertinent to the clinical efficacy and utility of three melanoma GEP tests that met the inclusion criteria (validated in peer-reviewed literature, US governmentally approved, and currently widely used) for review. A modified Delphi technique was used to achieve consensus and standard SORT criteria were applied. An expert panel of nine dermatologists/dermatologic surgeons/dermatopathologists developed a set of 29 clinical scenarios for the appropriate use of GEP assays and reviewed the available literature to make evidence-based recommendations for each indication.

Results: The 2-GEP assay for melanoma diagnosis received 1 B-strength and 6 C-strength recommendations. The 23-GEP diagnostic test received 1 A-strength, 3 B-strength, and 4 C-strength recommendations. The 31-GEP prognostic assay received 1 A-strength, 7 B-strength, and 6 C-strength recommendations.

Conclusions: These AUC recommendations provide an evidence-based framework for the integration of melanoma GEP tests into clinical practice.

Abstract 889 | Full Article PDF Downloads 343 Untitled Downloads 0


1. Prado G, Svoboda RM, Rigel DS. What's New in Melanoma. Dermatologic clinics 2019;37:159-68.

2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA: a cancer journal for clinicians 2019;69:7-34.

3. Nathanson KL. Using genetics and genomics strategies to personalize therapy for cancer: focus on melanoma. Biochemical pharmacology 2010;80:755-61.

4. Hyams DM, Cook RW, Buzaid AC. Identification of risk in cutaneous melanoma patients: Prognostic and predictive markers. Journal of surgical oncology 2019;119:175-86.

5. Clarke LE, Warf MB, Flake DD, 2nd, et al. Clinical validation of a gene expression signature that differentiates benign nevi from malignant melanoma. Journal of cutaneous pathology 2015;42:244-52.

6. Gerami P, Yao Z, Polsky D, et al. Development and validation of a noninvasive 2-gene molecular assay for cutaneous melanoma. Journal of the American Academy of Dermatology 2017;76:114-20.e2.

7. Clarke LE, Pimentel JD, Zalaznick H, Wang L, Busam KJ. Gene expression signature as an ancillary method in the diagnosis of desmoplastic melanoma. Human pathology 2017;70:113-20.

8. DecisionDx-Melanoma Test Overview. Accessed April 3, 2019.

9. Rivers JK, Copley MR, Svoboda R, Rigel DS. Non-Invasive Gene Expression Testing to Rule Out Melanoma. Skin therapy letter 2018;23:1-4.

10. Siegel DM, Hornberger J. Further Consideration of the Pigmented Lesion Assay-Reply. JAMA dermatology 2019.

11. myPath Melanoma FAQs. Accessed April 8, 2019.

12. Swetter SM, Tsao H, Bichakjian CK, et al. Guidelines of care for the management of primary cutaneous melanoma. Journal of the American Academy of Dermatology 2019;80:208-50.

13. Gerami P, Cook RW, Russell MC, et al. Gene expression profiling for molecular staging of cutaneous melanoma in patients undergoing sentinel lymph node biopsy. Journal of the American Academy of Dermatology 2015;72:780-5.e3.

14. Gerami P, Cook RW, Wilkinson J, et al. Development of a prognostic genetic signature to predict the metastatic risk associated with cutaneous melanoma. Clinical cancer research : an official journal of the American Association for Cancer Research 2015;21:175-83.

15. Ferris LK, Farberg AS, Middlebrook B, et al. Identification of high-risk cutaneous melanoma tumors is improved when combining the online American Joint Committee on Cancer Individualized Melanoma Patient Outcome Prediction Tool with a 31-gene expression profile-based classification. Journal of the American Academy of Dermatology 2017;76:818-25.e3.

16. Ebell MH, Siwek J, Weiss BD, et al. Strength of recommendation taxonomy (SORT): a patient-centered approach to grading evidence in the medical literature. American family physician 2004;69:548-56.

17. Hsu C-C, Sandford BA. The Delphi technique: making sense of consensus. Practical assessment, research & evaluation 2007;12:1-8.

18. Rossi AM, Sobanko J, Lawrence N, et al. Physician-Centered Outcomes for Skin Cancer Treatment. Dermatologic Surgery 2019;45:869-874.

19. Thiboutot DM, Dreno B, Abanmi A, et al. Practical management of acne for clinicians: An international consensus from the Global Alliance to Improve Outcomes in Acne. Journal of the American Academy of Dermatology 2018;78:S1-S23.e1.

20. Vidal CI, Armbrect EA, Andea AA, et al. Appropriate use criteria in dermatopathology: Initial recommendations from the American Society of Dermatopathology. Journal of the American Academy of Dermatology 2019;80:189-207.e11.

21. Ferris LK, Gerami P, Skelsey MK, et al. Real-world performance and utility of a noninvasive gene expression assay to evaluate melanoma risk in pigmented lesions. Melanoma research 2018;28:478-82.

22. Ferris LK, Jansen B, Ho J, et al. Utility of a Noninvasive 2-Gene Molecular Assay for Cutaneous Melanoma and Effect on the Decision to Biopsy. JAMA dermatology 2017;153:675-80.

23. Ferris LK, Moy RL, Gerami P, et al. Noninvasive Analysis of High-Risk Driver Mutations and Gene Expression Profiles in Primary Cutaneous Melanoma. J Invest Dermatol. 2019; 139(5):1127-1134.

24. Hornberger J, Siegel DM. Economic Analysis of a Noninvasive Molecular Pathologic Assay for Pigmented Skin Lesions. JAMA dermatology 2018;154:1025-31.

25. Jansen B, Hansen D, Moy R, Hanhan M, Yao Z. Gene Expression Analysis Differentiates Melanomas from Spitz Nevi. Journal of drugs in dermatology : JDD 2018;17:574-6.

26. Lee N, Scope A, Rabinovitz H. Assessing Skin Cancer Using Epidermal Genetic Information Retrieved by Adhesive Patch Skin Surface Sampling. Dermatologic clinics 2017;35:521-4.

27. Wachsman W, Morhenn V, Palmer T, et al. Noninvasive genomic detection of melanoma. The British journal of dermatology 2011;164:797-806.

28. Cassarino DS, Lewine N, Cole D, Wade B, Gustavsen G. Budget impact analysis of a novel gene expression assay for the diagnosis of malignant melanoma. Journal of medical economics 2014;17:782-91.

29. Clarke LE, Flake DD, Busam K, et al. An independent validation of a gene expression signature to differentiate malignant melanoma from benign melanocytic nevi. Cancer 2017;123:617-28.

30. Cockerell C, Tschen J, Billings SD, et al. The influence of a gene-expression signature on the treatment of diagnostically challenging melanocytic lesions. Personalized medicine 2017;14:123-30.

31. Cockerell CJ, Tschen J, Evans B, et al. The influence of a gene expression signature on the diagnosis and recommended treatment of melanocytic tumors by dermatopathologists. Medicine 2016;95:e4887.

32. Ko JS, Matharoo-Ball B, Billings SD, et al. Diagnostic Distinction of Malignant Melanoma and Benign Nevi by a Gene Expression Signature and Correlation to Clinical Outcomes. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2017;26:1107-13.

33. Leachman SA, Mengden Koon S, Korcheva VB, White KP. Assessing Genetic Expression Profiles in Melanoma Diagnosis. Dermatologic clinics 2017;35:537-44.

34. Reimann JDR, Salim S, Velazquez EF, et al. Comparison of melanoma gene expression score with histopathology, fluorescence in situ hybridization, and SNP array for the classification of melanocytic neoplasms. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 2018;31:1733-43.

35. Warf MB, Flake DD, 2nd, Adams D, et al. Analytical validation of a melanoma diagnostic gene signature using formalin-fixed paraffin-embedded melanocytic lesions. Biomarkers in medicine 2015;9:407-16.

36. Berger AC, Davidson RS, Poitras JK, et al. Clinical impact of a 31-gene expression profile test for cutaneous melanoma in 156 prospectively and consecutively tested patients. Current medical research and opinion 2016;32:1599-604.

37. Cook RW, Middlebrook B, Wilkinson J, et al. Analytic validity of DecisionDx-Melanoma, a gene expression profile test for determining metastatic risk in melanoma patients. Diagnostic pathology 2018;13:13.

38. Dillon LD, Gadzia JE, Davidson RS, et al. Prospective, multicenter clinical impact evaluation of a 31-gene expression profile test for management of melanoma patients. SKIN The Journal of Cutaneous Medicine 2018;2:111-21.

39. Farberg AS, Glazer AM, White R, Rigel DS. Impact of a 31-gene Expression Profiling Test for Cutaneous Melanoma on Dermatologists' Clinical Management Decisions. Journal of drugs in dermatology : JDD 2017;16:428-31.

40. Farberg AS, Glazer AM, Winkelmann RR, Rigel DS. Assessing Genetic Expression Profiles in Melanoma Prognosis. Dermatologic clinics 2017;35:545-50.

41. 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. Journal of the American Academy of Dermatology 2019;80:149-57.e4.

42. 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. Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al] 2018;44:1494-500.

43. 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. Journal of hematology & oncology 2017;10:152.

44. Schuitevoerder D, Heath M, Cook RW, et al. Impact of Gene Expression Profiling on Decision-Making in Clinically Node Negative Melanoma Patients after Surgical Staging. Journal of drugs in dermatology : JDD 2018;17:196-9.

45. Sidiropoulos M, Obregon R, Cooper C, Sholl LM, Guitart J, Gerami P. Primary dermal melanoma: a unique subtype of melanoma to be distinguished from cutaneous metastatic melanoma: a clinical, histologic, and gene expression-profiling study. Journal of the American Academy of Dermatology 2014;71:1083-92.

46. Svoboda RM, Glazer AM, Farberg AS, Rigel DS. Factors Affecting Dermatologists' Use of a 31-Gene Expression Profiling Test as an Adjunct for Predicting Metastatic Risk in Cutaneous Melanoma. Journal of drugs in dermatology : JDD 2018;17:544-7.

47. 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;18:130.

48. Coit DG, Thompson JA, Algazi A, et al. Melanoma, Version 2.2016, NCCN Clinical Practice Guidelines in Oncology. Journal of the National Comprehensive Cancer Network : JNCCN 2016;14:450-73.

49. Evidence-based medicine. A new approach to teaching the practice of medicine. Jama 1992;268:2420-5.
50. 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 oncology (London, England) 2019.