Skip to main content
Log in

Review of diagnostic, prognostic, and predictive biomarkers in melanoma

  • Review
  • Published:
Clinical & Experimental Metastasis Aims and scope Submit manuscript

Abstract

Melanoma is an aggressive cutaneous malignancy with rapidly rising incidence. Diagnosis of controversial melanocytic lesions, correct prognostication of patients, selection of appropriate adjuvant and systemic therapies, and prediction of response to a given therapy remain very real challenges. Despite these challenges, multiple high throughput, nucleic-acid based biomarkers have been developed that can be assayed from histologic tissue specimens. FISH, CGH, Decision-Dx, and other multi-marker assays have been combined to improve overall predictability. This review discusses some of the most promising nucleic acid based assays that can be obtained from tissue specimens to assist with diagnosis, prognostication, and prediction of treatment response.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Siegel RL, Miller KD, Jemal A (2017) Cancer statistics. CA Cancer J Clin 67(1):7–30

    Article  Google Scholar 

  2. Gimotty PA, Guerry D (2010) Prognostication in thin cutaneous melanomas. Arch Pathol Lab Med 134(12):1758–1763

    PubMed  Google Scholar 

  3. Whiteman DC, Baade PD, Olsen CM (2015) More people die from thin melanomas (1 mm) than from thick melanomas (> 4 mm) in Queensland, Australia. J Invest Dermatol 135(4):1190–1193

    Article  CAS  Google Scholar 

  4. Wong SL et al (2005) A nomogram that predicts the presence of sentinel node metastasis in melanoma with better discrimination than the American Joint Committee on Cancer staging system. Ann Surg Oncol 12(4):282–288

    Article  Google Scholar 

  5. Mahar AL, McShane LM, Groome PA, Compton CC (2013) A survey of clinical prediction tools in colorectal and lung cancers and melanoma. J Clin Oncol 31(15S): 1592

    Google Scholar 

  6. Mahar AL et al (2016) Critical assessment of clinical prognostic tools in melanoma. Ann Surg Oncol 23(9):2753–2761

    Article  Google Scholar 

  7. Gould Rothberg BE, Bracken MB, Rimm DL (2009) Tissue biomarkers for prognosis in cutaneous melanoma: a systematic review and meta-analysis. J Natl Cancer Inst 101(7):452–474

    Article  Google Scholar 

  8. Schramm SJ, Mann GJ (2011) Melanoma prognosis: a REMARK-based systematic review and bioinformatic analysis of immunohistochemical and gene microarray studies. Mol Cancer Ther 10(8):1520–1528

    Article  CAS  Google Scholar 

  9. Gould Rothberg BE et al (2009) Melanoma prognostic model using tissue microarrays and genetic algorithms. J Clin Oncol 27(34):5772–5780

    Article  Google Scholar 

  10. Piras F et al (2008) Combinations of apoptosis and cell-cycle control biomarkers predict the outcome of human melanoma. Oncol Rep 20(2):271–277

    PubMed  Google Scholar 

  11. Kashani-Sabet M et al (2009) A multimarker prognostic assay for primary cutaneous melanoma. Clin Cancer Res 15(22):6987–6992

    Article  CAS  Google Scholar 

  12. Gerami P et al (2015) Development of a prognostic genetic signature to predict the metastatic risk associated with cutaneous melanoma. Clin Cancer Res 21(1):175–183

    Article  CAS  Google Scholar 

  13. Gerami P et al (2015) Gene expression profiling for molecular staging of cutaneous melanoma in patients undergoing sentinel lymph node biopsy. J Am Acad Dermatol 72(5):780–785.e3

    Article  CAS  Google Scholar 

  14. Gerami P et al (2009) Fluorescence in situ hybridization (FISH) as an ancillary diagnostic tool in the diagnosis of melanoma. Am J Surg Pathol 33(8):1146–1156

    Article  Google Scholar 

  15. Scolyer RA et al (2010) Histologically ambiguous (“borderline”) primary cutaneous melanocytic tumors: approaches to patient management including the roles of molecular testing and sentinel lymph node biopsy. Arch Pathol Lab Med 134(12):1770–1777

    PubMed  Google Scholar 

  16. Dalton SR et al (2010) Use of fluorescence in situ hybridization (FISH) to distinguish intranodal nevus from metastatic melanoma. Am J Surg Pathol 34(2):231–237

    Article  Google Scholar 

  17. Pouryazdanparast P et al (2009) Distinguishing epithelioid blue nevus from blue nevus-like cutaneous melanoma metastasis using fluorescence in situ hybridization. Am J Surg Pathol 33(9):1396–1400

    Article  Google Scholar 

  18. Gerami P et al (2009) Fluorescence in situ hybridization for distinguishing nevoid melanomas from mitotically active nevi. Am J Surg Pathol 33(12):1783–1788

    Article  Google Scholar 

  19. Gerami P et al (2012) A highly specific and discriminatory FISH assay for distinguishing between benign and malignant melanocytic neoplasms. Am J Surg Pathol 36(6):808–817

    Article  Google Scholar 

  20. Gaiser T et al (2010) Classifying ambiguous melanocytic lesions with FISH and correlation with clinical long-term follow up. Mod Pathol 23(3):413–419

    Article  CAS  Google Scholar 

  21. Massi D et al (2011) Atypical Spitzoid melanocytic tumors: a morphological, mutational, and FISH analysis. J Am Acad Dermatol 64(5):919–935

    Article  Google Scholar 

  22. Tetzlaff MT et al (2013) Ambiguous melanocytic tumors in a tertiary referral center: the contribution of fluorescence in situ hybridization (FISH) to conventional histopathologic and immunophenotypic analyses. Am J Surg Pathol 37(12):1783–1796

    Article  Google Scholar 

  23. Vergier B et al (2011) Fluorescence in situ hybridization, a diagnostic aid in ambiguous melanocytic tumors: European study of 113 cases. Mod Pathol 24(5):613–623

    Article  CAS  Google Scholar 

  24. Bauer J, Bastian BC (2006) Distinguishing melanocytic nevi from melanoma by DNA copy number changes: comparative genomic hybridization as a research and diagnostic tool. Dermatol Ther 19(1):40–49

    Article  Google Scholar 

  25. Bastian BC et al (1998) Chromosomal gains and losses in primary cutaneous melanomas detected by comparative genomic hybridization. Cancer Res 58(10):2170–2175

    CAS  Google Scholar 

  26. Ali L et al. (2010) Correlating array comparative genomic hybridization findings with histology and outcome in spitzoid melanocytic neoplasms. Int J Clin Exp Pathol 3(6): 593–599

  27. Bastian BC et al (2003) Classifying melanocytic tumors based on DNA copy number changes. Am J Pathol 163(5):1765–1770

    Article  CAS  Google Scholar 

  28. Clarke LE et al (2015) Clinical validation of a gene expression signature that differentiates benign nevi from malignant melanoma. J Cutan Pathol 42(4):244–252

    Article  Google Scholar 

  29. Minca EC et al (2016) Comparison between melanoma gene expression score and fluorescence in situ hybridization for the classification of melanocytic lesions. Mod Pathol 29(8):832–843

    Article  CAS  Google Scholar 

  30. Network NCC (2018) NCCN Guidelines Version 2.2018, in NCCN Clnical Practice Guidelines in Oncology. Coit DG (ed) National Comprehensive Cancer Network

  31. Abbas O, Miller DD, Bhawan J (2014) Cutaneous malignant melanoma: update on diagnostic and prognostic biomarkers. Am J Dermatopathol 36(5):363–379

    Article  Google Scholar 

  32. Adler NR et al (2017) Metastatic pathways in patients with cutaneous melanoma. Pigment Cell Melanoma Res 30(1):13–27

    Article  Google Scholar 

  33. Nodin B et al (2012) High MCM3 expression is an independent biomarker of poor prognosis and correlates with reduced RBM3 expression in a prospective cohort of malignant melanoma. Diagn Pathol 7:82

    Article  CAS  Google Scholar 

  34. Nielsen PS et al (2013) Proliferation indices of phosphohistone H3 and Ki67: strong prognostic markers in a consecutive cohort with stage I/II melanoma. Mod Pathol 26(3):404–413

    Article  CAS  Google Scholar 

  35. Donizy P et al (2016) Golgi-related proteins GOLPH2 (GP73/GOLM1) and GOLPH3 (GOPP1/MIDAS) in cutaneous melanoma: patterns of expression and prognostic significance. Int J Mol Sci 17(10):1619

    Article  Google Scholar 

  36. Fohn LE et al (2011) D2-40 lymphatic marker for detecting lymphatic invasion in thin to intermediate thickness melanomas: association with sentinel lymph node status and prognostic value-a retrospective case study. J Am Acad Dermatol 64(2):336–345

    Article  Google Scholar 

  37. Han D et al (2013) Clinicopathologic predictors of sentinel lymph node metastasis in thin melanoma. J Clin Oncol 31(35):4387–4393

    Article  Google Scholar 

  38. Rangel J et al (2008) Osteopontin as a molecular prognostic marker for melanoma. Cancer 112(1):144–150

    Article  Google Scholar 

  39. Rangel J et al (2008) Novel role for RGS1 in melanoma progression. Am J Surg Pathol 32(8):1207–1212

    Article  Google Scholar 

  40. Leiter U et al (2016) Complete lymph node dissection versus no dissection in patients with sentinel lymph node biopsy positive melanoma (DeCOG-SLT): a multicentre, randomised, phase 3 trial. Lancet Oncol 17(6):757–767

    Article  Google Scholar 

  41. Faries MB et al (2017) Completion dissection or observation for sentinel-node metastasis in melanoma. N Engl J Med 376(23):2211–2222

    Article  Google Scholar 

  42. Damude S et al (2016) The predictive power of serum S-100B for non-sentinel node positivity in melanoma patients. Eur J Surg Oncol 42(4):545–551

    Article  CAS  Google Scholar 

  43. Wevers KP et al (2013) Assessment of a new scoring system for predicting non-sentinel node positivity in sentinel node-positive melanoma patients. Eur J Surg Oncol 39(2):179–184

    Article  CAS  Google Scholar 

  44. van der Ploeg AP et al (2011) Prognosis in patients with sentinel node-positive melanoma is accurately defined by the combined Rotterdam tumor load and Dewar topography criteria. J Clin Oncol 29(16):2206–2214

    Article  Google Scholar 

  45. Pastushenko I et al (2016) Increased angiogenesis and lymphangiogenesis in metastatic sentinel lymph nodes is associated with nonsentinel lymph node involvement and distant metastasis in patients with melanoma. Am J Dermatopathol 38(5):338–346

    Article  Google Scholar 

  46. Veronesi U et al (1988) Thin stage I primary cutaneous malignant melanoma: comparison of excision with margins of 1 or 3 cm. N Engl J Med 318(18):1159–1162

    Article  CAS  Google Scholar 

  47. Ringborg U et al (1996) Resection margins of 2 versus 5 cm for cutaneous malignant melanoma with a tumor thickness of 0.8 to 2.0 mm: randomized study by the Swedish Melanoma Study Group. Cancer 77(9):1809–1814

    Article  CAS  Google Scholar 

  48. Balch CM et al (2001) Long-term results of a prospective surgical trial comparing 2 cm vs. 4 cm excision margins for 740 patients with 1–4 mm melanomas. Ann Surg Oncol 8(2):101–108

    CAS  PubMed  Google Scholar 

  49. Thomas JM et al (2004) Excision margins in high-risk malignant melanoma. N Engl J Med 350(8):757–766

    Article  CAS  Google Scholar 

  50. Gillgren P et al (2011) 2-cm versus 4-cm surgical excision margins for primary cutaneous melanoma thicker than 2 mm: a randomised, multicentre trial. Lancet 378(9803):1635–1642

    Article  Google Scholar 

  51. Doepker MP et al (2016) Is a wider margin (2 cm vs. 1 cm) for a 1.01-2.0 mm melanoma necessary? Ann Surg Oncol 23(7):2336–2342

    Article  Google Scholar 

  52. Hsueh EC et al (2017) 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 10(1):152

    Article  Google Scholar 

  53. Zager JS et al (2018) Performance of a prognostic 31-gene expression profile in an independent cohort of 523 cutaneous melanoma patients. BMC Cancer 18(1):130

    Article  Google Scholar 

  54. Ferris LK et al (2017) 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. J Am Acad Dermatol 76(5):818–825 e3

    Article  Google Scholar 

  55. Eggermont AM et al (2015) Adjuvant ipilimumab versus placebo after complete resection of high-risk stage III melanoma (EORTC 18071): a randomised, double-blind, phase 3 trial. Lancet Oncol 16(5):522–530

    Article  CAS  Google Scholar 

  56. Weber J et al (2017) Adjuvant nivolumab versus ipilimumab in resected stage III or IV melanoma. N Engl J Med 377(19):1824–1835

    Article  CAS  Google Scholar 

  57. Long GV et al (2017) Adjuvant dabrafenib plus trametinib in stage III BRAF-mutated melanoma. N Engl J Med 377(19):1813–1823

    Article  CAS  Google Scholar 

  58. Wolchok JD et al (2017) Overall survival with combined nivolumab and ipilimumab in advanced melanoma. N Engl J Med 377(14):1345–1356

    Article  CAS  Google Scholar 

  59. Hodi FS et al (2010) Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med 363(8):711–723

    Article  CAS  Google Scholar 

  60. Robert C et al (2015) Pembrolizumab versus Ipilimumab in Advanced Melanoma. N Engl J Med 372(26):2521–2532

    Article  CAS  Google Scholar 

  61. Robert C et al (2015) Improved overall survival in melanoma with combined dabrafenib and trametinib. N Engl J Med 372(1):30–39

    Article  Google Scholar 

  62. Larkin J et al (2014) Combined vemurafenib and cobimetinib in BRAF-mutated melanoma. N Engl J Med 371(20):1867–1876

    Article  Google Scholar 

  63. Luke JJ et al (2017) Targeted agents and immunotherapies: optimizing outcomes in melanoma. Nat Rev Clin Oncol 14(8):463–482

    Article  CAS  Google Scholar 

  64. Daud AI et al (2016) Programmed death-ligand 1 expression and response to the anti-programmed death 1 antibody pembrolizumab in melanoma. J Clin Oncol 34(34):4102–4109

    Article  CAS  Google Scholar 

  65. Snyder A et al (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371(23):2189–2199

    Article  Google Scholar 

  66. Johnson DB et al (2016) Targeted next generation sequencing identifies markers of response to PD-1 blockade. Cancer Immunol Res 4(11):959–967

    Article  Google Scholar 

  67. Harlin H et al (2009) Chemokine expression in melanoma metastases associated with CD8 + T-cell recruitment. Cancer Res 69(7):3077–3085

    Article  CAS  Google Scholar 

  68. Ji RR et al (2012) An immune-active tumor microenvironment favors clinical response to ipilimumab. Cancer Immunol Immunother 61(7):1019–1031

    Article  CAS  Google Scholar 

  69. Gajewski TF, Schreiber H, Fu YX (2013) Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol 14(10):1014–1022

    Article  CAS  Google Scholar 

  70. Ayers M et al (2017) IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest 127(8):2930–2940

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jonathan S. Zager.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ankeny, J.S., Labadie, B., Luke, J. et al. Review of diagnostic, prognostic, and predictive biomarkers in melanoma. Clin Exp Metastasis 35, 487–493 (2018). https://doi.org/10.1007/s10585-018-9892-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10585-018-9892-z

Keywords

Navigation