Construction and analysis of multiparameter prognostic models for melanoma outcome

Methods Mol Biol. 2014:1102:227-58. doi: 10.1007/978-1-62703-727-3_13.

Abstract

The outcome of Stage II melanoma is uncertain. Despite that 10-year melanoma-specific survival can approach 50 % following curative-intent wide local excision and negative sentinel lymph node biopsy, the adverse risk-benefit ratio of interferon-based adjuvant regimens precludes their use in most patients. The discovery and translation of protein-based prognostic biomarkers into the clinic offers the promise for residual risk stratification of Stage II melanoma patients beyond conventional clinicopathologic criteria to identify an additional subset of patients who, based upon tumor molecular profiles, might also derive benefit from adjuvant regimens. Despite incorporation of Ki-67 assays into clinical practice, systematic review of REMARK-compliant, immunostain-based prognostic biomarker assays in melanoma suggests that residual risk of recurrence might be best explained by a composite score derived from a small panel of proteins representing independent features of melanoma biology. Reflecting this trend, to date, five such multiparameter melanoma prognostic models have been published. Here, we review these five models and provide detailed protocols for discovering and validating multiparameter models including: appropriate cohort recruitment strategies, comprehensive laboratory protocols supporting fully quantitative chromogenic or fluorescent immunostaining platforms, statistical approaches to create composite prognostic indices recommended steps for model validation in independent cohorts.

Publication types

  • Review
  • Systematic Review

MeSH terms

  • Biomarkers, Tumor / metabolism
  • Cohort Studies
  • Computational Biology
  • Humans
  • Melanoma / therapy*
  • Models, Biological*
  • Multivariate Analysis
  • Prognosis
  • Reproducibility of Results
  • Skin Neoplasms / therapy*
  • Tissue Array Analysis
  • Treatment Outcome

Substances

  • Biomarkers, Tumor