Review and cross-validation of gene expression signatures and melanoma prognosis

In melanoma, there is an urgent need to identify novel biomarkers with prognostic performance superior to traditional clinical and histological parameters. Gene expression-based prognostic signatures offer promise, but studies have been challenged by sample scarcity, cohort heterogeneity, and doubts about the efficacy of such signatures relative to current clinical practices. Motivated by new studies that have begun to address these challenges, we reviewed prognostic signatures derived from gene expression microarray analysis of human melanoma tissue. We used REMARK-based criteria to select the most relevant studies and directly compared their signature gene lists. Through functional ontology enrichment analysis, we observed that these independent data sets converge in part upon immune response processes and the G-protein signaling NRAS-regulation pathway, both important in melanoma development and progression. The signatures correctly predicted patient outcome in independent gene expression data sets with some notably low misclassification rates, particularly among studies involving more advanced-stage tumors. This successful cross-validation indicates that gene expression analysis-based signatures are becoming translationally relevant to care of melanoma patients, as well as improving understanding of the aspects of melanoma biology that determine patient outcome.