Sarah-Jane Schramm

BRAF Mutation, NRAS Mutation, and the Absence of an Immune-Related Expressed Gene Profile Predict Poor Outcome in Patients with Stage III Melanoma

Prediction of outcome for melanoma patients with surgically resected macroscopic nodal metastases is very imprecise. We performed a comprehensive clinico-pathologic assessment of fresh-frozen macroscopic nodal metastases and the preceding primary melanoma, somatic mutation profiling, and gene expression profiling to identify determinants of outcome in 79 melanoma patients. In addition to disease stage <II at initial presentation, the following clinical and pathologic factors were independent predictors of improved outcome (odds ratios for survival >4 years, 90% confidence interval): the presence of a nodular component in the primary melanoma (6.8, 0.6-76.0), and small cell size (11.1, 0.8-100.0) or low pigmentation (3.0, 0.8-100.0) in the nodal metastases. Absence of BRAF mutation (20.0, 1.0-1000.0) or NRAS mutation (16.7, 0.6-1000.0) were both favorable prognostic factors. A 46-gene expression signature with strong overrepresentation of immune response genes was predictive of better survival (10.9, 0.4-325.6); in the full cohort, median survival was >100 months in those with the signature, but 10 months in those without. This relationship was validated in two previously published independent stage III melanoma data sets. We conclude that the presence of BRAF mutation, NRAS mutation, and the absence of an immune-related expressed gene profile predict poor outcome in melanoma patients with macroscopic stage III disease.

Whole-genome landscapes of major melanoma subtypes

Melanoma of the skin is a common cancer only in Europeans, whereas it arises in internal body surfaces (mucosal sites) and on the hands and feet (acral sites) in people throughout the world. Here we report analysis of whole-genome sequences from cutaneous, acral and mucosal subtypes of melanoma. The heavily mutated landscape of coding and non-coding mutations in cutaneous melanoma resolved novel signatures of mutagenesis attributable to ultraviolet radiation. However, acral and mucosal melanomas were dominated by structural changes and mutation signatures of unknown aetiology, not previously identified in melanoma. The number of genes affected by recurrent mutations disrupting non-coding sequences was similar to that affected by recurrent mutations to coding sequences. Significantly mutated genes included BRAF, CDKN2A, NRAS and TP53 in cutaneous melanoma, BRAF, NRAS and NF1 in acral melanoma and SF3B1 in mucosal melanoma. Mutations affecting the TERT promoter were the most frequent of all; however, neither they nor ATRX mutations, which correlate with alternative telomere lengthening, were associated with greater telomere length. Most melanomas had potentially actionable mutations, most in components of the mitogen-activated protein kinase and phosphoinositol kinase pathways. The whole-genome mutation landscape of melanoma reveals diverse carcinogenic processes across its subtypes, some unrelated to sun exposure, and extends potential involvement of the non-coding genome in its pathogenesis.

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.

Melanoma Prognosis: A REMARK-Based Systematic Review and Bioinformatic Analysis of Immunohistochemical and Gene Microarray Studies

Despite intensive research efforts, within-stage survival rates for melanoma vary widely. Pursuit of molecular biomarkers with improved prognostic significance over clinicohistological measures has produced extensive literature. Reviews have synthesized these data, but none have systematically partitioned high-quality studies from the remainder across different molecular methods nor examined system properties of that output. Databases were searched for studies analyzing protein expression by immunohistochemistry (n = 617, extending the only systematic review to date by 102 studies) or for gene expression microarray studies (n = 45) in melanoma in relation to outcome. REMARK-derived criteria were applied to identify high-quality studies. Biomarkers and pathways were functionally assessed by using gene ontology software. Most manuscripts did not meet REMARK-based criteria, an ongoing trend that can impede translational research. Across REMARK-compliant literature, 41 proteins were significantly associated with outcome. Multimarker tests consistently emerged among the most promising potential biomarkers, indicating a need to continue assessing candidates in that composite setting. Twenty-one canonical pathways were populated by outcome-related proteins but not by those that failed to show such an association; we propose that this set of pathways warrants closer investigation to understand drivers of poor outcome in melanoma. Two-gene expression microarray studies met REMARK-based criteria reflecting a genuine paucity of literature in the area. The 254 outcome-related genes were examined for correspondences with the systematically identified protein signature. This analysis highlighted proliferating cell nuclear antigen and survivin as priorities for further examination as biomarkers in melanoma prognosis, and illustrated ongoing need to integrate alternative approaches to biomarker discovery in melanoma translational research. Mol Cancer Ther; 10(8); 1520–8. ©2011 AACR.

MicroRNA and mRNA expression profiling in metastatic melanoma reveal associations with BRAF mutation and patient prognosis.

The role of microRNAs (miRNAs) in melanoma is unclear. We examined global miRNA expression profiles in fresh‐frozen metastatic melanomas in relation to clinical outcome and BRAF mutation, with validation in independent cohorts of tumours and sera. We integrated miRNA and mRNA information from the same samples and elucidated networks associated with outcome and mutation. Associations with prognosis were replicated for miR‐150‐5p, miR‐142‐3p and miR‐142‐5p. Co‐analysis of miRNA and mRNA uncovered a network associated with poor prognosis (PP) that paradoxically favoured expression of miRNAs opposing tumorigenesis. These miRNAs are likely part of an autoregulatory response to oncogenic drivers, rather than drivers themselves. Robust association of miR‐150‐5p and the miR‐142 duplex with good prognosis and earlier stage metastatic melanoma supports their potential as biomarkers. miRNAs overexpressed in association with PP in an autoregulatory fashion will not be suitable therapeutic targets.

Determination of prognosis in metastatic melanoma through integration of clinico‐pathologic, mutation, mRNA, microRNA, and protein information

In patients with metastatic melanoma, the identification and validation of accurate prognostic biomarkers will assist rational treatment planning. Studies based on “‐omics” technologies have focussed on a single high‐throughput data type such as gene or microRNA transcripts. Occasionally, these features have been evaluated in conjunction with limited clinico‐pathologic data. With the increased availability of multiple data types, there is a pressing need to tease apart which of these sources contain the most valuable prognostic information. We evaluated and integrated several data types derived from the same tumor specimens in AJCC stage III melanoma patients—gene, protein, and microRNA expression as well as clinical, pathologic and mutation information—to determine their relative impact on prognosis. We used classification frameworks based on pre‐validation and bootstrap multiple imputation to compare the prognostic power of each data source, both individually as well as integratively. We found that the prognostic utility of clinico‐pathologic information was not out‐performed by any of the various “‐omics” platforms. Rather, a combination of clinico‐pathologic variables and mRNA expression data performed best. Furthermore, a patient‐based classification analysis revealed that the prognostic accuracy of various data types was not the same for different patients. This indicates that ongoing development in the individualized evaluation of melanoma patients must take account of the value of both traditional and novel “‐omics” measurements.

Identification, Review, and Systematic Cross-Validation of microRNA Prognostic Signatures in Metastatic Melanoma

In metastatic melanoma, it is vital to identify and validate biomarkers of prognosis. Previous studies have systematically evaluated protein biomarkers or mRNA-based expression signatures. No such analyses have been applied to microRNA (miRNA)-based prognostic signatures. As a first step, we identified two prognostic miRNA signatures from publicly available data sets (Gene Expression Omnibus/The Cancer Genome Atlas) of global miRNA expression profiling information. A 12-miRNA signature predicted longer survival after surgery for resection of American Joint Committee on Cancer stage III disease (>4 years, no sign of relapse) and outperformed American Joint Committee on Cancer standard-of-care prognostic markers in leave-one-out cross-validation analysis (error rates 34% and 38%, respectively). A similar 15-miRNA biomarker derived from The Cancer Genome Atlas miRNA-seq data performed slightly worse (39%) than these current biomarkers. Both signatures were then assessed for replication in two independent data sets and subjected to systematic cross-validation together with the three other miRNA-based prognostic signatures proposed in the literature to date. Five miRNAs (miR-142-5p, miR-150-5p, miR-342-3p, miR-155-5p, and miR-146b-5p) were reproducibly associated with patient outcome and have the greatest potential for application in the clinic. Our extensive validation approach highlighted among multiple independent cohorts the translational potential and limitations of miRNA signatures, and pointed to future directions in the analysis of this emerging class of markers.

Disturbed protein–protein interaction networks in metastatic melanoma are associated with worse prognosis and increased functional mutation burden

For disseminated melanoma, new prognostic biomarkers and therapeutic targets are urgently needed. The organization of protein–protein interaction networks was assessed via the transcriptomes of four independent studies of metastatic melanoma and related to clinical outcome and MAP‐kinase pathway mutations (BRAF/NRAS). We also examined patient outcome‐related differences in a predicted network of microRNAs and their targets. The 32 hub genes with the most reproducible survival‐related disturbances in co‐expression with their protein partner genes included oncogenes and tumor suppressors, previously known correlates of prognosis, and other proteins not previously associated with melanoma outcome. Notably, this network‐based gene set could classify patients according to clinical outcomes with 67–80% accuracy among cohorts. Reproducibly disturbed networks were also more likely to have a higher functional mutation burden than would be expected by chance. The disturbed regions of networks are therefore markers of clinically relevant, selectable tumor evolution in melanoma which may carry driver mutations.

Molecular interaction networks for the analysis of human disease: Utility, limitations, and considerations

High‐throughput ‘‐omics’ data can be combined with large‐scale molecular interaction networks, for example, protein–protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative ‘‐omics’ methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and “network‐type.” The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high‐throughput studies versus a meta‐database of smaller literature‐curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large‐scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.

Molecular biomarkers of prognosis in melanoma: how far are we from the clinic?

Interest in novel targeted and immunological therapies for advanced melanoma has never been greater. Novel prognostic biomarkers to stratify patients according to risk of relapse are also needed in both the primary and metastatic disease settings. Molecular methods have markedly increased the number and types of experimental hypotheses that can be evaluated in pursuit of these aims. Indeed, their contribution to a prioritized overview of the biology of melanoma has been substantial. But what are the barriers to integrating molecular biomarkers into clinical practice?