Colin B. Begg

Purpose:Patients with stage II and resected stage III melanomas have variable clinical outcomes, providing evidence of underlying biological differences in tumors and/or the patients themselves, beyond stage. The approval of adjuvant immunotherapy for stage IIB/C and resected stage III/IV disease (and adjuvant targeted therapy for resected stage III disease) has created a pressing need to develop biomarkers to accurately distinguish patients at low risk versus high risk for recurrence and death from melanoma. miRNAs are promising biomarkers because of their stability in tissues and fluids and their demonstrated functional and prognostic roles in melanoma. We hypothesized that miRNA expression could be integrated into prognostic models that would classify 5-year survival outcomes more accurately than clinical factors alone.Experimental Design:Using a NanoString miRNA Expression Assay, we analyzed 715 primary melanomas from patients with stage II or stage III disease within the InterMEL consortium and examined associations between miRNA expression and melanoma-specific death.Results:When integrated into clinical prognostic models for 5-year melanoma-specific survival, miRNA signatures improved the area under the receiver operating characteristic curve for patients in stage II from 0.71 for a “clinical factors–only” model to 0.81 for a “clinical plus miRNA” model in an independent test set, an improvement of 0.10 with a 95% confidence interval (0.03–0.19). The improvement was more modest for patients in stage III who were included in the analysis.Conclusions:Incorporating miRNA expression in primary melanomas may enhance the accuracy of clinical prognostic models and potentially aid in the selection of patients with melanoma for adjuvant treatment and clinical trials.

Purpose: Patients with stage II and resected stage III melanomas have variable clinical outcomes, providing evidence of underlying biological differences in tumors and/or the patients themselves, beyond stage. The approval of adjuvant immunotherapy for stage IIB/C and resected stage III/IV disease (and adjuvant targeted therapy for resected stage III disease) has created a pressing need to develop biomarkers to accurately distinguish patients at low risk versus high risk for recurrence and death from melanoma. miRNAs are promising biomarkers because of their stability in tissues and fluids and their demonstrated functional and prognostic roles in melanoma. We hypothesized that miRNA expression could be integrated into prognostic models that would classify 5-year survival outcomes more accurately than clinical factors alone. Experimental Design: Using a NanoString miRNA Expression Assay, we analyzed 715 primary melanomas from patients with stage II or stage III disease within the InterMEL consortium and examined associations between miRNA expression and melanoma-specific death. Results: When integrated into clinical prognostic models for 5-year melanoma-specific survival, miRNA signatures improved the area under the receiver operating characteristic curve for patients in stage II from 0.71 for a “clinical factors–only” model to 0.81 for a “clinical plus miRNA” model in an independent test set, an improvement of 0.10 with a 95% confidence interval (0.03–0.19). The improvement was more modest for patients in stage III who were included in the analysis. Conclusions: Incorporating miRNA expression in primary melanomas may enhance the accuracy of clinical prognostic models and potentially aid in the selection of patients with melanoma for adjuvant treatment and clinical trials.

Distinguishing driver mutations from the large background of passenger mutations remains a major challenge in cancer genomics. Evidence-based approaches to nominate driver mutations are often limited by the availability of experimental or clinical validation for specific variants. As clinical sequencing becomes integrated into patient care, computational methods provide powerful opportunities to analyze expanding genomic datasets and identify functional candidates beyond the current knowledge base. Among various analytical frameworks, mutual exclusivity, the observation that mutations in two or more genes tend not to co-occur within the same tumor, has been particularly attractive. Building on this principle, we propose BayesMAGPIE, a refined version of a statistical method, MAGPIE, developed previously for identifying driver genes within oncogenic pathways. The new method introduces two key innovations. First, it incorporates information on mutation type using a Bayesian hierarchical modeling framework, enabling the distinction between potential differences in functional effects among variants within the same gene, thereby improving the accuracy of driver identification. Second, it models gene-specific driver frequencies with a Dirichlet prior which effectively controls the sparsity of the inferred driver set and aligns with the biological expectation that most tumor types are driven by a small number of genes. We evaluate BayesMAGPIE through extensive simulation studies to assess its estimation bias and accuracy in driver identification, and benchmark its performance against MAGPIE using TCGA data from eight cancer types.

Supplementary Table S4 shows the estimated effects of SNPs on gene expression in human skin samples.

Supplementary Table S1 shows SNP information and their associations with MPM relative to SPM in the GEM study.

Background:Recent genome-wide association studies (GWAS) have identified new susceptibility loci for melanoma, but their associations with multiple primary melanoma (MPM) are unclear.Methods:We investigated the associations of 69 SNPs in 39 GWAS-identified loci with odds of MPM relative to single primary melanoma in the international, population-based Genes, Environment, and Melanoma study. Per-minor-allele ORs and 95% confidence intervals (CI) for individuals with MPM “cases” (n = 1,205) relative to single primary melanoma “controls” (n = 2,458) were estimated using multivariable logistic regression, and polygenic risk scores (PRS) were calculated and weighted based on a 2020 GWAS meta-analysis (57 of the 68 independent GWAS SNPs available).Results:Thirteen SNPs in 11 gene regions (PARP1, CYP1B1/RMDN3, TERT, RAPGEF5, TYRP1, MTAP, CDKN2A/CDKN2B, KLF4, TYR, SOX6, and ASIP) were statistically significantly associated (P < 0.05) with MPM adjusting for age, sex, age-by-sex interaction, and study center. The highest versus lowest PRS quintile was associated with a 2.81-fold higher odds of MPM (95% CI, 2.10–3.78; P = 7.5 × 10−13); this association was attenuated but remained statistically significant after excluding SNPs individually associated with MPM (OR = 1.75, 95% CI, 1.32–2.31).Conclusions:Inherited genetic variants spanning 11 gene regions were independently associated with MPM. Nonsignificant SNPs were associated with MPM when aggregated into a PRS, indicating that their cumulative effect may influence MPM risk despite lacking individual statistical significance in our study population.Impact:Our findings provide additional evidence that these loci are associated with melanoma risk and estimate the magnitude of their genetic effect on subsequent (multiple) primary melanoma risk.