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Melanocortin-1 receptor (MC1R) gene variants are very common and are associated with melanoma risk, but their contribution to melanoma risk prediction compared with traditional risk factors is unknown. We aimed to 1) evaluate the separate and incremental contribution of MC1R genotype to prediction of early-onset melanoma, and compare this with the contributions of physician-measured and self-reported traditional risk factors, and 2) develop risk prediction models that include MC1R, and externally validate these models using an independent dataset from a genetically similar melanoma population. Using data from an Australian population-based, case-control-family study, we included 413 case and 263 control participants with sequenced MC1R genotype, clinical skin examination and detailed questionnaire. We used unconditional logistic regression to estimate predicted probabilities of melanoma. Results were externally validated using data from a similar study in England. When added to a base multivariate model containing only demographic factors, MC1R genotype improved the area under the receiver operating characteristic curve (AUC) by 6% (from 0.67 to 0.73; P < 0.001) and improved the quartile classification by a net 26% of participants. In a more extensive multivariate model, the factors that contributed significantly to the AUC were MC1R genotype, number of nevi and previous non-melanoma skin cancer; the AUC was 0.78 (95% CI 0.75-0.82) for the model with self-reported nevi and 0.83 (95% CI 0.80-0.86) for the model with physician-counted nevi. Factors that did not further contribute were sun and sunbed exposure and pigmentation characteristics. Adding MC1R to a model containing pigmentation characteristics and other self-reported risk factors increased the AUC by 2.1% (P = 0.01) and improved the quartile classification by a net 10% (95% CI 1-18%, P = 0.03). Although MC1R genotype is strongly associated with skin and hair phenotype, it was a better predictor of early-onset melanoma than was pigmentation characteristics. Physician-measured nevi and previous non-melanoma skin cancer were also strong predictors. There might be modest benefit to measuring MC1R genotype for risk prediction even if information about traditional self-reported or clinically measured pigmentation characteristics and nevi is already available.

Background Mutations in the CDKN2A and CDK4 genes predispose to melanoma. From three case-control studies of cutaneous melanoma, we estimated the prevalence and predictors of these mutations for people from regions with widely differing latitudes and melanoma incidence. Methods Population-based cases and controls from the United Kingdom (1586 cases, 499 controls) and Australia (596 early-onset cases, 476 controls), and a hospital-based series from Spain (747 cases, 109 controls), were screened for variants in all exons of CDKN2A and the p16INK4A binding domain of CDK4 . Results The prevalence of mutations for people with melanoma was similar across regions: 2.3%, 2.5% and 2.0% for Australia, Spain and the United Kingdom respectively. The strongest predictors of carrying a mutation were having multiple primaries (odds ratio (OR) = 5.4, 95% confidence interval (CI: 2.5, 11.6) for 2 primaries and OR = 32.4 (95% CI: 14.7, 71.2) for 3 or more compared with 1 primary only); and family history (OR = 3.8; 95% CI:1.89, 7.5) for 1 affected first- or second-degree relative and OR = 23.2 (95% CI: 11.3, 47.6) for 2 or more compared with no affected relatives). Only 1.1% of melanoma cases with neither a family history nor multiple primaries had mutations. Conclusions There is a low probability (<2%) of detecting a germline CDKN2A mutation in people with melanoma except for those with a strong family history of melanoma (≥2 affected relatives, 25%), three or more primary melanomas (29%), or more than one primary melanoma who also have other affected relatives (27%).

BackgroundCDKN2A mutations confer a substantial risk of cutaneous melanoma; however, the magnitude of risk is uncertain.MethodsThe study estimated the hazard ratio (HR) and the average age specific cumulative risk (ie, penetrance) of reported melanoma for CDKN2A mutation carriers in case families using a modified segregation analysis of the first and higher degree relatives of 35 population-based cases. The study sample included 223 relatives of 13 melanoma cases diagnosed when aged 18–39 years from Melbourne, Sydney and Brisbane, Australia, and 322 relatives of 22 melanoma cases diagnosed at any age from Yorkshire, UK.ResultsThe estimated HR for melanoma for mutation carriers relative to the general population decreased with regions of increasing ambient ultraviolet (UV) irradiance, being higher for the UK than Australia (87, 95% CI 50 to 153 vs 31, 95% CI 20 to 50, p=0.008), and across Australia, 49 (95% CI 24 to 98) for Melbourne, 44 (95% CI 22 to 88) for Sydney, and 9 (95% CI 2 to 33) for Brisbane (p=0.02). Penetrance did not differ by geographic region. It is estimated that 16% (95% CI 10% to 27%) of UK and 20% (95% CI 13% to 30%) of Australian CDKN2A mutation carriers would be diagnosed with melanoma by age 50 years, and 45% (95% CI 29% to 65%) and 52% (95% CI 37% to 69%), respectively, by age 80 years.ConclusionsContrary to the strong association between UV radiation exposure and melanoma risk for the general population, CDKN2A mutation carriers appear to have the same cumulative risk of melanoma irrespective of the ambient UV irradiance of the region in which they live.

Background Melanocortin-1 receptor ( MC1R ) gene variants are very common and are associated with melanoma risk, but their contribution to melanoma risk prediction compared with traditional risk factors is unknown. We aimed to 1) evaluate the separate and incremental contribution of MC1R genotype to prediction of early-onset melanoma, and compare this with the contributions of physician-measured and self-reported traditional risk factors, and 2) develop risk prediction models that include MC1R , and externally validate these models using an independent dataset from a genetically similar melanoma population. Methods Using data from an Australian population-based, case-control-family study, we included 413 case and 263 control participants with sequenced MC1R genotype, clinical skin examination and detailed questionnaire. We used unconditional logistic regression to estimate predicted probabilities of melanoma. Results were externally validated using data from a similar study in England. Results When added to a base multivariate model containing only demographic factors, MC1R genotype improved the area under the receiver operating characteristic curve (AUC) by 6% (from 0.67 to 0.73; P  < 0.001) and improved the quartile classification by a net 26% of participants. In a more extensive multivariate model, the factors that contributed significantly to the AUC were MC1R genotype, number of nevi and previous non-melanoma skin cancer; the AUC was 0.78 (95% CI 0.75-0.82) for the model with self-reported nevi and 0.83 (95% CI 0.80-0.86) for the model with physician-counted nevi. Factors that did not further contribute were sun and sunbed exposure and pigmentation characteristics. Adding MC1R to a model containing pigmentation characteristics and other self-reported risk factors increased the AUC by 2.1% ( P  = 0.01) and improved the quartile classification by a net 10% (95% CI 1-18%, P = 0.03). Conclusions Although MC1R genotype is strongly associated with skin and hair phenotype, it was a better predictor of early-onset melanoma than was pigmentation characteristics. Physician-measured nevi and previous non-melanoma skin cancer were also strong predictors. There might be modest benefit to measuring MC1R genotype for risk prediction even if information about traditional self-reported or clinically measured pigmentation characteristics and nevi is already available.