Open Access Research article

MC1R genotype as a predictor of early-onset melanoma, compared with self-reported and physician-measured traditional risk factors: an Australian case-control-family study

Anne E Cust1*, Chris Goumas1, Kylie Vuong1, John R Davies2, Jennifer H Barrett2, Elizabeth A Holland3, Helen Schmid3, Chantelle Agha-Hamilton3, Bruce K Armstrong1, Richard F Kefford3, Joanne F Aitken4, Graham G Giles56, D Timothy Bishop2, Julia A Newton-Bishop2, John L Hopper5, Graham J Mann3 and Mark A Jenkins5

Author Affiliations

1 Cancer Epidemiology and Services Research (CESR), Sydney School of Public Health, The University of Sydney, Sydney, NSW 2006, Australia

2 Section of Epidemiology and Biostatistics, Leeds Institute of Molecular Medicine, University of Leeds, Leeds, UK

3 Westmead Institute for Cancer Research, University of Sydney at Westmead Millennium Institute and Melanoma Institute Australia, Sydney, Australia

4 Viertel Centre for Research in Cancer Control, Cancer Council Queensland, Spring Hill, Brisbane, Australia

5 Centre for Molecular, Environmental, Genetic and Analytic (MEGA) Epidemiology, Melbourne School of Population Health, The University of Melbourne, Melbourne, Australia

6 Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia

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BMC Cancer 2013, 13:406  doi:10.1186/1471-2407-13-406

Published: 4 September 2013

Abstract

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.

Keywords:
MC1R; Risk prediction; Accuracy; Melanoma; Sun exposure; Early-onset; Pigmentation; Nevi