Melanoma risk prediction based on a polygenic risk score and clinical risk factors

Abstract

Melanoma is one of the most commonly diagnosed cancers in the Western world: third in Australia, fifth in the USA and sixth in the European Union. Predicting an individual's personal risk of developing melanoma may aid them in undertaking effective risk reduction measures. The objective of this study was to use the UK Biobank to predict the 10-year risk of melanoma using a newly developed polygenic risk score (PRS) and an existing clinical risk model. We developed the PRS using a matched case-control training dataset ( N = 16 434) in which age and sex were controlled by design. The combined risk score was developed using a cohort development dataset ( N = 54 799) and its performance was tested using a cohort testing dataset ( N = 54 798). Our PRS comprises 68 single-nucleotide polymorphisms and had an area under the receiver operating characteristic curve of 0.639 [95% confidence interval (CI) = 0.618-0.661]. In the cohort testing data, the hazard ratio per SD of the combined risk score was 1.332 (95% CI = 1.263-1.406). Harrell's C-index was 0.685 (95% CI = 0.654-0.715). Overall, the standardized incidence ratio was 1.193 (95% CI = 1.067-1.335). By combining a PRS and a clinical risk score, we have developed a risk prediction model that performs well in terms of discrimination and calibration. At an individual level, information on the 10-year risk of melanoma can motivate people to take risk-reduction action. At the population level, risk stratification can allow more effective population-level screening strategies to be implemented.

Fig. 1

Standardized incidence ratios of the number of observed melanoma cases in the first 10 years of follow-up in the testing data compared with the expected number using population incidence rates by quintile of 10-year risk.

Fig. 2

Distribution of 10-year melanoma risk categorized by different percentage groups.