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

Chi Kuen Wong, Gillian S. Dite, Erika Spaeth, Nicholas M. Murphy, Richard Allman

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Pages (from-to)293-299
Number of pages7
JournalMelanoma Research
Volume33
Issue number4
DOIs
Publication statusPublished - 1 Aug 2023
Externally publishedYes

Keywords

  • melanoma
  • polygenic risk scores
  • risk prediction

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