Analysis: PRS Improves Melanoma Risk Prediction
Key Takeaways
- A new study suggests melanoma polygenic risk score (PRS) improved prediction beyond clinical factors alone.
- Adding PRS to MP16 increased the c-index from 0.713 to 0.729.
- The combined model identified more incident invasive melanoma cases in the top 2 risk deciles.
A polygenic risk score (PRS) improved prediction of invasive cutaneous melanoma when added to an established clinical risk model, according to findings from a recent genome-wide association analysis.
The PRS was derived from a genome-wide association study meta-analysis of cutaneous melanoma that included 28,849 melanoma cases and 78,922 controls from 20 studies across the United Kingdom, United States, Australia, and Europe. Investigators tuned the score in the Canadian Longitudinal Study on Aging cohort (528 melanoma cases and 17,787 controls).
The model was then independently tested in the QSkin prospective cohort, which included 16,282 participants aged 40 to 69 years at baseline with genetic data available. During 10 years of follow-up, 359 participants developed new invasive melanomas. Investigators compared the PRS with 14 self-reported clinical risk factors and the MP16 clinical prediction model.
In QSkin, the PRS outperformed any single clinical risk factor, with a c-index of 0.643. Adding PRS to a baseline model of age, sex, and the first 10 principal components increased the c-index from 0.603 to 0.670. Adding PRS to MP16 also improved discrimination, increasing the c-index from 0.713 to 0.729. The combined MP16 plus PRS model identified more true cases in the top 2 risk deciles than MP16 alone.
The reliance on self-reported clinical factors and the need to assess performance across broader ancestral and clinical populations were cited as a study limitations.
“Incorporating genetic risk information into existing clinical risk tools significantly improves prediction performance for melanoma,” the authors concluded.
Source: British Journal of Dermatology. 2026. Doi: