1036Independent evaluation of melanoma polygenic risk scores in UK and Australian prospective cohorts. (2nd September 2021) - Steinberg, Julia; Lee, Jin Yee; Wang, Harry; Law, Matthew; Smit, Amelia; Nguyen-Dumont, Tu; Giles, Graham; Southey, Melissa; Milne, Roger; Mann, Graham; MacInnis, Robert; Cust, Anne
Abstract: Background: To improve melanoma early detection, tools to predict personal risk based on genetic information (polygenic risk scores, PRS) have been developed, but require external validation. Methods: We analysed invasive melanoma incidence in UK Biobank (UKB; n = 395, 647; 1, 651 cases) and the Melbourne Collaborative Cohort Study (MCCS, Australia; n = 4, 765; 303 cases). Three PRS were evaluated: 68 genetic variants (SNPs) at 54 loci from a 2020 meta-analysis (PRS68); 50 SNPs significant in the 2020 meta-analysis excluding UKB (PRS50); 45 SNPs at 21 loci known pre-2020 (PRS45). 10-year melanoma risks were calculated from population-level cancer registry data by age group and sex, with and without PRS adjustment. Results: All PRS were strongly associated with melanoma incidence, including after adjustment for age, sex, ethnicity, and ease of tanning. Predicted absolute melanoma risks based on age and sex alone underestimated melanoma incidence in UKB (ratio expected/observed cases E/O=0.65, 95% confidence interval 0.62-0.68) and MCCS (E/O=0.65, 0.57-0.73). For UKB, this was reduced by PRS-adjustment, e.g. PRS50-adjusted risks E/O=0.91 (0.87-0.95). Discriminative ability for PRS68- and PRS50-adjusted absolute risks was higher than for risks based on age and sex alone (deltaAUC 0.07-0.1, p < 0.0001), and higher than for PRS45-adjusted risks (deltaAUC 0.02-0.04, p < 0.001). Conclusions: A PRS derived from a larger, more diverse meta-analysis improves melanoma risk prediction compared to an earlier PRS. Re-calibration of absolute risks may be necessary for application to specific populations. Key messages: A genetic score can improve prediction of melanoma risk and might help tailor melanoma prevention and early detection strategies to different risk levels.