Independent evaluation of melanoma polygenic risk scores in UK and Australian prospective cohorts*

Abstract

Summary Background Previous studies suggest that polygenic risk scores (PRSs) may improve melanoma risk stratification. However, there has been limited independent validation of PRS‐based risk prediction, particularly assessment of calibration (comparing predicted to observed risks). Objectives To evaluate PRS‐based melanoma risk prediction in prospective UK and Australian cohorts with European ancestry. Methods We analysed invasive melanoma incidence in the UK Biobank (UKB; n = 395 647, 1651 cases) and a case‐cohort nested within the Melbourne Collaborative Cohort Study (MCCS, Australia; n = 4765, 303 cases). Three PRSs were evaluated: 68 single‐nucleotide polymorphisms (SNPs) at 54 loci from a 2020 meta‐analysis (PRS68), 50 SNPs significant in the 2020 meta‐analysis excluding UKB (PRS50) and 45 SNPs at 21 loci known in 2018 (PRS45). Ten‐year melanoma risks were calculated from population‐level cancer registry data by age group and sex, with and without PRS adjustment. Results Predicted absolute melanoma risks based on age and sex alone underestimated melanoma incidence in the UKB [ratio of expected/observed cases: E/O = 0·65, 95% confidence interval (CI) 0·62–0·68] and MCCS (E/O = 0·63, 95% CI 0·56–0·72). For UKB, calibration was improved by PRS adjustment, with PRS50‐adjusted risks E/O = 0·91, 95% CI 0·87–0·95. The discriminative ability for PRS68‐ and PRS50‐adjusted absolute risks was higher than for risks based on age and sex alone (Δ area under the curve 0·07–0·10, P < 0·0001), and higher than for PRS45‐adjusted risks (Δ area under the curve 0·02–0·04, P < 0·001). Conclusions A PRS derived from a larger, more diverse meta‐analysis improves risk prediction compared with an earlier PRS, and might help tailor melanoma prevention and early detection strategies to different risk levels. Recalibration of absolute risks may be necessary for application to specific populations.

Management of cutaneous melanoma in Australia: a narrative review

“…20,21,23 By combining the odds ratios and frequencies of genome-wide association studies of single nucleotide polymorphisms, it is possible to generate an individual's polygenic risk score. Studies have shown that melanoma polygenic risk scores have similar risk prediction performance to models based on traditional risk factors, 24 and, when combined, they result in a modest incremental improvement in discrimination, sensitivity and specificity. 25 Further research is needed to guide the integration of sociodemographic, clinical and genomic risk factors into risk prediction models and their implementation in practice.…”

Polygenic risk scores for melanoma: a stepwise process towards clinical implementation

“…an integrated risk model. 3 However, the overall AUCROC was still moderate at 0Á69, suggesting that for population-based screening, the tested integrated risk models are not useful. The inclusion of single-nucleotide polymorphisms beyond those that meet stringent genome-wide association study significance levels or adding traditional melanoma risk factors may be considered to boost future predictive performance.…”

“…However, reproducibility is mentioned as an important issue in last year’s published PRS Reporting Standards (PRS‐RS) 2 . Therefore, the paper by Steinberg et al ., 3 in this issue, is an important study that evaluates three melanoma PRSs in addition to basic clinical characteristics derived from meta‐analysis in two independent large cohorts.…”

“…showed that in both the UK Biobank (UKB) and Melbourne Collaborative Cohort Study (MCCS) discriminant ability increased from 0·03 to 0·10 by adding a PRS to age and sex, i.e. an integrated risk model 3 . However, the overall AUCROC was still moderate at 0·69, suggesting that for population‐based screening, the tested integrated risk models are not useful.…”

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

“…Steinberg et al (2022) [19] investigated the performance of several PRSs developed from a meta-analysis [20] and previous studies [16], and found an AUC of 0.656 for the 50-SNP when testing in the UK Biobank data. The paper by Steinberg et al (2022) [19] is the best comparison with our work (AUC = 0.639).…”