Validation of lung cancer polygenic risk scores in a high-risk case-control cohort.

PURPOSE

Screening with low-dose computed tomography reduces lung cancer (LC) mortality. Risk prediction models used for screening selection do not include genetic variables. Here, we investigated the performance of previously published polygenic risk scores (PRSs) for LC, considering their potential to improve screening selection.

METHODS

We validated 9 PRSs in a high-risk case-control cohort, comprising genotype data from 652 surgical patients with LC and 550 cancer-free, high-risk (PLCO score ≥ 1.51%) participants of the Manchester Lung Health Check, a community-based LC screening program (n = 550). Discrimination (area under the curve [AUC]) between cases and controls was assessed for each PRS independently and alongside clinical risk factors.

RESULTS

Median age was 67 years, 53% were female, 46% were current smokers, and 76% were National Lung Screening Trial eligible. Median PLCO score among controls was 3.4%, 80% of cases were early stage. All PRSs significantly improved discrimination, AUC increased between +0.002 (P = .02) and +0.015 (P < .0001), compared with clinical risk factors alone. The best-performing PRS had an independent AUC of 0.59. Two novel loci, in the DAPK1 and MAGI2 genes, were significantly associated with LC risk.

CONCLUSION

PRSs may improve LC risk prediction and screening selection. Further research, particularly examining clinical utility and cost-effectiveness, is required.