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 (PLCOM2012 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 PLCOM2012 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.
Keywords: Lung cancer screening; Polygenic risk scores; Risk prediction.
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.