Clinical usefulness of polygenic risk scores in risk prediction models for lung cancer screening and lung nodule...

Lung cancer mortality decreases as a result of low-dose computed tomography (LDCT) screening, but suffers from low uptake and high false-positive rates. The impact of integrating genetic risk using a polygenic risk score (PRS) to optimize lung cancer screening remains underexplored.

We developed a genome-wide PRS and evaluated its performance in pre- and post-screening contexts. Screening eligibility was assessed using two UK Biobank (UKB) subsets: UKB_PLCO (n = 8957; PLCOm2012norace risk ≥2%) and UKB_{ScreeningCriteria} (n = 74,024 meeting screening eligibility criteria). To evaluate nodule management, we used a cohort of 669 ever-smokers with PLCO ≥2% and Lung-RADS score ≥3, referred to as SYNERGIQC_{PLCO_LungRADS}. Multivariable Cox models, time-dependent area under the curve (AUC), and decision curve analyses (DCA) evaluated association, discrimination, and clinical net benefit.

In UKB_PLCO, the PRS was associated with a hazard ratio (HR) of 1.18 per standard deviation. In UKB_{ScreeningCriteria}, PRS showed HR of 1.34. Adding PRS to the PLCOm2012 model improved discrimination (AUC: 0.707 vs 0.696; P = 6.85e^-27[likelihood ratio test]) and correctly reclassified 9.2% of incident lung cancer cases. Six-year absolute risks stratified by PRS deciles indicate 3.1-fold increase in the top compared to the bottom decile. In SYNERGIQC_{PLCO_LungRADS}, HR_PRS was 1.22. In this context, DCA indicated a modest net benefit for decision thresholds between 10% and 30%.

PRS in lung cancer reveals context-dependent net benefit across studied populations. Although PRS adds limited value for determining screening eligibility, it may help reclassify borderline individuals and inform decisions regarding closer follow-up or invasive diagnostic procedures for high-risk screened groups.