Assessing Lung Cancer Absolute Risk Trajectory Based on a Polygenic Risk Model

Abstract

Lung cancer is the leading cause of cancer death globally. An improved risk stratification strategy can increase efficiency of low-dose computed tomography (LDCT) screening. Here we assessed whether individual's genetic background has clinical utility for risk stratification in the context of LDCT screening. Based on 13,119 lung cancer patients and 10,008 controls with European ancestry in the International Lung Cancer Consortium, we constructed a polygenic risk score (PRS) via 10-fold cross-validation with regularized penalized regression. The performance of risk model integrating PRS, including calibration and ability to discriminate, was assessed using UK biobank data (N=335,931). Absolute risk was estimated based on age-specific lung cancer incidence and all-cause mortality as competing risk. To evaluate its potential clinical utility, the PRS distribution was simulated in the National Lung Screening Trial (N=50,772 participants). The lung cancer odds ratio (ORs) for individuals at the top decile of the PRS distribution versus those at bottom 10% was 2.39 (95%CI=1.92-3.00, P=1.80×10 −14 ) in the validation set (trend p-value of 5.26 × 10 −20 ). The OR per standard deviation of PRS increase was 1.26 (95%CI=1.20-1.32, P=9.69×10 −23 ) for overall lung cancer risk in the validation set. When considering absolute risks, individuals at different PRS deciles showed differential trajectories of 5-year and cumulative absolute risk. The age reaching the LDCT screening recommendation threshold can vary by 4 to 8 years, depending on the individual's genetic background, smoking status and family history. Collectively, these results suggest that individual's genetic background may inform the optimal lung cancer LDCT screening strategy.

High-Ambient Air Pollution Exposure Among Never Smokers Versus Ever Smokers With Lung Cancer

“…5,45 In addition, a recently published J o u r n a l P r e -p r o o f polygenic risk score validated in UK Biobank data found a significant association with lung cancer risk in never-smokers: OR per 1 SD = 1.28 (95% CI 1.13-1.46). 47 These findings show promise for the clinical utility of selected biomarkers, SNP panels and polygenic risk scores. However, they require external validation in different populations and if found to have reproducible, generalizable associations with lung cancer, their cost-effectiveness needs to be demonstrated, as these assays incur costs substantially greater than for administrating simple questionnaires.…”

Genetic analysis of lung cancer reveals novel susceptibility loci and germline impact on somatic mutation burden

“…It is also notable that when combining both sub-genome-wide PRS lists (smoking PRS and eQTL PRS) with GW significant results, the combined score for lung cancer is now an OR 1.304 per a SD increase in score. This is an improvement on previous PRS predictions (OR 1.17 and 1.26) (Hung et al, 2021;Kachuri et al, 2020), despite the conservative clumping approach (R2 < 0.1) employed. This suggests that integrating functional annotations may be of interest for PRS, particularly in rarer traits where assembling very large sample sizes may be problematic.…”

Gene-gene Interaction of AhR With and Within the Wnt Cascade Affects Susceptibility to Lung Cancer

“…Jia et al [43,44] build a PRS on 19 genome-wide associated SNPs (p < 0.5 10 − 8 ). Hung et al [45], integrated their PRS on 128 SNPs, including established LC-related loci and suggestive associated loci selected by LASSO-regression model, into the PLCO all2014 risk model. Both approaches have been validated using data from the UK Biobank.…”