Evaluation of diverse polygenic risk score models for lung cancer in a small-scale Chinese cohort.

INTRODUCTION

Lung cancer is a leading cause of cancer-related mortality globally, with distinct epidemiological and genetic patterns in East Asian populations. However, most polygenic risk score (PRS) models have been developed using European-ancestry cohorts, raising concerns about their applicability in non-European populations.

MATERIALS AND METHODS

In this study, we systematically evaluated the predictive performance of three PRS approaches in a Chinese lung cancer cohort consisting of 97 cases and 667 controls. We assessed (i) a previously reported 19-SNP PRS developed in Chinese individuals, (ii) genome-wide PRS derived using PRS-CS with East Asian and European GWAS summary statistics, and (iii) PRS-CSx, a cross-population Bayesian framework that integrates summary statistics across ancestries.

RESULTS

The 19-SNP PRS demonstrated limited discriminative power in our cohort. In contrast, PRS-CS using East Asian summary statistics showed significant associations with overall lung cancer and specific histological subtypes, particularly NSCLC and LUAD. PRS-CS based on European data yielded weaker performance, underscoring the importance of ancestry matching. Notably, PRS-CSx outperformed single-ancestry models, achieving improved risk stratification for NSCLC and LUAD. However, its predictive performance for LUSC and SCLC remained limited, likely due to sample size constraints and subtype heterogeneity.

CONCLUSION

Our findings emphasize the critical role of ancestry-matched data and integrative PRS approaches in enhancing risk prediction in underrepresented populations. PRS-CSx represents a promising tool for lung cancer risk assessment in East Asians, though further validation in larger cohorts are needed to improve generalizability and clinical utility.