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Abstract Background Early detection of colorectal neoplasms can reduce the colorectal cancer (CRC) burden by timely intervention for high-risk individuals. However, effective risk prediction models are lacking for personalized CRC early screening in East Asian (EAS) population. We aimed to develop, validate, and optimize a comprehensive risk prediction model across all stages of the dynamic adenoma-carcinoma sequence in EAS population. Methods To develop precision risk-stratification and intervention strategies, we developed three trans-ancestry PRSs targeting colorectal neoplasms: (1) using 148 previously identified CRC risk loci (PRS 148 ); (2) SNPs selection from large-scale meta-analysis data by clumping and thresholding (PRS 183 ); (3) PRS-CSx, a Bayesian approach for genome-wide risk prediction (PRS Genomewide ). Then, the performance of each PRS was assessed and validated in two independent cross-sectional screening sets, including 4600 patients with advanced colorectal neoplasm, 4495 patients with non-advanced adenoma, and 21,199 normal individuals from the ZJCRC (Zhejiang colorectal cancer set; EAS) and PLCO (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; European, EUR) studies. The optimal PRS was further incorporated with lifestyle factors to stratify individual risk and ultimately tested in the PLCO and UK Biobank prospective cohorts, totaling 350,013 participants. Results Three trans-ancestry PRSs achieved moderately improved predictive performance in EAS compared to EUR populations. Remarkably, the PRSs effectively facilitated a thorough risk assessment across all stages of the dynamic adenoma-carcinoma sequence. Among these models, PRS 183 demonstrated the optimal discriminatory ability in both EAS and EUR validation datasets, particularly for individuals at risk of colorectal neoplasms. Using two large-scale and independent prospective cohorts, we further confirmed a significant dose–response effect of PRS 183 on incident colorectal neoplasms. Incorporating PRS 183 with lifestyle factors into a comprehensive strategy improves risk stratification and discriminatory accuracy compared to using PRS or lifestyle factors separately. This comprehensive risk-stratified model shows potential in addressing missed diagnoses in screening tests (best NPV = 0.93), while moderately reducing unnecessary screening (best PPV = 0.32). Conclusions Our comprehensive risk-stratified model in population-based CRC screening trials represents a promising advancement in personalized risk assessment, facilitating tailored CRC screening in the EAS population. This approach enhances the transferability of PRSs across ancestries and thereby helps address health disparity. Graphical Abstract
Tian et al. (Thu,) studied this question.