Developing an optimal stratification model for colorectal cancer screening and reducing racial disparities in multi-center population-based studies

• Published in: Genome medicine 2024-06, Vol.16 (1), p.81-22, Article 81
• Main Authors: Tian, Jianbo, Zhang, Ming, Zhang, Fuwei, Gao, Kai, Lu, Zequn, Cai, Yimin, Chen, Can, Ning, Caibo, Li, Yanmin, Qian, Sangni, Bai, Hao, Liu, Yizhuo, Zhang, Heng, Chen, Shuoni, Li, Xiangpan, Wei, Yongchang, Li, Bin, Zhu, Ying, Yang, Jinhua, Jin, Mingjuan, Miao, Xiaoping, Chen, Kun
• Format: Article
• Language: English
• Subjects: Accuracy; Adenoma; Aged; Bayes Theorem; Bayesian analysis; Biobanks; Cancer; Cancer screening; Carcinoma; Clinical trials; Colonoscopy; Colorectal cancer; Colorectal carcinoma; Colorectal neoplasm; Colorectal Neoplasms - diagnosis; Colorectal Neoplasms - genetics; CRC early screening; Developing countries; Diagnosis; Early Detection of Cancer; Female; Genealogy; Genomes; Genomics; Genotype & phenotype; Health aspects; Humans; LDCs; Lifestyle factors; Lifestyles; Lung cancer; Male; Mathematical models; Medical screening; Middle Aged; Oncology, Experimental; Ovarian cancer; Performance evaluation; Polygenic risk score; Polymorphism, Single Nucleotide; Population; Population studies; Prediction models; Prevention; Principal components analysis; Prostate; Prostate cancer; Quality control; Racial differences; Risk Assessment; Risk Factors; Trans-ancestry; Tumors
• ISSN: 1756-994X; 1756-994X
• DOI: 10.1186/s13073-024-01355-y

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. 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 ); (2) SNPs selection from large-scale meta-analysis data by clumping and thresholding (PRS ); (3) PRS-CSx, a Bayesian approach for genome-wide risk prediction (PRS ). 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. 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 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 on incident colorectal neoplasms. Incorporating PRS 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). Our comprehensive risk-stratified model in population-based CRC screening trials represents a promising advancement in personalized risk assessment, facilitating tailored C