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
Department of Epidemiology and Biostatistics, School of Public Health, Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China.
Research Center of Public Health, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China.
Genome Med. 2024 Jun 13;16(1):81. 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 CRC screening in the EAS population. This approach enhances the transferability of PRSs across ancestries and thereby helps address health disparity.
早期发现结直肠肿瘤可通过对高危个体进行及时干预来减轻结直肠癌（CRC）负担。然而，在东亚（EAS）人群中，缺乏用于个性化CRC早期筛查的有效风险预测模型。我们旨在开发、验证和优化一个涵盖EAS人群动态腺瘤 - 癌序列所有阶段的综合风险预测模型。
为了制定精准的风险分层和干预策略，我们开发了三种针对结直肠肿瘤的跨祖先多基因风险评分（PRS）：（1）使用148个先前确定的CRC风险位点（PRS）；（2）通过聚类和阈值法从大规模荟萃分析数据中选择单核苷酸多态性（SNPs）（PRS）；（3）PRS - CSx，一种用于全基因组风险预测的贝叶斯方法（PRS）。然后，在两个独立的横断面筛查组中评估并验证每个PRS的性能，这两个筛查组包括来自ZJCRC（浙江结直肠癌组；EAS）和PLCO（前列腺、肺、结肠和卵巢癌筛查试验；欧洲，EUR）研究的4600例晚期结直肠肿瘤患者、4495例非晚期腺瘤患者和21199例正常个体。将最优的PRS进一步与生活方式因素相结合以分层个体风险，并最终在PLCO和英国生物银行前瞻性队列中进行测试，共有350013名参与者。
与EUR人群相比，三种跨祖先PRS在EAS人群中实现了适度改善的预测性能。值得注意的是，这些PRS有效地促进了对动态腺瘤 - 癌序列所有阶段的全面风险评估。在这些模型中，PRS在EAS和EUR验证数据集中均表现出最佳的鉴别能力，特别是对于有结直肠肿瘤风险的个体。使用两个大规模且独立的前瞻性队列，我们进一步证实了PRS对结直肠肿瘤发病的显著剂量反应效应。与单独使用PRS或生活方式因素相比，将PRS与生活方式因素纳入综合策略可改善风险分层和鉴别准确性。这种综合风险分层模型在解决筛查试验中的漏诊问题方面显示出潜力（最佳阴性预测值 = 0.93），同时适度减少不必要的筛查（最佳阳性预测值 = 0.32）。
我们在基于人群的CRC筛查试验中的综合风险分层模型代表了个性化风险评估方面的一项有前景的进展，有助于在EAS人群中进行针对性的CRC筛查。这种方法增强了PRS在不同祖先群体之间的可转移性，从而有助于解决健康差异问题。
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