Polygenic risk scores for pan-cancer risk prediction in the Chinese population: A population-based cohort study based on the China Kadoorie Biobank

Journal article

Background: Polygenic risk scores (PRSs) have been extensively developed for cancer risk prediction in European populations, but their effectiveness in the Chinese population remains uncertain. Methods and findings: We constructed 80 PRSs for the 13 most common cancers using seven schemes and evaluated these PRSs in 100,219 participants from the China Kadoorie Biobank (CKB). The optimal PRSs with the highest discriminatory ability were used to define genetic risk, and their site-specific and cross-cancer associations were assessed. We modeled 10-year absolute risk trajectories for each cancer across risk strata defined by PRSs and modifiable risk scores and quantified the explained relative risk (ERR) of PRSs with modifiable risk factors for different cancers. More than 60% (50/80) of the PRSs demonstrated significant associations with the corresponding cancer outcomes. Optimal PRSs for nine common cancers were identified, with each standard deviation increase significantly associated with corresponding cancer risk (hazard ratios (HRs) ranging from 1.20 to 1.76). Compared with participants at low genetic risk and reduced modifiable risk scores, those with high genetic risk and elevated modifiable risk scores had the highest risk of incident cancer, with HRs ranging from 1.97 (95% confidence interval (CI): 1.11–3.48 for cervical cancer, P = 0.020) to 8.26 (95% CI: 1.92–35.46 for prostate cancer, P = 0.005). We observed nine significant cross-cancer associations for PRSs and found the integration of PRSs significantly increased the prediction accuracy for most cancers. The PRSs contributed 2.6%–20.3%, while modifiable risk factors explained 2.3%–16.7% of the ERR in the Chinese population. Conclusions: The integration of existing evidence has facilitated the development of PRSs associated with nine common cancer risks in the Chinese population, potentially improving clinical risk assessment.

Authors: Zhu, M; Zhu, X; Han, Y; Ma, Z; Ji, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS Medicine
• Volume: 22
• Issue: 2
• Article number: e1004534
• Publication date: 2025-02-28
• Acceptance date: 2025-01-13
• DOI: 10.1371/journal.pmed.1004534
• EISSN: 1549-1676
• ISSN: 1549-1277
• Language: English