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

被引：0

作者：

Zhu, Meng ^{[1
,2
,3
,4
]}

Zhu, Xia ^{[1
]}

Han, Yuting ^{[5
]}

Ma, Zhimin ^{[1
]}

Ji, Chen ^{[1
]}

Wang, Tianpei ^{[1
]}

Yan, Caiwang ^{[1
,2
,3
,4
]}

Song, Ci ^{[1
,2
,3
,4
]}

Yu, Canqing ^{[5
,6
,7
]}

Sun, Dianjianyi ^{[5
,6
,7
]}

Jiang, Yue ^{[1
,2
,3
]}

Chen, Jiaping ^{[1
,2
,3
]}

Yang, Ling ^{[8
,9
,10
]}

Chen, Yiping ^{[8
,9
,10
]}

Du, Huaidong ^{[8
,9
,10
]}

Walters, Robin ^{[8
,9
,10
]}

Millwood, Iona Y. ^{[8
,9
,10
]}

Dai, Juncheng ^{[1
,2
,3
,11
]}

Ma, Hongxia ^{[1
,2
,3
,11
]}

Zhang, Zhengdong ^{[2
,3
,10
]}

Chen, Zhengming ^{[9
]}

Hu, Zhibin ^{[1
,2
,3
]}

Lv, Jun ^{[5
,6
,7
,12
]}

Jin, Guangfu ^{[1
,2
,3
,4
]}

Li, Liming ^{[5
,6
,7
]}

Shen, Hongbing ^{[1
,2
,3
]}

机构：

[1] Nanjing Med Univ, Ctr Global Hlth, Sch Publ Hlth, Dept Epidemiol, Nanjing, Peoples R China

[2] Nanjing Med Univ, Collaborat Innovat Ctr Canc Med, Jiangsu Key Lab Canc Biomarkers Prevent & Treatme, Nanjing, Peoples R China

[3] Nanjing Med Univ, China Int Cooperat Ctr Environm & Human Hlth, Nanjing, Peoples R China

[4] Nanjing Med Univ, Affiliated Wuxi Ctr Dis Control & Prevent, Wuxi Ctr Dis Control & Prevent, Wuxi Med Ctr,Dept Chron Noncommunicable Dis Contr, Wuxi, Peoples R China

[5] Peking Univ, Sch Publ Hlth, Dept Epidemiol & Biostat, Beijing, Peoples R China

[6] Peking Univ, Ctr Publ Hlth & Epidem Preparedness & Response, Beijing, Peoples R China

[7] Peking Univ, Key Lab Epidemiol Major Dis, Minist Educ, Beijing, Peoples R China

[8] Univ Oxford, Med Res Council, Populat Hlth Res Unit, Oxford, England

[9] Univ Oxford, Nuffield Dept Populat Hlth, Clin Trial Serv Unit, Oxford, England

[10] Univ Oxford, Nuffield Dept Populat Hlth, Epidemiol Studies Unit CTSU, Oxford, England

[11] Nanjing Med Univ, Genom Sci & Precis Med Inst, Gusu Sch, Nanjing, Peoples R China

[12] Peking Univ, State Key Lab Vasc Homeostasis & Remodeling, Beijing, Peoples R China

来源：

PLOS MEDICINE | 2025年 / 22卷 / 02期

基金：

中国国家自然科学基金;

关键词：

GENOME-WIDE ASSOCIATION; HERITABILITY; TWINS;

D O I：

10.1371/journal.pmed.1004534

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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.

引用

页数：22

共 35 条

[1] Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Et al., Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J Clin, 71, 3, pp. 209-249, (2021)
[2] Sud A, Kinnersley B, Houlston RS., Genome-wide association studies of cancer: current insights and future perspectives, Nat Rev Cancer, 17, 11, pp. 692-704, (2017)
[3] Popejoy AB, Fullerton SM., Genomics is failing on diversity, Nature, 538, 7624, pp. 161-164, (2016)
[4] Zhu M, Wang T, Huang Y, Zhao X, Ding Y, Zhu M, Et al., Genetic risk for overall cancer and the benefit of adherence to a healthy lifestyle, Cancer Res, 81, 17, pp. 4618-4627, (2021)
[5] Kachuri L, Graff RE, Smith-Byrne K, Meyers TJ, Rashkin SR, Ziv E, Et al., Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction, Nat Commun, 11, 1, (2020)
[6] Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, Et al., Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations, Nat Genet, 50, 9, pp. 1219-1224, (2018)
[7] Lambert SA, Gil L, Jupp S, Ritchie SC, Xu Y, Buniello A, Et al., The polygenic score catalog as an open database for reproducibility and systematic evaluation, Nat Genet, 53, 4, pp. 420-425, (2021)
[8] Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ., Clinical use of current polygenic risk scores may exacerbate health disparities, Nat Genet, 51, 4, pp. 584-591, (2019)
[9] Duncan L, Shen H, Gelaye B, Meijsen J, Ressler K, Feldman M, Et al., Analysis of polygenic risk score usage and performance in diverse human populations, Nat Commun, 10, 1, (2019)
[10] Ruan Y, Lin Y-F, Feng Y-CA, Chen C-Y, Lam M, Guo Z, Et al., Improving polygenic prediction in ancestrally diverse populations, Nat Genet, 54, 5, pp. 573-580, (2022)

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