Multiple polygenic score approach in colorectal cancer risk prediction

. 2025 Oct 30 ; 15 (1) : 38006. [epub] 20251030

Jazyk angličtina Země Anglie, Velká Británie Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid41168411

Grantová podpora
U01HG008657 NHGRI NIH HHS - United States
U01 CA164973, R01 CA126895, R01 CA060987, R01 CA072520, U24 CA074806 NCI NIH HHS - United States
U01 CA137088 NCI NIH HHS - United States

Odkazy

PubMed 41168411
PubMed Central PMC12575652
DOI 10.1038/s41598-025-21956-w
PII: 10.1038/s41598-025-21956-w
Knihovny.cz E-zdroje

Recent studies have demonstrated that for various diseases, incorporating polygenic risk scores (PRSs) for other traits and diseases into the PRS-based risk prediction model may improve predictive performance - known as Multiple Polygenic Score (MPS) approach. We aimed to examine whether the MPS approach improves colorectal cancer (CRC) risk prediction. We included 2,187 non-CRC PRSs from the polygenic Score (PGS) Catalog and used machine learning (ML) models to select the most predictive non-CRC PRSs, utilizing individual-level data from 31,257 CRC cases and 33,408 controls. An independent dataset from the Genetic Epidemiology Research in Adult Health and Aging (GERA) cohort (4,852 cases and 67,939 controls) was randomly split into subsets for model estimation and validation. The model combined MPS with two existing CRC-PRSs based on known loci and genome-wide genotyping. We then assessed model performance by calculating the area under the receiver operating curve (AUC) in the validation set and performed 1,000 bootstrapped iterations to evaluate AUC improvements. The ML model selected 337 non-CRC PRSs predictive of CRC risk. Adding MPS to the CRC-PRSs significantly improved AUC by 0.017 (95% CI: 0.011-0.022, p < 0.0001) when combined with known-loci CRC-PRS, 0.005 (95% CI: 0.002-0.007, p = 0.0005) with genome-wide CRC-PRS, and 0.004 (95% CI: 0.002-0.006, p = 0.0005) with both the known loci and genome-wide CRC-PRSs. These findings demonstrate MPS's potential to refine CRC risk prediction models and highlight opportunities for further advancements in risk prediction.

Broad Institute of Harvard and MIT Cambridge MA USA

Cancer Epidemiology and Prevention Research Unit School of Public Health Imperial College London London UK

Center for Cancer Research Medical University of Vienna Vienna Austria

Center for Gastrointestinal Biology and Disease University of North Carolina Chapel Hill NC USA

Center for Genetic Epidemiology Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles CA USA

Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health The University of Melbourne Melbourne VIC Australia

Channing Division of Network Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA

Clinical and Translational Epidemiology Unit Massachusetts General Hospital Harvard Medical School Boston MA USA

Colorado Center for Personalized Medicine University of Colorado Anschutz Medical Campus Aurora CO USA

Consortium for Biomedical Research in Epidemiology and Public Health Madrid 28029 Spain

Department of Biostatistics University of Washington Seattle WA USA

Department of Clinical Sciences Faculty of Medicine and health Sciences Universitat de Barcelona Institute of Complex Systems L'Hospitalet de Llobregat Barcelona 08908 Spain

Department of Diagnostics and Intervention Oncology Unit Umeå University Umeå Sweden

Department of Epidemiology Harvard T H Chan School of Public Health Harvard University Boston MA USA

Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA

Department of Epidemiology University of North Carolina at Chapel Hill Chapel Hill NC USA

Department of Epidemiology University of Washington Seattle WA USA

Department of Gastroenterology Kaiser Permanente San Francisco Medical Center San Francisco CA USA

Department of Genome Sciences University of Washington School of Medicine Seattle WA USA

Department of Health Systems Science Kaiser Permanente Bernard J Tyson School of Medicine Pasadena CA USA

Department of Immunology and Infectious Diseases Harvard T H Chan School of Public Health Harvard University Boston MA USA

Department of Medicine Division of Medical Genetics University of Washington Seattle WA USA

Department of Molecular Biology of Cancer Institute of Experimental Medicine of the Czech Academy of Sciences Prague Czech Republic

Department of Population Science American Cancer Society Atlanta Georgia

Department of Public Health Erasmus MC University Medical Center Rotterdam The Netherlands

Departments of Medicine and Epidemiology University of Pittsburgh Medical Center Pittsburgh PA USA

Discipline of Genetics Memorial University of Newfoundland St John's Canada

Division of Cancer Epidemiology and Genetics National Cancer Institute National Institutes of Health Bethesda MD USA

Division of Clinical Epidemiology and Aging Research German Cancer Research Center Heidelberg Germany

Division of Gastroenterology Massachusetts General Hospital Harvard Medical School Boston MA USA

Division of Gastroenterology University of California San Francisco San Francisco CA USA

Division of Human Genetics Department of Internal Medicine The Ohio State University Comprehensive Cancer Center Columbus OH USA

Division of Human Nutrition and Health Wageningen University and Research Wageningen The Netherlands

Gastroenterology Department Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas University of Barcelona Barcelona Spain

German Cancer Consortium Heidelberg Germany

Institute of Biology and Medical Genetics 1st Faculty of Medicine Charles University Prague Czech Republic

Institute of Science Tokyo Tokyo Japan

Intermountain Health Salt Lake City UT USA

Kaiser Permanente Division of Research Oakland CA USA

Leeds Institute of Cancer and Pathology University of Leeds Leeds UK

Nutrition and Metabolism Branch International Agency for Research on Cancer World Health Organization Lyon France

ONCOBELL Program Bellvitge Biomedical Research Institute L'Hospitalet de Llobregat Barcelona 08908 Spain

Oncology Data Analytics Program L'Hospitalet del Llobregat Barcelona 08908 Spain

Princess Margaret Cancer Centre University Health Network Toronto Canada

Program in MPE Molecular Pathological Epidemiology Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA USA

Public Health Sciences Division Fred Hutchinson Cancer Center Seattle WA USA

School of Public Health University of Washington Seattle WA USA

Service de Génétique médicale Nantes Université CHU de Nantes Nantes F 44000 France

Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins Baltimore MD USA

Slone Epidemiology Center at Boston University Boston MA USA

University of Hawaii Cancer Center Honolulu HI USA

University of Washington Seattle WA USA

Wallenberg Centre for Molecular Medicine Umeå University Umeå Sweden

Zobrazit více v PubMed

Siegel, R. L., Wagle, N. S., Cercek, A., Smith, R. A. & Jemal, A. Colorectal cancer statistics, 2023. PubMed

Siegel, R. L., Giaquinto, A. N. & Jemal, A. Cancer statistics, 2024. PubMed

Usher-Smith, J. A., Walter, F. M., Emery, J. D., Win, A. K. & Griffin, S. J. Risk prediction models for colorectal cancer: A systematic review. PubMed PMC

Peng, L., Balavarca, Y., Weigl, K., Hoffmeister, M. & Brenner, H. Head-to-Head comparison of the performance of 17 risk models for predicting presence of advanced neoplasms in colorectal cancer screening. PubMed PMC

McGeoch, L. et al. Risk prediction models for colorectal cancer incorporating common genetic variants: A systematic review. PubMed PMC

Thomas, M. et al. Genome-wide modeling of polygenic risk score in colorectal cancer risk. PubMed PMC

Thomas, M. et al. Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across Racial and ethnic populations. PubMed PMC

Choi, S. W., Mak, T. S. H. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. PubMed PMC

Briggs, S. E. W. et al. Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis using UK biobank data: population based cohort study. PubMed PMC

Huyghe, J. R. et al. Discovery of common and rare genetic risk variants for colorectal cancer. PubMed PMC

Privé, F., Arbel, J. & Vilhjálmsson, B. J. LDpred2: better, faster, stronger. PubMed PMC

Elgart, M. et al. Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations. PubMed PMC

Sawicki, T. et al. A review of colorectal cancer in terms of epidemiology, risk factors, development, symptoms and diagnosis. PubMed PMC

Ge, T. et al. Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations. PubMed PMC

Hahn, S. J., Kim, S., Choi, Y. S., Lee, J. & Kang, J. Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: A machine learning analysis of population-based 10-year prospective cohort study. PubMed PMC

Lambert, S. A. et al. The polygenic score catalog as an open database for reproducibility and systematic evaluation. PubMed PMC

Krapohl, E. et al. Multi-polygenic score approach to trait prediction. PubMed PMC

Sinnott-Armstrong, N. et al. Genetics of 35 blood and urine biomarkers in the UK biobank. PubMed PMC

Truong, B. et al. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. PubMed PMC

Neumann, A. et al. Combined polygenic risk scores of different psychiatric traits predict general and specific psychopathology in childhood. PubMed PMC

Pepe, M. S. & Cai, T. The analysis of placement values for evaluating discriminatory measures. PubMed

Fernandez-Rozadilla, C. et al. Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and East Asian ancestries. PubMed PMC

Peters, U., Bien, S. & Zubair, N. Genetic architecture of colorectal cancer. PubMed PMC

Argillander, T. E. et al. Features of incident colorectal cancer in Lynch syndrome. PubMed PMC

Gordon, A. S. et al. Rates of actionable genetic findings in individuals with colorectal cancer or polyps ascertained from a community medical setting. PubMed PMC

Su, Y. R. et al. Validation of a Genetic-Enhanced risk prediction model for colorectal cancer in a large Community-Based cohort. PubMed PMC

Keum, N. & Giovannucci, E. Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies. PubMed

Márquez-Luna, C. et al. Incorporating functional priors improves polygenic prediction accuracy in UK biobank and 23andMe data sets. PubMed PMC

Zhuang, Y., Kim, N. Y., Fritsche, L. G., Mukherjee, B. & Lee, S. Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction. PubMed PMC

Zheng, Z. et al. Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries. PubMed PMC

Bien, S. A. & Peters, U. Moving from one to many: insights from the growing list of pleiotropic cancer risk genes. PubMed PMC

Cheng, I. et al. Pleiotropic effects of genetic risk variants for other cancers on colorectal cancer risk: PAGE, GECCO and CCFR consortia. PubMed PMC

Sun, J. et al. Cross-cancer pleiotropic analysis identifies three novel genetic risk loci for colorectal cancer. PubMed PMC

Pardo-Cea, M. A. et al. Biological basis of extensive Pleiotropy between blood traits and cancer risk. PubMed PMC

Kim, H., Grueneberg, A., Vazquez, A. I. & Hsu, S. De Los Campos, G. Will big data close the missing heritability gap? PubMed PMC

Ge, T., Chen, C. Y., Ni, Y., Feng, Y. C. A. & Smoller, J. W. Polygenic prediction via bayesian regression and continuous shrinkage priors. PubMed PMC

Baker, E. & Escott-Price, V. Polygenic risk scores in alzheimer’s disease: current applications and future directions. PubMed PMC

Le Borgne, F. et al. Standardized and weighted time-dependent receiver operating characteristic curves to evaluate the intrinsic prognostic capacities of a marker by taking into account confounding factors. PubMed

Klau, J. H. et al. AI-based multi-PRS models outperform classical single-PRS models. PubMed PMC

Albiñana, C. et al. Multi-PGS enhances polygenic prediction by combining 937 polygenic scores. PubMed PMC

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