Karunamuni, Roshan A. and Huynh-Le, Minh-Phuong and Fan, Chun C. and Thompson, Wesley and Eeles, Rosalind A. and Kote-Jarai, Zsofia and Muir, Kenneth and Lophatananon, Artitaya and Schleutker, Johanna and Pashayan, Nora and Batra, Jyotsna and Groenberg, Henrik and Walsh, Eleanor I. and Turner, Emma L. and Lane, Athene and Martin, Richard M. and Neal, David E. and Donovan, Jenny L. and Hamdy, Freddie C. and Nordestgaard, Borge G. and Tangen, Catherine M. and MacInnis, Robert J. and Wolk, Alicja and Albanes, Demetrius and Haiman, Christopher A. and Travis, Ruth C. and Stanford, Janet L. and Mucci, Lorelei A. and West, Catharine M. L. and Nielsen, Sune F. and Kibel, Adam S. and Wiklund, Fredrik and Cussenot, Olivier and Berndt, Sonja I. and Koutros, Stella and Sorensen, Karina Dalsgaard and Cybulski, Cezary and Grindedal, Eli Marie and Park, Jong Y. and Ingles, Sue A. and Maier, Christiane and Hamilton, Robert J. and Rosenstein, Barry S. and Vega, Ana and Kogevinas, Manolis and Penney, Kathryn L. and Teixeira, Manuel R. and Brenner, Hermann and John, Esther M. and Kaneva, Radka and Logothetis, Christopher J. and Neuhausen, Susan L. and Razack, Azad and Newcomb, Lisa F. and Gamulin, Marija and Usmani, Nawaid and Claessens, Frank and Gago-Dominguez, Manuela and Townsend, Paul A. and Roobol, Monique J. and Zheng, Wei and Mills, Ian G. and Andreassen, Ole A. and Dale, Anders M. and Seibert, Tyler M. and Collaborators, UKGPCS and Prosta, APCB BioResource Australian and Collab, IMPACT Study Steering Comm and Investigators, Canary PASS and Comm, Profile Study Steering and Consortium, PRACTICAL (2021) Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer. Prostate Cancer and Prostatic Diseases, 24 (2). pp. 532-541. ISSN 1365-7852, DOI https://doi.org/10.1038/s41391-020-00311-2.
Full text not available from this repository.Abstract
Background Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46). Materials and method 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy. Results 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer. Conclusions Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.
| Item Type: | Article |
|---|---|
| Funders: | United States National Institute of Health/National Institute of Biomedical Imaging and Bioengineering [Grant No: K08EB026503], University of California System [Grant No: C21CR2060], Research Council of Norway [Grant No: 223273], KG Jebsen Stiftelsen, South East Norway Health Authority |
| Uncontrolled Keywords: | Polygenic hazard scores (PHS); Prostate cancer |
| Subjects: | R Medicine R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
| Divisions: | Faculty of Medicine |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 03 Aug 2022 03:34 |
| Last Modified: | 03 Aug 2022 03:36 |
| URI: | http://eprints.um.edu.my/id/eprint/34698 |
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