Naslov
Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer

Autori
Karunamuni, Roshan A. ; Huynh-Le, Minh-Phuong ; Fan, Chun C. ; Thompson, Wesley ; Eeles, Rosalind A. ; Kote-Jarai, Zsofia ; Muir, Kenneth ; Lophatananon, Artitaya ; Schleutker, Johanna ; Pashayan, Nora ; Batra, Jyotsna ; Grönberg, Henrik ; Walsh, Eleanor I. ; Turner, Emma L. ; Lane, Athene ; Martin, Richard M. ; Neal, David E. ; Donovan, Jenny L. ; Hamdy, Freddie C. ; Nordestgaard, Børge G. ; Tangen, Catherine M. ; MacInnis, Robert J. ; Wolk, Alicja ; Albanes, Demetrius ; Haiman, Christopher A. ; Travis, Ruth C. ; Stanford, Janet L. ; Mucci, Lorelei A. ; West, Catharine M. L. ; Nielsen, Sune F. ; Kibel, Adam S. ; Wiklund, Fredrik ; Cussenot, Olivier ; Berndt, Sonja I. ; Koutros, Stella ; Sørensen, Karina Dalsgaard ; Cybulski, Cezary ; Grindedal, Eli Marie ; Park, Jong Y. ; Ingles, Sue A. ; Maier, Christiane ; Hamilton, Robert J. ; Rosenstein, Barry S. ; Vega, Ana ; Kogevinas, Manolis ; Penney, Kathryn L. ; Teixeira, Manuel R. ; Brenner, Hermann ; John, Esther M. ; Kaneva, Radka ; Logothetis, Christopher J. ; Neuhausen, Susan L. ; Razack, Azad ; Newcomb, Lisa F. ; Gamulin, Marija ; Usmani, Nawaid ; Claessens, Frank ; Gago-Dominguez, Manuela ; Townsend, Paul A. ; Roobol, Monique J. ; Zheng, Wei ; Mills, Ian G. ; Andreassen, Ole A. ; Dale, Anders M. ; Seibert, Tyler M.

Kolaboracija
UKGPCS collaborators ; APCB BioResource (Australian Prostate Cancer BioResource) ; The IMPACT Study Steering Committee and Collaborators ; Canary PASS Investigators ; The Profile Study Steering Committee ; The PRACTICAL Consortium

Izvornik
Prostate cancer and prostatic diseases (1365-7852) 24 (2021), 2; 532-541

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
prostate cancer ; SNPs (PHS166 vs PHS46)

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Kliničke medicinske znanosti

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