Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer
Karunamuni, R. A. Huynh-Le, M. P. Fan, C. C. Thompson, W. Eeles, R. A. Kote-Jarai, Z. Muir, K. Lophatananon, A. Schleutker, J. Pashayan, N.
Karunamuni, R. A.
Huynh-Le, M. P.
Fan, C. C.
Thompson, W.
Eeles, R. A.
Kote-Jarai, Z.
Muir, K.
Lophatananon, A.
Schleutker, J.
Pashayan, N.
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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.
Authors
Karunamuni, R. A.
Huynh-Le, M. P.
Fan, C. C.
Thompson, W.
Eeles, R. A.
Kote-Jarai, Z.
Muir, K.
Lophatananon, A.
Schleutker, J.
Pashayan, N.
Batra, J.
Grönberg, H.
Walsh, E. I.
Turner, E. L.
Lane, A.
Martin, R. M.
Neal, D. E.
Donovan, J. L.
Hamdy, F. C.
Nordestgaard, B. G.
Tangen, C. M.
MacInnis, R. J.
Wolk, A.
Albanes, D.
Haiman, C. A.
Travis, R. C.
Stanford, J. L.
Mucci, L. A.
West, Catharine M L
Nielsen, S. F.
Kibel, A. S.
Wiklund, F.
Cussenot, O.
Berndt, S. I.
Koutros, S.
Sørensen, K. D.
Cybulski, C.
Grindedal, E. M.
Park, J. Y.
Ingles, S. A.
Maier, C.
Hamilton, R. J.
Rosenstein, B. S.
Vega, A.
Kogevinas, M.
Penney, K. L.
Teixeira, M. R.
Brenner, H.
John, E. M.
Kaneva, R.
Logothetis, C. J.
Neuhausen, S. L.
Razack, A.
Newcomb, L. F.
Gamulin, M.
Usmani, N.
Claessens, F.
Gago-Dominguez, M.
Townsend, P. A.
Roobol, M. J.
Zheng, W.
Mills, I. G.
Andreassen, O. A.
Dale, A. M.
Seibert, T. M.
Huynh-Le, M. P.
Fan, C. C.
Thompson, W.
Eeles, R. A.
Kote-Jarai, Z.
Muir, K.
Lophatananon, A.
Schleutker, J.
Pashayan, N.
Batra, J.
Grönberg, H.
Walsh, E. I.
Turner, E. L.
Lane, A.
Martin, R. M.
Neal, D. E.
Donovan, J. L.
Hamdy, F. C.
Nordestgaard, B. G.
Tangen, C. M.
MacInnis, R. J.
Wolk, A.
Albanes, D.
Haiman, C. A.
Travis, R. C.
Stanford, J. L.
Mucci, L. A.
West, Catharine M L
Nielsen, S. F.
Kibel, A. S.
Wiklund, F.
Cussenot, O.
Berndt, S. I.
Koutros, S.
Sørensen, K. D.
Cybulski, C.
Grindedal, E. M.
Park, J. Y.
Ingles, S. A.
Maier, C.
Hamilton, R. J.
Rosenstein, B. S.
Vega, A.
Kogevinas, M.
Penney, K. L.
Teixeira, M. R.
Brenner, H.
John, E. M.
Kaneva, R.
Logothetis, C. J.
Neuhausen, S. L.
Razack, A.
Newcomb, L. F.
Gamulin, M.
Usmani, N.
Claessens, F.
Gago-Dominguez, M.
Townsend, P. A.
Roobol, M. J.
Zheng, W.
Mills, I. G.
Andreassen, O. A.
Dale, A. M.
Seibert, T. M.
Description
Date
2021
Publisher
Collections
Keywords
Type
Article
Citation
Karunamuni RA, Huynh-Le MP, Fan CC, Thompson W, Eeles RA, Kote-Jarai Z, et al. Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer. Prostate Cancer Prostatic Dis. 2021.