Journal Article

DKFZ-2021-01162

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png 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. ; UKGPCS collaborators (Collaboration Author) ; Schleutker, J. ; Pashayan, N. ; Batra, J. ; BioResource, A. (Collaboration Author) ; 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, C. 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. ; Committee, I. S. S. ; Collaborators (Collaboration Author) ; Kogevinas, M. ; Penney, K. L. ; Teixeira, M. R. ; Brenner, H.DKFZ* ; John, E. M. ; Kaneva, R. ; Logothetis, C. J. ; Neuhausen, S. L. ; Razack, A. ; Newcomb, L. F. ; Investigators, C. P. (Collaboration Author) ; Gamulin, M. ; Usmani, N. ; Claessens, F. ; Gago-Dominguez, M. ; Townsend, P. A. ; Roobol, M. J. ; Zheng, W. ; Committee, P. S. S. (Collaboration Author) ; Mills, I. G. ; Andreassen, O. A. ; Dale, A. M. ; Seibert, T. M. ; Consortium, P. (Collaboration Author)

2021
Stockton Press Basingstoke

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Please use a persistent id in citations: doi:10.1038/s41391-020-00311-2

Abstract: 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).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.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.Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

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Contributing Institute(s):

1. C070 Klinische Epidemiologie und Alternf. (C070)
2. Präventive Onkologie (C120)
3. DKTK HD zentral (HD01)

Research Program(s):

1. 313 - Krebsrisikofaktoren und Prävention (POF4-313) (POF4-313)

Appears in the scientific report 2021

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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