TY - JOUR
T1 - Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer
AU - Karunamuni, Roshan A.
AU - Huynh-Le, Minh Phuong
AU - Fan, Chun C.
AU - Thompson, Wesley
AU - Eeles, Rosalind A.
AU - Kote-Jarai, Zsofia
AU - Muir, Kenneth
AU - Lophatananon, Artitaya
AU - Schleutker, Johanna
AU - Pashayan, Nora
AU - Batra, Jyotsna
AU - Grönberg, Henrik
AU - Walsh, Eleanor I.
AU - Turner, Emma L.
AU - Lane, Athene
AU - Martin, Richard M.
AU - Neal, David E.
AU - Donovan, Jenny L.
AU - Hamdy, Freddie C.
AU - Nordestgaard, Børge G.
AU - Tangen, Catherine M.
AU - MacInnis, Robert J.
AU - Wolk, Alicja
AU - Albanes, Demetrius
AU - Haiman, Christopher A.
AU - Travis, Ruth C.
AU - Stanford, Janet L.
AU - Mucci, Lorelei A.
AU - West, Catharine M.L.
AU - Nielsen, Sune F.
AU - Kibel, Adam S.
AU - Wiklund, Fredrik
AU - Cussenot, Olivier
AU - Berndt, Sonja I.
AU - Koutros, Stella
AU - Sørensen, Karina Dalsgaard
AU - Cybulski, Cezary
AU - Grindedal, Eli Marie
AU - Park, Jong Y.
AU - Ingles, Sue A.
AU - Maier, Christiane
AU - Hamilton, Robert J.
AU - Rosenstein, Barry S.
AU - Vega, Ana
AU - Kogevinas, Manolis
AU - Penney, Kathryn L.
AU - Teixeira, Manuel R.
AU - Brenner, Hermann
AU - John, Esther M.
AU - Kaneva, Radka
AU - UKGPCS Collaborators
AU - APCB BioResource (Australian Prostate Cancer BioResource)
AU - IMPACT Study Steering Committee and Collaborators
AU - Canary PASS Investigators
AU - Profile Study Steering Committee
AU - PRACTICAL consortium
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
U2 - 10.1038/s41391-020-00311-2
DO - 10.1038/s41391-020-00311-2
M3 - Journal article
C2 - 33420416
AN - SCOPUS:85102762943
VL - 24
SP - 532
EP - 541
JO - Prostate Cancer and Prostatic Diseases
JF - Prostate Cancer and Prostatic Diseases
SN - 1365-7852
IS - 2
ER -