Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer
: 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.; collaborators UKGPCS; APCB BioResource (Australian Prostate Cancer BioResource); The IMPACT Study Steering Committee and Collaborators; Canary PASS Investigators; The Profile Study Steering Committee; The PRACTICAL Consortium
Publisher: SPRINGERNATURE
: 2021
: Prostate Cancer and Prostatic Diseases
: PROSTATE CANCER AND PROSTATIC DISEASES
: PROSTATE CANCER P D
: 24
: 2
: 532
: 541
: 10
: 1365-7852
: 1476-5608
DOI: https://doi.org/10.1038/s41391-020-00311-2
: https://pureadmin.qub.ac.uk/ws/files/227954868/Karunamuni_et_al_2021_AAM.pdf
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.
