| Title: |
Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer. |
| Authors: |
Karunamuni, RA; Huynh-Le, M-P; Fan, CC; Thompson, W; Eeles, RA; Kote-Jarai, Z; Muir, K; Lophatananon, A; UKGPCS collaborators; Schleutker, J; Pashayan, N; Batra, J; APCB BioResource (Australian Prostate Cancer BioResource); Grönberg, H; Walsh, EI; Turner, EL; Lane, A; Martin, RM; Neal, DE; Donovan, JL; Hamdy, FC; Nordestgaard, BG; Tangen, CM; MacInnis, RJ; Wolk, A; Albanes, D; Haiman, CA; Travis, RC; Stanford, JL; Mucci, LA; West, CML; Nielsen, SF; Kibel, AS; Wiklund, F; Cussenot, O; Berndt, SI; Koutros, S; Sørensen, KD; Cybulski, C; Grindedal, EM; Park, JY; Ingles, SA; Maier, C; Hamilton, RJ; Rosenstein, BS; Vega, A; IMPACT Study Steering Committee and Collaborators; Kogevinas, M; Penney, KL; Teixeira, MR; Brenner, H; John, EM; Kaneva, R; Logothetis, CJ; Neuhausen, SL; Razack, A; Newcomb, LF; Canary PASS Investigators; Gamulin, M; Usmani, N; Claessens, F; Gago-Dominguez, M; Townsend, PA; Roobol, MJ; Zheng, W; Profile Study Steering Committee; Mills, IG; Andreassen, OA; Dale, AM; Seibert, TM; PRACTICAL Consortium |
| Contributors: |
Eeles, Rosalind; Kote-Jarai, Zsofia |
| Publisher Information: |
SPRINGERNATURE |
| Publication Year: |
2022 |
| Collection: |
The Institute of Cancer Research (ICR): Publications Repository |
| Subject Terms: |
UKGPCS collaborators; APCB BioResource (Australian Prostate Cancer BioResource); IMPACT Study Steering Committee and Collaborators; Canary PASS Investigators; Profile Study Steering Committee; PRACTICAL Consortium |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
Print-Electronic; application/pdf |
| Language: |
English |
| ISSN: |
1476-5608; 1365-7852 |
| Relation: |
Prostate cancer and prostatic diseases, 2021; https://repository.icr.ac.uk/handle/internal/4968 |
| Availability: |
https://repository.icr.ac.uk/handle/internal/4968 |
| Rights: |
http://www.rioxx.net/licenses/all-rights-reserved |
| Accession Number: |
edsbas.902AA4EE |
| Database: |
BASE |