Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score.
Journal
Prostate cancer and prostatic diseases
ISSN: 1476-5608
Titre abrégé: Prostate Cancer Prostatic Dis
Pays: England
ID NLM: 9815755
Informations de publication
Date de publication:
04 2022
04 2022
Historique:
received:
21
09
2021
accepted:
12
01
2022
pubmed:
14
2
2022
medline:
1
12
2022
entrez:
13
2
2022
Statut:
ppublish
Résumé
Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets. In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured. The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively. We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.
Sections du résumé
BACKGROUND
Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.
METHODS
In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.
RESULTS
The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.
CONCLUSIONS
We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.
Identifiants
pubmed: 35152271
doi: 10.1038/s41391-022-00497-7
pii: 10.1038/s41391-022-00497-7
pmc: PMC9372232
mid: NIHMS1771503
doi:
Substances chimiques
Prostate-Specific Antigen
EC 3.4.21.77
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
755-761Subventions
Organisme : NIBIB NIH HHS
ID : K08 EB026503
Pays : United States
Organisme : NCI NIH HHS
ID : UG1 CA189974
Pays : United States
Organisme : NCI NIH HHS
ID : UM1 CA182883
Pays : United States
Informations de copyright
© 2022. The Author(s).
Références
Seibert TM, Fan CC, Wang Y, Zuber V, Karunamuni R, Parsons JK, et al. Polygenic hazard score to guide screening for aggressive prostate cancer: Development and validation in large scale cohorts. BMJ. 2018;360:1–7.
Huynh-Le M-P, Fan CC, Karunamuni R, Thompson WK, Martinez ME, Eeles RA, et al. Polygenic hazard score is associated with prostate cancer in multi-ethnic populations. Nat Commun. 2021;12:1236.
doi: 10.1038/s41467-021-21287-0
Callender T, Emberton M, Morris S, Pharoah PDP, Pashayan N. Benefit, Harm, and Cost-effectiveness Associated with Magnetic Resonance Imaging before Biopsy in Age-based and Risk-stratified Screening for Prostate Cancer. JAMA Network Open. 2021;4:2037657.
doi: 10.1001/jamanetworkopen.2020.37657
Callender T, Emberton M, Morris S, Eeles R, Kote-Jarai Z, Pharoah PDP, et al. Polygenic risk-tailored screening for prostate cancer: A benefit-harm and cost-effectiveness modelling study. PLoS Med. 2019;16:e1002998.
doi: 10.1371/journal.pmed.1002998
Petrovski S, Goldstein DB. Unequal representation of genetic variation across ancestry groups creates healthcare inequality in the application of precision medicine. Genome Biol. 2016;17. https://doi.org/10.1186/s13059-016-1016-y .
Duncan L, Shen H, Gelaye B, Meijsen J, Ressler K, Feldman M, et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat Commun. 2019;10. https://doi.org/10.1038/s41467-019-11112-0 .
Huynh-Le MP, Fan CC, Karunamuni R, Walsh EI, Turner EL, Athene Lane J, et al. A genetic risk score to personalize prostate cancer screening, applied to population data. Cancer Epidemiol Biomark Preven. 2020;29:1731–8.
doi: 10.1158/1055-9965.EPI-19-1527
Karunamuni RA, Huynh-Le M-P, 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. https://doi.org/10.1038/s41391-020-00311-2 .
Karunamuni RA, Huynh‐Le M, Fan CC, Thompson W, Eeles RA, Kote‐Jarai Z, et al. African‐specific improvement of a polygenic hazard score for age at diagnosis of prostate cancer. International Journal of Cancer. 2021;148:99–105.
doi: 10.1002/ijc.33282
Karunamuni RA, Huynh-Le M-P, Fan CC, Thompson W, Lui A, Martinez ME, et al. Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24. Prostate Cancer Prostatic Dis. 2021;1–9.
Conti DV, Darst BF, Moss LC, Saunders EJ, Sheng X, Chou A, et al. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction. Nat Genet. 2021;53:65–75.
doi: 10.1038/s41588-020-00748-0
Schumacher FR, Al Olama AA, Berndt SI, Benlloch S, Ahmed M, Saunders EJ, et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat Genet. 2018;50:928–36.
doi: 10.1038/s41588-018-0142-8
Amos CI, Dennis J, Wang Z, Byun J, Schumacher FR, Gayther SA, et al. The OncoArray consortium: A network for understanding the genetic architecture of common cancers. Cancer Epidemiol Biomark Preven. 2017;26:126–35.
doi: 10.1158/1055-9965.EPI-16-0106
Eeles RA, Olama AAAl, Benlloch S, Saunders EJ, Leongamornlert DA, Tymrakiewicz M, et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat Genet. 2013;45:385–91.
doi: 10.1038/ng.2560
Karunamuni RA, Huynh-Le M-P, Fan CC, Eeles RA, Easton DF, Kote-Jarai ZS, et al. The effect of sample size on polygenic hazard models for prostate cancer. Eur J Hum Genet. 2020. https://doi.org/10.1038/s41431-020-0664-2 .
Huynh-Le M-P, Karunamuni R, Fan CC, Thompson WK, Muir K, Lophatananon A, et al. Common genetic and clinical risk factors: association with fatal prostate cancer in the Cohort of Swedish Men. Prostate Cancer Prostatic Dis. 2021;24:845–51.
doi: 10.1038/s41391-021-00341-4
Discacciati A, Orsini N, Andersson S-O, Andren O, Johansson J-E, Mantzoros CS, et al. Coffee consumption and risk of localized, advanced and fatal prostate cancer: a population-based prospective study. Annals of Oncology. 2013;24:1912–8.
doi: 10.1093/annonc/mdt105
Hamdy FC, Donovan JL, Lane JA, Mason M, Metcalfe C, Holding P, et al. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. New England Journal of Medicine. 2016;375:1415–24.
doi: 10.1056/NEJMoa1606220
Tibshirani R. The lasso method for variable selection in the cox model. Stat Med. 1997;16:385–95.
doi: 10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3
Tibshirani R. Regression Shrinkage and Selection via the Lasso. J R Stat Soc B. 1996;58:267–88.
Schaeffer E, Srinivas S, Antonarakis ES, Armstrong AJ, Bekelman JE, Cheng H, et al. Prostate cancer, version 1.2021: Featured updates to the nccn guidelines. JNCCN. 2021;19:134–43.
Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–91.
doi: 10.1038/s41588-019-0379-x
Grinde KE, Qi Q, Thornton TA, Liu S, Shadyab AH, Chan KHK, et al. Generalizing polygenic risk scores from Europeans to Hispanics/Latinos. Genet Epidemiol. 2019;43:50–62.
doi: 10.1002/gepi.22166
Popejoy AB, Fullerton SM. Genomics is failing on diversity. Nature. 2016;538:161–4.
doi: 10.1038/538161a
Riviere P, Luterstein E, Kumar A, Vitzthum LK, Deka R, Sarkar RR, et al. Survival of African American and non-Hispanic white men with prostate cancer in an equal-access health care system. Cancer. 2020;126:1683–90.
doi: 10.1002/cncr.32666
Dess RT, Hartman HE, Mahal BA, Soni PD, Jackson WC, Cooperberg MR, et al. Association of Black Race with Prostate Cancer-Specific and Other-Cause Mortality. JAMA Oncol. 2019;5:975–83.
doi: 10.1001/jamaoncol.2019.0826
Huynh-Le MP, Myklebust TÅ, Feng CH, Karunamuni R, Johannesen TB, Dale AM, et al. Age dependence of modern clinical risk groups for localized prostate cancer—A population-based study. Cancer. 2020;126:1691–9.
doi: 10.1002/cncr.32702
Brentnall AR, Cuzick J, Buist DSM, Bowles EJA. Long-term Accuracy of Breast Cancer Risk Assessment Combining Classic Risk Factors and Breast Density. JAMA Oncol. 2018;4:e180174.
doi: 10.1001/jamaoncol.2018.0174
Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Nat Rev Genet. 2018;1.
Yeh H-C, Duncan BB, Schmidt MI, Wang N-Y, Brancati FL. Smoking, smoking cessation, and risk for type 2 diabetes mellitus: a cohort study. Ann Intern Med. 2010;152:10–17.
doi: 10.7326/0003-4819-152-1-201001050-00005
Wang TJ, Larson MG, Levy D, Benjamin EJ, Leip EP, Omland T, et al. Plasma natriuretic peptide levels and the risk of cardiovascular events and death. N Engl J Med. 2004;350:655–63.
doi: 10.1056/NEJMoa031994
Tsodikov A, Gulati R, Carvalho TM, de, Heijnsdijk EAM, Hunter‐Merrill RA, Mariotto AB, et al. Is prostate cancer different in black men? Answers from 3 natural history models. Cancer. 2017;123:2312–9.
doi: 10.1002/cncr.30687
Shi Z, Platz EA, Wei J, Na R, Fantus RJ, Wang C-H, et al. Performance of Three Inherited Risk Measures for Predicting Prostate Cancer Incidence and Mortality: A Population-based Prospective Analysis. Eur Urol. 2020. https://doi.org/10.1016/j.eururo.2020.11.014 .
Carroll PR, Parsons JK, Box G, Carlsson S, Catalona WJ, Drake BF. National Comprehensive Cancer Network. Prostate Cancer Early Detection (Version 2.2021). https://www.nccn.org/professionals/physician_gls/pdf/prostate_detection.pdf Accessed October 10, 2021.
Giri VN, Knudsen KE, Kelly WK, Cheng HH, Cooney KA, Cookson MS, et al. Implementation of Germline Testing for Prostate Cancer: Philadelphia Prostate Cancer Consensus Conference 2019. JCO. 2020. https://doi.org/10.1200/JCO.20.00046 .
Hong H, Xu L, Liu J, Jones WD, Su Z, Ning B, et al. Technical Reproducibility of Genotyping SNP Arrays Used in Genome-Wide Association Studies. PLOS ONE. 2012;7:e44483.
doi: 10.1371/journal.pone.0044483
McNeish DM. Using Lasso for Predictor Selection and to Assuage Overfitting: A Method Long Overlooked in Behavioral Sciences. Multivariate Behav Res. 2015;50:471–84.
doi: 10.1080/00273171.2015.1036965