Risk Prediction of Prostate Cancer with Single Nucleotide Polymorphisms and Prostate Specific Antigen
Abstract
Purpose:
Combined information on single nucleotide polymorphisms and prostate specific antigen offers opportunities to improve the performance of screening by risk stratification. We aimed to predict the risk of prostate cancer based on prostate specific antigen together with single nucleotide polymorphism information.
Materials and Methods:
We performed a prospective study of 20,575 men with prostate specific antigen testing and 4,967 with a polygenic risk score for prostate cancer based on 66 single nucleotide polymorphisms from the Finnish population based screening trial of prostate cancer and 5,269 samples of 7 single nucleotide polymorphisms from the Finnish prostate cancer DNA study. A Bayesian predictive model was built to estimate the risk of prostate cancer by sequentially combining genetic information with prostate specific antigen compared with prostate specific antigen alone in study subjects limited to those with prostate specific antigen 4 ng/ml or above.
Results:
The posterior odds of prostate cancer based on 7 single nucleotide polymorphisms together with the prostate specific antigen level ranged from 3.7 at 4 ng/ml, 14.2 at 6 and 40.7 at 8 to 98.2 at 10 ng/ml. The ROC AUC was elevated to 88.8% (95% CI 88.6–89.1) for prostate specific antigen combined with the risk score based on 7 single nucleotide polymorphisms compared with 70.1% (95% CI 69.6–70.7) for prostate specific antigen alone. It was further escalated to 96.7% (95% CI 96.5–96.9) when all prostate cancer susceptibility polygenes were combined.
Conclusions:
Expedient use of multiple genetic variants together with information on prostate specific antigen levels better predicts the risk of prostate cancer than prostate specific antigen alone and allows for higher prostate specific antigen cutoffs. Combined information also provides a basis for risk stratification which can be used to optimize the performance of prostate cancer screening.
Reference
- 1. : A common variant associated with prostate cancer in European and African populations. Nat Genet 2006; 38: 652. Google Scholar
- 2. : Multiple regions within 8q24 independently affect risk for prostate cancer. Nat Genet 2007; 39: 638. Google Scholar
- 3. : Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat Genet 2007; 39: 645. Google Scholar
- 4. : Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat Genet 2007; 39: 631. Google Scholar
- 5. : Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat Genet 2007; 39: 977. Google Scholar
- 6. : Cumulative association of five genetic variants with prostate cancer. N Engl J Med 2008; 358: 910. Google Scholar
- 7. : Multigene panels in prostate cancer risk assessment: a systematic review. Genet Med 2016; 18: 535. Google Scholar
- 8. : Prostate-cancer mortality at 11 years of follow-up. N Engl J Med 2012; 366: 981. Google Scholar
- 9. : Mortality results from a randomized prostate-cancer screening trial. N Engl J Med 2009; 360: 1310. Google Scholar
- 10. : Prostate cancer mortality in the Finnish randomized screening trial. J Natl Cancer Inst 2013; 105: 719. Google Scholar
- 11. : The Finnish trial of prostate cancer screening: where are we now?BJU Int 2003; 92: 22. Google Scholar
- 12. : A stochastic model for survival of early prostate cancer with adjustments for leadtime, length bias, and over-detection. Biom J 2012; 54: 20. Google Scholar
- 13. : Multiple loci identified in a genomewide association study of prostate cancer. Nat Genet 2008; 40: 310. Google Scholar
- 14. : Germline mutations in HOXB13 and prostate-cancer risk. N Engl J Med 2012; 12; 366: 141. Google Scholar
- 15. : HOXB13 G84E mutation in Finland: population-based analysis of prostate, breast, and colorectal cancer risk. Cancer Epidemiol Biomarkers Prev 2013; 22: 452. Google Scholar
- 16. : Reducing overdiagnosis by polygenic risk-stratified screening: findings from the Finnish section of the ERSPC. Br J Cancer 2015; 113: 1086. Google Scholar
- 17. : Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat Genet 2018; 50: 928. Google Scholar
- 18. : Individual and cumulative association of prostate cancer susceptibility variants with clinicopathologic characteristics of the disease. Clin Chim Acta 2010; 411: 1232. Google Scholar
- 19. : Prevalence of the HOXB13 G84E germline mutation in British men and correlation with prostate cancer risk, tumour characteristics and clinical outcomes. Ann Oncol 2015; 26: 756. Google Scholar
- 20. : Optimal predictors of prostate cancer on repeat prostate biopsy: a prospective study of 1,051 men. J Urol 2000; 163: 1144. Link, Google Scholar
- 21. : Significance of prostate-specific antigen-alpha(1)-antichymotrypsin complex for diagnosis and staging of prostate cancer. Jpn J Clin Oncol 2001; 31: 506. Google Scholar
- 22. : Effect of verification bias on screening for prostate cancer by measurement of prostate-specific antigen. N Engl J Med 2003; 349: 335. Google Scholar
- 23. : Predictive properties of serum-prostate-specific antigen testing in a community-based setting. Arch Intern Med 1996; 156: 2462. Google Scholar
- 24. : Age-specific reference ranges for prostate-specific antigen in black men. N Engl J Med 1996; 335: 304. Google Scholar
- 25. : Adding genetic risk score to family history identifies twice as many high-risk men for prostate cancer: results from the prostate cancer prevention trial. Prostate 2016; 76: 1120. Google Scholar
The corresponding author certifies that, when applicable, a statement(s) has been included in the manuscript documenting institutional review board, ethics committee or ethical review board study approval; principles of Helsinki Declaration were followed in lieu of formal ethics committee approval; institutional animal care and use committee approval; all human subjects provided written informed consent with guarantees of confidentiality; IRB approved protocol number; animal approved project number.
Supported by the Ministry of Science and Technology (grant number MOST 107-3017-F-002-003) and the “Innovation and Policy Center for Population Health and Sustainable Environment (Population Health Research Center, PHRC), College of Public Health, National Taiwan University” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan (NTU-107L9003).
No direct or indirect commercial, personal, academic, political, religious or ethical incentive is associated with publishing this article.
