@article{081df15605094e3c91fd105eb540b7e0,
title = "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.",
author = "{Li-Sheng Chen}, Sam and {Ching-Yuan Fann}, Jean and Csilla Sipeky and Yang, {Teng Kai} and {Yueh-Hsia Chiu}, Sherry and {Ming-Fang Yen}, Amy and Virpi Laitinen and Tammela, {Teuvo L.J.} and Stenman, {Ulf H{\aa}kan} and Anssi Auvinen and Johanna Schleutker and Chen, {Hsiu Hsi}",
note = "Funding Information: 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. * Correspondence and requests for reprints: Division of Biostatistics, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Room 533, 5F, No. 17 Suchow Rd., Taipei 100, Taiwan (telephone: +886-2-33228033; FAX: +886-2-23587707; e-mail: chenlin@ntu.edu.tw). Funding Information: 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). Publisher Copyright: {\textcopyright} 2019 by AMERICAN UROLOGICAL ASSOCIATION EDUCATION AND RESEARCH, INC.",
year = "2019",
month = mar,
day = "1",
doi = "10.1016/j.juro.2018.10.015",
language = "English",
volume = "201",
pages = "486--495",
journal = "The Journal of Urology",
issn = "0022-5347",
publisher = "Elsevier Inc.",
number = "3",
}
