Common genetic variants in prostate cancer risk prediction – Results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3)
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Author
Lindström, Sara
Schumacher, Fredrick
Cox, David
Travis, Ruth
Albanes, Demetrius
Allen, Naomi
Andriole, Gerald
Berndt, Sonja
Boeing, Heiner
Bueno-de-Mesquita, H. Bas
Crawford, E. David
Diver, W. Ryan
Ganziano, J. Michael
Giles, Graham
Giovannucci, Edward
Gonzalez, Carlos
Henderson, Brian
Hunter, David
Johansson, Mattias
Kolonel, Laurence
Ma, Jing
Le Marchand, Loic
Pala, Valeria
Stram, Daniel
Thun, Michael
Tjonneland, Anne
Trichopoulos, Dimitrios
Virtamo, Jarmo
Weinstein, Stephanie
Willett, Walter C.::94559ea206eef8a8844fc5b80654fa5b::600
Yeager, Meredith
Hayes, Richard
Severi, Gianluca
Haiman, Christopher
Chanock, Stephen
Kraft, Peter
Published Version
https://doi.org/10.1158/1055-9965.EPI-11-1038Metadata
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Lindstrom, S., F. R. Schumacher, D. Cox, R. C. Travis, D. Albanes, N. E. Allen, G. Andriole, et al. 2012. “Common Genetic Variants in Prostate Cancer Risk Prediction--Results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3).” Cancer Epidemiology Biomarkers & Prevention 21 (3): 437–44. https://doi.org/10.1158/1055-9965.epi-11-1038.Abstract
Background: One of the goals of personalized medicine is to generate individual risk profiles that could identify individuals in the population that exhibit high risk. The discovery of more than two-dozen independent single-nucleotide polymorphism markers in prostate cancer has raised the possibility for such risk stratification. In this study, we evaluated the discriminative and predictive ability for prostate cancer risk models incorporating 25 common prostate cancer genetic markers, family history of prostate cancer, and age. Methods: We fit a series of risk models and estimated their performance in 7,509 prostate cancer cases and 7,652 controls within the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3). We also calculated absolute risks based on SEER incidence data. Results: The best risk model (C-statistic = 0.642) included individual genetic markers and family history of prostate cancer. We observed a decreasing trend in discriminative ability with advancing age (P = 0.009), with highest accuracy in men younger than 60 years (C-statistic = 0.679). The absolute ten-year risk for 50-year-old men with a family history ranged from 1.6% (10th percentile of genetic risk) to 6.7% (90th percentile of genetic risk). For men without family history, the risk ranged from 0.8% (10th percentile) to 3.4% (90th percentile). Conclusions: Our results indicate that incorporating genetic information and family history in prostate cancer risk models can be particularly useful for identifying younger men that might benefit from prostate-specific antigen screening.Impact: Although adding genetic risk markers improves model performance, the clinical utility of these genetic risk models is limited. Cancer Epidemiol Biomarkers Prev; 21(3); 437-44.Terms of Use
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