Common genetic variants in prostate cancer risk prediction - Results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3)
A partir de données portant sur 7 509 cas de cancer de la prostate et 7 652 témoins, cette étude évalue les performances de divers modèles, incluant marqueurs génétiques, histoire familiale et âge, pour estimer le risque de cancer de la prostate
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 SNP 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 NCI 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 PSA screening. Impact: Although adding genetic risk markers improves model performance, the clinical utility of these genetic risk models is limited.
Cancer Epidemiology Biomarkers & Prevention , résumé, 2012