Article

Common genetic variants in prostate cancer risk prediction--results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3).

Program in Molecular and Genetic Epidemiology,Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Cancer Epidemiology Biomarkers & Prevention (Impact Factor: 4.56). 03/2012; 21(3):437-44. DOI: 10.1158/1055-9965.EPI-11-1038
Source: PubMed

ABSTRACT 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.
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.
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).
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.
Although adding genetic risk markers improves model performance, the clinical utility of these genetic risk models is limited.

0 Bookmarks
 · 
153 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Cancer risk prediction models are important in identifying individuals at high risk of developing cancer, which could result in targeted screening and interventions to maximize the treatment benefit and minimize the burden of cancer. The cancer-associated genetic variants identified in genome-wide or candidate gene association studies have been shown to collectively enhance cancer risk prediction, improve our understanding of carcinogenesis, and possibly result in the development of targeted treatments for patients. In this article, we review the cancer risk prediction models that have been developed for popular cancers and assess their applicability, strengths, and weaknesses. We also discuss the factors to be considered for future development and improvement of models for cancer risk prediction.
    Cancer informatics 09/2014; 13(Suppl 2):19-28. DOI:10.4137/CIN.S13788
  • [Show abstract] [Hide abstract]
    ABSTRACT: Prostate cancer is considered a disease of older men (aged >65 years), but today over 10% of new diagnoses in the USA occur in young men aged ≤55 years. Early-onset prostate cancer, that is prostate cancer diagnosed at age ≤55 years, differs from prostate cancer diagnosed at an older age in several ways. Firstly, among men with high-grade and advanced-stage prostate cancer, those diagnosed at a young age have a higher cause-specific mortality than men diagnosed at an older age, except those over age 80 years. This finding suggests that important biological differences exist between early-onset prostate cancer and late-onset disease. Secondly, early-onset prostate cancer has a strong genetic component, which indicates that young men with prostate cancer could benefit from evaluation of genetic risk. Furthermore, although the majority of men with early-onset prostate cancer are diagnosed with low-risk disease, the extended life expectancy of these patients exposes them to long-term effects of treatment-related morbidities and to long-term risk of disease progression leading to death from prostate cancer. For these reasons, patients with early-onset prostate cancer pose unique challenges, as well as opportunities, for both research and clinical communities. Current data suggest that early-onset prostate cancer is a distinct phenotype-from both an aetiological and clinical perspective-that deserves further attention.
    Nature Reviews Urology 05/2014; 11(6). DOI:10.1038/nrurol.2014.91 · 4.79 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model.
    PLoS ONE 07/2014; 9(7):e101507. DOI:10.1371/journal.pone.0101507 · 3.53 Impact Factor

Full-text (2 Sources)

Download
45 Downloads
Available from
May 21, 2014