? ? There is growing awareness cancer susceptibility may be attributed to multiple genetic variations and biological pathways, rather than to single candidate genes. To date, few studies model the complex interaction among polymorphic carcinogen activation, clearance, transport, detoxification, as well as DNA repair genes on the risk of prostate cancer development, particularly within underserved populations. Objectives/Hypothesis: The primary goal of this project is to identify informative genetic markers to predict the risk of prostate cancer among men of African descent. Individuals possessing one or more putative rapid bioactivation genotypes, as well as compromised detoxification, transport, and DNA repair genotypes, are hypothesized to have a higher risk for developing prostate cancer, relative to those possessing low-risk genotypes. This increased risk may be partially attributed to an altered capacity to metabolically activate, extrude, or detoxify hazardous compounds from cells, and an inability to fully repair environmentally-induced DNA damage.
Specific Aims : 1) Determine or verify the prevalence of single nucleotide polymorphisms (SNPs) in selected metabolic activation, detoxification, transport, and DNA repair genes among men of African descent. 2) Evaluate the relationship between these polymorphic genes and prostate cancer risk. 3) Assess whether interaction among various genetic susceptibilities in selected metabolism, metabolic clearance, transport, and DNA repair genes can be used to predict or detect the risk of developing prostate cancer. Study Design: Germ-line DNA collected from 918 men of African descent will be analyzed, including 220 incident prostate cancer cases and 698 healthy controls. The occurrence of 28 putative high-impact markers will be analyzed using TaqMan allelic discrimination and polymerase chain reaction-restriction fragment length polymorphism strategies. In order to assess single-gene and combined effects on prostate cancer risk, traditional statistical methods will be combined with sophisticated computational algorithms and hierarchical interaction graphs. Impact: The proposed study identifies and evaluates whether a complex array of genetic markers can detect prostate cancer susceptibility using a combination of traditional and innovative bioinformatic approaches. This comprehensive approach provides an opportunity to visualize, verify, and evaluate the predictive accuracy of gene-gene interactions as indicators of risk. Ultimately, these study findings will help: 1) identify SNP signatures explaining disease progression and recurrence within high-risk populations; 2) clarify the role of multiple genes and biological pathways in prostate cancer detection; 3) predict patients benefiting from available chemoprevention and therapeutic regimens; and 4) reduce the mortality and morbidity of prostate cancer. ? ? ? ?
