Prostate Cancer (PC), the most frequently diagnosed solid tumor in men in the U.S., results in ~192,000 new cases and ~27,000 deaths per year. Although the variation of PC incidence is likely to be the result of several factors, there is a large body of literature that strongly implicates a genetic etiology. Genome-wide association studies (GWAS) have emerged as the most widely used contemporary approach to identify genetic variants (in particular SNPs) that are associated with increased risk of human disease. For PC, at least five GWAS have now been performed yielding a substantial number of well-validated SNPs (~30) that are associated with an increased risk of PC, and that number continues to grow. A significant problem for many of the PC risk-SNPs identified so far, however, is that they do not lie within or near a known gene and they have no obvious functional properties. These findings suggest that many (if not most) of these risk-SNPs will be located in regulatory regions that control gene expression rather than in coding regions that may directly affect protein function. Therefore, in order to define the functional role of currently known risk-SNPs, the target genes must first be identified. A promising strategy to address this problem involves the use of expression quantitative trait loci (eQTL) analysis. SNPs that contribute to differences in gene expression levels are widespread in the human genome. Unfortunately, most of the publically available SNP-Transcript eQTL datasets utilize lymphoblastoid cells with only a handful using tissue from target organs. Although useful, these datasets alone are unlikely to be sufficient. Recent studies have demonstrated that gene expression and gene regulation occur in both a tissue-specific and tissue- independent fashion and suggest that a complete repertoire of regulatory SNPs can only be uncovered in the context of cell type specificity. To date, such a tissue-specific dataset for normal prostate tissue does not exist. Overall, we hypothesize that many of the PC risk-SNPs identified to date will be located in regulatory domains involved in gene transcription. Furthermore, we hypothesize that candidate genes affected by these regulatory elements can be identified by taking advantage of eQTL datasets. We have three objectives for this grant proposal. First, we will identify candidate target genes for any current (or future) SNP identified to be associated with PC with the use of a normal prostate tissue-specific eQTL dataset. Second, for the candidate target genes identified, we will identify candidate causal-SNPs using a variety of both fine-mapping and bioinformatic strategies. Third, we will define the functional role of identified causal-SNPs using a variety of laboratory-based strategies. Our long-term goal is to identify the biologic pathways affected by these inherited factors and evaluate their impact on PC etiology.
In the case of prostate cancer, it is clear that early, localized disease is highly curable. Improved methods of identifying men who are at high risk for prostate cancer can lead to targeted populations to screen, resulting in better medical care for high-risk men, yet at reduced costs to the U.S. population. An understanding of the genetic mechanisms of prostate cancer may lead to better prognostic markers and to better treatment strategies, perhaps paving the way for pharmacogenomic interventions.
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