Elucidating the somatic genetics of prostate cancer

Dana-Farber Cancer Institute, Boston MA

Investigators

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Abstract

DESCRIPTION (provided by applicant): The genetics underlying the genesis and progression of prostate cancer remain poorly understood. This lack of understanding hampers both our current ability to rationally stratify patients with respect to existing therapies and to identify novel therapeutic targets. One mechanism by which we can accelerate the targeted therapy paradigm is to systematically define somatic mutations in prostate cancer. We have recently demonstrated the utility of high-density single nucleotide polymorphism (SNP) arrays for detecting large-scale genetic alterations of the cancer genome. Specifically, we have shown that SNP arrays are an effective high throughput approach to identify loss-of-heterozygosity (LOH) events, as well as copy number alterations including amplifications and deletions in human prostate cancer; and have developed a novel informatics platform (dChipSNP) to handle and analyze SNP array data. We have now shown that the 3rd generation of SNP arrays with probes for over 100,000 SNPs can robustly detect copy number changes including hemizygous deletions, homozygous deletions, and amplifications. The preliminary data suggest that it is now feasible to use SNP arrays to detect genomic alterations in human prostate cancer samples at unprecedented resolution. We propose to apply these methods to the systematic detection of large-scale genetic alterations in human prostate cancer. To this end the specific aims of this proposal are: 1. To generate genome-wide maps for loss-of-heterozygosity, homozygous deletions and amplifications in localized, androgen-dependent metastatic and androgen-independent metastatic prostate cancer using single-nucleotide polymorphism arrays containing probes for 120,000 SNPs. 2. To identify regions of LOH, gene amplification and homozygous deletion that are statistically enriched in metastatic or androgen-independent prostate cancer samples. 3. To use integrated analysis of matched expression data and genetic maps to stratify candidate genes targeted by the relevant genetic alterations. 4. To begin the functional validation of selected candidate tumor suppressor and oncogenes identified in aims 1 through 3.