Prostate cancer is a complex disease that presumably stratifies into different types of cancers with distinct progression rates, metastatic potentials and prognostic outcomes. We hypothesize that histologically similar primary prostate carcinomas can be stratified into distinct categories of clinical behavior based upon profiles of their expressed genes. We will characterize the gene expression profiles of prostate carcinoma that correlate with clinical phenotypes of progression or non-progression as determined by PSA-free survival at 5 years post-therapy. The expression profiles from the normal and neoplastic tissues of 100 individuals that have undergone radical prostatectomy will be determined. Outcomes will be ascertained by following serial PSA measurements for recurrent disease. Correlations of individual and cohorts of genes with outcome will be determined using statistical analyses. Independent predictors of recurrence such as PSA and Gleason Grade will be evaluated as co-variables. We will also determine the molecular indicators of response to chemotherapeutic intervention for highrisk prostate cancers by analyzing the gene expression profiles of prostate tissues obtained pre- and post-neoadjuvant chemotherapy. Correlations between individual genes as well as multiple genes will be examined for association with a) histological measures of response; b) PSA response immediately post-therapy; and c) time to recurrence as determined by PSA-free survival at 5 years post-therapy. The advancement in high throughput technology such as cDNA microarrays and MS/MS technology allows one to use a systems approach to study human diseases rather than analyzing one gene or one protein at a time. The objectives are to completely define the transcriptome (identify all mRNAs) and define much of the proteome (identify all proteins) of prostate cancer cells. We will apply high throughput technology and discovery-driven approaches to define the differences in transcriptomes and proteomes of the androgen-dependent LNCaP cell and its androgen-independent variant CL1 by 1) expression profiling using cDNA microarray containing 46k cDNAs, 2) MPSS-(TM) (Massively Parallel Signature Sequencing) technology to determine about 1 million sequences from each cell line, and 3) proteomics ICAT labeling coupled with MS/MS. We will then evaluate the findings from cell line models with 40 tumor biopsies each from androgen-dependent prostate carcinoma and hormone-refractory using real-time quantitative RT-PCR, quantitative Western blot analysis and immunohistochemical analysis with tissue microarrays.
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