Abstract: The main aim of this proposal is to analyze gene expression patterns in cancer of the prostate and to establish correlations with distinct groups of cancer behavior. These cancer subgroups are currently covered under histopathologic diagnoses that do not allow prediction of behavior from morphologic criteria. The studies will allow us to establish a molecular reclassification of prostate cancer based on coordinated expression of groups of specific genes. Complete prostatectomy specimens available in our Western Pennsylvania Prostate Tissue Bank (run by our department of Pathology) will be processed by microdissection and used to extract RNA. This will in turn be processed for analysis through the Affymetrix gene chip set, based on existing active strong and long term commitment of collaboration with the Molecular Oncology team of Hoffman LaRoche, Inc., at Nutley, New Jersey and our department of Pathology at the University of Pittsburgh. Our tissue bank contains complete and well stratified information that will be used by the bioinformatics teams of HLR and Pitt to provide correlation between coordinated expression of specific gene sets and distinct tumor behavior. We will be processing prostate cancer samples from the following groups: 1. Normal prostate. 2. Prostatic cancer without capsular invasion. 3. Prostatic cancer with capsular invasion that did not progress to systemic disease. 3. Prostatic cancer with capsular invasion that did progress to widespread systemic disease. 4. Metastatic foci. The data from the gene expression analysis will be processed by both the Pitt and the HLR bioinformatics team to provide cohesive and complete correlation from gene expression to clinical behavior, in order to establish new diagnostic groups of prostate cancer based on molecular sub- classification. Subsequent studies will also use the Differential Subtraction Chain technique and Fluorescence In Site Hybridization (FISH) to conduct complete genomic screening of the new sub-classification groups in order to detect genomic abnormalities that correlate with the gene expression patterns in the groups established from the above studies. While altered expression patterns are undoubtedly to become the basis for future tumor diagnostic methodology, repeated paradigms with all types of cancer suggest that the basis for altered gene expression patterns in tumors is the accumulation of genomic alterations linked to tumor progression. The integrated approach of this proposal will allow not only molecular sub-classification of prostate cancer but also establishment of easy to perform diagnostic tools (selective gene expression analysis by Real Time PCR Matrix, detection of genomic abnormality markers, etc.) that can be easily applied as predictors for tumor behavior. Preliminary results already provide strong evidence of correlation between invasive behavior and altered expression of specific genes. These include altered expression of membrane bound proteases and matrix bound growth factors, as well as increase in groups of G-protein linked receptors and the ligands, and decrease in enzymes responsible for their degradation.