Molecular Signatures of Lethal an Indolent Prostate Cancer

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One of the main challenges for medicine in the early part of 21st century is to incorporate knowledge of the human genome into the medical management of patients, thereby yielding more precise diagnoses and mechanism-based deployment of therapy. Nowhere is this need more pressing than in the treatment of men with prostate cancer. Prostate cancer is a major cause of death and morbidity, yet large numbers of men are believed to harbor tumors that are indolent even in the absence of therapy. As such, many men receive unnecessary treatment with its attendant effects, and others die of disease despite aggressive therapy. We hypothesize that the variability in the natural history of CaP is determined by heretofore unrecognized molecular heterogeneity of the disease. We therefore propose here to identify a gene expression signature that distinguishes indolent from lethal disease. This Project has 3 Specific Aims: Aim 1: Collect, register, and process archival formalin-fixed paraffin embedded (FFPE) samples from the Swedish Watchful Waiting (WW) cohorts with up to 30 years clinical follow up. Aim 2: Develop molecular signatures of lethal and indolent prostate cancer on the Watchful Waiting FFPE samples using an Illumina expression array platform with over 6000 genes developed specifically for molecular signature discovery. Aim 3: Validate the signatures on Swedish WW cases not used in Aim 2 and test for the presence of these profiles in tumor samples (n=100) from the Physicians' Health Study (PHS). At the conclusion of this proposal, we expect to have discovered and validated molecular predictors of prostate cancer outcome that are appropriate for further clinical development. To accomplish these goals, we have assembled a multidisciplinary team of investigators with a long track record of collaboration, and with particular expertise in the clinical, pathological and epidemiological aspects of prostate cancer, in the development and deployment of genomics technologies, and in advanced computational and statistical analysis.