Cancer is one of the leading causes of death in the modern world. The term cancer is used when cells lose their ability to regulate growth, become immortal, and in most cases migrate from their primary location into a new unrelated site. Among cancers, prostate cancer is the leading cause of cancer-related death among men in America, next only to lung cancer. Prostate cancer is clinically subdivided into various classes based on its progression. These include benign, localized, and metastatic (spread into other tissues) prostate cancer. Of these, surgical and radiation therapies exist for clinically localized prostate cancer. However, once the cancer becomes metastatic, it becomes incurable. The central dogma of any disease state is that the final outcome is regulated by the cohort of proteins it expresses. Many of these proteins could thus be used to study the occurrence of prostate cancer, classify the various stages of prostate cancer, and design therapeutic strategies to combat prostate cancer.

With the advent of microarray technology, it has become possible to study global gene expression profiles. However, a comparative global protein expression analysis for cancer tissues has not been accomplished to date. Hence, we propose to take advantage of the antibody microarray developed by us, to look for changes in levels of proteins during different stages of prostate cancer. This slide-based microarray platform relies on spotted antibodies which would be used to compare expression levels for various proteins in prostate cancer specimens. This proposal is based on the hypothesis that changes in protein levels or activities define cancer at all stages during its development. Here we aim to study the pattern of protein expression during various stages of prostate cancer, validate the expression pattern in hundreds of prostate cancer specimens, and correlate the expression data with clinical outcome.

As part of the first aim, about 500 antibodies would be spotted on a slide and used to monitor protein levels in various prostate cancer tissues. These would include antibodies against proteins involved in apoptosis, growth, migration, cell division, and so on. Also since most of the proteins orchestrate their effect by changing their state of activation (by phosphorylation or cleavage), antibodies to monitor such changes in activity of the proteins would also be included in the study. This would result in a cohort of proteins, henceforth termed biomarkers, that could then be used to monitor prostate cancer progression. In the second aim, we propose to study the expression pattern of these biomolecules using tissue microarrays (TMA). This is based on spotting hundreds of prostate cancer specimens on a single slide and allows comparative localization of the biomarkers across hundreds of prostate cancer specimens. Finally, as a part of the second aim, we would ascertain the clinical associations of candidate biomarkers using clinically stratified prostate cancer specimens.

In summary, we utilize an emerging proteomics platform to molecularly interrogate various stages of prostate cancer. The effectiveness of this paradigm for identifying and characterizing markers of prostate cancer is supported by earlier data on profiling of radiation-treated colon cancer cells using antibody microarrays. Our ultimate hope is that systematic molecular analysis of tissues derived from patients with advanced disease will one day lead to an effective therapy for prostate cancer.