Prostate cancer has the highest incidence and the second highest mortality of any cancer among men in the United States; the prostate cancer burden is especially high in African-Americans. The prognosis of this cancer is extremely heterogeneous, and it is of urgent clinical importance to be able to determine which early stage cancers should be treated aggressively and which should not. Although histologic grade and stage are the most important determinants of prostate cancer prognosis, some oncogene and tumor suppressor gene proteins have been linked to prostate cancer mortality in small studies; the most promising of these include PTEN, PIM-1, EGR1, P27 kipt, E-cadherin, alpha-catenin, P53, BCL-2, and HSP27. However, little information exists about interrelationships of the expression of different proteins and which proteins most strongly predict survival when other factors are taken into account. Since many clinical laboratories can now analyze the tumor protein expression levels through immunohistochemistry, there is great interest in finding immunohistochemical markers that can predict prostate cancer prognosis in the clinical setting. This application proposes to examine the relationship between protein expression of the above tumor genes and mortality from prostate cancer after prostatectomy using a population-based case-control study design. Subjects will be Caucasian and African-American prostate cancer patients of Kaiser Permanente Northwest, Kaiser Permanente Southern California, and Kaiser Permanente Northern California who have paraffin-embedded formalin-fixed tumor tissue stored in health plan laboratories. Laboratory scientists at Georgetown University's Lombardi Cancer Center will use both standard methods for immunohistochemistry and the Automated Cellular Imaging System (ChromaVision, Inc.) to assess protein expression in prostate tumor tissue. Additional subject data will be collected from automated sources and medical records to allow multivariate analysis of the association between tumor marker expression and prostate cancer mortality, taking into account demographic, pathologic, medical, and environmental variables. This study is unique in that, because of the very large health plan populations, study investigators will be able to assemble over 300 fatal cases of prostate cancer with available prostatectomy tissue and clinical data and will be able to compare them with a well-matched sample of non-fatal cases. The case-control study design will provide maximum statistical power for analysis of these relationships, particularly in the important African-American subgroup.
