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Tumor Biology |
Cancer Research Program, Garvan Institute of Medical Research, St. Vincent’s Hospital, Darlinghurst, Sydney, New South Wales 2010, Australia [S. M. H., L. G. H., D. I. Q., K. K. R., J. G. K., M. G-G., R. L. S.]; Genomics Research, Eos Biotechnology, South San Francisco, California [D. E. H. A., J. H., K. G., D. W., D. H. M.]; Department of Medical Oncology (J. J. G.), and Department of Urology, St. Vincent’s Hospital, Darlinghurst, Sydney, New South Wales 2010, Australia [P. D. S.]; Cedars-Sinai Prostate Cancer Center, Los Angeles, California [D. B. A.]; and Genitourinary Oncology Service, Memorial Sloan-Kettering Cancer Center, New York, New York [H. S.]
Current models of prostate cancer classification are poor at distinguishing between tumors that have similar histopathological features but vary in clinical course and outcome. Here, we applied classical survival analysis to genome-wide gene expression profiles of prostate cancers and preoperative prostate-specific antigen (PSA) levels from each patient, to identify prognostic markers of disease relapse that provide additional predictive value relative to PSA concentration. Three of 
200 probesets showing strongest correlation with relapse were identified as the gene for the putative calcium channel protein, trp-p8, with loss of trp-p8 mRNA expression associated with a significantly shorter time to PSA relapse-free survival. We observed subsequently that trp-p8 is lost in the transition to androgen independence in a prostate cancer xenograft model and in prostate cancer tissue from patients treated preoperatively with antiandrogen therapy, suggesting that trp-p8 is androgen regulated, and its loss may be associated with more advanced disease. The identification of trp-p8 and other proteins implicated in the phosphatidylinositol signal transduction pathway that are associated with prostate cancer outcome, both here and in other published work, suggests an integral role for this pathway in prostate carcinogenesis. Thus, our findings demonstrate that multivariable survival analysis can be applied to gene expression profiles of prostate cancers with censored follow-up data and used to identify molecular markers of prostate cancer relapse with strong predictive power and relevance to the etiology of this disease.
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