Review ArticlesThe Role of Tissue Microarrays in Prostate Cancer Biomarker DiscoveryDatta, Milton W. MD*; True, Lawrence D. MD†; Nelson, Peter S. MD‡; Amin, Mahul B. MD§Author Information *Hospital Pathology Associates and Department of Pathology, University of Minnesota, Minneapolis, MN †Department of Pathology, University of Washington ‡Department of Hematology-Oncology, Fred Hutchinson Cancer Research Center, Seattle, WA §Department of Pathology, Cedars-Sinai Medical Center and University of California, Los Angeles, CA Supported in part by the Department of Defense Consortium Grant (PC012003) to Mahul B. Amin, Peter S. Nelson, and Milton W. Datta, a Department of Defense Grant (PC060595) to Peter S. Nelson and Lawrence D. True, and NCI grant U01CA086743 to Milton W. Datta. Reprints: Milton W. Datta, MD, Urologic Pathology Abbott Northwestern Hospital, Pathology Lab-11136 800 East 28th Street, Minneapolis, MN 55407 (e-mail: [email protected]). Advances in Anatomic Pathology: November 2007 - Volume 14 - Issue 6 - p 408-418 doi: 10.1097/PAP.0b013e318155709a Buy Metrics Abstract Tissue microarrays (TMAs) offer the potential to rapidly translate genomics and basic science research findings to practical clinical application. This is particularly true in the field of cancer biomarker research, where TMAs can be used for candidate biomarker validation and association with patient clinical, pathologic, and outcomes parameters. In this review, we examine the effect of TMA use on prostate cancer biomarker research, focusing on the types of TMAs that have been used, and the biomarkers that have been examined. The results demonstrate that TMAs have been very effective in screening candidate biomarkers for subsequent, extended evaluation in large patient populations. In addition, the use of TMAs in multiple biomarker series allows for the statistical analysis of sets of biomarkers as diagnostic or prognostic tests. The processes used here can be applied to any tumor type to improve patient diagnosis, prognosis, and treatment response prediction. © 2007 Lippincott Williams & Wilkins, Inc.