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Protein Expression Profiles in Renal Cell Carcinoma: Staging, Prognosis, and Patient Selection for Clinical Trials

Authors' Affiliation: University of California-Los Angeles Kidney Cancer Program, Division of Urologic Oncology, Department of Urology and Division of Hematology-Oncology, Department of Medicine, David Geffen School of Medicine at University of California-Los Angeles, Los Angeles, California

Requests for reprints: Robert A. Figlin, Division of Medical Oncology and Therapeutics Research, City of Hope National Medical Center and Beckman Research Institute, 1500 East Duarte Road, Duarte, CA 91010. E-mail: rfiglin{at}coh.org.

Attempts to predict survival in patients with renal cell carcinoma (RCC) have traditionally relied on standard clinical variables, such as tumor-node-metastasis stage, histologic grade, and performance status. An accurate method for predicting patient survival is useful for patient counseling, planning follow-up, and selecting patients most likely to benefit from novel and established therapies. Furthermore, an improved prognostic system will allow for more accurate comparisons of clinical trials based on varying inclusion criteria. A large number of potential prognostic markers have recently been identified from methods based on gene arrays, which screen for differential expression of thousands of genes. The accepted method of clinical validation of novel markers is on formalin-fixed and paraffin-embedded specimens using immunohistochemistry. The development of tissue microarrays as a high-throughput technique has allowed for thousands of different cores of pathologic tissue to be assessed simultaneously in a timely and cost-efficient manner. This technology has enabled the analysis of protein expression profiles on specimens to determine their potential clinical significance and role in RCC biology. This article reviews the protein expression profiles in RCC and their association with pathobiology, prognosis, and response to treatment as well as their role in serving as potential molecular targets for therapy of RCC.