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Using Protein Expressions to Predict Survival in Clear Cell Renal Carcinoma

Departments of ¹ Urology, ² Pathology and Laboratory Medicine, ³ Biostatistics, ⁴ Human Genetics and Biostatistics, and ⁵ Medicine and Urology, University of California School of Medicine, Los Angeles, California

Purpose: An accurate system for predicting survival for patients with solid tumors will allow for better patient selection for both established and novel therapies. We propose a staging system for clear cell variants of renal cell carcinoma (RCC) that includes molecular predictors and standard clinical predictors such as tumor-node-metastasis (TNM) stage, histological grade, and performance status (PS).

Experimental Design: A custom tissue array was constructed using clear cell RCC from 318 patients, representing all stages of localized and metastatic RCC, and immunohistochemically stained for molecular markers Ki67, p53, gelsolin, CA9, CA12, PTEN, EpCAM, and vimentin. We present a strategy for evaluating individual candidate markers for prognostic information and integrating informative markers into a multivariate prognostic system.

Results: The overall median follow-up and the median follow-up for surviving patients were 28 and 55 months, respectively. A prognostic model based primarily on molecular markers included metastasis status, p53, CA9, gelsolin, and vimentin as predictors and had high discriminatory power: its statistically validated concordance index (C-index) was found to be 0.75. A prognostic model based on a combination of clinical and molecular predictors included metastasis status, T stage, Eastern Cooperative Oncology Group PS, p53, CA9, and vimentin as predictors and had a C-index of 0.79, which was significantly higher (P < 0.05) than that of prognostic models based on grade alone (C = 0.65), TNM stage alone (C = 0.73), or the University of California Los Angeles integrated staging system (C = 0.76).

Conclusions: Protein expressions obtained using widely available technology can complement standard clinical predictors such as TNM stage, histological grade, and PS.

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