Tissue Array-Based Predictions of Pathobiology, Prognosis, and Response to Treatment for Renal Cell Carcinoma Therapy

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Abstract
Renal cell carcinoma is the most lethal of the common urologic malignancies, with ∼40% of patients eventually dying of cancer progression. Approximately one third of patients present with metastatic disease, and up to 40% treated for localized disease have a recurrence. Historically, clinical factors have been used as prognostic markers for patients with renal cell carcinoma. Recent advances in the understanding of the pathogenesis, behavior, and molecular biology of renal cell carcinoma have paved the way for developments that may enhance early diagnosis, better predict tumor prognosis, and improve survival for renal cell carcinoma patients. Furthermore, reliable predictive factors are essential for the stratification of patients into clinically meaningful categories, which can be used to provide patients with counseling regarding prognosis, select treatment modalities, and determine eligibility for clinical trials. This has led to the creation of integrated staging systems that predict outcome by combining pathological and clinical variables. Although staging has been improved with the development of integrated systems, molecular tumor markers are expected to revolutionize the staging of renal cell carcinoma in the future. The development of methods based on gene and tissue arrays has created a powerful tool for evaluating hundreds to thousands of tumors simultaneously with histologic, immunohistochemical, and chromosomal analyses. Gene array analysis permits rapid molecular profiling, and tissue arrays enable the analysis of protein expression profiles on specimens to determine their potential clinical significance and role in renal cell carcinoma biology. This article reviews the tissue array-based predictors of pathobiology, prognosis, response to treatment, and potential molecular targets for therapy of renal cell carcinoma.