4-miRNA Signature To Predict Clear Cell Renal Cell Carcinoma Metastasis and Prognosis.
Huiqing Wu, Xiwei Wu, Lihong Weng, Xuejun Li, Chao Guo, Sumanta K Pal, Jennifer M Jin, Rebecca A Nelson, Bing Mu, Susan H Onami, Jeffrey J Wu, Nora H Ruel, Halin Gao, Maricela Covarrubias, Robert A Figlin, Lawrence M Weiss. City of Hope National Medical Center and Beckman Research Institute, Duarte, CA
Background: Clear cell renal cell carcinoma (ccRCC) represents the most common renal cancer histology. In the setting of metastatic disease, few patients achieve a durable remission with available therapies. The early detection of ccRCC metastatic potential may be beneficial for a more precise prediction of clinical outcomes and may ultimately be used to identify subsets of patients that stand to benefit from specific targeted therapies. RCC metastasis cannot be reliably predicted based on patients' clinical magnifications, pathologic findings or other currently available molecular tests. For this purpose, we analyzed microRNA (miRNA) expression in ccRCC and aimed to develop a miRNA expression signature to determine the risk of ccRCC metastasis and predict the prognosis of disease.
Design: We used the microarray technology to profile 10 benign kidney specimens and 68 ccRCC samples. Using a 28-sample training cohort of localized and metastatic ccRCCs and the univariate Logistic regression method, we developed a miRNA signature model to calculate a risk score for predicting the risk of metastasis. We validated the signature model in an independent 40-sample testing cohort of different stages of primary ccRCCs and further tested the signature model using a quantitative PCR (qPCR) platform.
Results: Utilizing the training cohort, we constructed a 4-miRNA expression signature model in which the expression levels of the 4 miRNAs were used to stratify ccRCC patients into high and low risk groups for metastasis. The signature was then validated in the testing cohort. With a 5-year follow-up if no metastasis developed, the signature showed a high sensitivity (75%) and specificity (100%). The risk status was proven to be associated with the cancer-specific survival. This molecular signature has further been developed into a qPCR-based assay, which showed to have the same high sensitivity and specificity.
Conclusions: The 4-miRNA signature can reliably predict ccRCC metastasis and prognosis. It is ready for further large clinical cohort validation and potential routine clinical application.
Category: Genitourinary (including renal tumors)
Monday, February 28, 2011 11:30 AM
Platform Session: Section A, Monday Morning