Survival Prediction of Clear Cell Renal Cell Carcinoma Based on Gene Expression Similarity to the Proximal Tubule of the Nephron

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Büttner F., Winter S., Rausch S., Reustle A., Kruck S., Junker K., Stenzl A., Agaimy A., Hartmann A., Bedke J., Schwab M., Schaeffeler E.

European Urology 2015

There is evidence that molecular features support subclassification of tumours, thereby improving prediction of patient outcome. Currently, two gene expression signatures (ccA/ccB and ClearCode34) have been established to classify clear cell renal cell carcinoma (ccRCC). Because RCC arises from nephron cell types, we aimed to explore its heterogeneity on a molecular level by comparing gene expression between tumour tissue and nephron regions. Based on genes that differ in expression between nephron regions, expression data of 479 ccRCCs and 212 papillary and 66 chromophobe RCCs from The Cancer Genome Atlas were correlated to those of nephron cell types. Cancer-specific survival (CSS) of ccRCC patients was significantly associated with gene expression similarity to the proximal tubules. Subsequently, a ccRCC risk score (S3-score) was established. Survival analyses indicated that the S3-score was significantly associated with CSS considering all cases of ccRCC, as well as metastatic and nonmetastatic ccRCC. Results could be validated in an independent cohort. The S3-score significantly improved the predictive ability of the ccA/ccB and ClearCode34 signatures, and the clinicopathologic-based stage, size, grade, and necrosis score (p [chi-square] = 1.56E-04). Intratumour heterogeneity of the S3-score was observed in 6 of 10 ccRCCs. In summary, the nephron-based S3-score enables prognostic risk stratification for ccRCC. Further studies are needed to evaluate its clinical utility. Patient summary: We developed a novel risk score for clear cell renal cell carcinoma to identify patients at risk of worse outcome that may improve patient care in the future. The present study established and validated a nephron-based model for clear cell renal cell carcinoma. This model significantly improved the currently established risk-stratification models for prediction of cancer-specific survival and may individualize patient care.


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