Abstract 1482: MicroRNA expression profiles classify renal cell carcinoma subtypes
Authors: Wach Sven , Nolte Elke , Anne Theil , Stoehr Christine , Rau Tilman T. , Hartmann Arndt , Ekici Arif B. , Keck Bastian , Taubert Helge , Wullich Bernd 
Published: May 2015

Introduction

Renal cell carcinoma (RCC) accounts for nearly 4% of all malignancies worldwide. According to the WHO classification of tumors of the urinary system, RCC is a collection of different subtypes rather than one entity. Besides the conventional clear cell RCC (ccRCC), the papillary RCC (pRCC), with an incidence of 10%-15%, is the second most common entity among renal malignancies. By histological characteristics, pRCCs can further be sub-divided into two distinct subtypes. Hereby, pRCCs type 2 are correlated with a worse clinical outcome. We sought to define characteristic miRNA expression profiles that could be useful for a molecular classification of RCC subtypes.

Material and Methods

Using miRNA microarrays (GeneChip miRNA arrays, Affymetrix), we established miRNA expression profiles of a discovery set of ccRCC and pRCC subtypes 1 and 2 tissue samples. The expression of a selected subset of miRNAs was validated by quantitative PCR. The best discriminating miRNAs of this initial set were used to establish a classification model for tissue samples. This core set of miRNAs was analyzed in a second, independent patient cohort of 69 tissue samples. Using the pre-defined classification rules, the samples of the independent patient cohort were classified in a double-blind fashion solely by their miRNA expression.

Results

Every RCC entity and subtype displayed a characteristic pattern of miRNA expression. Ten miRNAs were selected based on their ability to discriminate between tumor and normal tissue or between different subtypes of RCC. Binary logistic regression identified a core set of only five miRNAs that was able to fully classify any given sample with an overall accuracy of 88%. This classification scheme was applied to an independent patient cohort. Here five miRNAs (miRNAs miR-145, -200c, -210, -502-3p, and let-7c) were able to classify the samples with an overall accuracy of 64%. MiRNAs that are deregulated in every RCC entity target members of the family of multidrug-resistance proteins (MRPs) and miRNAs that distinguish between pRCC subtypes target components of the Jak-STAT signaling pathway.

Conclusions

Every entity or subtype of RCC displays a characteristic and unique pattern of miRNA expression. A core set of only five miRNAs can be used to distinguish not only between tumor and normal tissue samples but also between different tumor entities with high accuracy. Deregulated miRNAs might contribute to the high chemotherapy resistance of RCC. Furthermore, our results indicate that pRCC type 2 tumors could be dependent on oncogenic MYC signaling.

Citation Format: Sven Wach, Elke Nolte, Theil Anne, Christine Stoehr, Tilman T. Rau, Arndt Hartmann, Arif B. Ekici, Bastian Keck, Helge Taubert, Bernd Wullich. MicroRNA expression profiles classify renal cell carcinoma subtypes. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1482. doi:10.1158/1538-7445.AM2014-1482