Abstract 802: Development and validation of a microRNA-based diagnostic assay for the classification of renal cell tumors.
Authors: Robert Wassman, Brianna St. Cyr, Eddie Fridman, Iris Barshack, Yajue Huang, Sofia Zilber, Mats Sanden, Hila Benjamin, Noga Yerushalmi, Yael Spector
Journal: Cancer Research (2013)

Abstract Background: Renal cancers account for more than 3% of adult malignancies and result in more than 13,000 deaths per year in the US alone. The four most common types of kidney tumors include the malignant renal cell carcinomas: clear cell, papillary and chromophobe, as well as the benign oncocytoma. These histological subtypes vary in their clinical course and their prognosis, and different clinical strategies have been developed for their management. The differential diagnosis between the subtypes of kidney tumors based on morphology alone can be challenging, and is subjected to inter- and intra-observer variability. Even when utilizing immunohistochemistry (IHC) markers, the ability to differentiate between sub-types can be difficult, especially in the setting of uncommon morphology and biopsy sample with small amounts of tumor tissue. We present the development and validation of a microRNA-based test for classifying primary kidney tumors. Methods: 181 Formalin Fixed Paraffin Embedded (FFPE) samples from primary kidney tumors were collected and reviewed by pathologists from different institutes according to morphology and available IHC labeling data. High-quality total RNA, including the well-preserved microRNA fraction, was extracted from the FFPE samples using a proprietary protocol. Expression levels of hundreds of microRNAs were profiled using a custom microarray platform. Technical validation of the array results was performed using qRT-PCR. A diagnostic assay was developed using a K-nearest neighbor algorithm that searches for the 5 samples in the training database (181 samples used for assay development) that are most similar to the tested sample. The result for the tested sample is defined by a majority vote of the pre-determined subtypes of these 5 closest neighbors. A validation set of 201 independent samples was classified using the assay and analyzed blindly. Results: A set of 24 differentially expressed microRNAs were found to separate the four kidney tumor subtypes and were chosen as classifiers in the KNN algorithm. Clinical validation was performed using an independent, blinded sample set. The test was able to produce results for 92% of the validation set of 201 samples with accuracy of 95%. Conclusions: Expression levels of 24 microRNAs measured on a microarray platform were found to accurately differentiate the four main types of primary kidney tumors. These findings were the basis for the development and validation of a standardized diagnostic assay for the classification of renal cell tumors in FFPE samples from resections or biopsies. This assay can serve as a reliable diagnostic tool to aid physicians with the growing unmet need for kidney tumor classification. Citation Format: Robert Wassman, Brianna St. Cyr, Eddie Fridman, Iris Barshack, Yajue Huang, Sofia Zilber, Mats Sanden, Hila Benjamin, Noga Yerushalmi, Yael Spector. Development and validation of a microRNA-based diagnostic assay for the classification of renal cell tumors. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 802. doi:10.1158/1538-7445.AM2013-802