Platinum Priority – Kidney Cancer Editorial by Maria J. Ribal on pp. 731–733 of this issue Accurate Molecular Classification of Kidney Cancer Subtypes Using MicroRNA Signature Youssef M. Youssef a,b , Nicole M.A. White a,b , Jo ¨rg Grigull c , Adriana Krizova a,b , Christina Samy a,b , Salvador Mejia-Guerrero a,b , Andrew Evans a,d , George M. Yousef a,b, * a Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada b Department of Laboratory Medicine and the Keenan Research Centre in the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada c Department of Mathematics and Statistics, York University, Toronto, Canada d Department of Pathology, Toronto General Hospital, Toronto, Canada EUROPEAN UROLOGY 59 (2011) 721–730 available at www.sciencedirect.com journal homepage: www.europeanurology.com Article info Article history: Accepted January 3, 2011 Published online ahead of print on January 13, 2011 Keywords: Biomarker Cancer Clear cell renal cell carcinoma Chromophobe Diagnosis Kidney cancer MicroRNA miRNA Oncocytoma Papillary Pathology Profiling Statistical classifier Prognosis RCC Renal cancer Subtypes Tumour markers Unclassified Abstract Background: Renal cell carcinoma (RCC) encompasses different histologic subtypes. Distinguishing between the subtypes is usually made by morphologic assessment, which is not always accurate. Objective: Our aim was to identify microRNA (miRNA) signatures that can distinguish the different RCC subtypes accurately. Design, setting, and participants: A total of 94 different subtype cases were analysed. miRNA microarray analysis was performed on fresh frozen tissues of three common RCC subtypes (clear cell, chromophobe, and papillary) and on oncocytoma. Results were validated on the original as well as on an independent set of tumours, using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis with miRNA- specific primers. Measurements: Microarray data were analysed by standard approaches. Relative ex- pression for qRT-PCR was determined using the DDC T method, and expression values were normalised to small nucleolar RNA, C/D box 44 (SNORD44, formerly RNU44). Experiments were done in triplicate, and an average was calculated. Fold change was expressed as a log 2 value. The top-scoring pairs classifier identified operational decision rules for distinguishing between different RCC subtypes and was robust under cross-validation. Results and limitations: We developed a classification system that can distinguish the different RCC subtypes using unique miRNA signatures in a maximum of four steps. The system has a sensitivity of 97% in distinguishing normal from RCC, 100% for clear cell RCC (ccRCC) subtype, 97% for papillary RCC (pRCC) subtype, and 100% accuracy in distinguishing oncocytoma from chromophobe RCC (chRCC) subtype. This system was cross-validated and showed an accuracy of about 90%. The oncogenesis of ccRCC is more closely related to pRCC, whereas chRCC is comparable with oncocytoma. We also developed a binary classification system that can distinguish between two individual subtypes. Conclusions: MiRNA expression patterns can distinguish between RCC subtypes. # 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved. * Corresponding author. Department of Laboratory Medicine, St. Michael’s Hospital, 30 Bond Street, Toronto, ON, M5B 1W8, Canada. Tel. +1 416 864 6060x6129; Fax: +1 416 864 5648. E-mail address: yousefg@smh.ca (G.M. Yousef). 0302-2838/$ – see back matter # 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.eururo.2011.01.004