372 MOLECULAR SUB-CLASSIFICATION OF RENAL EPITHELIAL NEOPLASMS (2010)

You have accessJournal of UrologyKidney Cancer: Basic Research III1 Apr 2010372 MOLECULAR SUB-CLASSIFICATION OF RENAL EPITHELIAL NEOPLASMS Thomas Sanford, Paul Chung, Ariel Reinish, Ramaprasad Srinivasan, Peter Pinto, W. Marston Linehan, and Gennady Bratslavsky Thomas SanfordThomas Sanford More articles by this author , Paul ChungPaul Chung More articles by this author , Ariel ReinishAriel Reinish More articles by this author , Ramaprasad SrinivasanRamaprasad Srinivasan More articles by this author , Peter PintoPeter Pinto More articles by this author , W. Marston LinehanW. Marston Linehan More articles by this author , and Gennady BratslavskyGennady Bratslavsky More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2010.02.439AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Sub-classification of renal epithelial neoplasms poses a major challenge to both pathologists and treating physicians. Despite the use of immunohistochemistry to distinguish the sub-classes of renal neoplasms, diagnostic accuracy is still suboptimal and frequently yields confusing results. In this study, we sought to improve the accuracy of sub-classification of renal cell carcinoma using molecular signatures. METHODS We searched the Gene Expression Omnibus (GEO) database for publically available gene expression microarray datasets with multiple histologic sub-types of renal cortical neoplasms. The data were normalized and filtered using BRB array-tools (http://linus.nci.nih.gov/BRB-ArrayTools.html). Meta-analysis was performed to identify differentially expressed genes for each histologic subtype. The list of genes obtained from the meta-analysis was used to create 50-gene predictive signatures through a pair-based method. These predictive signatures were organized into an algorithm to sub-classify renal neoplasms based on molecular characteristics. The signatures were then validated on independent datasets identified in GEO. The training and validation datasets were obtained from different sources. RESULTS We identified three GEO datasets that fit our criteria to develop a training set. The training set included 149 samples and consisted of 69 clear cell RCC, 41 papillary type 1 RCC, 16 chromophobe RCC, and 23 oncocytomas. From these samples, we created predictive signatures that were applied in sequential fashion according to our algorithm. We evaluated the performance of these signatures on a validation set comprised of data from five separate GEO datasets. We were able to correctly classify 68 of the 72 samples (94%) in our validation set. The correct classification by subtype was: 19/20 (95%) for clear cell, 14/14 (100%) for papillary, 17/19 (89%) for chromophobe, 18/19 (95%) for oncocytomas. CONCLUSIONS Through the use of meta-analytic techniques, we were able to create an algorithm that sub-classifies renal neoplasms on a molecular level with 94% accuracy across multiple independent datasets. This algorithm may improve the accuracy of sub-classification of renal neoplasms, aid in selection of molecular therapies, and potentially allow for increased diagnostic yield by decreasing the number of “unclassified” or “indeterminate” renal tumors and biopsies. Bethesda, MD© 2010 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 183Issue 4SApril 2010Page: e147 Peer Review Report Advertisement Copyright & Permissions© 2010 by American Urological Association Education and Research, Inc.MetricsAuthor Information Thomas Sanford More articles by this author Paul Chung More articles by this author Ariel Reinish More articles by this author Ramaprasad Srinivasan More articles by this author Peter Pinto More articles by this author W. Marston Linehan More articles by this author Gennady Bratslavsky More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...