Molecular sub-classification of renal epithelial tumors using meta-analysis of gene expression microarrays.

Thomas Sanford,Gennady Bratslavsky,Ariel Reinish,Paul H Chung,Ramaprasad Srinivasan,Vladimir Valera,W Marston Linehan,Chad Creighton

doi:10.1371/journal.pone.0021260

Thomas Sanford, Gennady Bratslavsky + Show 6 more

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https://doi.org/10.1371/journal.pone.0021260

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Abstract

PurposeTo evaluate the accuracy of the sub-classification of renal cortical neoplasms using molecular signatures.Experimental DesignA search of publicly available databases was performed to identify microarray datasets with multiple histologic sub-types of renal cortical neoplasms. Meta-analytic techniques were utilized to identify differentially expressed genes for each histologic subtype. The lists of genes obtained from the meta-analysis were used to create predictive signatures through the use of a pair-based method. These signatures were organized into an algorithm to sub-classify renal neoplasms. The use of these signatures according to our algorithm was validated on several independent datasets.ResultsWe identified three Gene Expression Omnibus datasets that fit our criteria to develop a training set. All of the datasets in our study utilized the Affymetrix platform. The final training dataset included 149 samples represented by the four most common histologic subtypes of renal cortical neoplasms: 69 clear cell, 41 papillary, 16 chromophobe, and 23 oncocytomas. When validation of our signatures was performed on external datasets, we were able to correctly classify 68 of the 72 samples (94%). 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.ConclusionsThrough the use of meta-analytic techniques, we were able to create an algorithm that sub-classified renal neoplasms on a molecular level with 94% accuracy across multiple independent datasets. This algorithm may aid in selecting molecular therapies and may improve the accuracy of subtyping of renal cortical tumors.

Highlights

Renal epithelial tumors are a diverse group of neoplasms that have been sub-classified based on histologic morphology [1]
Clear cell tumors are most commonly associated with mutations in the VHL tumor suppressor gene, familial and a subset of papillary tumors are associated with dysregulation of the MET proto-oncogene, and familial chromophobe tumors and oncocytomas are associated with dysregulation of the BHD gene [4,5,6,7,8]
Dataset search The Gene Expression Omnibus (GEO) and Array Express databases were searched for published microarray datasets involving renal neoplasms

Summary

Introduction

Renal epithelial tumors are a diverse group of neoplasms that have been sub-classified based on histologic morphology [1]. The four most common types of renal cortical neoplasms are clear cell renal cell carcinoma (RCC) (63–89%,), papillary RCC (7–19%), chromophobe RCC (2–6%), and oncocytoma (5–7%) [2,3]. Clear cell tumors are most commonly associated with mutations in the VHL tumor suppressor gene, familial and a subset of papillary (type I) tumors are associated with dysregulation of the MET proto-oncogene, and familial chromophobe tumors and oncocytomas are associated with dysregulation of the BHD gene [4,5,6,7,8]. Improved understanding of the genetic alterations and downstream molecular pathways of the histologic subtypes of renal epithelial neoplasms has led to the development of targeted molecular therapies and the tailoring of treatment and follow-up to the subtype of the tumor. The FDA has approved a number of targeted therapies for clear cell histology and there are promising clinical trials underway for papillary histology [9,10]

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