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Robust Classification of Renal Cell Carcinoma Based on Gene Expression Data and Predicted Cytogenetic Profiles

¹ Bioinformatics Special Program and ² Laboratory of Cancer Genetics, Van Andel Research Institute, Grand Rapids, Michigan; ³ Department of Urology, School of Medicine, The University of Tokyshima, Tokushima, Japan; ⁴ Department of Urology, School of Medicine, Iwate Medical University, Morika, Japan; ⁵ Department of Pathology, University of Chicago Cancer Research Center, Chicago, Illinois; and ⁶ Divison of Urology, Spectrum Health Hospital, Grand Rapids, Michigan

Renal cell carcinoma (RCC) is a heterogeneous disease that includes several histologically distinct subtypes. The most common RCC subtypes are clear cell, papillary, and chromophobe, and recent gene expression profiling studies suggest that classification of RCC based on transcriptional signatures could be beneficial. Traditionally, however, patterns of chromosomal alterations have been used to assist in the molecular classification of RCC. The purpose of this study was to determine whether it was possible to develop a classification model for the three major RCC subtypes that utilizes gene expression profiles as the bases for both molecular genetic and cytogenetic classification. Gene expression profiles were first used to build an expression-based RCC classifier. The RCC gene expression profiles were then examined for the presence of regional gene expression biases. Regional expression biases are genetic intervals that contain a disproportionate number of genes that are coordinately up- or down-regulated. The presence of a regional gene expression bias often indicates the presence of a chromosomal abnormality. In this study, we demonstrate an expression-based classifier can distinguish between the three most common RCC subtypes in 99% of cases (n = 73). We also demonstrate that detection of regional expression biases accurately identifies cytogenetic features common to RCC. Additionally, the in silico-derived cytogenetic profiles could be used to classify 81% of cases. Taken together, these data demonstrate that it is possible to construct a robust classification model for RCC using both transcriptional and cytogenetic features derived from a gene expression profile.

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