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

Abstract

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 upor 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 silicoderived 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.

Aneuploidy and Cancer: From Correlation to Causation

“…Thus, drug resistance must be polygenic. The same is likely to be true for the other cancer-specific phenotypes such as grossly altered metabolism, invasiveness, metastasis, and immortality [40,45], because all of these phenotypes correlate with altered expressions of thousands of genes [34,87,[116][117][118] and with highly abnormal concentrations of thousands of normal proteins [16,40,51,119]. Moreover, in highly aneuploid cancer cells the number of centrosomes is increased up to 5-fold -from a normal of two to around ten -and at the same time their structures are often altered [120][121][122][123].…”

“…By contrast, the chromosomal theory of cancer explains the complexity of cancer-specific phenotypes by the complexicity of the genetic units that are varied, namely chromosomes with thousands of genes. Accordingly, the complex phenotypes of cancer cells have recently been shown to correlate with over-and underexpressions of thousands of genes [34,87,[116][117][118]136]. Likewise, cancer cells over-and underproduce thousands of normal proteins [16,40,51,119].…”

Identification of deregulated oncogenic pathways in renal cell carcinoma: an integrated oncogenomic approach based on gene expression profiling

“…Subsequent studies have shown that it is possible to predict regions of likely DNA copy number changes through examination of the population of mRNA generated from the genes that map to each chromosome (Crawley and Furge, 2002;Harding et al, 2002). Previously we have shown that we could predict cytogenetic abnormalities with >80% accuracy across the different subtypes of RCC (Furge et al, 2004). As such, it is possible to correlate specific cytogenetic abnormalities with specific pathway abnormalities through examination of the gene expression data.…”

“…In an era of limited resources, it will be increasingly important to develop and apply methods such as these to extract as much information as possible from existing data sets. (Furge et al, 2004;Kim and Volsky, 2005) method combined with MYC, E2F3, SRC, RAS, CTNNB1 (b-catenin), synergistic HGF/VEGF VHL, FH, IGF1, wound response, and hypoxia signatures obtained from the literature (Dupont et al, 2001;Gerritsen et al, 2003;Staller et al, 2003;Bild et al, 2005;Adler et al, 2006;Chi et al, 2006;Vanharanta et al, 2006) and the HGF, VEGF, and NF-kB signatures generated using data from the Gene Expression Omnibus (GDS406, GDS495 and GSE2489) were used. Statistics were computed for each up-and downregulated component and then combined.…”

465 Rna Sequencing Reveals Up-Regulation of Runx1-Runx1t1 Gene Signatures in Clear Cell Renal Cell Carcinoma

“…Gene expression profiling, based on microarray hybridization, has been successfully used for the identification of genes that are differentially expressed among RCC subtypes and in the search for new therapeutic targets [3][4][5][6]. This method has also been correlated with chromosomal abnormalities and deregulated oncogenic pathways.…”