Expression profiling of human renal carcinomas with functional taxonomic analysis

Abstract

Background: Molecular characterization has contributed to the understanding of the inception, progression, treatment and prognosis of cancer. Nucleic acid array-based technologies extend molecular characterization of tumors to thousands of gene products. To effectively discriminate between tumor sub-types, reliable laboratory techniques and analytic methods are required.

Results: We derived mRNA expression profiles from 21 human tissue samples (eight normal kidneys and 13 kidney tumors) and two pooled samples using the Affymetrix GeneChip platform. A panel of ten clustering algorithms combined with four data pre-processing methods identified a consensus cluster dendrogram in 18 of 40 analyses and of these 16 used a logarithmic transformation. Within the consensus dendrogram the expression profiles of the samples grouped according to tissue type; clear cell and chromophobe carcinomas displayed distinctly different gene expression patterns. By using a rigorous statistical selection based method we identified 355 genes that showed significant (p < 0.001) gene expression changes in clear cell renal carcinomas compared to normal kidney. These genes were classified with a tool to conceptualize expression patterns called "Functional Taxonomy". Each tumor type had a distinct "signature," with a high number of genes in the categories of Metabolism, Signal Transduction, and Cellular and Matrix Organization and Adhesion.

Conclusions: Affymetrix GeneChip profiling differentiated clear cell and chromophobe carcinomas from one another and from normal kidney cortex. Clustering methods that used logarithmic transformation of data sets produced dendrograms consistent with the sample biology. Functional taxonomy provided a practical approach to the interpretation of gene expression data.

Figure 1

Histology of tissue samples. Frozen sections of normal kidney with glomerulus in center (A) and clear cell RCCa (B). Representative paraffin sections of clear cell (C, E, G) and chromophobe RCCa (D, F, H). Immunohistochemical stains for CD 31 (E and F) and CD 3 (G and H). Note that the clear cell tumor has an the extensive network of CD 31-positive vessels (E) and numerous scattered CD 3-positive lymphoctyes (G). In contrast, in the chromophobe there is a relative paucity of vessels (F) and few T cells (H). Original magnification:A, B 100× C – H 200×. Staining: A – D, hematoxylin and eosin; E – H, diaminobenzidine immunoperoxidase with hematoxylin.

Figure 2

Sample cluster of the pilot data set (A) and sample and gene cluster of the complete data set (B). The sample cluster dendrogram form the pilot data set (A) uses the average linkage clustering method and represents the most frequently occurring dendrogram out of the 40 derived for the data set. Horizontal distance indicates relatedness. The sample and gene cluster (B) from all 23 samples shows a colored representation of the gene expression data where columns are individual genes and rows are separate samples. The color in each cell of the table represents the median adjusted expression value of each gene.

Figure 3

Functional taxonomy signatures of clear cell and chromophobe RCCa. The 456 genes that were selected according to the criteria in Table 3 were annotated and placed into 16 cellular function categories (A). The number of genes in each cellular function category is displayed. A positive value indicates the genes were increased in expression while a negative value indicates the genes were decreased in expression. The second-level signatures for the subcategories of Signal Transduction, and of Cellular and Matrix Organization and Adhesion are shown in panels (B) and (C) respectively.

Figure 4

Functional taxonomy signatures of clear cell RCCa. The 355 genes that were selected from the complete data set were annotated and placed into 16 cellular function categories (A). The number of genes in each cellular function category is displayed. A positive value indicates the genes were increased in expression while a negative value indicates the genes were decreased in expression. The second-level signatures for the subcategories of Signal Transduction, and of Cellular and Matrix Organization and Adhesion are shown in panels (B) and (C) respectively.