Expression profiling of human renal carcinomas with functional taxonomic analysis
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Published on: January 22, 2013
Michael A Gieseg1, Theresa Cody, Michael Z Man
1Pfizer Global Research & Development, 2800 Plymouth Rd, Ann Arbor, Michigan 48105, USA. Michael.Gieseg@pfizer.com
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.
Insights
Gene expression profiling using Affymetrix GeneChip successfully distinguished kidney tumor subtypes, clear cell and chromophobe carcinomas, from normal kidney tissue. Logarithmic data transformation and functional taxonomy aided in interpreting these distinct molecular signatures.
Area of Science:
- Molecular biology
- Genomics
- Cancer research
Background:
- Molecular characterization is crucial for understanding cancer.
- Nucleic acid array technologies enable large-scale gene product analysis.
- Accurate methods are needed to differentiate tumor subtypes.
Purpose of the Study:
- To differentiate kidney tumor subtypes using gene expression profiling.
- To identify molecular signatures distinguishing clear cell and chromophobe carcinomas from normal kidney tissue.
Main Methods:
- Utilized Affymetrix GeneChip for mRNA expression profiling of 21 human kidney tissue samples.
- Applied ten clustering algorithms and four data pre-processing methods.
- Identified differentially expressed genes using statistical selection and classified them with Functional Taxonomy.
Main Results:
- Gene expression profiles clearly separated clear cell and chromophobe carcinomas from normal kidney.
- Identified 355 genes with significant expression changes in clear cell renal carcinomas.
- Distinct molecular signatures were observed for each tumor type, enriched in Metabolism, Signal Transduction, and Cellular Organization categories.
Conclusions:
- Affymetrix GeneChip profiling effectively differentiated kidney tumor subtypes and normal tissue.
- Clustering methods with logarithmic data transformation yielded biologically relevant groupings.
- Functional Taxonomy proved useful for interpreting complex gene expression data.