Distinguishing tumor sub-types

[编者的话]

文章主题是利用聚类方法来分析芯片所产生的基因表达谱数据，从而来区分癌症亚型。

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.

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