Abstract
Abstract Gene expression profiling has yielded important insights about tumor biology that may improve treatment decisions in patients. However, it is difficult to collect a specimen of pure tumor cells, and thus microarray measurements usually reflect the contribution of tumor cells as well as stromal and other normal cells. We applied unsupervised matrix factorization methods to gene expression data to derive several sets of co-expressed genes, or signatures, that together comprise a set of independent descriptors of breast tumors. Some of these signatures correspond to specific cell types (adipocytes, lymphocytes, fibroblasts), while others reflect well-known tumor-intrinsic expression programs (ER, ERBB2, proliferation). We confirmed the specificity of the signatures using expression data from purified normal cells and tumor cell lines, microdissected tumors, and bulk tumors with corresponding histological cellularity estimates. In several data sets, cell-type signatures were highly variable and anticorrelated with tumor-intrinsic signatures, confirming that variation in normal cell content is a potential source of measurement bias.Overall, these results provide an intuitive framework for the interpetation of tumor expression profiles and may lead to an improved understanding of the physiological mechanisms involved in tumor development. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 1166.
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