Abstract
Gene expression signatures can predict the activation of oncogenic pathways and other phenotypes of interest via quantitative models that combine the expression levels of multiple genes. However, as the number of platforms to measure genome-wide gene expression proliferates, there is an increasing need to develop models that can be ported across diverse platforms. Because of the range of technologies that measure gene expression, the resulting signal values can vary greatly. To understand how this variation can affect the prediction of gene expression signatures, we have investigated the ability of gene expression signatures to predict pathway activation across Affymetrix and Illumina microarrays. We hybridized the same RNA samples to both platforms and compared the resultant gene expression readings, as well as the signature predictions. Using a new approach to map probes across platforms, we found that the genes in the signatures from the two platforms were highly similar, and that the predictions they generated were also strongly correlated. This demonstrates that our method can map probes from Affymetrix and Illumina microarrays, and that this mapping can be used to predict gene expression signatures across platforms.
Highlights
Biological processes are driven by the coordinated actions of multiple gene products
Similarity of Genes in Gene Expression Signatures While the previous analysis showed that gene expression patterns could be, overall, concordant between the Affymetrix HG-U133A 2.0 (AFFY) and Illumina Human HT-12 v4.0 (ILLU) platforms, we examined the similarity of the genes that comprised gene expression signatures
This was already a highly significant overlap, we examined whether the genes from the AFFY signature that did not meet the cutoff for the ILLU signature still had a strong score (Figure 2C)
Summary
Biological processes are driven by the coordinated actions of multiple gene products. The activation of a process, whether it is a cellular activity, activation of a signaling pathway, or other molecular event, is marked by a characteristic change in the expression of a set of genes, which denotes the signature of that process [1,2,3]. Gene expression signatures developed in vitro can be used to predict phenotypes in vivo and have been used to predict activation of oncogenic pathways, outcomes in cancers, subtypes of cancers, and sites of cancer metastases [2,4,5,6]. Genes that can distinguish these two states are selected and combined into a model that can predict the activation of the pathway in another sample. An implementation to score activation of oncogenic pathways was recently made available on the web as part of the SIGNATURE project [9]
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