Gene Expression Phenotypes of Oncogenic Signaling Pathways (2003)

The study of oncogenic signaling pathways has progressed remarkably over the past few decades, resulting in the identification of a large collection of activated receptors, receptorcoupled activators, kinases, phosphatases, transcription factors, and various negative regulators of these activities.1-3 Nevertheless, most of the details regarding the pathways regulated by these oncogenic proteins is limited to events linearly associated with the pathway—by their nature, the discoveries have been incremental, adding new information piece by piece. Now, however, with the advent of technologies that have the power to greatly expand the scope of such studies through genome-scale analyses of gene expression patterns, large-scale analysis of patterns of protein accumulation, protein modification, and protein interaction, we have the potential to rapidly expand the understanding of these pathways and, most importantly, to integrate the action of the pathways into a unified picture. Our recent study4 represents a first step towards this goal of building a broader understanding of oncogenic signaling events. This work aims to lay a foundation for using the complex, massively multivariate data of genome-scale expression analyses for identifying and quantifying fundamental physiologic states in cell proliferation as well as pathologic states in neoplasia. Although the application of DNA microarray technology for the analysis of gene expression has been applied in numerous studies, including many focused on the analysis of oncogenic pathways,5-8 the focus and concepts underlying our analytic methods define an approach that differs from past studies and that offers the potential to broaden the understanding of oncogenic pathways. DNA microarrays have the capacity to score the activity of thousands of genes simultaneously—in one sense, providing a very highthroughput northern analysis. For a molecular biologist, analyses of gene expression are all about fold change—a gene induced 10-fold is often considered more important than a gene induced 2-fold. Indeed, invoking statistics in a molecular biology experiment to ascertain the significance of a 2-fold change is simply not done—the observed change is either visually clear or not and no level of statistical significance will alter this conclusion. Genomic technology in the form of DNA microarray data has changed all of this dramatically. The use of DNA microarray data is not about the simple measures of differential expression gene-by-gene. Rather, the power lies in the ability to assay many thousands of genes simultaneously and evaluate the multivariate patterns of change across subsets that characterize a physiological or clinical state. This complexity opens the way to powerful tools of statistical analysis—not merely simple measures of reproducibility but identification of complex patterns within the data that reflect biology. In a very real sense, this approach is no different than the analysis of complex financial datasets to identify underlying social or political factors that influence currency valuation. The power of genomic technology, generating datasets of enormous complexity, heralds the transformation of biology into a quantitative science.