Gene Expression Phenotypic Models That Predict the Activity of Oncogenic Pathways
Abstract
High-density DNA microarrays provide measures of very large numbers of genes in one assay. It is the ability to take highly complex gene expression datasets, find underlying structure, and rigorously test association of that structure with biological conditions that is critical to developing a multi-faceted views of the gene activity that defines cellular phenotype. We have sought to connect features of gene expression data with biological hypotheses by integrating "metagene" patterns from DNA microarray experiments in the characterization and prediction of oncogenic phenotypes. We apply these techniques to the analysis of the Ras, Myc, and E2F regulatory pathways. The phenotypic models accurately predict the activity of these pathways in the context of normal cell proliferation. Moreover, the metagene models trained with gene expression patterns evoked by ectopic Myc or Ras expression in primary tissue culture cells properly predict the activity of in vivo tumor models that result from the deregulation of the Myc or Ras pathways. We conclude that these gene expression phenotypes have the potential to characterize the complex genetic alterations that typify the neoplastic state, whether in vitro or in vivo, in a way that truly reflects the complexity of the regulatory pathways that are affected.
Authors
Erich Huang, Seiichi Ishida, Jennifer Pittman, Holly Dressman, Andrea Bild, Mark Kloos, Mark D'Amico, Richard G. Pestell, Mike West, and Joseph R. Nevins
Contact
- Mike West (mw@stat.duke.edu)
- Joseph Nevins (j.nevins@duke.edu)