Nature Genetics
34, 226 - 230 (2003)
Published online: 18 May 2003; | doi:10.1038/ng1167
There is an Erratum (August 2003) associated with this Letter.
Gene expression phenotypic models that predict the activity of oncogenic pathwaysErich Huang1, 2, Seiichi Ishida1, 7, Jennifer Pittman2, 3, Holly Dressman1, 2, 4, Andrea Bild1, 2, Mark Kloos1, Mark D'Amico5, Richard G Pestell5, Mike West2, 3
& Joseph R Nevins1, 2, 4, 61
Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina 27710, USA. 2
Computational and Applied Genomics Program, Duke Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina 27710, USA. 3
Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina 27710, USA. 4
Center for Genome Technology, Duke Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina 27710, USA. 5
Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA. 6
Howard Hughes Medical Institute, Duke University Medical Center, Durham, North Carolina 27710, USA. 7
Present address: Division of Pharmacology, National Institute of Health Sciences 1-18-1, Kamiyoga, Setagaya-Ku Tokyo 158-8501, Japan.
Correspondence should be addressed to Joseph R Nevins j.nevins@duke.eduHigh-density DNA microarrays measure expression of large numbers of genes in one assay. The ability to find underlying structure in complex gene expression data sets and rigorously test association of that structure with biological conditions is essential to developing multi-faceted views of the gene activity that defines cellular phenotype. We 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 applied these techniques to the analysis of regulatory pathways controlled by the genes HRAS (Harvey rat sarcoma viral oncogene homolog), MYC (myelocytomatosis viral oncogene homolog) and E2F1, E2F2 and E2F3 (encoding E2F transcription factors 1, 2 and 3, respectively). 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 production of Myc or Ras proteins in primary tissue culture cells properly predict the activity of in vivo tumor models that result from deregulation of the MYC or HRAS 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.
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