Gene expression phenotypes of oncogenic pathways and breast cancers
aDepartment of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC, United States, bHoward Hughes Medical Institute, Durham, NC, United States, cInstitute of Statistics and Decision Sciences, Duke University, Durham, NC, United States, dComputational and Applied Genomics Program, Duke Institute for Genome Sciences and Policy, Durham, NC, United States
Genome-scale measures of gene expression derived from DNA microarray studies has the potential for adding enormous information to the analysis of biological phenotypes. Perhaps the most successful application of this data has been in the characterization of human cancers, including the ability to predict clinical outcomes. Nevertheless, most analyses have used gene expression profiles to define broad group distinctions, similar to the use of traditional clinical risk factors, leaving considerable heterogeneity within the broadly defined groups. One strategy to resolve this heterogeneity is to make use of multiple gene expression patterns that are more powerful in defining individual characteristics and predicting outcomes than any single gene expression pattern. Statistical tree-based classification systems provide a framework for assessing multiple patterns, that we term metagenes, selecting those that are most capable of resolving the biological heterogeneity. Moreover, this framework provides a mechanism to combine multiple forms of data, both genomic and clinical, to most effectively characterize individual patients and achieve the goal of personalized predictions of clinical outcomes. We have also applied similar strategies to identify gene expression phenotypes of oncogenic signaling pathways including Ras, Myc, and Rb-E2F pathways. Our phenotypic models accurately predict the activity of these pathways, including in the context of mouse tumor models involving the deregulation of these pathways. We suggest that these gene expression phenotypes have the potential to characterize the complex genetic alterations that typify the neoplastic state in a way that truly reflects the complexity of the regulatory pathways that are affected.
Paper presented at the International Symposium on Predictive Oncology and Intervention Strategies; Nice, France; February 7 - 10, 2004; in plenary session 703 (Oncogenic pathways).