The phenotypic heterogeneity of human cancers presents major challenges to advancing our understanding of disease mechanisms as well as to developing effective strategies for therapeutic design. This heterogeneity is also reflected in the variation in activity of cell signaling pathways that control cell growth and determine cell fate, processes critical for driving the cancer phenotype. The primary goal of the work described in this proposal is to take advantage of recent developments in the use of genome-scale measures of gene expression, together with advanced computational tools, to develop a more detailed understanding of the gene regulatory networks associated with the action of various oncogenic activities. Our focus is two-fold: develop a better understanding of the function and inter-connection of cell signaling pathways and second, utilize this information to translate to opportunities in clinical practice. A central focus of our work has been the development of expression signatures as a representation of a biological state, in this instance the activation of a pathway. These signatures will be expanded to a large collection of pathways relevant for cancer phenotypes;we will also develop and utilize novel statistical methodologies to dissect the complexity of cell signaling pathways, developing a library of verified pathway sub-signatures. In addition, the gene expression signatures reflecting cell signaling pathway activation, including pathway sub-signatures, will be used to define subgroups of cancer as the basis for defining distinct mechanisms of disease. And finally, genome-wide siRNA targeting will be used to identify cellular that influence the activity of cell signaling pathways.

Public Health Relevance

Recent studies describing in-depth analyses of gene mutations in a number of human cancers have emphasized the complexity and heterogeneity of cancer and the importance of placing such analyses in pathway-specific contexts. A major focus of our work has been the use of gene expression signatures to define and predict the activity of a variety of cell signaling pathways that contribute to the oncogenic phenotype. Importantly, not only do these signatures provide a mechanism to dissect the heterogeneity of human cancers but they also provide an understanding of the events associated with this heterogeneity. Furthermore, since these signatures also predict sensitivity to various targeted therapeutics, these tools provide a framework for developing a strategy for treatment of individual patients.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Cancer Genetics Study Section (CG)
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Li, Jerry
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Duke University
Schools of Medicine
United States
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