Tumor suppressors act as components of complex networks whose overall function can be impaired by many different genetic or epigenetic alterations. While many of these alterations have been elegantly recapitulated in mouse, existing technology has been limited in its ability to model the significant range and complexity of gene suppression that occurs during neoplastic progression. The recent development of retroviral systems capable of mediating stable gene silencing has vastly increased our capacity to recreate the precise gene expression profiles seen in tumor cells. We propose to exploit the emerging power of RNA interference (RNAi) to study tumor suppressor gene networks in vivo, in particular, the impact of tumor suppressor hypomorphs on tumor development and responses to cancer therapy. Our team includes investigators that have been pioneers in establishing methods for using short hairpin RNAs (shRNAs) to stably suppress gene function in culture cells and in animals. As well as investigators with substantial expertise in modeling cancer and cancer therapy in the mouse who have successfully demonstrated that shRNAs can create 'epi-allelic' series of hypomorphs that produce distinct tumor phenotypes in vivo. Our experimental approach will be to: 1) develop RNAi technology for suppressing gene function in both chimeric (genetic mosaic) and germline settings; 2) use conditional systems to determine the extent to which tumor suppressor gene inactivation is required for tumor maintenance, and the consequences of gene reactivation on tumor behavior; 3) use this technology to produce an 'epi-allelic' series of tumor suppressor hypomorphs that may produce different pathologies depending on the strength of suppression; 4) take advantage of new shRNA libraries presently capable of targeting cancer relevant genes in the mouse genome (and likely to expand genome-wide) to conduct unbiased genetic screens for modulators of tumor phenotypes; 5) build upon our previous success with the hematopoeitic system to model carcinomas, with a particular emphasis on developing rapid methods to evaluate genetic interactions during breast carcinogenesis and therapy. We expect that these studies will provide new insights into how tumor suppressor networks are disabled during the development of particular neoplasias, and ultimately identify key nodes in these networks that may be sensitive to therapeutic intervention. Moreover, they will produce new mouse models of human cancer that can be used to understand treatment responses and as preclinical models for testing novel therapies.
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