It is now generally accepted that cancer is a genetic disease, mediated in large part by somatic mutations in specific genes. However, many of the key tumor suppressor genes and oncogenes responsible for cancer initiation and progression remain to be identified. Although technical hurdles have limited our ability to identify such genes in a comprehensive fashion, the delineation of the sequence of the human genome, coupled with recent advances DMAanalysis technologies, have created an unprecedented opportunity for progress in this area. Over the past several years we have developed high throughput technologies for sequencing and mutational analyses to rapidly analyze gene families in human cancer. We have specifically focused on gene families involved in signal transduction, as these have been implicated in tumorigenesis and may be amenable to therapeutic intervention. These approaches have recently permitted the mutational analysis of all members of the PI-3 kinase, tyrosine kinase, tyrosine phosphatase, and serine/threonine kinase gene families. These genetic analyses have identified a high frequency of somatic mutations in PIK3CA as well as mutations in several kinases and phosphatases not previously implicated in human cancers. The purpose of this proposal is to use our cancer sequencing technologies to perform large-scale genetic analyses of gene families involved in signal transduction in human cancer. These families will include the serine/threonine protein phosphatase family, the lipid phosphatase gene family, the G protein-coupled receptor gene family, the heterotrimeric G protein gene family, the GTPase gene superfamily, and the G protein modulator gene family. Initially, the genes comprising these families will be analyzed for somatic alterations in colorectal cancers. Subsequently, selected genes will be further analyzed in lung, breast, gastric, brain, pancreatic and ovarian cancers to determine whether mutations in these genes provide common mechanisms of tumorigenesis shared by different cancer types. Finally, we will examine the mutation spectrum observed in the different tumors in order to determine if the mutated genes may have equivalent tumorigenic effects and are involved in specific signaling pathways. We envision that analyses of these gene families will allow us to identify genes not previously implicated in human cancer and provide insights into signaling pathways involved in tumor progression. The studies described in this application should lead to a greater understanding of cancer etiology, improved tools for cancer detection and diagnosis, new targets for therapeutic and preventative intervention, and opportunities for individualized treatment.
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