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- W2524567910 abstract "Gene interference screens are a widely adopted andpopular tool for uncovering gene function but imperfections in thetechnology limit the power of these investigations. There are manycompleted and on-going RNAi investigations across a multitude ofbiological systems because these experiments are scalable,cost-effective, and relatively easily adapted to multipleexperimental environments. The most influential disadvantage isthat many of the individual reagents are non-specific and interferewith genes other than the intended target. Efforts to improvelimitations in RNAi have focused on statistical models andimproving reagents, yet have not explored using biological contextto select gene targets. This thesis uses network modeling and dataintegration to provide context for gene interference studies, anddemonstrates the utility of this approach in two systems: AcuteLymphoblastic Leukemia (ALL) is a disease of undifferentiatedB-cells that results from accumulation of genetic lesions, yet wehave an incomplete understanding of all genes contributing to thedisease and how they interact. To discover genetic mediators ofthis disease, we employ a genome-scale shRNA screen, and complementthis data with differential mRNA expression and ChIP-seq data usingnetwork integration. The integrated model identifies processes notrepresented in any input set and predicts novel genes contributingto disease. We specifically validate the role of Wwpl as a tumorsuppressor in ALL. Aberrant growth factor pathway activity drivescancer pathology and is the target of molecular cancer therapies.Specifically, the epidermal growth factor receptor (EFGR) pathwayand its ligand, transforming growth factor alpha (TGF[alpha]) areclinically relevant to gastric cancer. We use an shRNA screen andPrize Collecting Steiner Forest (PCSF) algorithm to discover thepathway regulating TGF shedding. This pathway identifies commonregulators of TGF[alpha] shedding and NF[chi]B regulation, yettargeting NF[chi]B and the EGFR pathway has thus far beenunsuccessful in cancer therapies. Our network identifies IRAK1 as aviable path forward for modulating both TGF[alpha] and NF[chi]B ingastric cancer." @default.
- W2524567910 created "2016-10-07" @default.
- W2524567910 creator A5065995488 @default.
- W2524567910 date "2016-01-01" @default.
- W2524567910 modified "2023-09-26" @default.
- W2524567910 title "Network analyses for functional genomic screens in cancer" @default.
- W2524567910 hasPublicationYear "2016" @default.
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