Network analyses for functional genomic screens in cancer
Author(s)Wilson, Jennifer L. (Jennifer Lynn)
Massachusetts Institute of Technology. Department of Biological Engineering.
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Gene interference screens are a widely adopted and popular tool for uncovering gene function but imperfections in the technology limit the power of these investigations. There are many completed and on-going RNAi investigations across a multitude of biological systems because these experiments are scalable, cost-effective, and relatively easily adapted to multiple experimental environments. The most influential disadvantage is that many of the individual reagents are non-specific and interfere with genes other than the intended target. Efforts to improve limitations in RNAi have focused on statistical models and improving reagents, yet have not explored using biological context to select gene targets. This thesis uses network modeling and data integration to provide context for gene interference studies, and demonstrates the utility of this approach in two systems: Acute Lymphoblastic Leukemia (ALL) is a disease of undifferentiated B-cells that results from accumulation of genetic lesions, yet we have an incomplete understanding of all genes contributing to the disease and how they interact. To discover genetic mediators of this disease, we employ a genome-scale shRNA screen, and complement this data with differential mRNA expression and ChIP-seq data using network integration. The integrated model identifies processes not represented in any input set and predicts novel genes contributing to disease. We specifically validate the role of Wwpl as a tumor suppressor in ALL. Aberrant growth factor pathway activity drives cancer pathology and is the target of molecular cancer therapies. Specifically, the epidermal growth factor receptor (EFGR) pathway and its ligand, transforming growth factor alpha (TGF[alpha]) are clinically relevant to gastric cancer. We use an shRNA screen and Prize Collecting Steiner Forest (PCSF) algorithm to discover the pathway regulating TGF shedding. This pathway identifies common regulators of TGF[alpha] shedding and NF[chi]B regulation, yet targeting NF[chi]B and the EGFR pathway has thus far been unsuccessful in cancer therapies. Our network identifies IRAK1 as a viable path forward for modulating both TGF[alpha] and NF[chi]B in gastric cancer.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages 128-151).
DepartmentMassachusetts Institute of Technology. Department of Biological Engineering.; Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology