Genome-wide RNAi screening has emerged as a powerful tool for loss-of-function studies that may lead to therapeutic target discovery for human malignancies in the era of personalized medicine. However, due to high false-positive and false-negative rates arising from noise of high-throughput measurements and off-target effects, powerful computational tools and additional knowledge are much needed to analyze and complement it. Availability of high-throughput genomic data including gene expression profiles, copy number variations from large-sampled primary patients and cell lines allows us to tackle underlying drivers causally associated with tumorigenesis or drug-resistance. In my dissertation, I have developed a framework to integrate functional RNAi screens with systems biology of cancer genomics to tailor potential therapeutics for reversal of drug-resistance or treatment of aggressive tumors. I developed a series of algorithms and tools to deconvolute, QC and post-analyze high-throughput shRNA screening data by next-generation sequencing technology (shSeq), particularly a novel Bayesian hierarchical modeling approach to integrate multiple shRNAs targeting the same gene, which outperforms existing methods. In parallel, I developed a systems biology algorithm, NetBID2, to infer disease drivers from high-throughput genomic data by reverse-engineering network and Bayesian inference, which is able to detect hidden drivers that traditional methods fail to find. Integrating NetBID2 with functional RNAi screens, I have identified known and novel driver-type therapeutic targets in various disease contexts. For example, I discovered that AKT1 is a driver for glucocorticoid (GC) resistance, a problem in the treatment of T-ALL. The inhibition of AKT1 was validated to reverse GC-resistance. Additionally, upon silencing predicted master regulators of GC resistance with shRNA screens, 13 out of 16 were validated to significantly overcome resistance. In breast cancer, I discovered that STAT3 is required for transformation of HER2+ breast cancer, an aggressive breast tumor subtype. The suppression of STAT3 was confirmed in vitro and in vivo to be an effective therapy for HER2+ breast cancer. Moreover, my analysis revealed that STAT3 silencing only works in ER- cases. Using my framework, I have also identified potential therapeutic targets for ABC or GCB-type DLBCL and subtype-based breast cancer that are currently being validated.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D82V2P8W |
| Date | January 2012 |
| Creators | Yu, Jiyang |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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