| Description |
Cancer is extremely challenging to treat as every patient responds differently to treatments, depending on the specific molecular aberrations and deregulated signaling pathways driving their tumors. To address this heterogeneity and improve patient outcomes, therapies targeting specific pathways have been developed. The use of computational pathway analysis tools and genomic data can help guide the use of targeted therapies by assessing which pathways are deregulated in patient subpopulations and individual tumors. However, most pathway analysis tools do not account for complex interactions inherent to signaling pathways, and are not capable of integrating different types of genomic data (multiomic data). To address these limitations, this dissertation focuses on developing user-friendly multiomic gene set analysis tools, and utilizing bioinformatics tools to measure pathway activation for multiple pathways simultaneously in cancer. Chapter 2 first describes the need for genomics and pathway-based analyses in cancer using the commonly aberrant RAS pathway as an example. Chapter 3 utilizes pathway-based gene expression signatures and the pathway analysis toolkit ASSIGN to interrogate pathways from the growth factor receptor network (GFRN) in breast cancer. Two discrete phenotypes, which correlated with mechanisms of apoptosis and drug response, were characterized from GFRN activity. These phenotypes have the potential to pinpoint more effective breast cancer treatments. Chapter 4 describes the development of Gene Set Omic Analysis (GSOA), a novel gene set analysis tool which uses machine learning to identify pathway differences between two given biologicalconditions from multiomic data. GSOA demonstrated its capacity to identify pathways known to play a role in various cancers, and improves upon other methods because of its ability to decipher complex multigene and multiomic patterns. Chapter 5 describes GSOA-shiny, a novel web application for GSOA, which provides biologists with lack of bioinformatics experience access to multiomic gene set analysis from an easy-to-use interface. Overall, this dissertation presents novel breast cancer phenotypes with clinical implications, provides the research community with gene expression signatures for GFRN components, and presents an innovative method and web application for gene set analysisâ€"all contributing to furthering the field of personalized oncology. |
