Computational Analyses, Methods, and Tools Supporting Cancer Biomarker Identification and Targeted Therapy Development

Over time, much has been done in attempt to understand the various causes and complex molecular mechanisms of cancer, yet it still represents one of the leading causes of mortality worldwide. Fortunately, cancer therapeutics have evolved, from broad chemotherapies with multiple harsh side effects to molecular missiles which target specific cancer causing genes, leaving a patient's normal cells largely untouched. Similarly, cancer detection strategies and prognosis methods have also advanced, allowing doctors and patients to better manage and control the disease. The main challenge currently is to identify those genes that are specific markers for a particular cancer and can inform prognosis and those that may be "targeted therapies". This can be accomplished most rapidly through the use of large-scale cancer genomic datasets and sophisticated integrative analyses, methods, and tools to detect and prioritize candidate genes and biomarkers. As such, the goal of this work is to develop analyses, methods, and frameworks that benefit the translational research community by identifying and prioritizing genes for biomarker and drug development. Specifically, using integrative approaches on The Cancer Genome Atlas (TCGA) and various datasets from Gene Expression Omnibus (GEO), we perform analyses to identify a marker of survival and Epithelial-mesenchymal transition (EMT) in ovarian serous adenocarcinoma and a 5-gene signature of survival and molecular subtype in pancreatic ductal adenocarcinoma. Additionally, we highlight associated oncogenic pathways and suggest potential therapeutic strategies in these analyses. In order to improve detection of these survival markers we also evaluate a suite of techniques used commonly in the literature for survival analysis and determine best practices when using RNA-Sequencing data. Finally, we develop an application that allows researcher to access cancer 'big data' and apply their experience and domain expertise alongside the application logic of the tool to identify survival markers, therapeutic avenues, and genes that may represent an 'Achilles heel' for a set of tumors. This undertaking involves many different facets of bioinformatics, including statistical methods of analysis, high-performance computing, graph theory, web programming, and UI/UX interaction, as well as domain expertise in cancer target discovery. While there is much activity in the translational cancer informatics domain, the current study adds to the wealth of knowledge and tools in the community and presents another foothold to gain novel insights into this devastating disease.Ph.D., Biomedical Science -- Drexel University, 201