High-grade serous ovarian cancer (HG-SOC) is the most lethal gynecologic malignancy. Although most respond to initial therapy, the vast majority of patients eventually recur and die of their disease. Understanding intra-tumor cellular heterogeneity and inter-patient variability is necessary to effectively cure HG-SOC. The work described in this thesis should help to speed the progress of ovarian cancer research in several ways. First, I generated a robust xenograft model that recapitulates the cellular heterogeneity of HG-SOC. In addition, I performed gene expression profiling on a subset of xenografts and showed that they recapitulate the inter-patient diversity of this disease. Second, I applied this model to pre-clinical testing of a folate-targeted imaging agent and showed that it can identify metastatic studding by PET/CT and fluorescence imaging. Using my xenograft model, I investigated the properties of tumor-initiating cells (TIC) and demonstrated that TIC in HG-SOC are rare. Furthermore, although CD133 marks most TIC, heterogeneity in the phenotype is observed within individual tumors and between different patients. Finally, I used a transformative technology, CyTOF, to develop a novel pipeline for prioritization of candidate TIC markers, as well as for characterization of cellular heterogeneity in primary HG-SOC samples.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/36007 |
| Date | 13 August 2013 |
| Creators | Stewart, Jocelyn Melissa |
| Contributors | Neel, Benjamin |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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