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UBC Theses and Dissertations

Identification of molecular and morphological alterations in head and neck cancer Jabalee, James

Abstract

Head and neck squamous cell carcinoma (HNSC) represents the 6th most common cancer type worldwide. A better understanding of the molecular alterations that occur in cells of the tumor and surrounding environment will allow for the identification of new therapeutic targets and biomarkers. This thesis focuses on two distinct but related issues: (1) identification and functional elucidation of epigenetically deregulated genes in oral squamous cell carcinoma (OSCC), and (2) use of tumor-induced changes in non-cancerous epithelial cells adjacent to human papillomavirus-positive oropharyngeal cancers (HPV+ OPCs) as screening biomarkers. OSCC displays a dismal 5-year survival of ~50%. We previously performed whole-genome DNA methylation and gene expression profiling of tissues from the oral cavity and found the gene SMPD3 to be frequently hypermethylated and downregulated. Overexpression of SMPD3 in oral dysplasia and cancer cell lines did not alter proliferation but decreased migration and invasion and increased resistance to erlotinib. Further, SMPD3 has been linked to the biogenesis of extracellular vesicles. Although SMPD3 overexpression did not alter vesicle size or concentration, it did significantly alter vesicle microRNA content. Once a tumor is established, it extrudes signals in the form of EVs, cytokines, and other molecules that affect nearby non-malignant cells. Such changes, called malignancy-associated changes (MACs), can be used as biomarkers to screen for the presence of a tumor. We applied this idea to HPV+ OPCs, which are difficult to detect due to their formation at the base of large invaginations. To detect MACs, we compared cells from tumor-adjacent and contralateral normal epithelia, tumors, and patients without cancer in terms of microRNA expression, gene expression, and nuclear morphology. Using RNA from tissue biopsies, we identified 55 genes and 10 microRNAs that fit our criteria for MACs. We then built a machine learning model that could classify nuclei based on measurement of morphological features. Nuclei from tissue and brush biopsies could be classified according to their site of origin with high accuracy. This observation could form the basis of a brush biopsy-based screening method for HPV+ OPC. Together, these data contribute to our understanding of cancer progression at the molecular level

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