UBC Theses and Dissertations
An investigation into the non-coding genomic landscape and effects of chemotherapeutics in pre-treated advanced cancers Kwan, Harwood
Cancer is a disease which arises due to somatic alterations in the genome. However, most studies on cancer genetics only explore the impact that coding mutations have on the progression of the disease. Furthermore, many genomic inquiries on cancer only implicate primary untreated tumours, which misses the impact of metastasis and treatment. Here we present a cohort of 638 advanced cancer patients with whole genomic, transcriptomic and clinical information. Through this cohort, we attempt to better characterize the non-coding region of metastatic cancers as well as attempt to understand the mutational impact of chemotherapeutics. Using a positional clustering method, we identified 1,567 significant mutational hotspots in the genome. 86 genes were identified as being affected by a hotspot in a regulatory region, including in the TERT promoter, a region with well-known driving mutations. To characterize the biological function of the hotspots, we analyzed the impact of mutation on corresponding gene expression. We show an increased expression for TERT and AP2A1 when their respective promoter regions are mutated, the latter being a novel association. Mutational clusters affecting non-coding RNAs were also examined for any functional impact, but no significant associations were seen. Large non-coding mutational events such as kataegis were seen in multiple cancer types and across all chromosomes. However, little recurrence was seen for kataegis. Additionally, using observed mutational frequencies, we attempt to identify any mutations that may be treatment-induced. Examining the breast, lung, colon and pancreas and ovarian cohorts, we were able to extract known resistance mutations such as ESR1 mutations after aromatase inhibitor treatment and EGFR T790M mutations post anti-EGFR therapy. Further insights are required to confirm the expressional change seen in the cohort. Additional studies to determine AP2A1’s role in cancer would help understand this correlation. Overall, our study shows the presence of important mutations in the non-coding space of metastatic cancers, and the power of whole genome sequencing. Furthermore, we display the need for similar datasets to extrapolate mutations which correlate to resistance.
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