Developing computational methods to enhance understanding in glioma progression

However, thus far it has only been used in a phylogentic sense to investigate relationships between organisms. This thesis explores the possibility of repurposing these methods so that they can explore relationships between multiple samples taken from an individual with a cancer diagnosis. This required the development of a bespoke software, given the additional complexities of the genomic landscape of cancer, followed by an in-depth study of the appropriate parameters to be applied.

Finally, the utility of the software was assessed in the context of genomic cohorts generated from longitudinal sampling of cancer in a single patient for three patients with glioma and three patients with clear cell renal cell cancer. Of the six patients evaluated, two produced trees with the same topologies as those produced using traditional, alignment-based methods, two displayed minor variations and two displayed larger variations in topology.

These results indicate that alignment-free methods for sequence comparison can be a useful exploratory tool in investigating the relationship between tumour samples within a single patient. Further investigation is required to determine the cause of this variation, whether it represents noise in the data or genetic variability not accounted for when using traditional methods.<br><br><br>