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Abstract

Next generation sequencing (NGS) technology provides researchers an opportunity to study cancer genomes at different resolutions. In particular, detection and interpretation of the smallest somatic changes of the genome (single nucleotide variants) are now tractable at scale. However, significant challenges in the analysis of both bulk tumour and single cell sequencing methods remain to fully exploit the advance in technology development. Two emerging areas of applying sequencing technology to better ascertain properties of cancer evolution are (i) sequencing multiple tumour biopsies from the same patient, and (ii) single cell genome sequencing. Both of these advances represent computational challenges that I address through development of novel methods in this thesis. The first proposed method (Chapter 2) incorporates prior clonal information to improve the accuracy of detecting SNVs across the genome of multiple bulk tumour samples. The second proposed method (Chapter 3) is a statistical model that exploits the underlying phylogeny of individually sequenced cells to detect SNVs in every individual cell. The latter method identifies clone specific SNVs without the requirement of deconvolving the results from bulk sequencing data. The resultant accurate detection of SNVs (Chapter 4) helps enhance insight on the evolutionary process of tumours and genetic pathways. Together, the methods provide a toolbox for comprehensive profiling of SNVs for the study of tumour dynamics.