Grant: Doctoral Research Award: Canada Graduate Scholarships

Cancer is an evolving disease. The evolutionary trajectory of cancer is defined by the selective pressure within the tissue microenvironment, or ecological niche, of origin. As new mutations arise, the potential for new cellular phenotypes, or subclones, with varying degrees of fitness also increases. This intratumor heterogeneity, or clonality, is known to be an important factor in predicting cancer progression, survival, therapeutic response, and drug resistance. Currently, there are issues with quantifying and detecting clonality in cancer. Existing methods utilize short-read sequencing to reconstruct subclonal populations through either single-nucleotide variant (SNV) analysis or copy number aberration (CNA) analysis. SNV methods use variant allele frequencies to calculate cancer cell fractions, but they require statistical correction because copy number changes and variable whole-genome duplication events impact allele frequencies and mask linkage information. As well, CNA approaches for subclonal reconstruction face issues with statistical variation and sequence coverage noise across SNVs. These limitations constrain the accuracy of subclonal reconstruction as we cannot assign specific groups of sequences to specific subclones. Accurately quantifying clonality is critical for making evolutionary inferences about mutation rate and selection both temporally and spatially within tumours. Therefore, methodological improvements are needed to produce evolutionary biomarkers that are clinically relevant for patients. Overall this research will improve on subclonal reconstruction and haplotype assignment methods. This will enable better analysis of tumour's past while simultaneously generating evolutionary biomarkers that inform on survival, optimal therapy, and drug resistance. Ultimately our methods will help understand the evolvability of an individual's cancer with the goal of improving clinical outcomes, and, most importantly, saving patients' lives.