Application of SVDQuartets to CNA tree inference in simulated cancer cells

<p dir="ltr">Identifying tumor cell biomarkers has clinical relevance in disease screening, prognosis, and treatment decision-making. Cancer cells’ uncontrolled reproduction provides plentiful opportunities for these biomarkers to evolve, engendering treatment resistance and disease recurrence. Improving clinical outcomes thus requires further investigation of the largely unknown processes of tumor evolution. Existing phylogenetics methods have been adapted for cancer cell mutation, though there is room for improvement in inference accuracy and computational scalability. This thesis demonstrates the efficacy and theoretic validity of adapting an existing species phylogenetics method, SVDQuartets, for simulated cancer cell copy number alteration data.</p>