Cancer is the leading cause of death in Canada and worldwide. Cancer tumours generally arise from a single cell which has acquired genetic mutations leading to uncontrolled cell division. As cancer cells divide, they acquire additional mutations which are passed down to their descendents. Drug treatment often results in widespread tumour cell death. However, if even a small subset of cells feature mutations which confer treatment resistance, these cells will survive and the cancer will reoccur. While recent discoveries have greatly improved outcomes for cancer patients, the likelihood of survival remains poor following tumour recurrence. This project aims to characterize tumour-cell diversity though computational analysis of data from novel single-cell sequencing experiments in breast cancer models. Together with our collaborators, we will develop and test devices designed to gather data on mutations in individual tumour cells, and create computational models to interpret this data. We will then compare the findings from our model systems with information derived from donated patient tumour samples in order to identify common mutations responsible for drug resistance. This research will lead to a better understanding of breast cancer tumours with drug-resistant tendencies, and permit the development of new tests and targeted treatments to prevent patient relapse. In addition to shedding light on drug resistance in breast cancer, the novel experimental and computational tools developed through this project could later be widely applied to study drug resistance in diverse cancer types.