Development and Application of Computational Methods for Profiling Cancers at Single Cell Resolution

Cancer is a complex disease with many factors which determine how rapidly cancer cells can grow and spread throughout the body. Significant differences exist within the cancer cell population of a patient. These differences shape the interaction of cancer cells with the surrounding healthy tissue, with dramatic variation between patients. This so called cancer heterogeneity has profound implication for patient prognosis, and is one of the primary challenges to developing effective cancer treatments. Recent technological advances now allow for the measurement of multiple aspects of individual cells within a cancer. This has created an opportunity to precisely characterize the set of mutations in each cancer cell, along with their functional consequences and how they impact interactions with surrounding cells. My group will develop statistical machine learning approaches to analyze the complex datasets generated by these technologies.

Working alongside clinicians and biologists at BC Cancer, part of the Provincial Health Services Authority (BC Cancer), we will apply these computational methods to study the evolution of metastatic breast cancer and the mechanisms of relapse in follicular lymphoma. Ultimately this research will provide important insights that can guide the development of better strategies for the diagnosis and treatment of these cancers.