Dec - Quaid Morris¶
Speaker: Quaid Morris
Talk Title: Reconstructing Cancer Evolution
Thursday, December 12th, 2019 6:00pm
Affiliation: Professor, University of Toronto
Bio: Dr. Quaid Morris is a faculty member and associate professor at the Donnelly Centre for Cellular and Biomolecular research at the University of Toronto where he holds cross-appointments in the Faculty of Medicine, Arts and Sciences and Applied Sciences. He is currently the Co-director of the Graduate Stream in Computational Biology, Department of Molecular Genetics. Dr. Morris pursued graduate training and research in machine learning at the Gatsby Computational Neuroscience Unit, University College London, England and obtained his PhD in Computational Neuroscience at the Massachusetts Institute of Technology. His areas of research include Computational Biology, Machine Learning, Cancer Genomics, RNA-binding proteins, Functional Genomics, Post-transcriptional regulation, Electronic Health Records and Health Informatics. His lab studies the evolutionary history of tumors, using machine learning and statistical modeling to understand how different populations of cells evolve over time.
There are 10 trillion mutated copies of your genome in your body; a thousand times more copies than humans who have ever lived. Each of these somatic genomes, if you are as old as I am, carries between 50 and 50,000 somatic DNA mutations. In contrast, each new human is born with only about 50 germline mutations. So, there are between a thousand and a million times more somatic mutations events occurring in your body, in your lifetime, than have ever occurred in the human germline. And unlike humans, who have an exponentially expanding population; you have a fixed number of cells that are in constant competition for resources.
Cancer is a tragic, but common, result of this massive-scale experiment in human genome evolution. A handful of the 1000s of somatic mutations in cancerous and pre-cancerous cells are “driver” mutations that bestow fitness advantages that cause selective sweeps that drastically increase the frequency of mutated cells. These sweeps also increase the frequency of “passenger” mutations accumulated since the last such sweep. These latter mutations have little impact on cell function but provide information about the mutational processes that generated them. Both their type (i.e., A to C) and genomic locations depend not only what caused the mutation — e.g., UV light – but also the chromatin state of the cell that acquired them. I will describe our work developing methods to classify somatic mutations into different ‘subclones’ that correspond to different sweeps. Our methods also use phylogenetic approaches to determine the relative order in which the sweeps occurred. We have also developed methods to interpret this historical record of the cancer. Specifically, we are attempting to use the timing and time-dependent patterns of somatic mutations to reconstruct the historical changes that a normal precursor cell underwent during its transformation into a cancerous cell. We also study pre-cancerous patterns of somatic evolution that can be read out of deeply sequenced blood populations. I will also briefly describe our preliminary work in using deep neural networks to use these data to infer properties of an individual’s hematopoietic stem cells which cause age-related clonal hematopoiesis.
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