Research
Welcome to the Computational Cancer Genomics Lab at the Princess Margaret Cancer Center at the University Health Network. We study role of genomic variations in driving tumor progression.
Our lab studies cancer biology by building computational tools with state-of-the art genomics, biophysics, and machine learning techniques. We are looking for enthusiastic researchers and students interested in computational biology to JOIN US!
Rapid declines in sequencing costs have enabled large-scale genome and exome sequencing for various cancer cohorts. A critical shared objective among such studies has been to understand how genomic variants affect tumor etiology. How may we develop robust quantitative models to predict the impact of somatic mutations on gene expression and protein function? Furthermore, how may we leverage these quantitative models to prioritize genomic variants and utilize this knowledge to develop new cancer therapeutics? Our lab is interested in developing integrative methods that use multiple data resources and cross-disciplinary approaches to address questions of this nature.
Previously, we have developed methods that integrate protein structure and protein motion information to evaluate the molecular impact of cancer mutations and identify putative cancer driver genes. Currently, we are building machine learning methods integrating protein structure, cancer genomics, and clinical data to identify novel drug targets and predict drugs' efficacy & side effects among cancer patients.
The canonical model of cancer progression dichotomizes cancer mutations as drivers and passengers. However, our recent analysis of thousands of cancer genomes indicates the presence of a continuum where strong and weak drivers can contribute to cancer progression via epistatic interactions or their aggregated/additive effects. As a follow-up to this work, we are currently developing novel methods to investigate the role of cooperative genetic and cellular level interactions in driving tumor growth and metastases.
The overwhelming majority of cancer mutations fall within non-coding regions of the genome. Clear insights into how non-coding mutations play causal roles in various cancer types remain limited. Similar to non-coding mutations, we have little understanding of how SVs influence cancer progression. My group is interested in building methodologies for understanding the role of non-coding mutations and SVs in different cancer cohorts.
