The last decade has seen an explosion of genomic and health-related data. These data can advance precision medicine, but only if we apply the right analyses. I use statistical methods that link together many different types of large genomic and health datasets. My research identifies genomic mechanisms that lead to disease, which is the first step towards improving patient care. A primary goal of my research is to learn about the genes that cause mental illnesses like attention-deficit / hyperactivity disorder (ADHD) in children. We know that genes are important to ADHD risk. We also know that babies born small are at increased risk of ADHD, and that the placenta influences a baby’s growth in the womb. What we do not know, however, is how genes that are important to placenta function also affect a baby’s future risk of ADHD. Answers to this question will help us understand ADHD biology so that we can develop better prevention and treatment strategies and give all children the best start in life.
Research Pillar: Biomedical Research
Towards a mathematical theory of development
New technologies like single-cell RNA sequencing can observe biological processes at unprecedented resolution. One of the most exciting prospects associated with this new trove of data is the possibility of studying temporal processes, such as differentiation and development. How are cell types stabilized? How do they destabilize in diseases like cancer and with age? However, it is not currently possible to record dynamic changes in gene expression, because current measurement technologies are destructive. A number of recent efforts have tackled this by collecting snap-shots of single cell expression profiles along a time-course and then computationally inferring trajectories from the static snap-shots. We argue that this inference problem is easier with more data, and the right way to measure the “size” of a data set is really the number of time-points, not the number of cells. We propose to collect the first single cell RNA-seq time-course with more than one thousand distinct temporal snapshots, and we develop a novel mathematical and conceptual framework to analyze the data. This tremendous temporal resolution will give us unprecedented statistical power to discover the genetic forces controlling development.
Light and drug delivery coupled with biomaterials to improve motor function after spinal cord injury in animal models
Spinal cord injury (SCI) is a debilitating condition with no available cure directly affecting ~80,000 Canadians. The major challenges to overcome include: i) the limited spontaneous regeneration of nerve fibers (axons) after the injury; ii) scar tissue formation at the injury site (lesion), which inhibits the growth of axons; and iii) the difficulty in guiding axons to grow across the lesion. The present work proposes a novel solution that combines optical stimulation technology and biomaterials to promote axonal growth, inhibit the formation of scar tissue using targeted drug delivery, and guide growing axons across the lesion. My team has developed fully implantable multifunctional neural probes for the delivery of both light and drugs to the spinal cord injury site as well as biomaterials to guide the growth to axons across the lesion. The MSFHR Scholar Program would support our work to integrate these strategies and create a therapy that helps us understand the combined effects of light stimulation, drug delivery, and axon guidance on motor function recovery after SCI in animal models. The outcomes will support treatment development for SCI based on a better mechanistic understanding of regeneration.
Using high-throughput experiments and machine learning to understand the role of non-coding mutations in cancer
Cancer is caused by mutations in the DNA that cause a patient’s cells to grow out of control. Some of these cancer-causing mutations change how genes are regulated; that is, which genes are turned on or off in the cell. Essentially all cancers have activated the TERT gene because TERT is essential for cancer growth. We understand TERT regulation better than most genes, but even here we cannot predict how mutations alter TERT expression. Overall, we do not understand which genes or mutations can promote cancer via altered gene regulation. Our work aims to learn the code that cancer cells use to interpret regulatory mutations. We will make many artificial mutations in large scale, and measure how much each mutation affects the amount of gene made. We will model how the cells interpret these mutations using a computer, and apply the model to find new cancer mutations. We will these computer models to discover how often mutations alter gene regulation in cancer, and highlight genes whose regulation is important in particular cancers. In the long-term, our work will allow us to better diagnose and treat cancer by showing how a particular patient’s tumor’s mutations alter gene regulation and cancer growth.
Single cell methods for characterizing genomic alterations in cancer
Cancer arises when a single cell acquires genetic alterations leading to uncontrolled replication. As tumour cells divide they continue to acquire genetic mutations which they pass on to their descendants, forming distinct subpopulations with different characteristics. The ability of tumours to generate genetic diversity and evolve in response to selective pressures can enable them to develop resistance to treatment. Certain forms of genetic alteration have been associated with poor patient survival in high grade serous ovarian cancer. Understanding the frequency with which these alterations arise within tumours and the diversity they generate requires profiling the genetic material of individual cancer cells. We will optimize experimental approaches for sequencing single tumour cells and develop computational and statistical methods to characterize this genetic diversity. This will provide researchers with new tools with which to study the mechanisms that underlie treatment resistance and patient relapse, and open the door for the development of new prognostic measures and therapeutic approaches.