I'm a Senior Bioinformatician, Data Scientist with the Mital lab at The Hospital for Sick Children (SickKids).
I'm investigating the genomics of childhood heart disease. Our goals are to identify gene defects that cause heart disease in children, to understand how these variants influence disease severity and outcome, to apply this knowledge to inform the clinical care of children with heart disease, and to discover new therapies for pediatric heart failure.
I previously worked as a Bioinformatician, Data Scientist with the Boutros lab at the Ontario Institute for Cancer Research (OICR). There I developed a machine learning pipeline to identify the optimal sets of data types and parameters for diagnostic and prognostic biomarkers in prostate cancer. I also led development of our group's in-house, Perl-based genomics analysis pipeline, which ties together software tools into a unified framework for automated processing and QC of sequencing data.
As a Postdoctoral Research Associate with Obi Griffith and Elaine Mardis at the McDonnell Genome Institute of Washington University in St. Louis, I investigated the genomics of response to neoadjuvant trastuzumab and chemotherapy in HER2-positive breast cancer, including the identification of recurrent somatic mutations, gene fusions, copy number alterations, neoantigens, and transcriptional changes. Our study aimed to identify alternative, druggable targets for those patients that do not respond to the current standard of care in HER2-positive breast cancer. In a separate study, I identified highly curated regulatory sites within the human genome and then worked with NimbleGen/Roche to design a capture reagent targeting the human regulome. We then performed targeted sequencing of these regions in breast cancer cases in order to identify recurrent regulatory mutations in the disease, and sought to associate these mutations with gene expression changes and prognostic outcome.
My Ph.D. research was in Mike Hallett's group at McGill University, and focused on applying bioinformatics tools to breast cancer. This included the development of a de novo framework, termed Absolute Inference of Patient Signatures (AIPS), for analyzing molecular signatures in breast cancer. As a part of this project, we collected and annotated thousands of signatures and breast cancer samples, and further developed statistical tests to determine associations between signatures and clinicopathological features such as patient outcome. Another set of projects involved investigating the role of the oncogene MET in mouse models of breast cancer. This was in collaboration with Marisa Ponzo and Jennifer Knight from Morag Park's group. We determined that MET is associated with triple-negative breast cancer, and that expression of MET synergizes with loss of p53 to induce a claudin-low phenotype. Finally, in collaboration with Therese Sørlie, we investigated molecular features of progression from non-invasive to invasive breast cancer. We observed that many of the strongest discriminators of tumor invasiveness are subtype-specific, indicating that vastly different mechanisms may lead to disease progression in breast cancer.