I'm a Data Scientist, Bioinformatician with the Boutros lab at the Ontario Institute for Cancer Research (OICR).
I'm developing a machine learning pipeline to increase accuracy of diagnostic and prognostic biomarkers in prostate cancer. Using this pipeline, I've identified optimal sets of data types and parameters to validate during the next project phase.
I'm also leading a team that's maintaining an in-house, Perl-based genomics analysis pipeline. This ties together software tools into a unified framework for automated processing and QC of data. It uses SGE to parallelize processes onto either the compute cluster at our institute or AWS servers.
I previously worked as a Postdoctoral Research Associate with Obi Griffith and Elaine Mardis at the McDonnell Genome Institute of Washington University in St. Louis, where we investigated the genomic architecture of cancer.
The genomics of response to neoadjuvant trastuzumab and chemotherapy in HER2-positive breast cancer: we investigated features associated with response to trastuzumab in HER2-positive breast cancer. This included the identification of recurrent somatic mutations, gene fusions, copy number alterations, as well as other genomic and transcriptomic 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.
The prognostic significance of regulatory mutations in breast cancer: we identified highly curated regulatory sites within the human genome. In turn, we used this information to design a capture reagent targeting the human regulome. We have 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 Breast Signature Analysis Tool (BreSAT), 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.