PERFORM Colloquium: Prognostic and predictive signatures in breast cancer using surrogate tissue
In this talk I will present an overview of how genomics and bioinformatics have been used to develop gene signatures for clinical end-points in breast cancer primarily using primary tumor tissue. I will then describe our new methods focused on the same end-points using only surrogate tissue which can be obtained in a minimally invasive manner.
- Prognostic and predictive gene signatures.
- Primary tumor versus microenvironment versus patient systemic response.
- Single cell sequencing, immune profiles.
- Molecular interactions between the primary tumor and surrogate tissue.
Dr. Hallett is a computational and systems biologist in the Department of Biology at Concordia since January 2017 and holds a CRC Tier 1 in Algorithmic Bioinformatics. He was previously at McGill University from 2000–2017 in the School of Computer Science, Department of Biochemistry and the Goodman Cancer Centre as an associate professor. He has been primarily interested in breast cancer wit hmany projects based on high-throughput profiles (gene expression, epigenetic, microRNA) in both human clinical samples and mouse models of the disease. The general goal is to develop bioinformatics tools that provide a global perspective on the dynamics of the disease with emphasis on translating these approaches to clinical (prognostic or predictive) use. His group is focused on understanding how differences in the tumor microenvironment and patient systemic response can be used in determining patient treatment.