From theory to immunotherapy: universal antigen encoding of T cell activation

Paul François, PhD
Professor
Department of Biochemistry and Molecular Medicine
Université de Montréal

This conference is hosted by Éric A. Cohen, PhD . This conference is part of the 2023-2024 IRCM conference calendar.

In person: 
IRCM Auditorium
110, avenue des Pins O, H2W 1R7 Montreal

Online:
Zoom Link : https://zoom.us/j/95269762104
ID : 952 6976 2104
Code : 476372

IRCM conferences are set to occur under a hybrid format. However, please note that last-minute changes to online-only lectures may occur due to unforeseen circumstances. We invite you to visit this webpage again a few days before attending.

About this conference
Systems immunology lacks a framework to derive theoretical understanding from high-dimensional experimental datasets. We combined a robotic platform with machine learning to measure and theoretically model CD8+ T cell activation. High-dimensional cytokine dynamics could be compressed onto a low-dimensional latent space in an antigen-specific manner (so-called “antigen encoding”). We used antigen encoding to model and reconstruct patterns of T cell immune activation, and relate it to the theoretical framework of Adaptive Kinetic Proofreading that we have developed. We generalized antigen encoding to multiple immune settings, including chimeric antigen receptor T cells (CAR-T). Our approach leads to direct predictions and applications to cancer immunotherapy. We developed an Antagonism-Enforced Braking System to refine specificity of CAR-T cells, that we validated both in vitro and in vivo.

About Paul François
Dr. Paul François is a Professor of Bioinformatics and Biophysics, the Founder of the McGill Molecular and Cellular Biophysics Center, and an Associate Member of MILA Québec. In 2023, he left his faculty position at McGill University to join the Department of Department of Biochemistry and Molecular Medicine at Université de Montréal. His research program combines tools from physics, mathematics, computer science and machine learning to build models of complex biological systems, from the sub-cellular to the evolutionary scale. His work, currently supported by grants from CIHR and NSERC, has been published in top journals such as Science, PNAS, and eLife.

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