Leveraging Systems Immunology To Understand The Molecular Underpinnings Of Chimeric Antigen Receptor T-Cell Therapy

Loading...
Thumbnail Image
Degree type
Doctor of Philosophy (PhD)
Graduate group
Genomics & Computational Biology
Discipline
Subject
Bioinformatics
Funder
Grant number
License
Copyright date
2022-09-17T20:21:00-07:00
Distributor
Related resources
Author
Chen, Gregory
Contributor
Abstract

Chimeric Antigen Receptor (CAR) T-cell therapy is a promising strategy for the treatment of human cancers, and in the last decade has progressed from breakthrough early trials to first in class FDA approvals for B-cell leukemias and lymphomas. Concurrent with the promise and challenges in cancer immunotherapy, there have been exciting technological advances in the field of genomics and systems biology, including approaches to capture and analyze RNA expression, protein expression, and chromatin accessibility at a single-cell resolution. In this dissertation, I investigate the molecular basis of CAR T-cell therapy response and resistance using a systems immunology approach, leveraging these technologies and computational analysis to shed new light on key clinical questions. First, I discuss our work investigating the molecular differences between pre-manufacture T-cells in a cohort of pediatric patients on trial to receive CAR T-cell therapy, in which we identified subtype-specific factors associated with clinical CAR T-cell persistence. Next, I discuss our work studying CAR T-cells from two of the earliest cancer patients successfully treated with CAR T-cell therapy, in which we discovered novel populations of long-persisting CD4+ CAR T-cells nearly a decade post-infusion. Finally, I discuss our work in understanding the genomic fate of leukemic cells in patients undergoing anti-CD19 CAR T-cell therapy and discuss the broader insights that have arisen from this work. Together, these data expand our understanding of the T-cell and cancer cell characteristics involved in successful CAR T-cell therapy, and highlight the power of integrative genomics and computational biology in spearheading the discovery process in cancer therapy.

Advisor
Kai Tan
Date of degree
2021-01-01
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Recommended citation