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Mapping CAR T-cell design space using agent-based models

View ORCID ProfileAlexis N. Prybutok, View ORCID ProfileJessica S. Yu, View ORCID ProfileJoshua N. Leonard, View ORCID ProfileNeda Bagheri
doi: https://doi.org/10.1101/2022.04.07.487561
Alexis N. Prybutok
1Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
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Jessica S. Yu
2Department of Biology, University of Washington, Seattle 98195, United States
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Joshua N. Leonard
1Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
3Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
4Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
5Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, Illinois 60208, United States
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  • For correspondence: nbagheri@uw.edu j-leonard@northwestern.edu
Neda Bagheri
1Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
2Department of Biology, University of Washington, Seattle 98195, United States
3Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
4Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
6Department Chemical Engineering, University of Washington, Seattle 98195, United States
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  • For correspondence: nbagheri@uw.edu j-leonard@northwestern.edu
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Abstract

Chimeric antigen receptor (CAR) T-cell therapy shows promise for treating liquid cancers and increasingly for solid tumors as well. While potential design strategies exist to address translational challenges, including the lack of unique tumor antigens and the presence of an immunosuppressive tumor microenvironment, testing all possible design choices in vitro and in vivo is prohibitively expensive, time consuming, and laborious. To address this gap, we extended the modeling framework ARCADE (Agent-based Representation of Cells And Dynamic Environments) to include CAR T-cell agents (CAR T-cell ARCADE, or CARCADE). We conducted in silico experiments to investigate how clinically relevant design choices and inherent tumor features—CAR T-cell dose, CD4+:CD8+ CAR T-cell ratio, CAR-antigen affinity, cancer and healthy cell antigen expression—individually and collectively impact treatment outcomes. Our analysis revealed that tuning CAR affinity modulates IL-2 production by balancing CAR T-cell proliferation and effector function. It also identified a novel multi-feature tuned treatment strategy for balancing selectivity and efficacy and provided insights into how spatial effects can impact relative treatment performance in different contexts. CARCADE facilitates deeper biological understanding of treatment design and could ultimately enable identification of promising treatment strategies to accelerate solid tumor CAR T-cell design-build-test cycles.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 09, 2022.
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Mapping CAR T-cell design space using agent-based models
Alexis N. Prybutok, Jessica S. Yu, Joshua N. Leonard, Neda Bagheri
bioRxiv 2022.04.07.487561; doi: https://doi.org/10.1101/2022.04.07.487561
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Mapping CAR T-cell design space using agent-based models
Alexis N. Prybutok, Jessica S. Yu, Joshua N. Leonard, Neda Bagheri
bioRxiv 2022.04.07.487561; doi: https://doi.org/10.1101/2022.04.07.487561

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