(A-28) Simulating Signaling Dynamics in Chimeric Antigen Receptor T-cells

Introduction:: Chimeric antigen receptor T-cell (CART-cell) therapy is an adoptive cell therapy in which a patient’s T-cells are genetically modified to express chimeric antigen receptor (CAR) constructs. The CAR is a fusion of protein domains consisting of extracellular antigen targeting and intracellular signaling and costimulatory domains. The targeting domains are antibody-derived single chain variable fragments (scFv). The signaling domain is T-cell receptor (TCR)-derived CD3ζ with CD28, 4-1BB, and ICOS typically used as costimulatory domains [1]. This construct allows CART-cells to target and eliminate antigen-expressing cancer cells. Therapy dynamics involve interconnected activation, signaling, inhibitive, cytotoxic pathways including crosstalk and feedback between and within effector and target cells. Differences in any one variable can lead to non-intuitive effects on CART function and impact therapy dynamics. It is not feasible to test clinically or experimentally the vast range of perturbations which may influence therapy responses, but these perturbations can offer significant insight on how CART products may be improved. There remains an unmet need to develop computational frameworks that more accurately predict CART-cell function and clinical efficacy. Here we present a coarse-grained model simulated with logical rules that investigates signaling processes following the interaction between CART-cells and tumor cells and allows for in silico based prediction of CART-cell functionality prior to experimentation. The model is used to explore how changes in signaling pathways impact signaling signatures indicative of specific functional states of the interaction network.


Materials and Methods:: Signaling Network: A minimal model (Fig. 1A, B) of the signaling network of CART-cells (consisting of CD3ζ activating and varying costimulatory domains) during interactions with targeted tumor cells was derived using curated pathway diagrams for T-cell activation and literature sources for transcriptional and gene regulation. The constructed network diagrams the signaling activity and cellular responses following engagement of tumor cells by CART-cells. Major activation (CAR, MAP kinase (MAPK), PI3K-AKT-MTOR1, calcium, and other signaling intermediates), cytokine (IL2), cytotoxic, and inhibitory (PD1 and CTLA4) signaling pathways leading to transcription regulation and gene expression which influence CART-cell function are considered. Relevant crosstalks and feedback mechanisms are also considered. This base model [2] is currently being expanded to include additional CAR costimulatory domains and regulatory mechanisms. Dynamic Simulation: The logic model is designed and simulated using the stochastic discrete logic based formalism described in [3] for modeling probability Boolean networks (PBNs). Each node in the model is defined by a logical rule (update function), which when satisfied switches (or keeps) the node ON (value of 1) or otherwise switches (or keeps) the node OFF (value of 0). The update function is supported by corresponding propensity probabilities, pa and pd, which respectively set the probability that a given update function will update during a given iteration based on its current state (either ON or OFF). This is done to account for inherent randomness in the system where not all process (reactions or sets of reactions) can be assumed to occur during each iteration.


Results, Conclusions, and Discussions:: Results: The model simulates possible signaling network dynamics following activation of the CAR, PD1, CTLA4, and IL2 receptors and restates available experimental findings. The utility of the model can be observed in simulations of in silico CART-cell function in knockout (KO) and over-expression conditions at each network node compared with steady state function The model matches expected behavior where deficiencies in critical activation nodes lead to curtailed ability to maintain functional signaling (Fig. 1C), whilst overexpression of these pathways induces inhibition, which aligns with experimental findings [4]. Upregulation of intermediate activation signaling molecules are shown to have slight propensity to increase functional signaling (PI3K, ERK, PKC), while abrogation of inhibitory pathways and downstream TF (PD1, BATF) significantly improve function but not tumor apoptosis signatures. Discussion: Beyond PD1 inhibition, disrupting additional central signaling nodes generally resulted in loss of function, as we have defined it here. In contrast, we failed to observe substantial increases in CART-cell activities upon simulating overexpression of activating nodes. Therefore, the model simulations predict that significant improvement to baseline functional and cytotoxic ability by perturbing the intercellular network are possibly intrinsically limited. While the activity of inhibitory pathways dampens functional signaling, the ability of the interaction network to promote apoptosis with a non-negligible probability is maintained. In the context of response to CART-cell therapy, this indicates that disrupted intercellular signaling instead of increased inhibitory signaling are more likely to abrogate function. Consequently, optimization strategies focused on supporting low steady-state activity of the activation network are potentially more likely to promote therapy response.


Acknowledgements (Optional): : This work was partly supported by NIH grant R21-AI149039 to S.T and R.D. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


References (Optional): : [1] G. Dotti, S. Gottschalk, B. Savoldo, and M. K. Brenner, "Design and development of therapies using chimeric antigen receptor-expressing T cells," Immunological Reviews, vol. 257, no. 1, pp. 107-126, 2014.

[2] V. Shah, J. Womack, A. E. Zamora, S. S. Terhune, and R. K. Dash, "Simulating the Evolution of Signaling Signatures during CART-Cell -- Tumor Cell Interactions," p. arXiv:2302.04338Accessed on: February 01, 2023Available: https://doi.org/10.48550/arXiv.2302.04338

[3] D. Murrugarra, A. Veliz-Cuba, B. Aguilar, S. Arat, and R. Laubenbacher, "Modeling stochasticity and variability in gene regulatory networks," EURASIP Journal on Bioinformatics and Systems Biology, vol. 2012, no. 1, 2012.

[4] L. Tang, Y. Zhang, Y. Hu, H. Mei, and Y. Li, "T Cell Exhaustion and CAR-T Immunotherapy in Hematological Malignancies," BioMed Research International, vol. 2021, pp. 1-8, 2021.