Abstract
Adoptive cell therapies (ACT) leverage tumor-immune interactions to cure cancer. Despite promising phase I/II clinical trials of chimeric-antigen-receptor natural killer (CAR-NK) cell therapies, molecular mechanisms and cellular properties required to achieve clinical benefits in broad cancer spectra remain underexplored. While in vitro and in vivo experiments are essential, they are expensive, laborious, and limited to targeted investigations. Here, we present ABMACT (Agent-Based Model for Adoptive Cell Therapy), an in silico approach employing agent-based models (ABM) to simulate the continuous course and dynamics of an evolving tumor-immune ecosystem, consisting of heterogeneous “virtual cells” created based on knowledge and omics data observed in experiments and patients. Applying ABMACT in multiple therapeutic contexts indicates that to achieve optimal ACT efficacy, it is key to enhance immune cellular proliferation, cytotoxicity, and serial killing capacity. With ABMACT, in silico trials can be performed systematically to inform ACT product development and predict optimal treatment strategies.
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Data availability
Data for generating figures are provided in Supplementary Data 4 and at https://doi.org/10.5281/zenodo.17818689. Public scRNA-seq data used in this work can be obtained from GSE190976, GSE227098, and syn52600685.
Code availability
The feature selection using LME model was performed in R (v4.2.2)108. ABM simulations were performed in Python (v3.9)109 using the MESA framework (v2.2.4)64. The source code for reproducing the work is accessible at: https://github.com/KChen-lab/ABMACT.
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Acknowledgements
This work is made possible by 2024-345892 from the Chan Zuckerberg Initiative DAF, an advised fund of the Chan Zuckerberg Initiative Foundation, 5U01CA281902 from National Cancer Institute, and 75N99223S0001 from the Advanced Research Projects Agency for Health (ARPA-H). This work was supported in part by the University of Texas MD Anderson Cancer Center Institute for Cell Therapy Discovery & Innovation. S.C. and E.D. were supported by the Cancer Prevention and Research Institute of Texas (RP230160) and the National Institutes of Health (R21 CA267312-01A1). S.C. was additionally supported by the John F. Jr and Carolyn Bookout Presidential Distinguished Chair fund. The data used in this study were supported, in part, by grants (1 R01 CA211044-01, 5 P01CA148600-03, and U01CA247760) from the National Institutes of Health (NIH), the Cancer Prevention and Research Institute of Texas (grants RP180466 and RP180248), the Leukemia Specialized Program of Research Excellence (SPORE) Grant (P50CA100632), the Specialized Program of Research Excellence (SPORE) in Brain Cancer grant (P50CA127001), and CPRIT Single Core (RP180684), and a grant (P30 CA016672) from the NIH to the MD Anderson Cancer Center Flow Cytometry and Cellular Imaging Core Facility that assisted with the mass cytometry studies. We sincerely thank Drs. Peng Wei, Ziyi Li, Enli Liu, Li Li, and Ms. Xiaohan Chi for their support and insightful feedback during the development of the study.
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Y.W. designed the study, developed all computational code, performed the analyses, and wrote the manuscript. S.C. contributed to study design, model development, interpretation of results, and manuscript writing. K.C. conceived and supervised the project, contributed to study design, model development, interpretation of results, and contributed to manuscript writing. M.Da., V.M., and E.D. generated and provided the experimental data, provided guidance throughout, and contributed to manuscript writing. M.S., L.L.P., R.B., and K.R. generated and provided the experimental data. M.De. contributed to project ideation and manuscript writing. All authors discussed the results and approved the final manuscript.
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The authors declare the following competing interests: M.Da., R.B., M.S., and K.R., and the University of Texas MD Anderson Cancer Center have an institutional financial conflict of interest with Takeda Pharmaceutical. R.B., K.R., and the University of Texas MD Anderson Cancer Center have an institutional financial conflict of interest with Affimed GmbH. K.R. participates on the Scientific Advisory Board for Avenge Bio, Virogin Biotech, Navan Technologies, Caribou Biosciences, Bit Bio Limited, Replay Holdings, oNKo Innate, and The Alliance for Cancer Gene Therapy ACGT. K.R. is the Scientific founder of Syena. M. Da. participates on the Scientific Advisory Board for Cellsbin.
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Wang, Y., Casarin, S., Daher, M. et al. Agent-based modeling of cellular dynamics in adoptive cell therapy. Commun Biol (2026). https://doi.org/10.1038/s42003-026-09653-4
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DOI: https://doi.org/10.1038/s42003-026-09653-4
