Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables

In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical mod...

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Bibliographic Details
Published inbioRxiv
Main Authors Brummer, Alexander B, Xella, Agata, Woodall, Ryan, Adhikarla, Vikram, Cho, Heyrim, Gutova, Margarita B, Brown, Christine E, Rockne, Russell C
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 13.12.2022
Subjects
Cancer
Cell culture
Cell death
Cell therapy
Chimeric antigen receptors
Glioblastoma
Glioblastoma cells
Lymphocytes T
Mathematical models
Tumors
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Summary:In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. This data-driven model-discover based approach has the potential to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Updated text, figures, and supplemental tables.* https://github.com/alexbbrummer/CART_SINDy
DOI:10.1101/2022.09.22.508748