Mathematical modeling of patient-specific multiphasic dynamics of CAR-T cells, their interaction with healthy B cells, and the development of resistance.

Mathematical modeling of patient-specific multiphasic dynamics of CAR-T cells, their interaction with healthy B cells, and the development of resistance.

CAR-T cell Immunotherapy uses immune system cells (T lymphocytes) extracted from a patient, which are genetically modified to express a CAR gene. This gene enhances the T lymphocyte's ability to recognize and attack the patient's own tumor cells expressing a particular antigen. Considerable success has been registered to this therapy in treating certain types of blood system cancers, such as leukemias and lymphomas.After being infused into the patient's body, CAR-T cells go through several stages when they are capable of killing cancer cells (functional cells), when they exhaust this capacity (exhausted cells), and when they form immunological memory (memory cells) against the same antigen-associated cancer. We have recently developed a mathematical model (referred to as model 1) capable of capturing this multiphasic behavior of CAR-T cells against different cancers from 12 patients. Specifically, patient data informed the estimation of model parameters, yielding 12 patient-specific models. These models were then employed to assess potential markers for disease progression. Through our analysis, we have been able to propose a clinical indicator of response to therapy over longer periods, an unmet medical need. A clinical marker associated with each patient's specific predictive model holds the promise of assisting in the strategic planning of supplementary therapeutic interventions. Consequently, this research plan endeavors to broaden our investigation to encompass a larger patient cohort, ultimately validating the predictive capability of the proposed clinical marker or uncovering alternative markers.Furthermore, the reasons why certain patients treated with CAR-T cells remain cancer-free for extensive periods, spanning even up to a decade, while others experience disease progression, remains elusive. Existing evidence indicates that the levels of antigens expressed by tumor cells significantly impact the efficacy of the therapy. This drove the development of the second model (referred to as model 2), which integrates healthy B cells expressing the same target antigen into model 1. Additionally, a third model (referred to as model 3) was conceived to investigate resistance-associated mechanisms, influenced by both antigen density and mutations. The experimental data required in this project will enable the estimation of parameters within these models, ultimately contributing to the finer characterization of patient-specific profiles and shedding light on the emergence of resistance.

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