Data-driven analysis of a mechanistic model of CAR T cell signaling predicts effects of cell-to-cell heterogeneity

Abstract

Due to the variability of protein expression, cells of the same population can exhibit different responses to stimuli. It is important to understand this heterogeneity at the individual level, as population averages mask these underlying differences. Using computational modeling, we can interrogate a system much more precisely than by using experiments alone, in order to learn how the expression of each protein affects a biological system. Here, we examine a mechanistic model of CAR T cell signaling, which connects receptor-antigen binding to MAPK activation, to determine intracellular modulations that can increase cellular response. CAR T cell cancer therapy involves removing a patient’s T cells, modifying them to express engineered receptors that can bind to tumor-associated antigens to promote tumor cell killing, and then injecting the cells back into the patient. This population of cells, like all cell populations, would have heterogeneous protein expression, which could affect the efficacy of treatment. Thus, it is important to examine the effects of cell-to-cell heterogeneity. We first generated a dataset of simulated cell responses via Monte Carlo simulations of the mechanistic model, where the initial protein concentrations were randomly sampled. We analyzed the dataset using partial least-squares modeling to determine the relationships between protein expression and ERK phosphorylation, the output of the mechanistic model. Using this data-driven analysis, we found that only the expressions of proteins relating directly to the receptor and the MAPK cascade, the beginning and end of the network, respectively, are relevant to the cells’ response. We also found, surprisingly, that increasing the amount of receptor present can actually inhibit the cell’s ability to respond due to increasing the strength of negative feedback from phosphatases. Overall, we have combined data-driven and mechanistic modeling to generate detailed insight into CAR T cell signaling.

The roles of T cell competition and stochastic extinction events in chimeric antigen receptor T cell therapy

“…Recent works used an empirical time-dependent modelling approach and compartment modelling [6] to describe the complicated temporal kinetics of the CAR T cell drug tisagenlecleucel [10]. Others sought to quantify ecological dynamics of CAR T cells to explain expansion and exhaustion [8], and signalling-induced cell state variability [9], both using in vitro data. Current modelling has not considered interactions between CAR and normal T cells, nor paid much attention to feedback between tumour and CAR T cells [11][12][13].…”

“…Cellular immunotherapies, such as CAR T cell therapy, encompass a new frontier for predictive mathematical biological modelling [6][7][8][9]. One of the first goals of this new field is to describe and predict CAR T cell expansion and decay after administration.…”

Tumor-immune ecosystem dynamics define an individual Radiation Immune Score to predict pan-cancer radiocurability

“…Simulating the highly dynamic tumor responses to radiation based on individual patient properties holds promise for innovative translational opportunities [14] , [15] , [16] , [17] , 18 , [19] , [20] . Similarly, significant inroads have been made in mathematical modeling of tumor-immune interactions and immunotherapy response prediction [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , as well as understanding the local and systemic mechanistic consequences of radio-immunotherapy combinations [31] , [32] , [33] , [34] .…”

A computational model of CAR T-cell immunotherapy predicts leukemia patient responses at remission, resistance, and relapse

“…For example, a multiscale physiologically based pharmacokinetic-pharmacodynamic model had been developed for a quantitative study of relationship between CAR-affinity, antigen abundance, tumor cell depletion and CAR T-cell expansion using data collected from xenograft mouse models (8). Other approaches focus mostly on modeling factors underlying CAR T-cell dynamics, such as ecological dynamics regulated CAR T-cell explain expansion (9) and exhaustion (10), signaling-induced cell state variability (11), and CAR T-cell expansion due to lymphodepletion and competitive growth between CAR T-cell and normal T-cell (12). Lately, Liu et al developed a model to characterize clinical CAR T-cell kinetics across response status, patient populations, and tumor types, yet only in a retrospective manner (13).…”