Predator-Prey in Tumor-Immune Interactions: A Wrong Model or Just an Incomplete One?

Kareva, Irina; Luddy, Kimberly A.; O’Farrelly, Cliona; Gatenby, Robert A.; Brown, Joel S.

doi:10.3389/fimmu.2021.668221

Cited by 28 publications

(21 citation statements)

References 108 publications

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“…This is despite its relative simplicity compared to other mathematical models of immunotherapies [ 13 , 17 , 43 , 44 ]. A recent commentary regarding predator-prey like models, including the model presented here, is the possibility of oscillating solutions which are unlikely to be observed in patients [ 45 , 46 ]. We note that the coexistence equilibrium is accompanied with phase-space trajectories that accurately describe experimental data.…”

Section: Discussionmentioning

confidence: 99%

Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy

Brummer

Yang

et al. 2022

PLoS Comput Biol

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Chimeric antigen receptor (CAR) T-cell therapy is potentially an effective targeted immunotherapy for glioblastoma, yet there is presently little known about the efficacy of CAR T-cell treatment when combined with the widely used anti-inflammatory and immunosuppressant glucocorticoid, dexamethasone. Here we present a mathematical model-based analysis of three patient-derived glioblastoma cell lines treated in vitro with CAR T-cells and dexamethasone. Advanced in vitro experimental cell killing assay technologies allow for highly resolved temporal dynamics of tumor cells treated with CAR T-cells and dexamethasone, making this a valuable model system for studying the rich dynamics of nonlinear biological processes with translational applications. We model the system as a nonautonomous, two-species predator-prey interaction of tumor cells and CAR T-cells, with explicit time-dependence in the clearance rate of dexamethasone. Using time as a bifurcation parameter, we show that (1) dexamethasone destabilizes coexistence equilibria between CAR T-cells and tumor cells in a dose-dependent manner and (2) as dexamethasone is cleared from the system, a stable coexistence equilibrium returns in the form of a Hopf bifurcation. With the model fit to experimental data, we demonstrate that high concentrations of dexamethasone antagonizes CAR T-cell efficacy by exhausting, or reducing the activity of CAR T-cells, and by promoting tumor cell growth. Finally, we identify a critical threshold in the ratio of CAR T-cell death to CAR T-cell proliferation rates that predicts eventual treatment success or failure that may be used to guide the dose and timing of CAR T-cell therapy in the presence of dexamethasone in patients.

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Section: Discussionmentioning

confidence: 99%

Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy

Brummer

Yang

et al. 2022

PLoS Comput Biol

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“…We do not attempt to generalize our quantitative results outside of our specific in vitro system (see e.g. [31]), but argue that heterogeneity in ecological dynamics across treatment conditions can contribute to the identified population cycling behavior in empirical adaptive therapeutic systems (see e.g. [17, 14]) and that pro-growth interactions can suppress competitive dynamics more than commonly assumed in the adaptive therapy literature.…”

Section: Discussionmentioning

confidence: 99%

Borrowing ecological theory to infer interactions between sensitive and resistant breast cancer cell populations

Susswein

Sengupta

Clarke

et al. 2022

Preprint

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While some forms of breast cancer are highly responsive to treatment, endocrine therapy-resistant breast cancers are disproportionately lethal. There has been significant progress in understanding how endocrine therapy-resistant strains evolve from therapy-susceptible strains of cancer, but little is understood about the proliferation of resistance through cancer cell populations or the interactions that occur between populations of resistant and sensitive cells. In this study, we characterize the nature of the ecological interaction between populations of resistant and susceptible breast cancer cells to reveal novel methods of controlling drug resistance. Using in-vitro data on fluorescent-tagged resistant and susceptible cells, we use an image processing algorithm to identify and count cell growth till equilibrium. We then borrow theory from population biology to infer the type of ecological interaction that occurs between populations of resistant and sensitive cells. In particular, we use a Bayesian approach to fit single culture cell populations to infer density-dependent growth parameters (growth rate, carrying capacity) and a Generalized Lotka-Volterra model to understand how susceptible and resistant co-culture populations may be depressing or supporting growth of the other. Our results identify a net mutualistic interaction between the susceptible and resistant cancer strains, demonstrating that there are ecological dynamics to cancer resistance. Our findings also suggest that ecological dynamics change in the presence of therapy and that an adaptive treatment protocol can induce cycling behavior suggesting that heterogeneous ecological effects contribute to empirically observed adaptive-therapeutic dynamics.

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“…As an additional difference, we treated the immunotherapy as either on or off, whereas the chemotherapy dosing existed on a continuum from 0 to 1 (maximum tolerable dose). While immune system models can explicitly consider the dynamics of other elements of the immune system [42, 38, 32, 18], when the patient data are just time series of tumor size, without any measures of immune infiltration or immune system dynamics, then the model we used here should suffice as an abstraction.…”

Section: Discussionmentioning

confidence: 99%

Improving Mathematical Models of Cancer through Game-Theoretic Modelling: A Study in Non-Small Cell Lung Cancer

Martinez¹,

Laleh

Salvioli³

et al. 2021

Preprint

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In this paper, a large dataset of 590 Non-Small Cell Lung Patients treated with either chemotherapy or immunotherapy was used to determine whether a game-theoretic model including both evolution of therapy resistance and cost of resistance provides a better fit than classical mathematical models of population growth (exponential, logistic, classic Bertalanffy, general Bertalanffy, Gompertz, general Gompertz). This is the first time a large clinical patient cohort (as opposed to only in-vitro data) has been used to apply a game-theoretic cancer model. The game-theoretic model provides a better fit to the tumor dynamics of the 590 Non-Small Cell Lung Cancer patients than any of the non-evolutionary population growth models. This is not simply due to having more parameters in the game-theoretic model. The game-theoretic model is able to fit accurately patients whose tumor burden exhibit a U-shaped trajectory over time. We then demonstrate how this game-theoretic model provides predictions of tumor growth based on just a few initial measurements. Assuming that treatment-specific parameters define the treatment impact completely, we then explore alternative treatment protocols and their impact on the tumor growth. As such, the model can be used to suggest patient-specific optimal treatment regimens with the goal of minimizing final tumor burden. Therapeutic protocols based on game-theoretic modeling can predict tumor growth, and improve patient outcome. The model invites evolutionary therapies that anticipate and steer the evolution of therapy resistance.

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Predator-Prey in Tumor-Immune Interactions: A Wrong Model or Just an Incomplete One?

Cited by 28 publications

References 108 publications

Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy

Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy

Borrowing ecological theory to infer interactions between sensitive and resistant breast cancer cell populations

Improving Mathematical Models of Cancer through Game-Theoretic Modelling: A Study in Non-Small Cell Lung Cancer

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