Optimal Treatment Strategy for a Tumor Model under Immune Suppression

Kim, Kwang Su; Cho, Gi Phil; Jung, Il Hyo

doi:10.1155/2014/206287

Cited by 15 publications

(12 citation statements)

References 19 publications

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“…For example, the units of concentration of the cytokines were converted from mg/L to IU/L by referring to the published specific activity of each cytokine. See [33] for details on how to perform these conversions. Cytokines whose specific activity was not available in the published literature were assigned an average specific activity of 1.3 × 10 10 IU/g.…”

Section: Model Parameters and Treatment Robustnessmentioning

confidence: 99%

Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success

Morales¹,

Soto-Ortiz²

2018

OJI

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Positive feedback loops drive immune cell polarization toward a pro-tumor phenotype that accentuates immunosuppression and tumor angiogenesis. This phenotypic switch leads to the escape of cancer cells from immune destruction. These positive feedback loops are generated by cytokines such as TGF-β, Interleukin-10 and Interleukin-4, which are responsible for the polarization of monocytes and M1 macrophages into pro-tumor M2 macrophages, and the polarization of naive helper T cells intopro-tumor Th2 cells. In this article, we present a deterministic ordinary differential equation (ODE) model that includes key cellular interactions and cytokine signaling pathways that lead to immune cell polarization in the tumor microenvironment. The model was used to simulate various cancer treatments in silico. We identified combination therapies that consist of M1 macrophages or Th1 helper cells, coupled with an anti-angiogenic treatment, that are robust with respect to immune response strength, initial tumor size and treatment resistance. We also identified IL-4 and IL-10 as the targets that should be neutralized in order to make these combination treatments robust with respect to immune cell polarization. The model simulations confirmed a hypothesis based on published experimental evidence that a polarization into the M1 and Th1 phenotypes to increase the M1-to-M2 and Th1-to-Th2 ratios plays a significant role in treatment success. Our results highlight the importance of immune cell reprogramming as a viable strategy to eradicate a highly vascularized tumor when the strength of the immune response is characteristically weak and cell polarization to the pro-tumor phenotype has occurred. [8] [9] [10] [11]. The high proportion of M2 macrophages and Th2 helper cells in the tumor microenvironment makes them an important target of cancer therapies [12] [13] [14] [15] [16]. Positive Feedback Loops Perpetuate Cell PolarizationA tumor is a complex dynamical system, and its survival depends on a diverse set of signaling networks characterized by cytokine-driven positive feedback loops that can reinforce the anti-tumor phenotype or the pro-tumor phenotype of tumor-infiltrating immune cells. For example, M1 macrophages secrete IL-12 which leads to the differentiation of immature helper T cells into Th1 cells. Th1 cells secrete IFN-ϒ which reinforces the M1 macrophage phenotype. This positive feedback loop perpetuates the M1 and Th1 anti-tumor polarization of these cells, which can lead to tumor destruction. On the other hand, M2 macrophages secrete IL-4 and IL-6 [17], [18] which lead to the differentiation of immature helper T cells into Th2 cells. Th2 cells secrete which reinforces the M2 macrophage phenotype. This positive feedback loop perpetuates the M2 and Th2 pro-tumor polarization, leading to tumor escape. More complex immune cell interactions exist. M2 macrophages and Th2 cells secrete TGF-β which converts naïve helper T cells into pro-tumor regulatory T cells (Tregs) [20] and B cells into pro-tumor regulatory B cells (Bregs). Tre...

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Section: Model Parameters and Treatment Robustnessmentioning

confidence: 99%

Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success

Morales¹,

Soto-Ortiz²

2018

OJI

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“…The recruitment of all the uninfected CD4 + T cells is s 3 , from a source such as the thymus since HIV infection does not discriminate between different CD4 + T cells. The naive helper cells are activated by IL-12 in conjunction with the antigen presented in the 52,53 Abbreviations: IL, interleukin; TGF, transforming growth factor.…”

Section: State Variables Equationsmentioning

confidence: 99%

“…Lastly, following de Pillis et al Kwang et al, 52,53 most of the chemotherapeutic drugs are only effective during certain phases of cell cycle (cell cycle specific) and pharmacokinetics indicate that the effectiveness of the chemotherapeutic drugs is bounded. We therefore have the term (1 − e −D ), a saturation term representing the fraction of the cell killed by a dose, D of chemotherapy.…”

Section: State Variables Equationsmentioning

confidence: 99%

Modelling the dynamics of HIV‐related non‐Hodgkin lymphomas in the presence of HIV treatment and chemotherapy

Aogo

Nyabadza

2017

Math Methods in App Sciences

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Communicated by: R. Anguelov MOS Classification: 92C50; 92B05; 34C60; 34D20 Human immunodeficiency virus (HIV)/AIDS and cancer coexistence both in vivo and in vitro in a cancer-immune environment leads to specific cytokines being produced by various immune cells and the cancer cells. Most of the studies have suggested that specific cytokines produced by the immune system cells and the tumor play an important role in the dynamics of non-Hodgkin lymphomas (NHLs). In this paper, a mathematical model describing the NHL-immune system interaction in the presence of the HIV, HIV treatment, and chemotherapy is developed. The formulated model, described by nonlinear ODEs, shows existence of multiple equilibria whose stability and bifurcation analysis are presented. From the bifurcation analysis, bistability regions are evident. We observe that with and without HIV treatment, the system results in a nonaggressive tumor size or aggressive tumor (full-blown tumor) depending on the initial conditions. The results further suggest that at a low endemic state, patients can live for longer period with the tumor, which might explain why some patients can live with cancer for many years. However, initiation of HIV treatment in patients with NHL is observed to lower these endemic states of the tumor. Our results explain why late initiation of HIV treatment might not be helpful to NHL patients. We further investigated the effect of chemotherapy on the dynamics of the tumor. Our simulation results might explain why a few of these chemotherapeutic drugs are more effective when given at a slow continuous rate. The model provides a unique opportunity to influence policy on HIV-related cancer treatment and management.

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“…In the past, mathematical modeling about tumorimmune interactions has been studied by many scholars (see [1][2][3]). Particularly, Gałach [4] provided three kinds of different models which describe a competition between the tumor and immune cells by using a mathematical model with delay.…”

Section: Introductionmentioning

confidence: 99%

“…Actually they investigated the stability of the steady state and observed a state of the "returning" tumor in the model with time delay. In several papers, there are several mathematical models that describe treatment effects: immunotherapy, chemotherapy, radiation therapy, and so on [1,5].…”

Section: Introductionmentioning

confidence: 99%

State-Dependent Impulsive Control Strategies for a Tumor-Immune Model

Kim

Cho

Nie

et al. 2016

Discrete Dynamics in Nature and Society

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Controlling the number of tumor cells leads us to expect more efficient strategies for treatment of tumor. Towards this goal, a tumor-immune model with state-dependent impulsive treatments is established. This model may give an efficient treatment schedule to control tumor’s abnormal growth. By using the Poincaré map and analogue of Poincaré criterion, some conditions for the existence and stability of a positive order-1 periodic solution of this model are obtained. Moreover, we carry out numerical simulations to illustrate the feasibility of our main results and compare fixed-time impulsive treatment effects with state-dependent impulsive treatment effects. The results of our simulations say that, in determining optimal treatment timing, the model with state-dependent impulsive control is more efficient than that with fixed-time impulsive control.

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Optimal Treatment Strategy for a Tumor Model under Immune Suppression

Cited by 15 publications

References 19 publications

Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success

Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success

Modelling the dynamics of HIV‐related non‐Hodgkin lymphomas in the presence of HIV treatment and chemotherapy

State-Dependent Impulsive Control Strategies for a Tumor-Immune Model

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