A model for effects of adaptive immunity on tumor response to chemotherapy and chemoimmunotherapy

Robertson-Tessi, Mark; El-Kareh, Ardith W.; Goriely, Alain

doi:10.1016/j.jtbi.2015.06.009

Cited by 24 publications

(21 citation statements)

References 81 publications

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“…A comprehensive model was presented by (Robertson-Tessi et al, 2015), in which T helper cells, T regulatory cells (Tregs), CTLs, and dendritic cells (DCs), along with interleukins and their effects on tumor cells, are modeled together with combinations of chemo-and immunotherapy. The model is based primarily on (Robertson-Tessi et al, 2012), for which the evolution of tumor cells T is described as follows:…”

Section: Cd4+ T Helper Cellsmentioning

confidence: 99%

Mathematical modeling of tumor-immune cell interactions

Mahlbacher

Reihmer

Frieboes

2019

Journal of Theoretical Biology

125

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The anti-tumor activity of the immune system is increasingly recognized as critical for the mounting of a prolonged and effective response to cancer growth and invasion, and for preventing recurrence following resection or treatment. As the knowledge of tumor-immune cell interactions has advanced, experimental investigation has been complemented by mathematical modeling with the goal to quantify and predict these interactions. This succinct review offers an overview of recent tumor-immune continuum modeling approaches, highlighting spatial models. The focus is on work published in the past decade, incorporating one or more immune cell types and evaluating immune cell effects on tumor progression. Due to their relevance to cancer, the following immune cells and their combinations are described: macrophages, Cytotoxic T Lymphocytes, Natural Killer cells, dendritic cells, T regulatory cells, and CD4+ T helper cells. Although important insight has been gained from a mathematical modeling perspective, the development of models incorporating patient-specific data remains an important goal yet to be realized for potential clinical benefit.

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Section: Cd4+ T Helper Cellsmentioning

confidence: 99%

Mathematical modeling of tumor-immune cell interactions

Mahlbacher

Reihmer

Frieboes

2019

Journal of Theoretical Biology

125

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“…26 Mathematical models have been used to study tumorimmune dynamics and system stability in a variety of cancer types. [27][28][29][30][31] Neoadjuvant Therapy in Estrogen Receptor-Positive Breast Cancer…”

Section: Combination Therapy With Immune Checkpoint Inhibitorsmentioning

confidence: 99%

The Immune Checkpoint Kick Start: Optimization of Neoadjuvant Combination Therapy Using Game Theory

West

Robertson-Tessi

Luddy

et al. 2019

JCO Clinical Cancer Informatics

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Purpose In an upcoming clinical trial at the Moffitt Cancer Center for women with stage 2/3 estrogen receptor–positive breast cancer, treatment with an aromatase inhibitor and a PD-L1 checkpoint inhibitor combination will be investigated to lower a preoperative endocrine prognostic index (PEPI) that correlates with relapse-free survival. PEPI is fundamentally a static index, measured at the end of neoadjuvant therapy before surgery. We have developed a mathematical model of the essential components of the PEPI score to identify successful combination therapy regimens that minimize tumor burden and metastatic potential, on the basis of time-dependent trade-offs in the system. Methods We considered two molecular traits, CCR7 and PD-L1, which correlate with treatment response and increased metastatic risk. We used a matrix game model with the four phenotypic strategies to examine the frequency-dependent interactions of cancer cells. This game was embedded in an ecological model of tumor population-growth dynamics. The resulting model predicts evolutionary and ecological dynamics that track with changes in the PEPI score. Results We considered various treatment regimens on the basis of combinations of the two therapies with drug holidays. By considering the trade off between tumor burden and metastatic potential, the optimal therapy plan was a 1-month kick start of the immune checkpoint inhibitor followed by 5 months of continuous combination therapy. Relative to a protocol giving both therapeutics together from the start, this delayed regimen resulted in transient suboptimal tumor regression while maintaining a phenotypic constitution that is more amenable to fast tumor regression for the final 5 months of therapy. Conclusion The mathematical model provides a useful abstraction of clinical intuition, enabling hypothesis generation and testing of clinical assumptions.

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“…and any one of the seven conditions (2)(3)(4)(5)(6)(7)(8) above is satisfied; ie, at least one of m 1 , m 2 , m 3 is positive.…”

Section: Coexisting Equilibrium Pointmentioning

confidence: 99%

“…In the study, 3 importance of scheduling immunotherapy is underlined to eradicate and control tumor population. Moreover, immune competition is analyzed in case of tumor population by Brazzoli et al 4 In the work by Robertson-Tessi et al, 5 the effect of chemotherapy and immunotherapy on control of the tumor growth has been presented. The combined effect of immunotherapy and chemotherapy is investigated on the basis of optimal control theory by Pang et al 6 The aim behind chemotherapy is to eliminate the tumor cells.…”

Section: Introductionmentioning

confidence: 99%

Optimal chemotherapy and immunotherapy schedules for a cancer‐obesity model with Caputo time fractional derivative

Yıldız

Arshad

Băleanu

2018

Math Methods in App Sciences

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Communicated by: M. Kirane MSC Classification: 49K99; 34A08; 37N25; 92B05; 65L07This work presents a new mathematical model to depict the effect of obesity on cancerous tumor growth when chemotherapy and immunotherapy have been administered. We consider an optimal control problem to destroy the tumor population and minimize the drug dose over a finite time interval. The constraint is a model including tumor cells, immune cells, fat cells, and chemotherapeutic and immunotherapeutic drug concentrations with the Caputo time fractional derivative. We investigate the existence and stability of the equilibrium points, namely, tumor-free equilibrium and coexisting equilibrium, analytically. We discretize the cancer-obesity model using the L1 method. Simulation results of the proposed model are presented to compare three different treatment strategies: chemotherapy, immunotherapy, and their combination. In addition, we investigate the effect of the differentiation order and the value of the decay rate of the amount of chemotherapeutic drug to the value of the cost functional. We find out the optimal treatment schedule in case of chemotherapy and immunotherapy. KEYWORDS chemotherapy, fractional differential equations, immunotherapy, optimal control, stability 9390

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A model for effects of adaptive immunity on tumor response to chemotherapy and chemoimmunotherapy

Cited by 24 publications

References 81 publications

Mathematical modeling of tumor-immune cell interactions

Mathematical modeling of tumor-immune cell interactions

The Immune Checkpoint Kick Start: Optimization of Neoadjuvant Combination Therapy Using Game Theory

Optimal chemotherapy and immunotherapy schedules for a cancer‐obesity model with Caputo time fractional derivative

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