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Shafiekhani S, Dehghanbanadaki H, Fatemi AS, Rahbar S, Hadjati J, Jafari AH. Prediction of anti-CD25 and 5-FU treatments efficacy for pancreatic cancer using a mathematical model. BMC Cancer 2021; 21:1226. [PMID: 34781899 PMCID: PMC8594222 DOI: 10.1186/s12885-021-08770-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 09/09/2021] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with rising incidence and with 5-years overall survival of less than 8%. PDAC creates an immune-suppressive tumor microenvironment to escape immune-mediated eradication. Regulatory T (Treg) cells and myeloid-derived suppressor cells (MDSC) are critical components of the immune-suppressive tumor microenvironment. Shifting from tumor escape or tolerance to elimination is the major challenge in the treatment of PDAC. RESULTS In a mathematical model, we combine distinct treatment modalities for PDAC, including 5-FU chemotherapy and anti- CD25 immunotherapy to improve clinical outcome and therapeutic efficacy. To address and optimize 5-FU and anti- CD25 treatment (to suppress MDSCs and Tregs, respectively) schedule in-silico and simultaneously unravel the processes driving therapeutic responses, we designed an in vivo calibrated mathematical model of tumor-immune system (TIS) interactions. We designed a user-friendly graphical user interface (GUI) unit which is configurable for treatment timings to implement an in-silico clinical trial to test different timings of both 5-FU and anti- CD25 therapies. By optimizing combination regimens, we improved treatment efficacy. In-silico assessment of 5-FU and anti- CD25 combination therapy for PDAC significantly showed better treatment outcomes when compared to 5-FU and anti- CD25 therapies separately. Due to imprecise, missing, or incomplete experimental data, the kinetic parameters of the TIS model are uncertain that this can be captured by the fuzzy theorem. We have predicted the uncertainty band of cell/cytokines dynamics based on the parametric uncertainty, and we have shown the effect of the treatments on the displacement of the uncertainty band of the cells/cytokines. We performed global sensitivity analysis methods to identify the most influential kinetic parameters and simulate the effect of the perturbation on kinetic parameters on the dynamics of cells/cytokines. CONCLUSION Our findings outline a rational approach to therapy optimization with meaningful consequences for how we effectively design treatment schedules (timing) to maximize their success, and how we treat PDAC with combined 5-FU and anti- CD25 therapies. Our data revealed that a synergistic combinatorial regimen targeting the Tregs and MDSCs in both crisp and fuzzy settings of model parameters can lead to tumor eradication.
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Affiliation(s)
- Sajad Shafiekhani
- Departments of Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics, Tehran, Iran.,Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Hojat Dehghanbanadaki
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran.,Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Azam Sadat Fatemi
- Departments of Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics, Tehran, Iran
| | - Sara Rahbar
- Departments of Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Research Center for Biomedical Technologies and Robotics, Tehran, Iran
| | - Jamshid Hadjati
- Departments of Medical Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Homayoun Jafari
- Departments of Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. .,Research Center for Biomedical Technologies and Robotics, Tehran, Iran.
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YILDIZ TUĞBAAKMAN, KÖSE EMEK, ELLIOTT SAMANTHAL. MATHEMATICAL MODELING OF PANCREATIC CANCER TREATMENT WITH CANCER STEM CELLS. J BIOL SYST 2021. [DOI: 10.1142/s0218339021500182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Of all cancers, pancreatic cancer has a significantly low rate of survival, mostly due to lack of early screening. Thus, once detected, pancreatic cancer is usually in later stages, reducing the likelihood of full recovery. The most common treatment strategy is chemotherapy, although several immunotherapeutic drugs show promising results in extending the patient’s lifespan. In this paper, we provide a validated mathematical model for the pancreatic cancer after fitting the parameter values, such as tumor growth rate, inverse carrying capacity, activation and decay rate of pancreatic stellate cells, with the use of the experimental data presented by Cioffi et al. cioffi2015inhibition For treatments with the chemotherapeutic drugs, Abraxane and Gemcitabine, and the immunotherapeutic drug, Anti-CD47, we modified the model accurately and compared the simulation results with the experimental data not only to model pancreatic cancer treatment correctly but also to move forward with other drug trials. Then, we include the cancer stem cells, which are known to initiate tumors and cause a relapse post-chemotherapy, per cancer stem cell hypothesis so that cancer progression can be assessed based on this phenomenon. In addition, we investigate optimal drug protocols. We find out that the most effective treatment is dual therapy due to extending survival time when compared to other drugs. Moreover, this study reveals that drug dose is more effectual than frequency of drug injection on account of different treatment scheduling with the same dose over a week. The model could be a starting point to investigate pancreatic cancer progression based on cancer stem cell hypothesis and shed light on novel drug discoveries.
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Affiliation(s)
- TUĞBA AKMAN YILDIZ
- Department of Computer Engineering, University of Turkish Aeronautical Association, 06790 Ankara, Turkey
| | - EMEK KÖSE
- Department of Mathematics and Computer Science, St. Mary’s College of Maryland, St. Mary’s City, MD 20619, USA
| | - SAMANTHA L. ELLIOTT
- Department of Biology, St. Mary’s College of Maryland, St. Mary’s City, MD 20619, USA
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Frascoli F, Flood E, Kim PS. A model of the effects of cancer cell motility and cellular adhesion properties on tumour-immune dynamics. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2017; 34:215-240. [PMID: 27094601 DOI: 10.1093/imammb/dqw004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 01/18/2016] [Indexed: 02/07/2023]
Abstract
We present a three-dimensional model simulating the dynamics of an anti-cancer T-cell response against a small, avascular, early-stage tumour. Interactions at the tumour site are accounted for using an agent-based model (ABM), while immune cell dynamics in the lymph node are modelled as a system of delay differential equations (DDEs). We combine these separate approaches into a two-compartment hybrid ABM-DDE system to capture the T-cell response against the tumour. In the ABM at the tumour site, movement of tumour cells is modelled using effective physical forces with a specific focus on cell-to-cell adhesion properties and varying levels of tumour cell motility, thus taking into account the ability of cancer cells to spread and form clusters. We consider the effectiveness of the immune response over a range of parameters pertaining to tumour cell motility, cell-to-cell adhesion strength and growth rate. We also investigate the dependence of outcomes on the distribution of tumour cells. Low tumour cell motility is generally a good indicator for successful tumour eradication before relapse, while high motility leads, almost invariably, to relapse and tumour escape. In general, the effect of cell-to-cell adhesion on prognosis is dependent on the level of tumour cell motility, with an often unpredictable cross influence between adhesion and motility, which can lead to counterintuitive effects. In terms of overall tumour shape and structure, the spatial distribution of cancer cells in clusters of various sizes has shown to be strongly related to the likelihood of extinction.
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Affiliation(s)
- Federico Frascoli
- Faculty of Science, Engineering and Technology, Department of Mathematics, Swinburne University of Technology, Melbourne, Victoria, Australia
| | - Emelie Flood
- School of Applied Sciences, RMIT University, Melbourne, Victoria, Australia
| | - Peter S Kim
- School of Mathematics and Statistics, University of Sydney, New South Wales, Australia
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Mahasa KJ, Ouifki R, Eladdadi A, Pillis LD. Mathematical model of tumor-immune surveillance. J Theor Biol 2016; 404:312-330. [PMID: 27317864 DOI: 10.1016/j.jtbi.2016.06.012] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 06/09/2016] [Accepted: 06/09/2016] [Indexed: 12/26/2022]
Abstract
We present a novel mathematical model involving various immune cell populations and tumor cell populations. The model describes how tumor cells evolve and survive the brief encounter with the immune system mediated by natural killer (NK) cells and the activated CD8(+) cytotoxic T lymphocytes (CTLs). The model is composed of ordinary differential equations describing the interactions between these important immune lymphocytes and various tumor cell populations. Based on up-to-date knowledge of immune evasion and rational considerations, the model is designed to illustrate how tumors evade both arms of host immunity (i.e. innate and adaptive immunity). The model predicts that (a) an influx of an external source of NK cells might play a crucial role in enhancing NK-cell immune surveillance; (b) the host immune system alone is not fully effective against progression of tumor cells; (c) the development of immunoresistance by tumor cells is inevitable in tumor immune surveillance. Our model also supports the importance of infiltrating NK cells in tumor immune surveillance, which can be enhanced by NK cell-based immunotherapeutic approaches.
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Affiliation(s)
- Khaphetsi Joseph Mahasa
- DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa.
| | - Rachid Ouifki
- DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa
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Abstract
Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.
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Affiliation(s)
- Philipp M Altrock
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard T.H. Chan School of Public Health, 450 Brookline Avenue, Boston, Massachusetts 02115, USA
- Program for Evolutionary Dynamics, Harvard University, 1 Brattle Square, Suite 6, Cambridge, Massachusetts 02138, USA
| | - Lin L Liu
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard T.H. Chan School of Public Health, 450 Brookline Avenue, Boston, Massachusetts 02115, USA
| | - Franziska Michor
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard T.H. Chan School of Public Health, 450 Brookline Avenue, Boston, Massachusetts 02115, USA
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dePillis LG, Eladdadi A, Radunskaya AE. Modeling cancer-immune responses to therapy. J Pharmacokinet Pharmacodyn 2014; 41:461-78. [DOI: 10.1007/s10928-014-9386-9] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Accepted: 09/17/2014] [Indexed: 12/26/2022]
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FORYŚ URSZULA, WANIEWSKI JACEK, ZHIVKOV PETAR. ANTI-TUMOR IMMUNITY AND TUMOR ANTI-IMMUNITY IN A MATHEMATICAL MODEL OF TUMOR IMMUNOTHERAPY. J BIOL SYST 2011. [DOI: 10.1142/s0218339006001702] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A two-dimensional system of ordinary differential equations is used to characterize the basic types of phase portraits of the immune system — tumor interactions model, and to study the impact of anti-immune activity by tumor on the outcome of immunotherapy. The focus is on specific (acquired) immunity and different forms of immunotherapy as active therapy with in vivo stimulation of the immunity and passive one with infusion of ex vivo produced specific immunity. The analysis is performed for two families of stimulation function, which describes the dynamics of the stimulation of the immune system by tumor antigens: (1) antigen dependent and (2) antigen per one immunity unit dependent functions, with Michaelis-Menten and sigmoid functions in each family. We show that there are no limit cycles in the system and that anti-immune activity by tumor changes all equilibrium points from global to local ones. In the latter case, the immune system has no control over the growth of large tumors. Furthermore, if the immunity is weak, the immune system cannot eradicate even small tumors. The weak immunity and stimulation strength result in unrestricted tumor growth. The patterns of asymptotic behavior of the system do not depend on the type of the stimulation function, but do depend on its parameters. Our results reflect the basic clinical and experimental knowledge about immunotherapy and its effectiveness and yield new suggestions for an efficient immunotherapy.
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Affiliation(s)
- URSZULA FORYŚ
- Institute of Applied Mathematics and Mechanics, Faculty of Mathematics, Informatics and Mechanics, Warsaw University, Banacha 2, 02-097 Warsaw, Poland
| | - JACEK WANIEWSKI
- Institute of Biocybernetics and Biomedical Engineering PAS, Trojdena 4, 02-109 Warsaw, Poland
- Interdisciplinary Center for Mathematical and Computer Modelling, Pawinskiego 5A, 02-106 Warsaw, Poland
| | - PETAR ZHIVKOV
- Institute of Applied Mathematics and Mechanics, Faculty of Mathematics, Informatics and Mechanics, Warsaw University, Banacha 2, 02-097 Warsaw, Poland
- Institute of Nuclear Research and Nuclear Energy, Nuclear Spectroscopy Laboratory, 72 Tzarigradsko chaussee, Blvd., BG1784 Sofia, Bulgaria
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