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Lazebnik T, Bunimovich-Mendrazitsky S. Predicting lung cancer's metastats' locations using bioclinical model. Front Med (Lausanne) 2024; 11:1388702. [PMID: 38846148 PMCID: PMC11153684 DOI: 10.3389/fmed.2024.1388702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/13/2024] [Indexed: 06/09/2024] Open
Abstract
Background Lung cancer is a global leading cause of cancer-related deaths, and metastasis profoundly influences treatment outcomes. The limitations of conventional imaging in detecting small metastases highlight the crucial need for advanced diagnostic approaches. Methods This study developed a bioclinical model using three-dimensional CT scans to predict the spatial spread of lung cancer metastasis. Utilizing a three-layer biological model, we identified regions with a high probability of metastasis colonization and validated the model on real-world data from 10 patients. Findings The validated bioclinical model demonstrated a promising 74% accuracy in predicting metastasis locations, showcasing the potential of integrating biophysical and machine learning models. These findings underscore the significance of a more comprehensive approach to lung cancer diagnosis and treatment. Interpretation This study's integration of biophysical and machine learning models contributes to advancing lung cancer diagnosis and treatment, providing nuanced insights for informed decision-making.
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Affiliation(s)
- Teddy Lazebnik
- Department of Cancer Biology, Cancer Institute, University College London, London, United Kingdom
- Department of Mathematics, Ariel University, Ariel, Israel
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2
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Golmankhaneh AK, Tunç S, Schlichtinger AM, Asanza DM, Golmankhaneh AK. Modeling tumor growth using fractal calculus: Insights into tumor dynamics. Biosystems 2024; 235:105071. [PMID: 37944632 DOI: 10.1016/j.biosystems.2023.105071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 10/23/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023]
Abstract
Important concepts like fractal calculus and fractal analysis, the sum of squared residuals, and Aikaike's information criterion must be thoroughly understood in order to correctly fit cancer-related data using the proposed models. The fractal growth models employed in this work are classified in three main categories: Sigmoidal growth models (Logistic, Gompertz, and Richards models), Power Law growth model, and Exponential growth models (Exponential and Exponential-Lineal models)". We fitted the data, computed the sum of squared residuals, and determined Aikaike's information criteria using Matlab and the web tool WebPlotDigitizer. In addition, the research investigates "double-size cancer" in the fractal temporal dimension with respect to various mathematical models.
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Affiliation(s)
| | - Sümeyye Tunç
- Department of Physiotherapy and Rehabilitation, IMU Vocational School, Istanbul Medipol University, Unkapani, Fatih, Istanbul, 34083, Turkey.
| | - Agnieszka Matylda Schlichtinger
- Faculty of Physics and Astronomy, Institute of Theoretical Physics, University of Wroclaw, pl. M. Borna 9, Wroclaw, 50-204, Poland.
| | - Dachel Martinez Asanza
- Department of Scientific-Technical Results Management, National School of Public Health (ENSAP), Havana Medical Sciences University, Havana, 10800, Cuba.
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3
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Jenner AL, Kelly W, Dallaston M, Araujo R, Parfitt I, Steinitz D, Pooladvand P, Kim PS, Wade SJ, Vine KL. Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model. PLoS Comput Biol 2023; 19:e1010104. [PMID: 36649330 PMCID: PMC9891514 DOI: 10.1371/journal.pcbi.1010104] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 02/01/2023] [Accepted: 12/21/2022] [Indexed: 01/18/2023] Open
Abstract
The prognosis for pancreatic ductal adenocarcinoma (PDAC) patients has not significantly improved in the past 3 decades, highlighting the need for more effective treatment approaches. Poor patient outcomes and lack of response to therapy can be attributed, in part, to a lack of uptake of perfusion of systemically administered chemotherapeutic drugs into the tumour. Wet-spun alginate fibres loaded with the chemotherapeutic agent gemcitabine have been developed as a potential tool for overcoming the barriers in delivery of systemically administrated drugs to the PDAC tumour microenvironment by delivering high concentrations of drug to the tumour directly over an extended period. While exciting, the practicality, safety, and effectiveness of these devices in a clinical setting requires further investigation. Furthermore, an in-depth assessment of the drug-release rate from these devices needs to be undertaken to determine whether an optimal release profile exists. Using a hybrid computational model (agent-based model and partial differential equation system), we developed a simulation of pancreatic tumour growth and response to treatment with gemcitabine loaded alginate fibres. The model was calibrated using in vitro and in vivo data and simulated using a finite volume method discretisation. We then used the model to compare different intratumoural implantation protocols and gemcitabine-release rates. In our model, the primary driver of pancreatic tumour growth was the rate of tumour cell division. We were able to demonstrate that intratumoural placement of gemcitabine loaded fibres was more effective than peritumoural placement. Additionally, we quantified the efficacy of different release profiles from the implanted fibres that have not yet been tested experimentally. Altogether, the model developed here is a tool that can be used to investigate other drug delivery devices to improve the arsenal of treatments available for PDAC and other difficult-to-treat cancers in the future.
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Affiliation(s)
- Adrianne L. Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- * E-mail:
| | - Wayne Kelly
- School of Computer Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Michael Dallaston
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Robyn Araujo
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Isobelle Parfitt
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Dominic Steinitz
- Tweag Software Innovation Lab, London, United Kingdom
- Kingston University, Kingston, United Kingdom
| | - Pantea Pooladvand
- School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
| | - Peter S. Kim
- School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
| | - Samantha J. Wade
- Illawarra Health and Medical Research Institute, Wollongong, New South Wales, Australia
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales, Australia
| | - Kara L. Vine
- Illawarra Health and Medical Research Institute, Wollongong, New South Wales, Australia
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales, Australia
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4
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Chauvet S, Hubert F, Mann F, Mezache M. Tumorigenesis and axons regulation for the pancreatic cancer: A mathematical approach. J Theor Biol 2023; 556:111301. [PMID: 36270328 DOI: 10.1016/j.jtbi.2022.111301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 09/23/2022] [Accepted: 09/30/2022] [Indexed: 11/06/2022]
Abstract
The nervous system is today recognized to play an important role in the development of cancer. Indeed, neurons extend long processes (axons) that grow and infiltrate tumors in order to regulate the progression of the disease in a positive or negative way, depending on the type of neuron considered. Mathematical modeling of this biological process allows to formalize the nerve-tumor interactions and to test hypotheses in silico to better understand this phenomenon. In this work, we introduce a system of differential equations modeling the progression of pancreatic ductal adenocarcinoma (PDAC) coupled with associated changes in axonal innervation. The study of the asymptotic behavior of the model confirms the experimental observations that PDAC development is correlated with the type and densities of axons in the tissue. We study then the identifiability and the sensitivity of the model parameters. The identifiability analysis informs on the adequacy between the parameters of the model and the experimental data and the sensitivity analysis on the most contributing factors on the development of cancer. It leads to significant insights on the main neural checkpoints and mechanisms controlling the progression of pancreatic cancer. Finally, we give an example of a simulation of the effects of partial or complete denervation that sheds lights on complex correlation between the healthy, pre-cancerous and cancerous cell densities and axons with opposite functions.
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Affiliation(s)
- Sophie Chauvet
- Aix Marseille Univ, CNRS, IBDM (UMR 7288), Turing Centre for Living systems, Marseille, France
| | - Florence Hubert
- Aix Marseille Univ, CNRS, Centrale Marseille, I2M (UMR 7373), Turing Centre for Living systems, Marseille, France
| | - Fanny Mann
- Aix Marseille Univ, CNRS, IBDM (UMR 7288), Turing Centre for Living systems, Marseille, France
| | - Mathieu Mezache
- Aix Marseille Univ, CNRS, Centrale Marseille, I2M (UMR 7373), Turing Centre for Living systems, Marseille, France; Université Paris-Saclay, INRAE, MaIAGE (UR 1404), 78350 Jouy-en-Josas, France.
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5
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Guhe V, Ingale P, Tambekar A, Singh S. Systems biology of autophagy in leishmanial infection and its diverse role in precision medicine. Front Mol Biosci 2023; 10:1113249. [PMID: 37152895 PMCID: PMC10160387 DOI: 10.3389/fmolb.2023.1113249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 04/05/2023] [Indexed: 05/09/2023] Open
Abstract
Autophagy is a contentious issue in leishmaniasis and is emerging as a promising therapeutic regimen. Published research on the impact of autophagic regulation on Leishmania survival is inconclusive, despite numerous pieces of evidence that Leishmania spp. triggers autophagy in a variety of cell types. The mechanistic approach is poorly understood in the Leishmania parasite as autophagy is significant in both Leishmania and the host. Herein, this review discusses the autophagy proteins that are being investigated as potential therapeutic targets, the connection between autophagy and lipid metabolism, and microRNAs that regulate autophagy and lipid metabolism. It also highlights the use of systems biology to develop novel autophagy-dependent therapeutics for leishmaniasis by utilizing artificial intelligence (AI), machine learning (ML), mathematical modeling, network analysis, and other computational methods. Additionally, we have shown many databases for autophagy and metabolism in Leishmania parasites that suggest potential therapeutic targets for intricate signaling in the autophagy system. In a nutshell, the detailed understanding of the dynamics of autophagy in conjunction with lipids and miRNAs unfolds larger dimensions for future research.
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Shojaee P, Mornata F, Deutsch A, Locati M, Hatzikirou H. The impact of tumor associated macrophages on tumor biology under the lens of mathematical modelling: A review. Front Immunol 2022; 13:1050067. [PMID: 36439180 PMCID: PMC9685623 DOI: 10.3389/fimmu.2022.1050067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 10/18/2022] [Indexed: 09/10/2023] Open
Abstract
In this article, we review the role of mathematical modelling to elucidate the impact of tumor-associated macrophages (TAMs) in tumor progression and therapy design. We first outline the biology of TAMs, and its current application in tumor therapies, and their experimental methods that provide insights into tumor cell-macrophage interactions. We then focus on the mechanistic mathematical models describing the role of macrophages as drug carriers, the impact of macrophage polarized activation on tumor growth, and the role of tumor microenvironment (TME) parameters on the tumor-macrophage interactions. This review aims to identify the synergies between biological and mathematical approaches that allow us to translate knowledge on fundamental TAMs biology in addressing current clinical challenges.
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Affiliation(s)
- Pejman Shojaee
- Centre for Information Services and High Performance Computing, Technische Universität (TU) Dresden, Dresden, Germany
| | - Federica Mornata
- Leukocyte Biology Lab, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Andreas Deutsch
- Centre for Information Services and High Performance Computing, Technische Universität (TU) Dresden, Dresden, Germany
| | - Massimo Locati
- Leukocyte Biology Lab, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Medical Biotechnologies and Translational Medicine, Universitàdegli Studi di Milano, Milan, Italy
| | - Haralampos Hatzikirou
- Centre for Information Services and High Performance Computing, Technische Universität (TU) Dresden, Dresden, Germany
- Mathematics Department, Khalifa University, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Centre (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
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7
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Liao KL, Watt KD. Mathematical Modeling and Analysis of CD200-CD200R in Cancer Treatment. Bull Math Biol 2022; 84:82. [PMID: 35792958 DOI: 10.1007/s11538-022-01039-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 06/01/2022] [Indexed: 11/26/2022]
Abstract
CD200 is a cell membrane protein that binds to its receptor, CD200 receptor (CD200R). The CD200 positive tumor cells inhibit the cellular functions of M1 and M2 macrophages and dendritic cells (DCs) through the CD200-CD200R complex, resulting in downregulation of Interleukin-10 and Interleukin-12 productions and affecting the activation of cytotoxic T lymphocytes. In this work, we provide two ordinary differential equation models, one complete model and one simplified model, to investigate how the binding affinities of CD200R and the populations of M1 and M2 macrophages affect the functions of the CD200-CD200R complex in tumor growth. Our simulations demonstrate that (i) the impact of the CD200-CD200R complex on tumor promotion or inhibition highly depends on the binding affinity of the CD200R on M2 macrophages and DCs to the CD200 on tumor cells, and (ii) a stronger binding affinity of the CD200R on M1 macrophages or DCs to the CD200 on tumor cells induces a higher tumor cell density in the CD200 positive tumor. Thus, the CD200 blockade would be an efficient treatment method in this case. Moreover, the simplified model shows that the binding affinity of CD200R on macrophages is the major factor to determine the treatment efficacy of CD200 blockade when the binding affinities of CD200R on M1 and M2 macrophages are significantly different to each other. On the other hand, both the binding affinity of CD200R and the population of macrophages are the major factors to determine the treatment efficacy of CD200 blockade when the binding affinities of CD200R on M1 and M2 macrophages are close to each other. We also analyze the simplified model to investigate the dynamics of the positive and trivial equilibria of the CD200 positive tumor case and the CD200 deficient tumor case. The bifurcation diagrams show that when M1 macrophages dominate the population, the tumor cell density of the CD200 positive tumor is higher than the one of CD200 deficient tumor. Moreover, the dynamics of tumor cell density change from tumor elimination to tumor persistence to oscillation, as the maximal proliferation rate of tumor cells increases.
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Affiliation(s)
- Kang-Ling Liao
- Department of Mathematics, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada.
| | - Kenton D Watt
- Department of Mathematics, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
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Bartha L, Eftimie R. Mathematical investigation into the role of macrophage heterogeneity on the temporal and spatio-temporal dynamics of non-small cell lung cancers. J Theor Biol 2022; 549:111207. [PMID: 35772491 DOI: 10.1016/j.jtbi.2022.111207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 05/23/2022] [Accepted: 06/21/2022] [Indexed: 10/17/2022]
Abstract
Non Small Cell Lung Cancer (NSCLC) is the most common type of lung cancer, and represents the leading cause of cancer-related deaths worldwide. Experimental studies have shown that these solid cancers are heavily infiltrated with macrophages: anti-tumour M1 macrophages, pro-tumour M2 macrophages, and macrophage subtypes sharing both M1 and M2 properties. In this study we aim to investigate qualitatively the role of macrophages with different functional phenotypes (especially those with mixed phenotypes) on cancer dynamics and the success of different immunotherapies for cancer. To this end, we start with two time-evolving mathematical models for cancer-immune interactions that consider: (i) the effect of the two extreme phenotypes, M1 and M2 cells; (ii) the effect of M1 and M2 cells, as well as a macrophage sub-population with a mixed phenotype (throughout this theoretical study we call these cells "M12 cells"). We compare the dynamics of the two models using computational approaches, paying particular attention to the effect of different anti-cancer immunotherapies that focus on macrophages. Since data available for NSCLC and macrophage interactions are incomplete, we perform a global sensitivity analysis to see the influence of input parameters on model outcomes. Finally, we consider extensions of the previous two models to include also the spatial movement of cells, and investigate the role of macrophages with extreme phenotypes and with mixed phenotypes, on the invasion of cancer cells into the surrounding extracellular matrix (ECM). We use numerical simulations to investigate the macrophages phenotypes at the tumour center versus the invasive margin. Again, we examine the impact of immunotherapies for cancer on the spatial dynamics of cancers and immune cells, and observe a shift in the phenotype of macrophages distributed at the tumour center and invasive margin.
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Affiliation(s)
- Liza Bartha
- Former address: Mathematics, University of Dundee, Dundee, DD1 4HN, United Kingdom
| | - Raluca Eftimie
- Former address: Mathematics, University of Dundee, Dundee, DD1 4HN, United Kingdom; Laboratoire Mathématiques de Besançon, UMR-CNRS 6623, Université de Bourgogne Franche-Comté, 16 Route de Gray, 25200 Besançon, France.
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9
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Mathematical Modeling of Tumor and Cancer Stem Cells Treated with CAR-T Therapy and Inhibition of TGF-$$\beta $$. Bull Math Biol 2022; 84:58. [DOI: 10.1007/s11538-022-01015-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 03/22/2022] [Indexed: 11/26/2022]
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10
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Patsatzis DG. Algorithmic asymptotic analysis: Extending the arsenal of cancer immunology modeling. J Theor Biol 2022; 534:110975. [PMID: 34883121 DOI: 10.1016/j.jtbi.2021.110975] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/23/2021] [Accepted: 11/25/2021] [Indexed: 12/25/2022]
Abstract
The recent advances in cancer immunotherapy boosted the development of tumor-immune system models, with the aim to indicate more efficient treatments. Physical understanding is however difficult to be acquired, due to the complexity and the multi-scale dynamics of these models. In this work, the dynamics of a fundamental model formulating the interactions of tumor cells with natural killer cells, CD8+ T cells and circulating lymphocytes is examined. It is first shown that the long-term evolution of the system towards high-tumor or tumor-free equilibria is determined by the dynamics of an initial explosive stage of tumor progression. Focusing on this stage, the algorithmic Computational Singular Perturbation methodology is employed to identify the underlying mechanisms confining the system's evolution and the governing slow dynamics along them. These insights are preserved along different tumor-immune system and patient-dependent realizations. On top of these identifications, a novel reduced model is algorithmically constructed, which accurately predicts the dynamics of the system during the explosive stage and includes half of the parameters of the detailed model. The present analysis demonstrates the potential of algorithmic asymptotic analysis for acquiring physical understanding and for simplifying the complexity of cancer immunology models. Along with the current techniques on the field, this analysis can provide guidelines for more effective treatment development.
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Affiliation(s)
- Dimitrios G Patsatzis
- School of Chemical Engineering, National Technical University of Athens, 15772 Athens, Greece.
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11
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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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DARANDIS N, NAZARI M. A NEW MATHEMATICAL MODELING AND SUB-OPTIMAL CHEMOTHERAPY OF CANCER. J BIOL SYST 2021. [DOI: 10.1142/s0218339021500133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The development of more accurate cancer mathematical models leads to present more realistic treatment protocols, especially in model-based treatment protocol design. Hence, a cancer mathematical model is presented by considering tumor cells, immune cells, interleukins, macrophages polarization, and chemotherapy based on biological concepts. Both local and global sensitivity analyses are done to examine the effect of changing the parameters on the final tumor population. Then, the tumor-free equilibrium points of the system are derived, and their stabilities are studied. The main target of chemotherapy is to eliminate the tumor while limiting drug toxicity. The SDRE method is used to construct a sub-optimal control strategy by using the developed nonlinear cancer model. For simulation, three patients are considered: a young patient, an old patient, and a pregnant patient. These cases have different immune system strengths. Also, three initial tumor sizes are regarded for each case. So, different treatment strategies are suggested. Eradication of tumor cells in a finite duration with a desired amount of chemo-drug is shown in the simulation results. The results confirmed that the immune system’s ability plays an important role in treatment success. It is shown that there are different treatment protocols for different patients, and the SDRE method is more flexible and effective than the others.
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Affiliation(s)
- N. DARANDIS
- Faculty of Mechanical and Mechatronics Engineering, Shahrood, University of Technology, P. O. Box: 3619995161, Shahrood, Iran
| | - M. NAZARI
- Faculty of Mechanical and Mechatronics Engineering, Shahrood, University of Technology, P. O. Box: 3619995161, Shahrood, Iran
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14
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Wilkie KP, Aktar F. Mathematically modelling inflammation as a promoter of tumour growth. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2021; 37:491-514. [PMID: 32430508 DOI: 10.1093/imammb/dqaa005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 03/19/2020] [Accepted: 03/25/2020] [Indexed: 12/23/2022]
Abstract
Inflammation is now known to play a significant role in tumour growth and progression. It is also difficult to adequately quantify systemic inflammation and the resulting localized effects in cancer. Here, we use experimental data to infer the possible contributions of inflammation in a mouse model of cancer. The model is validated by predicting tumour growth under anti-inflammatory treatments, and combination cancer therapies are explored. We then extend the model to consider simultaneous tumour implants at two distinct sites, which experimentally was shown to result in one large and one small tumour. We use this model to examine the role inflammation may play in the growth rate separation. Finally, we use this predictive two-tumour model to explore implications of inflammation on metastases, surgical removal of the primary and adjuvant anti-inflammatory treatments. This work suggests that improved tumour control can be obtained by targeting both the cancer and host, through anti-inflammatory treatments, including reduced metastatic burden post-surgical removal of primary tumours.
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Affiliation(s)
- Kathleen P Wilkie
- Department of Mathematics, Ryerson University, Toronto, Ontario, Canada
| | - Farjana Aktar
- Department of Mathematics, Ryerson University, Toronto, Ontario, Canada
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15
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Bergman DR, Karikomi MK, Yu M, Nie Q, MacLean AL. Modeling the effects of EMT-immune dynamics on carcinoma disease progression. Commun Biol 2021; 4:983. [PMID: 34408236 PMCID: PMC8373868 DOI: 10.1038/s42003-021-02499-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 07/27/2021] [Indexed: 02/07/2023] Open
Abstract
During progression from carcinoma in situ to an invasive tumor, the immune system is engaged in complex sets of interactions with various tumor cells. Tumor cell plasticity alters disease trajectories via epithelial-to-mesenchymal transition (EMT). Several of the same pathways that regulate EMT are involved in tumor-immune interactions, yet little is known about the mechanisms and consequences of crosstalk between these regulatory processes. Here we introduce a multiscale evolutionary model to describe tumor-immune-EMT interactions and their impact on epithelial cancer progression from in situ to invasive disease. Through simulation of patient cohorts in silico, the model predicts that a controllable region maximizes invasion-free survival. This controllable region depends on properties of the mesenchymal tumor cell phenotype: its growth rate and its immune-evasiveness. In light of the model predictions, we analyze EMT-inflammation-associated data from The Cancer Genome Atlas, and find that association with EMT worsens invasion-free survival probabilities. This result supports the predictions of the model, and leads to the identification of genes that influence outcomes in bladder and uterine cancer, including FGF pathway members. These results suggest new means to delay disease progression, and demonstrate the importance of studying cancer-immune interactions in light of EMT.
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Affiliation(s)
- Daniel R. Bergman
- grid.266093.80000 0001 0668 7243Department of Mathematics, University of California, Irvine, CA USA
| | - Matthew K. Karikomi
- grid.266093.80000 0001 0668 7243Department of Mathematics, University of California, Irvine, CA USA
| | - Min Yu
- grid.42505.360000 0001 2156 6853USC Norris Comprehensive Cancer Center, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA ,grid.42505.360000 0001 2156 6853Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Qing Nie
- grid.266093.80000 0001 0668 7243Department of Mathematics, University of California, Irvine, CA USA ,grid.266093.80000 0001 0668 7243Department of Cell and Developmental Biology, University of California, Irvine, CA USA
| | - Adam L. MacLean
- grid.266093.80000 0001 0668 7243Department of Mathematics, University of California, Irvine, CA USA ,grid.42505.360000 0001 2156 6853USC Norris Comprehensive Cancer Center, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA ,grid.42505.360000 0001 2156 6853Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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16
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Eftimie R, Barelle C. Mathematical investigation of innate immune responses to lung cancer: The role of macrophages with mixed phenotypes. J Theor Biol 2021; 524:110739. [PMID: 33930438 DOI: 10.1016/j.jtbi.2021.110739] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 04/21/2021] [Accepted: 04/23/2021] [Indexed: 01/01/2023]
Abstract
Macrophages' role in the evolution of solid tumours is a well accepted fact, with the M1-like macrophages having an anti-tumour role and the M2-like macrophages having a pro-tumour role. Despite the fact that some clinical studies on lung tumours have emphasised also the presence of macrophages with mixed M1 and M2 phenotypes in addition to macrophages with distinct phenotypes, the majority of studies still use the distinct M1-M2 classification to predict the evolution of tumours and patient survival. In this theoretical study we use a mathematical modelling and computational approach to investigate the role of macrophages with mixed phenotype on growth/control/elimination of lung tumours. We show that tumour control in the presence of M2→M1 re-polarising treatments is mainly the result of macrophages with mixed phenotypes (due to the assumption of short half-life of M1-like macrophages). We also show that the half-life of various macrophage phenotypes (distinct M1 or mixed M1/M2 phenotypes) impacts the outcome of various therapeutic strategies targeting tumour-associated macrophages. All these results suggest the need for a better experimental understanding of the kinetics of macrophages inside solid tumours.
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Affiliation(s)
- Raluca Eftimie
- Division of Mathematics, University of Dundee, Dundee DD1 4HN, United Kingdom; Laboratoire Mathématiques de Besançon, UMR - CNRS 6623, Université de Bourgogne Franche-Comté, 25000 Besançon, France.
| | - Charlotte Barelle
- Division of Mathematics, University of Dundee, Dundee DD1 4HN, United Kingdom
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17
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Mathematical modeling of ventilator-induced lung inflammation. J Theor Biol 2021; 526:110738. [PMID: 33930440 DOI: 10.1016/j.jtbi.2021.110738] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/20/2021] [Accepted: 04/22/2021] [Indexed: 12/13/2022]
Abstract
Despite the benefits of mechanical ventilators, prolonged or misuse of ventilators may lead to ventilation-associated/ventilation-induced lung injury (VILI). Lung insults, such as respiratory infections and lung injuries, can damage the pulmonary epithelium, with the most severe cases needing mechanical ventilation for effective breathing and survival. Damaged epithelial cells within the alveoli trigger a local immune response. A key immune cell is the macrophage, which can differentiate into a spectrum of phenotypes ranging from pro- to anti-inflammatory. To gain a greater understanding of the mechanisms of the immune response to VILI and post-ventilation outcomes, we developed a mathematical model of interactions between the immune system and site of damage while accounting for macrophage phenotype. Through Latin hypercube sampling we generated a collection of parameter sets that are associated with a numerical steady state. We then simulated ventilation-induced damage using these steady state values as the initial conditions in order to evaluate how baseline immune state and lung health affect outcomes. We used a variety of methods to analyze the resulting parameter sets, transients, and outcomes, including a random forest decision tree algorithm and parameter sensitivity with eFAST. Analysis shows that parameters and properties of transients related to epithelial repair and M1 activation are important factors. Using the results of this analysis, we hypothesized interventions and used these treatment strategies to modulate the response to ventilation for particular parameters sets.
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18
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Kaondera-Shava RF, Lungu E, Szomolay B. A novel mathematical model of AIDS-associated Kaposi's sarcoma: Analysis and optimal control. Biosystems 2020; 200:104318. [PMID: 33309554 DOI: 10.1016/j.biosystems.2020.104318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 11/30/2020] [Indexed: 12/27/2022]
Abstract
Kaposi's sarcoma (KS) has been the most common HHV-8 virus-induced neoplasm associated with HIV-1 infection. Although the standard KS therapy has not changed in 20 years, not all cases of KS will respond to the same therapy. The goal of current AIDS-KS treatment modalities is to reconstitute the immune system and suppress HIV-1 replication, but newer treatment modalities are on horizon. There are very few mathematical models that have included HIV-1 viral load (VL) measures, despite VL being a key determinant of treatment outcome. Here we introduce a mathematical model that consolidates the effect of both HIV-1 and HHV-8 VL on KS tumor progression by incorporating low or high VLs into the proliferation terms of the immune cell populations. Regulation of HIV-1/HHV-8 VL and viral reservoir cells is crucial for restoring a patient to an asymptomatic stage. Therefore, an optimal control strategy given by a combined antiretroviral therapy (cART) is derived. The results indicate that the drug treatment strategies are capable of removing the viral reservoirs faster and consequently, the HIV-1 and KS tumor burden is reduced. The predictions of the mathematical model have the potential to offer more effective therapeutic interventions based on viral and virus-infected cell load and support new studies addressing the superiority of VL over CD4+ T-cell count in HIV-1 pathogenesis.
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Affiliation(s)
- R F Kaondera-Shava
- Department of Mathematics and Statistical Sciences, Botswana International University of Science and Technology, Private Bag 16, Palapye, Botswana.
| | - E Lungu
- Department of Mathematics and Statistical Sciences, Botswana International University of Science and Technology, Private Bag 16, Palapye, Botswana.
| | - B Szomolay
- Systems Immunity Research Institute, Cardiff University School of Medicine, Cardiff, CF14 4XN, United Kingdom.
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19
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Modelling within-host macrophage dynamics in influenza virus infection. J Theor Biol 2020; 508:110492. [PMID: 32966828 DOI: 10.1016/j.jtbi.2020.110492] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 08/24/2020] [Accepted: 09/11/2020] [Indexed: 12/13/2022]
Abstract
Human respiratory disease associated with influenza virus infection is of significant public health concern. Macrophages, as part of the front line of host innate cellular defence, have been shown to play an important role in controlling viral replication. However, fatal outcomes of infection, as evidenced in patients infected with highly pathogenic viral strains, are often associated with prompt activation and excessive accumulation of macrophages. Activated macrophages can produce a large amount of pro-inflammatory cytokines, which leads to severe symptoms and at times death. However, the mechanism for rapid activation and excessive accumulation of macrophages during infection remains unclear. It has been suggested that the phenomena may arise from complex interactions between macrophages and influenza virus. In this work, we develop a novel mathematical model to study the relationship between the level of macrophage activation and the level of viral load in influenza infection. Our model combines a dynamic model of viral infection, a dynamic model of macrophages and the essential interactions between the virus and macrophages. Our model predicts that the level of macrophage activation can be negatively correlated with the level of viral load when viral infectivity is sufficiently high. We further identify that temporary depletion of resting macrophages in response to viral infection is a major driver in our model for the negative relationship between the level of macrophage activation and viral load, providing new insight into the mechanisms that regulate macrophage activation. Our model serves as a framework to study the complex dynamics of virus-macrophage interactions and provides a mechanistic explanation for existing experimental observations, contributing to an enhanced understanding of the role of macrophages in influenza viral infection.
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20
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Frieboes HB, Raghavan S, Godin B. Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis. Front Bioeng Biotechnol 2020; 8:1011. [PMID: 32974325 PMCID: PMC7466654 DOI: 10.3389/fbioe.2020.01011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 08/03/2020] [Indexed: 12/13/2022] Open
Abstract
The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.
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Affiliation(s)
- Hermann B. Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, United States
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, United States
- Center for Predictive Medicine, University of Louisville, Louisville, KY, United States
| | - Shreya Raghavan
- Department of Biomedical Engineering, College of Engineering, Texas A&M University, College Station, TX, United States
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, United States
| | - Biana Godin
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, United States
- Department of Obstetrics and Gynecology, Houston Methodist Hospital, Houston, TX, United States
- Developmental Therapeutics Program, Houston Methodist Cancer Center, Houston Methodist Hospital, Houston, TX, United States
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21
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Chelliah V, Lazarou G, Bhatnagar S, Gibbs JP, Nijsen M, Ray A, Stoll B, Thompson RA, Gulati A, Soukharev S, Yamada A, Weddell J, Sayama H, Oishi M, Wittemer-Rump S, Patel C, Niederalt C, Burghaus R, Scheerans C, Lippert J, Kabilan S, Kareva I, Belousova N, Rolfe A, Zutshi A, Chenel M, Venezia F, Fouliard S, Oberwittler H, Scholer-Dahirel A, Lelievre H, Bottino D, Collins SC, Nguyen HQ, Wang H, Yoneyama T, Zhu AZX, van der Graaf PH, Kierzek AM. Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm. Clin Pharmacol Ther 2020; 109:605-618. [PMID: 32686076 PMCID: PMC7983940 DOI: 10.1002/cpt.1987] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 07/06/2020] [Indexed: 12/12/2022]
Abstract
Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.
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Affiliation(s)
| | | | | | | | | | - Avijit Ray
- Abbvie Inc., North Chicago, Illinois, USA
| | | | | | - Abhishek Gulati
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Serguei Soukharev
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Akihiro Yamada
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Jared Weddell
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Hiroyuki Sayama
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Masayo Oishi
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | | | | | | | | | | | | | | | - Irina Kareva
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | | | - Alex Rolfe
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | - Anup Zutshi
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | | | | | | | | | | | | | - Dean Bottino
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Sabrina C Collins
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Hoa Q Nguyen
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Haiqing Wang
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Tomoki Yoneyama
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Andy Z X Zhu
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
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22
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Eftimie R. Investigation into the role of macrophages heterogeneity on solid tumour aggregations. Math Biosci 2020; 322:108325. [DOI: 10.1016/j.mbs.2020.108325] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 02/03/2020] [Accepted: 02/16/2020] [Indexed: 01/01/2023]
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23
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Dogra P, Ramírez JR, Peláez MJ, Wang Z, Cristini V, Parasher G, Rawat M. Mathematical Modeling to Address Challenges in Pancreatic Cancer. Curr Top Med Chem 2020; 20:367-376. [PMID: 31893993 PMCID: PMC7279939 DOI: 10.2174/1568026620666200101095641] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 09/10/2019] [Accepted: 10/20/2019] [Indexed: 12/30/2022]
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents, thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidisciplinary approach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis, and treatment. To this end, the application of quantitative tools can help improve the understanding of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - María J. Peláez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
- Applied Physics Graduate Program, Rice University, Houston, TX 77005, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Gulshan Parasher
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Manmeet Rawat
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
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24
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Halkola AS, Parvinen K, Kasanen H, Mustjoki S, Aittokallio T. Modelling of killer T-cell and cancer cell subpopulation dynamics under immuno- and chemotherapies. J Theor Biol 2019; 488:110136. [PMID: 31887273 DOI: 10.1016/j.jtbi.2019.110136] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 11/25/2019] [Accepted: 12/21/2019] [Indexed: 12/22/2022]
Abstract
Each patient's cancer has a unique molecular makeup, often comprised of distinct cancer cell subpopulations. Improved understanding of dynamic processes between cancer cell populations is therefore critical for making treatment more effective and personalized. It has been shown that immunotherapy increases the survival of melanoma patients. However, there remain critical open questions, such as timing and duration of immunotherapy and its added benefits when combined with other types of treatments. We introduce a model for the dynamics of active killer T-cells and cancer cell subpopulations. Rather than defining the cancer cell populations based on their genetic makeup alone, we consider also other, non-genetic differences that make the cell populations either sensitive or resistant to a therapy. Using the model, we make predictions of possible outcomes of the various treatment strategies in virtual melanoma patients, providing hypotheses regarding therapeutic efficacy and side-effects. It is shown, for instance, that starting immunotherapy with a denser treatment schedule may enable changing to a sparser schedule later during the treatment. Furthermore, combination of targeted and immunotherapy results in a better treatment effect, compared to mono-immunotherapy, and a stable disease can be reached with a patient-tailored combination. These results offer better understanding of the competition between T-cells and cancer cells, toward personalized immunotherapy regimens.
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Affiliation(s)
- Anni S Halkola
- Department of Mathematics and Statistics, University of Turku, Turku, Finland; Western Finland Cancer Centre (FICAN West), Turku University Hospital, Turku, Finland.
| | - Kalle Parvinen
- Department of Mathematics and Statistics, University of Turku, Turku, Finland; Western Finland Cancer Centre (FICAN West), Turku University Hospital, Turku, Finland; Evolution and Ecology Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.
| | - Henna Kasanen
- Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland; Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
| | - Satu Mustjoki
- Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland; Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Department of Mathematics and Statistics, University of Turku, Turku, Finland; Western Finland Cancer Centre (FICAN West), Turku University Hospital, Turku, Finland; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
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25
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Investigating Macrophages Plasticity Following Tumour-Immune Interactions During Oncolytic Therapies. Acta Biotheor 2019; 67:321-359. [PMID: 31410657 PMCID: PMC6825040 DOI: 10.1007/s10441-019-09357-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Accepted: 08/02/2019] [Indexed: 12/22/2022]
Abstract
Over the last few years, oncolytic virus therapy has been recognised as a promising approach in cancer treatment, due to the potential of these viruses to induce systemic anti-tumour immunity and selectively killing tumour cells. However, the effectiveness of these viruses depends significantly on their interactions with the host immune responses, both innate (e.g., macrophages, which accumulate in high numbers inside solid tumours) and adaptive (e.g., \documentclass[12pt]{minimal}
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\begin{document}$$\hbox {CD8}^{+}$$\end{document}CD8+ T cells). In this article, we consider a mathematical approach to investigate the possible outcomes of the complex interactions between two extreme types of macrophages (M1 and M2 cells), effector \documentclass[12pt]{minimal}
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\begin{document}$$\hbox {CD8}^{+}$$\end{document}CD8+ T cells and an oncolytic Vesicular Stomatitis Virus (VSV), on the growth/elimination of B16F10 melanoma. We discuss, in terms of VSV, \documentclass[12pt]{minimal}
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\begin{document}$$\hbox {CD8}^{+}$$\end{document}CD8+ and macrophages levels, two different types of immune responses which could ensure tumour control and eventual elimination. We show that both innate and adaptive anti-tumour immune responses, as well as the oncolytic virus, could be very important in delaying tumour relapse and eventually eliminating the tumour. Overall this study supports the use mathematical modelling to increase our understanding of the complex immune interaction following oncolytic virotherapies. However, the complexity of the model combined with a lack of sufficient data for model parametrisation has an impact on the possibility of making quantitative predictions.
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26
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Computational modeling of therapy on pancreatic cancer in its early stages. Biomech Model Mechanobiol 2019; 19:427-444. [PMID: 31501963 PMCID: PMC7105451 DOI: 10.1007/s10237-019-01219-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 08/23/2019] [Indexed: 12/18/2022]
Abstract
More than eighty percent of pancreatic cancer involves ductal adenocarcinoma with an abundant desmoplastic extracellular matrix surrounding the solid tumor entity. This aberrant tumor microenvironment facilitates a strong resistance of pancreatic cancer to medication. Although various therapeutic strategies have been reported to be effective in mice with pancreatic cancer, they still need to be tested quantitatively in wider animal-based experiments before being applied as therapies. To aid the design of experiments, we develop a cell-based mathematical model to describe cancer progression under therapy with a specific application to pancreatic cancer. The displacement of cells is simulated by solving a large system of stochastic differential equations with the Euler-Maruyama method. We consider treatment with the PEGylated drug PEGPH20 that breaks down hyaluronan in desmoplastic stroma followed by administration of the chemotherapy drug gemcitabine to inhibit the proliferation of cancer cells. Modeling the effects of PEGPH20 + gemcitabine concentrations is based on Green's fundamental solutions of the reaction-diffusion equation. Moreover, Monte Carlo simulations are performed to quantitatively investigate uncertainties in the input parameters as well as predictions for the likelihood of success of cancer therapy. Our simplified model is able to simulate cancer progression and evaluate treatments to inhibit the progression of cancer.
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27
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Tumor Clearance Analysis on a Cancer Chemo-Immunotherapy Mathematical Model. Bull Math Biol 2019; 81:4144-4173. [PMID: 31264136 DOI: 10.1007/s11538-019-00636-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Accepted: 06/20/2019] [Indexed: 01/08/2023]
Abstract
Mathematical models may allow us to improve our knowledge on tumor evolution and to better comprehend the dynamics between cancer, the immune system and the application of treatments such as chemotherapy and immunotherapy in both short and long term. In this paper, we solve the tumor clearance problem for a six-dimensional mathematical model that describes tumor evolution under immune response and chemo-immunotherapy treatments. First, by means of the localization of compact invariant sets method, we determine lower and upper bounds for all cells populations considered by the model and we use these results to establish sufficient conditions for the existence of a bounded positively invariant domain in the nonnegative orthant by applying LaSalle's invariance principle. Then, by exploiting a candidate Lyapunov function we determine sufficient conditions on the chemotherapy treatment to ensure tumor clearance. Further, we investigate the local stability of the tumor-free equilibrium point and compute conditions for asymptotic stability and tumor persistence. All conditions are given by inequalities in terms of the system parameters, and we perform numerical simulations with different values on the chemotherapy treatment to illustrate our results. Finally, we discuss the biological implications of our work.
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28
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Abstract
In this paper, a mathematical model of drug release from polymeric matrix and consequent intracellular drug transport is proposed and analyzed. Modeling of drug release is done through solubilization dynamics of drug particles, diffusion of the solubilized drug through the polymeric matrix in addition to reversible dissociation/recrystallization process. The interaction between drug-receptor, drug-plasma proteins along with other intracellular endosomal events is modeled. This leads to a mixed system of partial and ordinary differential equations with associated pertinent set of initial and boundary conditions. Furthermore, besides the stability of the proposed model, several sub-models are also studied for their stability criteria. Prominence is provided to the reduced model system having requisite relevance to the original system where quasi steady state approximation (QSSA) theory is utilized. For the model to be potent enough to generate appropriate predictive results for drug delivery, the stability properties of equilibrium in the mathematical model are analyzed both analytically and numerically. Numerical simulation in the embodiment of graphical representations speaks about various vital characteristics of the underlying physical phenomena along with the importance and sensitized impact of the model parameters controlling significant biological functions. Probed new therapies and clinical procedures could be assessed considering the present mathematical model and its analysis as the basis framework in order to effectively enhance therapeutic efficacy and improved patient compliance. The present study confirms the necessity of stability analysis study so that advocated mathematical model can effectively complement the real physiological behavior of pharmacokinetics.
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Affiliation(s)
- Koyel Chakravarty
- Department of Mathematics, Indian Institute of Technology Guwahati, Guwahati 781039, India
| | - D. C. Dalal
- Department of Mathematics, Indian Institute of Technology Guwahati, Guwahati 781039, India
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29
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Jia D, Li X, Bocci F, Tripathi S, Deng Y, Jolly MK, Onuchic JN, Levine H. Quantifying Cancer Epithelial-Mesenchymal Plasticity and its Association with Stemness and Immune Response. J Clin Med 2019; 8:E725. [PMID: 31121840 PMCID: PMC6572429 DOI: 10.3390/jcm8050725] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 05/14/2019] [Accepted: 05/20/2019] [Indexed: 12/19/2022] Open
Abstract
Cancer cells can acquire a spectrum of stable hybrid epithelial/mesenchymal (E/M) states during epithelial-mesenchymal transition (EMT). Cells in these hybrid E/M phenotypes often combine epithelial and mesenchymal features and tend to migrate collectively commonly as small clusters. Such collectively migrating cancer cells play a pivotal role in seeding metastases and their presence in cancer patients indicates an adverse prognostic factor. Moreover, cancer cells in hybrid E/M phenotypes tend to be more associated with stemness which endows them with tumor-initiation ability and therapy resistance. Most recently, cells undergoing EMT have been shown to promote immune suppression for better survival. A systematic understanding of the emergence of hybrid E/M phenotypes and the connection of EMT with stemness and immune suppression would contribute to more effective therapeutic strategies. In this review, we first discuss recent efforts combining theoretical and experimental approaches to elucidate mechanisms underlying EMT multi-stability (i.e., the existence of multiple stable phenotypes during EMT) and the properties of hybrid E/M phenotypes. Following we discuss non-cell-autonomous regulation of EMT by cell cooperation and extracellular matrix. Afterwards, we discuss various metrics that can be used to quantify EMT spectrum. We further describe possible mechanisms underlying the formation of clusters of circulating tumor cells. Last but not least, we summarize recent systems biology analysis of the role of EMT in the acquisition of stemness and immune suppression.
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Affiliation(s)
- Dongya Jia
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
| | - Xuefei Li
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
| | - Federico Bocci
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
- Department of Chemistry, Rice University, Houston, TX 77005, USA.
| | - Shubham Tripathi
- PhD Program in Systems, Synthetic, and Physical Biology, Rice University, Houston, TX 77005, USA.
| | - Youyuan Deng
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
- Applied Physics Graduate Program, Rice University, Houston, TX 77005, USA.
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India.
| | - José N Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
- Department of Chemistry, Rice University, Houston, TX 77005, USA.
- Department of Biosciences, Rice University, Houston, TX 77005, USA.
- Department of Physics and Astronomy, Rice University, Houston, TX 77005, USA.
| | - Herbert Levine
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA.
- Department of Physics, Northeastern University, Boston, MA 02115, USA.
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30
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Hu X, Ke G, Jang SRJ. Modeling Pancreatic Cancer Dynamics with Immunotherapy. Bull Math Biol 2019; 81:1885-1915. [PMID: 30843136 DOI: 10.1007/s11538-019-00591-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Accepted: 02/22/2019] [Indexed: 12/11/2022]
Abstract
We develop a mathematical model of pancreatic cancer that includes pancreatic cancer cells, pancreatic stellate cells, effector cells and tumor-promoting and tumor-suppressing cytokines to investigate the effects of immunotherapies on patient survival. The model is first validated using the survival data of two clinical trials. Local sensitivity analysis of the parameters indicates there exists a critical activation rate of pro-tumor cytokines beyond which the cancer can be eradicated if four adoptive transfers of immune cells are applied. Optimal control theory is explored as a potential tool for searching the best adoptive cellular immunotherapies. Combined immunotherapies between adoptive ex vivo expanded immune cells and TGF-[Formula: see text] inhibition by siRNA treatments are investigated. This study concludes that mono-immunotherapy is unlikely to control the pancreatic cancer and combined immunotherapies between anti-TGF-[Formula: see text] and adoptive transfers of immune cells can prolong patient survival. We show through numerical explorations that how these two types of immunotherapies are scheduled is important to survival. Applying TGF-[Formula: see text] inhibition first followed by adoptive immune cell transfers can yield better survival outcomes.
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Affiliation(s)
- Xiaochuan Hu
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, 79409-1042, USA.,Department of Mathematics and Statistics, University of the Incarnate Word, San Antonio, TX, 78209, USA
| | - Guoyi Ke
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, 79409-1042, USA.,Department of Mathematics and Physical Sciences, Louisiana State University of Alexandria, Alexandria, LA, 71302, USA
| | - Sophia R-J Jang
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, 79409-1042, USA.
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31
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Sun S, Klebaner F, Zhang X, Tian T. Instantaneous mutation rate in cancer initiation and progression. BMC SYSTEMS BIOLOGY 2018; 12:110. [PMID: 30463617 PMCID: PMC6249718 DOI: 10.1186/s12918-018-0629-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Cancer is one of the leading causes for the morbidity and mortality worldwide. Although substantial studies have been conducted theoretically and experimentally in recent years, it is still a challenge to explore the mechanisms of cancer initiation and progression. The investigation for these problems is very important for the diagnosis of cancer diseases and development of treatment schemes. RESULTS To accurately describe the process of cancer initiation, we propose a new concept of gene initial mutation rate based on our recently designed mathematical model using the non-constant mutation rate. Unlike the widely-used average gene mutation rate that depends on the number of mutations, the gene initial mutation rate can be used to describe the initiation process of a single patient. In addition, we propose the instantaneous tumour doubling time that is a continuous function of time based on the non-constant mutation rate. Our proposed concepts are supported by the clinic data of seven patients with advanced pancreatic cancer. The regression results suggest that, compared with the average mutation rate, the estimated initial mutation rate has a larger value of correlation coefficient with the patient survival time. We also provide the estimated tumour size of these seven patients over time. CONCLUSIONS The proposed concepts can be used to describe the cancer initiation and progression for different patients more accurately. Since a quantitative understanding of cancer progression is important for clinical treatment, our proposed model and calculated results may provide insights into the development of treatment schemes and also have other clinic implications.
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Affiliation(s)
- Shuhao Sun
- School of Mathematical Sciences, Monash University, Melbourne, 3800, VIC, Australia
| | - Fima Klebaner
- School of Mathematical Sciences, Monash University, Melbourne, 3800, VIC, Australia
| | - Xinan Zhang
- School of Mathematics and Statistics, Central China Normal University, Wuhan, 430079, People's Republic of China
| | - Tianhai Tian
- School of Mathematical Sciences, Monash University, Melbourne, 3800, VIC, Australia.
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32
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A structural methodology for modeling immune-tumor interactions including pro- and anti-tumor factors for clinical applications. Math Biosci 2018; 304:48-61. [PMID: 30055212 DOI: 10.1016/j.mbs.2018.07.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 07/10/2018] [Accepted: 07/17/2018] [Indexed: 12/17/2022]
Abstract
The immune system turns out to have both stimulatory and inhibitory factors influencing on tumor growth. In recent years, the pro-tumor role of immunity factors such as regulatory T cells and TGF-β cytokines has specially been considered in mathematical modeling of tumor-immune interactions. This paper presents a novel structural methodology for reviewing these models and classifies them into five subgroups on the basis of immune factors included. By using our experimental data due to immunotherapy experimentation in mice, these five modeling groups are evaluated and scored. The results show that a model with a small number of variables and coefficients performs efficiently in predicting the tumor-immune system interactions. Though immunology theorems suggest to employ a larger number of variables and coefficients, more complicated models are here shown to be inefficient due to redundant parallel pathways. So, these models are trapped in local minima and restricted in prediction capability. This paper investigates the mathematical models that were previously developed and proposes variables and pathways that are essential for modeling tumor-immune. Using these variables and pathways, a minimal structure for modeling tumor-immune interactions is proposed for future studies.
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33
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El-Gamal MI, Al-Ameen SK, Al-Koumi DM, Hamad MG, Jalal NA, Oh CH. Recent Advances of Colony-Stimulating Factor-1 Receptor (CSF-1R) Kinase and Its Inhibitors. J Med Chem 2018; 61:5450-5466. [PMID: 29293000 DOI: 10.1021/acs.jmedchem.7b00873] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Colony stimulation factor-1 receptor (CSF-1R), which is also known as FMS kinase, plays an important role in initiating inflammatory, cancer, and bone disorders when it is overstimulated by its ligand, CSF-1. Innate immunity, as well as macrophage differentiation and survival, are regulated by the stimulation of the CSF-1R. Another ligand, interlukin-34 (IL-34), was recently reported to activate the CSF-1R receptor in a different manner. The relationship between CSF-1R and microglia has been reviewed. Both CSF-1 antibodies and small molecule CSF-1R kinase inhibitors have now been tested in animal models and in humans. In this Perspective, we discuss the role of CSF-1 and IL-34 in producing cancer, bone disorders, and inflammation. We also review the newly discovered and improved small molecule kinase inhibitors and monoclonal antibodies that have shown potent activity toward CSF-1R, reported from 2012 until 2017.
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Affiliation(s)
- Mohammed I El-Gamal
- College of Pharmacy , University of Sharjah , Sharjah 27272 , United Arab Emirates.,Department of Medicinal Chemistry, Faculty of Pharmacy , University of Mansoura , Mansoura 35516 , Egypt
| | - Shahad K Al-Ameen
- College of Pharmacy , University of Sharjah , Sharjah 27272 , United Arab Emirates
| | - Dania M Al-Koumi
- College of Pharmacy , University of Sharjah , Sharjah 27272 , United Arab Emirates
| | - Mawadda G Hamad
- College of Pharmacy , University of Sharjah , Sharjah 27272 , United Arab Emirates
| | - Nouran A Jalal
- College of Pharmacy , University of Sharjah , Sharjah 27272 , United Arab Emirates
| | - Chang-Hyun Oh
- Center for Biomaterials , Korea Institute of Science and Technology , P.O. Box 131, Cheongryang , Seoul 130-650 , Republic of Korea.,Department of Biomolecular Science , University of Science and Technology , 113 Gwahangno, Yuseong-gu , Daejeon 305-333 , Republic of Korea
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34
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Abstract
Inflammatory and fibrotic events that drive chronic pancreatitis (CP) are likely orchestrated via signaling of soluble cytokines and chemokines systemically and within the pancreas. However, a comprehensive summary of the expression of such factors during CP has not been reported to date. This information is important given continued interest in targeting cytokines that influence CP pathogenesis. Reported data on the expression change of soluble immunomodulatory factors in human CP patients were identified via a literature search using a single search term. Thirty-one articles meeting the prespecified inclusion criteria were identified to generate a compiled data summary. Compiled data demonstrated up-regulation of several factors in the blood or pancreas microenvironment of CP patients. Nine factors were elevated in both compartments, including fractalkine, IFN-γ, interleukin 1β, IL-6, IL-8, macrophage inhibitory cytokine 1, neutrophil gelatinase-associated lipocalin, transforming growth factor β, and tumor necrosis factor α. Most up-regulated factors could be classified into one of several functional groups, including inflammation, chemotaxis, angiogenesis, bone remodeling, extracellular matrix remodeling, and pain. After further validation, these factors may be used as biomarkers for disease diagnosis and identification of comorbidities, or as potential therapeutic targets.
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35
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Seager RJ, Hajal C, Spill F, Kamm RD, Zaman MH. Dynamic interplay between tumour, stroma and immune system can drive or prevent tumour progression. CONVERGENT SCIENCE PHYSICAL ONCOLOGY 2017; 3. [PMID: 30079253 DOI: 10.1088/2057-1739/aa7e86] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
In the tumour microenvironment, cancer cells directly interact with both the immune system and the stroma. It is firmly established that the immune system, historically believed to be a major part of the body's defence against tumour progression, can be reprogrammed by tumour cells to be ineffective, inactivated, or even acquire tumour promoting phenotypes. Likewise, stromal cells and extracellular matrix can also have pro-and anti-tumour properties. However, there is strong evidence that the stroma and immune system also directly interact, therefore creating a tripartite interaction that exists between cancer cells, immune cells and tumour stroma. This interaction contributes to the maintenance of a chronically inflamed tumour microenvironment with pro-tumorigenic immune phenotypes and facilitated metastatic dissemination. A comprehensive understanding of cancer in the context of dynamical interactions of the immune system and the tumour stroma is therefore required to truly understand the progression toward and past malignancy.
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Affiliation(s)
- R J Seager
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston MA 02215
| | - Cynthia Hajal
- Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139
| | - Fabian Spill
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston MA 02215.,Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139
| | - Roger D Kamm
- Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139
| | - Muhammad H Zaman
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston MA 02215.,Howard Hughes Medical Institute, Boston University, Boston, MA 02215
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36
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Wilkie KP, Hahnfeldt P. Modeling the Dichotomy of the Immune Response to Cancer: Cytotoxic Effects and Tumor-Promoting Inflammation. Bull Math Biol 2017; 79:1426-1448. [PMID: 28585066 DOI: 10.1007/s11538-017-0291-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Accepted: 05/04/2017] [Indexed: 12/18/2022]
Abstract
Although the immune response is often regarded as acting to suppress tumor growth, it is now clear that it can be both stimulatory and inhibitory. The interplay between these competing influences has complex implications for tumor development, cancer dormancy, and immunotherapies. In fact, early immunotherapy failures were partly due to a lack in understanding of the nonlinear growth dynamics these competing immune actions may cause. To study this biological phenomenon theoretically, we construct a minimally parameterized framework that incorporates all aspects of the immune response. We combine the effects of all immune cell types, general principles of self-limited logistic growth, and the physical process of inflammation into one quantitative setting. Simulations suggest that while there are pro-tumor or antitumor immunogenic responses characterized by larger or smaller final tumor volumes, respectively, each response involves an initial period where tumor growth is stimulated beyond that of growth without an immune response. The mathematical description is non-identifiable which allows an ensemble of parameter sets to capture inherent biological variability in tumor growth that can significantly alter tumor-immune dynamics and thus treatment success rates. The ability of this model to predict non-intuitive yet clinically observed patterns of immunomodulated tumor growth suggests that it may provide a means to help classify patient response dynamics to aid identification of appropriate treatments exploiting immune response to improve tumor suppression, including the potential attainment of an immune-induced dormant state.
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Affiliation(s)
- Kathleen P Wilkie
- Center of Cancer Systems Biology, Boston, MA, USA.
- Department of Mathematics, Ryerson University, Toronto, ON, Canada.
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37
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Modelling and investigation of theCD4+T cells – Macrophages paradox in melanoma immunotherapies. J Theor Biol 2017; 420:82-104. [DOI: 10.1016/j.jtbi.2017.02.022] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2016] [Revised: 02/12/2017] [Accepted: 02/16/2017] [Indexed: 12/18/2022]
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38
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Li X, Levine H. Bistability of the cytokine-immune cell network in a cancer microenvironment. CONVERGENT SCIENCE PHYSICAL ONCOLOGY 2017. [DOI: 10.1088/2057-1739/aa6c07] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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39
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Abstract
In this paper, we investigate a mathematical model of pancreatic cancer, which extends the existing pancreatic cancer models with regulatory T cells (Tregs) and Treg inhibitory therapy. The model consists of tumor-immune interaction and immune suppression from Tregs. In the absence of treatments, we first characterize the system dynamics by locating equilibrium points and determining stability properties. Next, cytokine induced killer (CIK) immunotherapy is incorporated. Numerical simulations of prognostic results illustrate that the median overall survival associated with treatment can be prolonged approximately from 7 to 13 months, which is consistent with the clinical data. Furthermore, we consider cyclophosphamide (CTX) therapy as well as the combined therapy with CIK and CTX. Intensive simulation results suggest that both CTX therapy and the combined CIK/CTX therapy can reduce the number of Tregs and increase the overall survival (OS), but Tregs and tumor cells will gradually rise to equilibrium state as long as therapies are ceased.
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Affiliation(s)
- DAN-HUA HE
- Department of Mathematics, Zhejiang International Studies University, HangZhou 310012, P. R. China
| | - JIAN-XIN XU
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
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40
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Towards the Design of a Patient-Specific Virtual Tumour. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:7851789. [PMID: 28096895 PMCID: PMC5206790 DOI: 10.1155/2016/7851789] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 11/21/2016] [Indexed: 01/24/2023]
Abstract
The design of a patient-specific virtual tumour is an important step towards Personalized Medicine. However this requires to capture the description of many key events of tumour development, including angiogenesis, matrix remodelling, hypoxia, and cell state heterogeneity that will all influence the tumour growth kinetics and degree of tumour invasiveness. To that end, an integrated hybrid and multiscale approach has been developed based on data acquired on a preclinical mouse model as a proof of concept. Fluorescence imaging is exploited to build case-specific virtual tumours. Numerical simulations show that the virtual tumour matches the characteristics and spatiotemporal evolution of its real counterpart. We achieved this by combining image analysis and physiological modelling to accurately described the evolution of different tumour cases over a month. The development of such models is essential since a dedicated virtual tumour would be the perfect tool to identify the optimum therapeutic strategies that would make Personalized Medicine truly reachable and achievable.
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41
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CA19-9-related tumor kinetics after first-line chemotherapy of patients with advanced pancreatic cancer: a monoinstitutional experience. Med Oncol 2016; 33:103. [PMID: 27522503 DOI: 10.1007/s12032-016-0817-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2016] [Accepted: 08/05/2016] [Indexed: 01/04/2023]
Abstract
The absolute value of carbohydrate antigen 19-9 (CA19-9) pretreatment and its reduction after chemotherapy are established prognostic variables for patients with advanced pancreatic cancer. The present study is a retrospective monoinstitutional evaluation of the prognostic role of the CA19-9 reduction and some CA19-9-related tumor kinetics parameters, such as tumor growth rate constant (G), kinetic tumor response and log ratio. Forty-one cases met the selection criteria. After 8 weeks only G reported an inverse relationship with OS (r = -0.494) that was confirmed by regression analysis (R (2) = 0.192). G after 8 weeks of chemotherapy appears as a possible surrogate end point of overall survival.
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42
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A mathematical prognosis model for pancreatic cancer patients receiving immunotherapy. J Theor Biol 2016; 406:42-51. [PMID: 27338302 DOI: 10.1016/j.jtbi.2016.06.021] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Revised: 06/16/2016] [Accepted: 06/17/2016] [Indexed: 11/22/2022]
Abstract
Pancreatic cancer is one of the most deadly types of cancer since it typically spreads rapidly and can seldom be detected in its early stage. Pancreatic cancer therapy is thus a challenging task, and appropriate prognosis or assessment for pancreatic cancer therapy is of critical importance. In this work, based on available clinical data in Niu et al. (2013) we develop a mathematical prognosis model that can predict the overall survival of pancreatic cancer patients who receive immunotherapy. The mathematical model incorporates pancreatic cancer cells, pancreatic stellate cells, three major classes of immune effector cells CD8+ T cells, natural killer cells, helper T cells, and two major classes of cytokines interleukin-2 (IL-2) and interferon-γ (IFN-γ). The proposed model describes the dynamic interaction between tumor and immune cells. In order for the model to be able to generate appropriate prognostic results for disease progression, the distribution and stability properties of equilibria in the mathematical model are computed and analysed in absence of treatments. In addition, numerical simulations for disease progression with or without treatments are performed. It turns out that the median overall survival associated with CIK immunotherapy is prolonged from 7 to 13months compared with the survival without treatment, this is consistent with the clinical data observed in Niu et al. (2013). The validity of the proposed mathematical prognosis model is thus verified. Our study confirms that immunotherapy offers a better prognosis for pancreatic cancer patients. As a direct extension of this work, various new therapy methods that are under exploration and clinical trials could be assessed or evaluated using the newly developed mathematical prognosis model.
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43
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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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44
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Hao W, Friedman A. Serum uPAR as Biomarker in Breast Cancer Recurrence: A Mathematical Model. PLoS One 2016; 11:e0153508. [PMID: 27078836 PMCID: PMC4831695 DOI: 10.1371/journal.pone.0153508] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 03/30/2016] [Indexed: 12/22/2022] Open
Abstract
There are currently over 2.5 million breast cancer survivors in the United States and, according to the American Cancer Society, 10 to 20 percent of these women will develop recurrent breast cancer. Early detection of recurrence can avoid unnecessary radical treatment. However, self-examination or mammography screening may not discover a recurring cancer if the number of surviving cancer cells is small, while biopsy is too invasive and cannot be frequently repeated. It is therefore important to identify non-invasive biomarkers that can detect early recurrence. The present paper develops a mathematical model of cancer recurrence. The model, based on a system of partial differential equations, focuses on tissue biomarkers that include the plasminogen system. Among them, only uPAR is known to have significant correlation to its concentration in serum and could therefore be a good candidate for serum biomarker. The model includes uPAR and other associated cytokines and cells. It is assumed that the residual cancer cells that survived primary cancer therapy are concentrated in the same location within a region with a very small diameter. Model simulations establish a quantitative relation between the diameter of the growing cancer and the total uPAR mass in the cancer. This relation is used to identify uPAR as a potential serum biomarker for breast cancer recurrence.
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Affiliation(s)
- Wenrui Hao
- Mathematical Biosciences Institute, The Ohio State University, Columbus, OH, United States of America
| | - Avner Friedman
- Mathematical Biosciences Institute, The Ohio State University, Columbus, OH, United States of America
- Department of Mathematics, The Ohio State University, Columbus, OH, United States of America
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45
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Liu Y, Li S, Liu Z, Wang R. Bifurcation-based approach reveals synergism and optimal combinatorial perturbation. J Biol Phys 2016; 42:399-414. [PMID: 27079194 DOI: 10.1007/s10867-016-9414-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2015] [Accepted: 03/02/2016] [Indexed: 12/28/2022] Open
Abstract
Cells accomplish the process of fate decisions and form terminal lineages through a series of binary choices in which cells switch stable states from one branch to another as the interacting strengths of regulatory factors continuously vary. Various combinatorial effects may occur because almost all regulatory processes are managed in a combinatorial fashion. Combinatorial regulation is crucial for cell fate decisions because it may effectively integrate many different signaling pathways to meet the higher regulation demand during cell development. However, whether the contribution of combinatorial regulation to the state transition is better than that of a single one and if so, what the optimal combination strategy is, seem to be significant issue from the point of view of both biology and mathematics. Using the approaches of combinatorial perturbations and bifurcation analysis, we provide a general framework for the quantitative analysis of synergism in molecular networks. Different from the known methods, the bifurcation-based approach depends only on stable state responses to stimuli because the state transition induced by combinatorial perturbations occurs between stable states. More importantly, an optimal combinatorial perturbation strategy can be determined by investigating the relationship between the bifurcation curve of a synergistic perturbation pair and the level set of a specific objective function. The approach is applied to two models, i.e., a theoretical multistable decision model and a biologically realistic CREB model, to show its validity, although the approach holds for a general class of biological systems.
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Affiliation(s)
- Yanwei Liu
- Department of Mathematics, Shanghai University, Shanghai, China
| | - Shanshan Li
- Department of Mathematics, Shanghai University, Shanghai, China
| | - Zengrong Liu
- Department of Mathematics, Shanghai University, Shanghai, China
| | - Ruiqi Wang
- Department of Mathematics, Shanghai University, Shanghai, China.
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Modeling the Treatment of Glioblastoma Multiforme and Cancer Stem Cells with Ordinary Differential Equations. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:1239861. [PMID: 27022405 PMCID: PMC4745194 DOI: 10.1155/2016/1239861] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Revised: 11/12/2015] [Accepted: 12/22/2015] [Indexed: 11/24/2022]
Abstract
Despite improvements in cancer therapy and treatments, tumor recurrence is a common event in cancer patients. One explanation of recurrence is that cancer therapy focuses on treatment of tumor cells and does not eradicate cancer stem cells (CSCs). CSCs are postulated to behave similar to normal stem cells in that their role is to maintain homeostasis. That is, when the population of tumor cells is reduced or depleted by treatment, CSCs will repopulate the tumor, causing recurrence. In this paper, we study the application of the CSC Hypothesis to the treatment of glioblastoma multiforme by immunotherapy. We extend the work of Kogan et al. (2008) to incorporate the dynamics of CSCs, prove the existence of a recurrence state, and provide an analysis of possible cancerous states and their dependence on treatment levels.
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A cancer treatment based on synergy between anti-angiogenic and immune cell therapies. J Theor Biol 2016; 394:197-211. [PMID: 26826488 DOI: 10.1016/j.jtbi.2016.01.026] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2015] [Revised: 07/06/2015] [Accepted: 01/13/2016] [Indexed: 02/06/2023]
Abstract
A mathematical model integrating tumor angiogenesis and tumor-targeted cytotoxicity by immune cells was developed to identify the therapeutic window of two distinct modes to treat cancer: (1) an anti-angiogenesis treatment based on the monoclonal antibody bevacizumab that targets tumor vasculature, and (2) immunotherapy involving the injection of unlicensed dendritic cells to boost the anti-tumor adaptive response. The angiogenic cytokine Vascular Endothelial Growth Factor (VEGF) contributes to the immunosuppressive tumor microenvironment, which is responsible for the short-lived therapeutic effect of cancer-targeted immunotherapy. The effect of immunosuppression on the width of the therapeutic window of each treatment was quantified. Experimental evidence has shown that neutralizing immunosuppressive cytokines results in an enhanced immune response against infections and chronic diseases. The model was used to determine treatment protocols involving the combination of anti-VEGF and unlicensed dendritic cell injections that enhance tumor regression. The model simulations predicted that the most effective method to treat tumors involves administering a series of biweekly anti-VEGF injections to disrupt angiogenic processes and limit tumor growth. The simulations also verified the hypothesis that reducing the concentration of the immunosuppressive factor VEGF prior to an injection of unlicensed dendritic cells enhances the cytotoxicity of CD8+ T cells and results in complete tumor elimination. Feasible treatment protocols for tumors that are diagnosed late and have grown to a relatively large size were identified.
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Friedman A, Liao KL. The role of the cytokines IL-27 and IL-35 in cancer. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2015; 12:1203-1217. [PMID: 26775857 DOI: 10.3934/mbe.2015.12.1203] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The cancer-immune interaction is a fast growing field of research in biology, where the goal is to harness the immune system to fight cancer more effectively. In the present paper we review recent work of the interaction between T cells and cancer. CD8+ T cells are activated by IL-27 cytokine and they kill tumor cells. Regulatory T cells produce IL-35 which promotes cancer cells by enhancing angiogenesis, and inhibit CD8+ T cells via TGF-β production. Hence injections of IL-27 and anti-IL-35 are both potentially anti-tumor drugs. The models presented here are based on experimental mouse experiments, and their simulations agree with these experiments. The models are used to suggest effective schedules for drug treatment.
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Affiliation(s)
- Avner Friedman
- Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43210, United States
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The re-polarisation of M2 and M1 macrophages and its role on cancer outcomes. J Theor Biol 2015; 390:23-39. [PMID: 26551154 DOI: 10.1016/j.jtbi.2015.10.034] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2015] [Revised: 10/08/2015] [Accepted: 10/19/2015] [Indexed: 01/18/2023]
Abstract
The anti-tumour and pro-tumour roles of Th1/Th2 immune cells and M1/M2 macrophages have been documented by numerous experimental studies. However, it is still unknown how these immune cells interact with each other to control tumour dynamics. Here, we use a mathematical model for the interactions between mouse melanoma cells, Th2/Th1 cells and M2/M1 macrophages, to investigate the unknown role of the re-polarisation between M1 and M2 macrophages on tumour growth. The results show that tumour growth is associated with a type-II immune response described by large numbers of Th2 and M2 cells. Moreover, we show that (i) the ratio k of the transition rates k12 (for the re-polarisation M1→M2) and k21 (for the re-polarisation M2→M1) is important in reducing tumour population, and (ii) the particular values of these transition rates control the delay in tumour growth and the final tumour size. We also perform a sensitivity analysis to investigate the effect of various model parameters on changes in the tumour cell population, and confirm that the ratio k alone and the ratio of M2 and M1 macrophage populations at earlier times (e.g., day 7) cannot always predict the final tumour size.
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