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Lee ND, Kaveh K, Bozic I. Clonal interactions in cancer: integrating quantitative models with experimental and clinical data. Semin Cancer Biol 2023; 92:61-73. [PMID: 37023969 DOI: 10.1016/j.semcancer.2023.04.002] [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: 11/30/2022] [Revised: 02/16/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023]
Abstract
Tumors consist of different genotypically distinct subpopulations-or subclones-of cells. These subclones can influence neighboring clones in a process called "clonal interaction." Conventionally, research on driver mutations in cancer has focused on their cell-autonomous effects that lead to an increase in fitness of the cells containing the driver. Recently, with the advent of improved experimental and computational technologies for investigating tumor heterogeneity and clonal dynamics, new studies have shown the importance of clonal interactions in cancer initiation, progression, and metastasis. In this review we provide an overview of clonal interactions in cancer, discussing key discoveries from a diverse range of approaches to cancer biology research. We discuss common types of clonal interactions, such as cooperation and competition, its mechanisms, and the overall effect on tumorigenesis, with important implications for tumor heterogeneity, resistance to treatment, and tumor suppression. Quantitative models-in coordination with cell culture and animal model experiments-have played a vital role in investigating the nature of clonal interactions and the complex clonal dynamics they generate. We present mathematical and computational models that can be used to represent clonal interactions and provide examples of the roles they have played in identifying and quantifying the strength of clonal interactions in experimental systems. Clonal interactions have proved difficult to observe in clinical data; however, several very recent quantitative approaches enable their detection. We conclude by discussing ways in which researchers can further integrate quantitative methods with experimental and clinical data to elucidate the critical-and often surprising-roles of clonal interactions in human cancers.
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Affiliation(s)
- Nathan D Lee
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America
| | - Kamran Kaveh
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America
| | - Ivana Bozic
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America; Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
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2
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Achuthan S, Chatterjee R, Kotnala S, Mohanty A, Bhattacharya S, Salgia R, Kulkarni P. Leveraging deep learning algorithms for synthetic data generation to design and analyze biological networks. J Biosci 2022. [DOI: 10.1007/s12038-022-00278-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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3
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Coggan H, Page KM. The role of evolutionary game theory in spatial and non-spatial models of the survival of cooperation in cancer: a review. JOURNAL OF THE ROYAL SOCIETY, INTERFACE 2022; 19:20220346. [PMID: 35975562 PMCID: PMC9382458 DOI: 10.1098/rsif.2022.0346] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Evolutionary game theory (EGT) is a branch of mathematics which considers populations of individuals interacting with each other to receive pay-offs. An individual’s pay-off is dependent on the strategy of its opponent(s) as well as on its own, and the higher its pay-off, the higher its reproductive fitness. Its offspring generally inherit its interaction strategy, subject to random mutation. Over time, the composition of the population shifts as different strategies spread or are driven extinct. In the last 25 years there has been a flood of interest in applying EGT to cancer modelling, with the aim of explaining how cancerous mutations spread through healthy tissue and how intercellular cooperation persists in tumour-cell populations. This review traces this body of work from theoretical analyses of well-mixed infinite populations through to more realistic spatial models of the development of cooperation between epithelial cells. We also consider work in which EGT has been used to make experimental predictions about the evolution of cancer, and discuss work that remains to be done before EGT can make large-scale contributions to clinical treatment and patient outcomes.
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Affiliation(s)
- Helena Coggan
- Department of Mathematics, University College London, London, UK
| | - Karen M Page
- Department of Mathematics, University College London, London, UK
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4
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Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy. Biomolecules 2021; 12:biom12010008. [PMID: 35053156 PMCID: PMC8773639 DOI: 10.3390/biom12010008] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/14/2021] [Accepted: 12/17/2021] [Indexed: 12/21/2022] Open
Abstract
Drug resistance, a major challenge in cancer therapy, is typically attributed to mutations and genetic heterogeneity. Emerging evidence suggests that dynamic cellular interactions and group behavior also contribute to drug resistance. However, the underlying mechanisms remain poorly understood. Here, we present a new mathematical approach with game theoretical underpinnings that we developed to model real-time growth data of non-small cell lung cancer (NSCLC) cells and discern patterns in response to treatment with cisplatin. We show that the cisplatin-sensitive and cisplatin-tolerant NSCLC cells, when co-cultured in the absence or presence of the drug, display dynamic group behavior strategies. Tolerant cells exhibit a 'persister-like' behavior and are attenuated by sensitive cells; they also appear to 'educate' sensitive cells to evade chemotherapy. Further, tolerant cells can switch phenotypes to become sensitive, especially at low cisplatin concentrations. Finally, switching treatment from continuous to an intermittent regimen can attenuate the emergence of tolerant cells, suggesting that intermittent chemotherapy may improve outcomes in lung cancer.
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Wölfl B, te Rietmole H, Salvioli M, Kaznatcheev A, Thuijsman F, Brown JS, Burgering B, Staňková K. The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer. DYNAMIC GAMES AND APPLICATIONS 2021; 12:313-342. [PMID: 35601872 PMCID: PMC9117378 DOI: 10.1007/s13235-021-00397-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 05/05/2023]
Abstract
Evolutionary game theory mathematically conceptualizes and analyzes biological interactions where one's fitness not only depends on one's own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer's eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.
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Affiliation(s)
- Benjamin Wölfl
- Department of Mathematics, University of Vienna, Vienna, Austria
- Vienna Graduate School of Population Genetics, Vienna, Austria
| | - Hedy te Rietmole
- Department of Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Monica Salvioli
- Department of Mathematics, University of Trento, Trento, Italy
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Artem Kaznatcheev
- Department of Biology, University of Pennsylvania, Philadelphia, USA
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Frank Thuijsman
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Joel S. Brown
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL USA
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL USA
| | - Boudewijn Burgering
- Department of Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
- The Oncode Institute, Utrecht, The Netherlands
| | - Kateřina Staňková
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
- Department of Engineering Systems and Services, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands
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6
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Abstract
Choosing and optimizing treatment strategies for cancer requires
capturing its complex dynamics sufficiently well for understanding but
without being overwhelmed. Mathematical models are essential to
achieve this understanding, and we discuss the challenge of choosing
the right level of complexity to address the full range of tumor
complexity from growth, the generation of tumor heterogeneity, and
interactions within tumors and with treatments and the tumor
microenvironment. We discuss the differences between conceptual and
descriptive models, and compare the use of predator-prey models,
evolutionary game theory, and dynamic precision medicine approaches in
the face of uncertainty about mechanisms and parameter values.
Although there is of course no one-size-fits-all approach, we conclude
that broad and flexible thinking about cancer, based on combined
modeling approaches, will play a key role in finding creative and
improved treatments.
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Affiliation(s)
- Robert A Beckman
- Departments of Oncology and Biostatistics, Bioinformatics, & Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, 12231Georgetown University Medical Center, Washington, DC, USA
| | - Irina Kareva
- Mathematical and Computational Sciences Center, School of Human Evolution and Social Change, 7864Arizona State University, Tempe, AZ, USA
| | - Frederick R Adler
- School of Biological Sciences, 415772University of Utah, Salt Lake City, UT, USA.,Department of Mathematics, 415772University of Utah, Salt Lake City, UT, USA
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Tavakoli F, Sartakhti JS, Manshaei MH, Basanta D. Cancer immunoediting: A game theoretical approach. In Silico Biol 2021; 14:1-12. [PMID: 33216021 PMCID: PMC8203245 DOI: 10.3233/isb-200475] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The role of the immune system in tumor development increasingly includes the idea of cancer immunoediting. It comprises three phases: elimination, equilibrium, and escape. In the first phase, elimination, transformed cells are recognized and destroyed by immune system. The rare tumor cells that are not destroyed in this phase may then enter the equilibrium phase, where their growth is prevented by immunity mechanisms. The escape phase represents the final phase of this process, where cancer cells begin to grow unconstrained by the immune system. In this study, we describe and analyze an evolutionary game theoretical model of proliferating, quiescent, and immune cells interactions for the first time. The proposed model is evaluated with constant and dynamic approaches. Population dynamics and interactions between the immune system and cancer cells are investigated. Stability of equilibria or critical points are analyzed by applying algebraic analysis. This model allows us to understand the process of cancer development and might help us design better treatment strategies to account for immunoediting.
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Affiliation(s)
- Fatemeh Tavakoli
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | | | | | - David Basanta
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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Zheng Y, Sun Y, Torga G, Pienta K, Austin R. Game Theory Cancer Models of Cancer Cell-Stromal Cell Dynamics using Interacting Particle Systems. ACTA ACUST UNITED AC 2020. [DOI: 10.1142/s1793048020500058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We describe an evolutionary game theory model that has been used to predict the population dynamics of interacting cancer and stromal cells. We first consider the mean field case assuming homogeneous and nondiscrete populations. Interacting Particle Systems (IPS) are then presented as a discrete and spatial alternative to the mean field approach. Finally, we discuss cases where IPS gives results different from the mean field approach.
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Affiliation(s)
- Yinan Zheng
- Department of Physics, Princeton University, Princeton, NJ, USA
| | - Yusha Sun
- Department of Physics, Princeton University, Princeton, NJ, USA
| | | | | | - Robert Austin
- Department of Physics, Princeton University, Princeton, NJ, USA
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9
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Gluzman M, Scott JG, Vladimirsky A. Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory. Proc Biol Sci 2020; 287:20192454. [PMID: 32315588 DOI: 10.1098/rspb.2019.2454] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Recent clinical trials have shown that adaptive drug therapies can be more efficient than a standard cancer treatment based on a continuous use of maximum tolerated doses (MTD). The adaptive therapy paradigm is not based on a preset schedule; instead, the doses are administered based on the current state of tumour. But the adaptive treatment policies examined so far have been largely ad hoc. We propose a method for systematically optimizing adaptive policies based on an evolutionary game theory model of cancer dynamics. Given a set of treatment objectives, we use the framework of dynamic programming to find the optimal treatment strategies. In particular, we optimize the total drug usage and time to recovery by solving a Hamilton-Jacobi-Bellman equation. We compare MTD-based treatment strategy with optimal adaptive treatment policies and show that the latter can significantly decrease the total amount of drugs prescribed while also increasing the fraction of initial tumour states from which the recovery is possible. We conclude that the use of optimal control theory to improve adaptive policies is a promising concept in cancer treatment and should be integrated into clinical trial design.
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Affiliation(s)
- Mark Gluzman
- Center for Applied Mathematics, Cornell University, Ithaca, NY, USA
| | - Jacob G Scott
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
| | - Alexander Vladimirsky
- Department of Mathematics and Center for Applied Mathematics, Cornell University, 561 Malott Hall, Ithaca, NY 14853-4201, USA
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10
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WITHDRAWN: Evolutionary Game Dynamics and Cancer. Trends Cancer 2019. [DOI: 10.1016/j.trecan.2019.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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11
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Carotenuto A, Cutolo A, Petrillo A, Fusco R, Arra C, Sansone M, Larobina D, Cardoso L, Fraldi M. Growth and in vivo stresses traced through tumor mechanics enriched with predator-prey cells dynamics. J Mech Behav Biomed Mater 2018; 86:55-70. [DOI: 10.1016/j.jmbbm.2018.06.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 05/10/2018] [Accepted: 06/05/2018] [Indexed: 12/27/2022]
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12
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Wu A, Liao D, Kirilin V, Lin KC, Torga G, Qu J, Liu L, Sturm JC, Pienta K, Austin R. Cancer dormancy and criticality from a game theory perspective. CANCER CONVERGENCE 2018; 2:1. [PMID: 29623956 PMCID: PMC5876693 DOI: 10.1186/s41236-018-0008-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 01/05/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The physics of cancer dormancy, the time between initial cancer treatment and re-emergence after a protracted period, is a puzzle. Cancer cells interact with host cells via complex, non-linear population dynamics, which can lead to very non-intuitive but perhaps deterministic and understandable progression dynamics of cancer and dormancy. RESULTS We explore here the dynamics of host-cancer cell populations in the presence of (1) payoffs gradients and (2) perturbations due to cell migration. CONCLUSIONS We determine to what extent the time-dependence of the populations can be quantitively understood in spite of the underlying complexity of the individual agents and model the phenomena of dormancy.
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Affiliation(s)
- Amy Wu
- Banter AI, 408 Florence St., Palo Alto CA, 94301 USA
| | - David Liao
- Department of Pathology, University of California at San Francisco, San Francisco, 94143 USA
| | - Vlamimir Kirilin
- Department of Physics, Princeton University, Princeton, 08544 NJ USA
| | - Ke-Chih Lin
- Department of Electrical Engineering, Princeton University, Princeton, 08544 USA
| | - Gonzalo Torga
- The Johns Hopkins Hospital, 1800 Orleans St., Baltimore MD, 21287 USA
| | - Junle Qu
- College of Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060 China
| | - Liyu Liu
- College of Physics, Chongqing University, Chongqing China, 400044 China
| | - James C. Sturm
- Department of Electrical Engineering, Princeton University, Princeton, 08544 USA
| | - Kenneth Pienta
- The Johns Hopkins Hospital, 1800 Orleans St., Baltimore MD, 21287 USA
| | - Robert Austin
- Department of Physics, Princeton University, Princeton, 08544 NJ USA
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13
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Yuan R, Zhu X, Wang G, Li S, Ao P. Cancer as robust intrinsic state shaped by evolution: a key issues review. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2017; 80:042701. [PMID: 28212112 DOI: 10.1088/1361-6633/aa538e] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Cancer is a complex disease: its pathology cannot be properly understood in terms of independent players-genes, proteins, molecular pathways, or their simple combinations. This is similar to many-body physics of a condensed phase that many important properties are not determined by a single atom or molecule. The rapidly accumulating large 'omics' data also require a new mechanistic and global underpinning to organize for rationalizing cancer complexity. A unifying and quantitative theory was proposed by some of the present authors that cancer is a robust state formed by the endogenous molecular-cellular network, which is evolutionarily built for the developmental processes and physiological functions. Cancer state is not optimized for the whole organism. The discovery of crucial players in cancer, together with their developmental and physiological roles, in turn, suggests the existence of a hierarchical structure within molecular biology systems. Such a structure enables a decision network to be constructed from experimental knowledge. By examining the nonlinear stochastic dynamics of the network, robust states corresponding to normal physiological and abnormal pathological phenotypes, including cancer, emerge naturally. The nonlinear dynamical model of the network leads to a more encompassing understanding than the prevailing linear-additive thinking in cancer research. So far, this theory has been applied to prostate, hepatocellular, gastric cancers and acute promyelocytic leukemia with initial success. It may offer an example of carrying physics inquiring spirit beyond its traditional domain: while quantitative approaches can address individual cases, however there must be general rules/laws to be discovered in biology and medicine.
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Affiliation(s)
- Ruoshi Yuan
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
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14
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Eyre HA, Lavretsky H, Forbes M, Raji C, Small G, McGorry P, Baune BT, Reynolds C. Convergence Science Arrives: How Does It Relate to Psychiatry? ACADEMIC PSYCHIATRY : THE JOURNAL OF THE AMERICAN ASSOCIATION OF DIRECTORS OF PSYCHIATRIC RESIDENCY TRAINING AND THE ASSOCIATION FOR ACADEMIC PSYCHIATRY 2017; 41:91-99. [PMID: 26964782 PMCID: PMC5540327 DOI: 10.1007/s40596-016-0496-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Accepted: 01/25/2016] [Indexed: 05/12/2023]
Affiliation(s)
- Harris A Eyre
- University of Adelaide, Adelaide, South Australia, Australia.
| | - Helen Lavretsky
- Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, California, USA
| | - Malcolm Forbes
- James Cook University, Townsville, Queensland, Australia
| | | | - Gary Small
- Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, California, USA
| | | | | | - Charles Reynolds
- University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
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15
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Salimi Sartakhti J, Manshaei MH, Sadeghi M. MMP-TIMP interactions in cancer invasion: An evolutionary game-theoretical framework. J Theor Biol 2016; 412:17-26. [PMID: 27670802 DOI: 10.1016/j.jtbi.2016.09.019] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Revised: 08/31/2016] [Accepted: 09/22/2016] [Indexed: 10/21/2022]
Abstract
One of the main steps in solid cancers to invade surrounding tissues is degradation of tissue barriers in the extracellular matrix. This operation that leads to initiate, angiogenesis and metastasis to other organs, is essentially consequence of collapsing dynamic balance between matrix metalloproteinases (MMP) and tissue inhibitors of metalloproteinases (TIMP). In this work, we model the MMP-TIMP interaction in both normal tissue and invasive cancer using evolutionary game theory. Our model explains how invasive cancer cells get the upper hand in MMP-TIMP imbalance scenarios. We investigate dynamics of them over time and discuss stable and nonstable states in the population. Numerical simulations presented here provide the identification of key genotypic features in the tumor invasion and a natural description for phenotypic variability. The simulation results are consistent with the experimental results in vitro observations presented in medical literature. Finally, by the provided results the necessary conditions to inhibit cancer invasion or prolong its course are explained. In this way, two therapeutic approaches with respect to how they could meet the required conditions are considered.
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Affiliation(s)
- Javad Salimi Sartakhti
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
| | - Mohammad Hossein Manshaei
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
| | - Mehdi Sadeghi
- School of Biological Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran; National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
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16
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Eyre HA, Forbes M, Raji C, Cork N, Durning S, Armstrong E, Wheeler E, Meyers A, Baune BT, Berk M. Strengthening the role of convergence science in medicine. CONVERGENT SCIENCE PHYSICAL ONCOLOGY 2015. [DOI: 10.1088/2057-1739/1/2/026001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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17
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Oxygen-Driven Tumour Growth Model: A Pathology-Relevant Mathematical Approach. PLoS Comput Biol 2015; 11:e1004550. [PMID: 26517813 PMCID: PMC4627780 DOI: 10.1371/journal.pcbi.1004550] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Accepted: 09/12/2015] [Indexed: 02/04/2023] Open
Abstract
Xenografts -as simplified animal models of cancer- differ substantially in vasculature and stromal architecture when compared to clinical tumours. This makes mathematical model-based predictions of clinical outcome challenging. Our objective is to further understand differences in tumour progression and physiology between animal models and the clinic. To achieve that, we propose a mathematical model based upon tumour pathophysiology, where oxygen -as a surrogate for endocrine delivery- is our main focus. The Oxygen-Driven Model (ODM), using oxygen diffusion equations, describes tumour growth, hypoxia and necrosis. The ODM describes two key physiological parameters. Apparent oxygen uptake rate ( kR′) represents the amount of oxygen cells seem to need to proliferate. The more oxygen they appear to need, the more the oxygen transport. kR′ gathers variability from the vasculature, stroma and tumour morphology. Proliferating rate (kp) deals with cell line specific factors to promote growth. The KH,KN describe the switch of hypoxia and necrosis. Retrospectively, using archived data, we looked at longitudinal tumour volume datasets for 38 xenografted cell lines and 5 patient-derived xenograft-like models. Exploration of the parameter space allows us to distinguish 2 groups of parameters. Group 1 of cell lines shows a spread in values of kR′ and lower kp, indicating that tumours are poorly perfused and slow growing. Group 2 share the value of the oxygen uptake rate ( kR′) and vary greatly in kp, which we interpret as having similar oxygen transport, but more tumour intrinsic variability in growth. However, the ODM has some limitations when tested in explant-like animal models, whose complex tumour-stromal morphology may not be captured in the current version of the model. Incorporation of stroma in the ODM will help explain these discrepancies. We have provided an example. The ODM is a very simple -and versatile- model suitable for the design of preclinical experiments, which can be modified and enhanced whilst maintaining confidence in its predictions. Tumour-bearing animal models of cancer are needed to discover new drugs to treat cancer. We aim in this article to capture—through mathematics- some underlying phenomena of tumour growth in animals. We propose a set of equations that, despite being very simple, describe tumour growth, hypoxia and necrosis. Cells under low oxygen levels change into a stress state called “hypoxia”, which will ultimately lead to tissue death, also known as “necrosis” and “apoptosis”. Hypoxic cells undergo a variety of changes which impact tumour growth, development, metastasis and -most importantly- response to therapy. Hence, oxygen distribution is important. We simulate oxygen profiles to locate hypoxic and necrotic tumour regions. Finally, this mathematical model allows us to compare and classify animal models from a grounded and physiological perspective, rather than a more convenient and empirical one. This will help us understand how well (or poorly) animal tumours mimic tumours in patients. The simplicity of our mathematical model allows us to obtain more information out of the same animal sets without any further experiments, hopefully saving time, money and animal usage.
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18
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Wu A, Liao D, Austin R. Evolutionary game theory in cancer: first steps in prediction of metastatic cancer progression? Future Oncol 2015; 11:881-3. [DOI: 10.2217/fon.15.5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Affiliation(s)
- Amy Wu
- Department of Electrical Engineering, Princeton University, Princeton, NJ 08540, USA
| | - David Liao
- Department of Pathology, University of California at San Francisco, 500 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Robert Austin
- Department of Physics, Princeton University, Princeton, NJ 08544, USA
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19
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A cellular automaton model for tumor dormancy: emergence of a proliferative switch. PLoS One 2014; 9:e109934. [PMID: 25329892 PMCID: PMC4199683 DOI: 10.1371/journal.pone.0109934] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Accepted: 09/12/2014] [Indexed: 01/06/2023] Open
Abstract
Malignant cancers that lead to fatal outcomes for patients may remain dormant for very long periods of time. Although individual mechanisms such as cellular dormancy, angiogenic dormancy and immunosurveillance have been proposed, a comprehensive understanding of cancer dormancy and the “switch” from a dormant to a proliferative state still needs to be strengthened from both a basic and clinical point of view. Computational modeling enables one to explore a variety of scenarios for possible but realistic microscopic dormancy mechanisms and their predicted outcomes. The aim of this paper is to devise such a predictive computational model of dormancy with an emergent “switch” behavior. Specifically, we generalize a previous cellular automaton (CA) model for proliferative growth of solid tumor that now incorporates a variety of cell-level tumor-host interactions and different mechanisms for tumor dormancy, for example the effects of the immune system. Our new CA rules induce a natural “competition” between the tumor and tumor suppression factors in the microenvironment. This competition either results in a “stalemate” for a period of time in which the tumor either eventually wins (spontaneously emerges) or is eradicated; or it leads to a situation in which the tumor is eradicated before such a “stalemate” could ever develop. We also predict that if the number of actively dividing cells within the proliferative rim of the tumor reaches a critical, yet low level, the dormant tumor has a high probability to resume rapid growth. Our findings may shed light on the fundamental understanding of cancer dormancy.
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20
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Austin R. Beyond evolution: game theory and the progression of cancer. Interface Focus 2014. [DOI: 10.1098/rsfs.2014.0044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Robert Austin
- Department of Physics, Princeton University, Princeton, NJ, USA
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Liao D, Tlsty TD. Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations. Interface Focus 2014; 4:20140037. [PMID: 25097751 PMCID: PMC4071513 DOI: 10.1098/rsfs.2014.0037] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Failure to understand evolutionary dynamics has been hypothesized as limiting our ability to control biological systems. An increasing awareness of similarities between macroscopic ecosystems and cellular tissues has inspired optimism that game theory will provide insights into the progression and control of cancer. To realize this potential, the ability to compare game theoretic models and experimental measurements of population dynamics should be broadly disseminated. In this tutorial, we present an analysis method that can be used to train parameters in game theoretic dynamics equations, used to validate the resulting equations, and used to make predictions to challenge these equations and to design treatment strategies. The data analysis techniques in this tutorial are adapted from the analysis of reaction kinetics using the method of initial rates taught in undergraduate general chemistry courses. Reliance on computer programming is avoided to encourage the adoption of these methods as routine bench activities.
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Affiliation(s)
- David Liao
- Department of Pathology , University of California San Francisco , San Francisco, CA 94143 , USA
| | - Thea D Tlsty
- Department of Pathology , University of California San Francisco , San Francisco, CA 94143 , USA
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