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Murali A, Sarkar RR. Mechano-immunology in microgravity. LIFE SCIENCES IN SPACE RESEARCH 2023; 37:50-64. [PMID: 37087179 DOI: 10.1016/j.lssr.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/16/2023] [Accepted: 03/05/2023] [Indexed: 05/03/2023]
Abstract
Life on Earth has evolved to thrive in the Earth's natural gravitational field; however, as space technology advances, we must revisit and investigate the effects of unnatural conditions on human health, such as gravitational change. Studies have shown that microgravity has a negative impact on various systemic parts of humans, with the effects being more severe in the human immune system. Increasing costs, limited experimental time, and sample handling issues hampered our understanding of this field. To address the existing knowledge gap and provide confidence in modelling the phenomena, in this review, we highlight experimental works in mechano-immunology under microgravity and different computational modelling approaches that can be used to address the existing problems.
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Affiliation(s)
- Anirudh Murali
- Chemical Engineering and Process Development, CSIR - National Chemical Laboratory, Pune, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development, CSIR - National Chemical Laboratory, Pune, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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2
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West J, Robertson-Tessi M, Anderson ARA. Agent-based methods facilitate integrative science in cancer. Trends Cell Biol 2023; 33:300-311. [PMID: 36404257 PMCID: PMC10918696 DOI: 10.1016/j.tcb.2022.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/25/2022] [Accepted: 10/31/2022] [Indexed: 11/19/2022]
Abstract
In this opinion, we highlight agent-based modeling as a key tool for exploration of cell-cell and cell-environment interactions that drive cancer progression, therapeutic resistance, and metastasis. These biological phenomena are particularly suited to be captured at the cell-scale resolution possible only within agent-based or individual-based mathematical models. These modeling approaches complement experimental work (in vitro and in vivo systems) through parameterization and data extrapolation but also feed forward to drive new experiments that test model-generated predictions.
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Affiliation(s)
- Jeffrey West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Mark Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
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3
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Luque LM, Carlevaro CM, Llamoza Torres CJ, Lomba E. Physics-based tissue simulator to model multicellular systems: A study of liver regeneration and hepatocellular carcinoma recurrence. PLoS Comput Biol 2023; 19:e1010920. [PMID: 36877741 PMCID: PMC10019748 DOI: 10.1371/journal.pcbi.1010920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/16/2023] [Accepted: 02/03/2023] [Indexed: 03/07/2023] Open
Abstract
We present a multiagent-based model that captures the interactions between different types of cells with their microenvironment, and enables the analysis of the emergent global behavior during tissue regeneration and tumor development. Using this model, we are able to reproduce the temporal dynamics of regular healthy cells and cancer cells, as well as the evolution of their three-dimensional spatial distributions. By tuning the system with the characteristics of the individual patients, our model reproduces a variety of spatial patterns of tissue regeneration and tumor growth, resembling those found in clinical imaging or biopsies. In order to calibrate and validate our model we study the process of liver regeneration after surgical hepatectomy in different degrees. In the clinical context, our model is able to predict the recurrence of a hepatocellular carcinoma after a 70% partial hepatectomy. The outcomes of our simulations are in agreement with experimental and clinical observations. By fitting the model parameters to specific patient factors, it might well become a useful platform for hypotheses testing in treatments protocols.
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Affiliation(s)
- Luciana Melina Luque
- Instituto de Física de Líquidos y Sistemas Biológicos - CONICET. La Plata, Argentina
- * E-mail: (LML); (CMC)
| | - Carlos Manuel Carlevaro
- Instituto de Física de Líquidos y Sistemas Biológicos - CONICET. La Plata, Argentina
- Departamento de Ingeniería Mecánica, Universidad Tecnológica Nacional, Facultad Regional La Plata, La Plata, Argentina
- * E-mail: (LML); (CMC)
| | | | - Enrique Lomba
- Instituto de Química Física Rocasolano - CSIC. Madrid, España
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4
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Qiao L, Sinha S, Abd El-Hafeez AA, Lo IC, Midde KK, Ngo T, Aznar N, Lopez-Sanchez I, Gupta V, Farquhar MG, Rangamani P, Ghosh P. A circuit for secretion-coupled cellular autonomy in multicellular eukaryotic cells. Mol Syst Biol 2023; 19:e11127. [PMID: 36856068 PMCID: PMC10090951 DOI: 10.15252/msb.202211127] [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: 05/18/2022] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 03/02/2023] Open
Abstract
Cancers represent complex autonomous systems, displaying self-sufficiency in growth signaling. Autonomous growth is fueled by a cancer cell's ability to "secrete-and-sense" growth factors (GFs): a poorly understood phenomenon. Using an integrated computational and experimental approach, here we dissect the impact of a feedback-coupled GTPase circuit within the secretory pathway that imparts secretion-coupled autonomy. The circuit is assembled when the Ras-superfamily monomeric GTPase Arf1, and the heterotrimeric GTPase Giαβγ and their corresponding GAPs and GEFs are coupled by GIV/Girdin, a protein that is known to fuel aggressive traits in diverse cancers. One forward and two key negative feedback loops within the circuit create closed-loop control, allow the two GTPases to coregulate each other, and convert the expected switch-like behavior of Arf1-dependent secretion into an unexpected dose-response alignment behavior of sensing and secretion. Such behavior translates into cell survival that is self-sustained by stimulus-proportionate secretion. Proteomic studies and protein-protein interaction network analyses pinpoint GFs (e.g., the epidermal GF) as key stimuli for such self-sustenance. Findings highlight how the enhanced coupling of two biological switches in cancer cells is critical for multiscale feedback control to achieve secretion-coupled autonomy of growth factors.
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Affiliation(s)
- Lingxia Qiao
- Department of Mechanical and Aerospace Engineering, Jacob's School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Saptarshi Sinha
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Amer Ali Abd El-Hafeez
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - I-Chung Lo
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Krishna K Midde
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Tony Ngo
- Skaggs School of Pharmacy and Pharmaceutical Science, University of California San Diego, La Jolla, CA, USA
| | - Nicolas Aznar
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Inmaculada Lopez-Sanchez
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Vijay Gupta
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Marilyn G Farquhar
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Padmini Rangamani
- Department of Mechanical and Aerospace Engineering, Jacob's School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Pradipta Ghosh
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA.,Moores Comprehensive Cancer Center, University of California San Diego, La Jolla, CA, USA.,Department of Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA.,Veterans Affairs Medical Center, La Jolla, CA, USA
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5
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Grajek J, Kather JN, Poleszczuk J. An in silico model to study the impact of carbonic anhydrase IX expression on tumour growth and anti-PD-1 therapy. J R Soc Interface 2023; 20:20220654. [PMID: 36695125 PMCID: PMC9874981 DOI: 10.1098/rsif.2022.0654] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 01/04/2023] [Indexed: 01/26/2023] Open
Abstract
Immune checkpoint inhibitors (ICIs) are revolutionary cancer treatments. However, the mechanisms behind their effectiveness are not yet fully understood. Here, we aimed to investigate the role of the pH-regulatory enzyme carbonic anhydrase IX (CAIX) in ICI success. Consequently, we developed an in silico model of the tumour microenvironment. The hybrid model consists of an agent-based model of tumour-immune cell interactions, coupled with a set of diffusion-reaction equations describing substances in the environment. It is calibrated with data from the literature, enabling the study of its qualitative behaviour. In our model, CAIX-expressing tumours acidified their neighbourhood, thereby reducing immune infiltration by 90% (p < 0.001) and resulting in a 25% increase in tumour burden (p < 0.001). Moreover, suppression of CAIX improved the response to anti-PD-1 (23% tumour reduction in CAIX knockouts and 6% in CAIX-expressing tumours, p < 0.001), independently of initial PD-L1 expression. Our simulations suggest that patients with CAIX-expressing tumours could respond favourably to combining ICIs with CAIX suppression, even in the absence of pre-treatment PD-L1 expression. Furthermore, when calibrated with tumour-type-specific data, our model could serve as a high-throughput tool for testing the effectiveness of such a combinatorial approach.
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Affiliation(s)
- Julia Grajek
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw 02-109, Poland
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden 01309, Germany
| | - Jan Poleszczuk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw 02-109, Poland
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6
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Liu F, Heiner M, Gilbert D. Hybrid modelling of biological systems: current progress and future prospects. Brief Bioinform 2022; 23:6555400. [PMID: 35352101 PMCID: PMC9116374 DOI: 10.1093/bib/bbac081] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/12/2022] [Accepted: 02/16/2022] [Indexed: 11/15/2022] Open
Abstract
Integrated modelling of biological systems is becoming a necessity for constructing models containing the major biochemical processes of such systems in order to obtain a holistic understanding of their dynamics and to elucidate emergent behaviours. Hybrid modelling methods are crucial to achieve integrated modelling of biological systems. This paper reviews currently popular hybrid modelling methods, developed for systems biology, mainly revealing why they are proposed, how they are formed from single modelling formalisms and how to simulate them. By doing this, we identify future research requirements regarding hybrid approaches for further promoting integrated modelling of biological systems.
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Affiliation(s)
- Fei Liu
- School of Software Engineering, South China University of Technology, Guangzhou 510006, P.R. China
- Corresponding author: Fei Liu, School of Software Engineering, South China University of Technology, Guangzhou 510006, P.R. China. E-mail:
| | - Monika Heiner
- Department of Computer Science, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus 03046, Germany
| | - David Gilbert
- Department of Computer Science, Brunel University London, Middlesex UB8 3PH, UK
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7
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Qiao L, Sinha S, El-hafeez AAA, Lo I, Midde KK, Ngo T, Aznar N, Lopez-sanchez I, Gupta V, Farquhar MG, Rangamani P, Ghosh P. A Circuit for Secretion-coupled Cellular Autonomy in Multicellular Eukaryotes.. [DOI: 10.1101/2021.03.18.436048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
ABSTRACTCancers represent complex autonomous systems, displaying self-sufficiency in growth signaling. Autonomous growth is fueled by a cancer cell’s ability to ‘secrete-and-sense’ growth factors: a poorly understood phenomenon. Using an integrated systems and experimental approach, here we dissect the impact of a feedback-coupled GTPase circuit within the secretory pathway that imparts secretion-coupled autonomy. The circuit is assembled when the Ras-superfamily monomeric GTPase Arf1, and the heterotrimeric GTPase Giαβγ and their corresponding GAPs and GEFs are coupled by GIV/Girdin, a protein that is known to fuel aggressive traits in diverse cancers. One forward and two key negative feedback loops within the circuit create closed-loop control (CLC), allow the two GTPases to coregulate each other, and convert the expected switch-like behavior of Arf1-dependent secretion into an unexpected dose response alignment behavior of sensing and secretion. Such behavior translates into cell survival that is self-sustained by stimulus-proportionate secretion. Proteomic studies and protein-protein interaction network analyses pinpoint growth factors (e.g., the epidermal growth factor; EGF) as a key stimuli for such self-sustenance. Findings highlight how enhanced coupling of two biological switches in cancer cells is critical for multiscale feedback control to achieve secretion-coupled autonomy of growth factors.SYNOPSIS IMAGESTANDFIRST TEXTThis work defines the inner workings of a Golgi-localized molecular circuitry comprised of coupled GTPases, which empowers cells to achieve self-sufficiency in growth factor signaling by creating a secrete-and-sense autocrine loop.HIGHLIGHTS/MAIN FINDINGSModeling and experimental approaches were used to dissect a coupled GTPase circuit.Coupling enables closed loop feedback and mutual control of GTPases.Coupling generates dose response alignment behavior of sensing and secretion of growth factors.Coupling is critical for multiscale feedback control to achieve secretion-coupled autonomy.
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8
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A New Mathematical Model for Controlling Tumor Growth Based on Microenvironment Acidity and Oxygen Concentration. BIOMED RESEARCH INTERNATIONAL 2021; 2021:8886050. [PMID: 33575354 PMCID: PMC7857879 DOI: 10.1155/2021/8886050] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 12/29/2020] [Accepted: 01/08/2021] [Indexed: 12/12/2022]
Abstract
Hypoxia and the pH level of the tumor microenvironment have a great impact on the treatment of tumors. Here, the tumor growth is controlled by regulating the oxygen concentration and the acidity of the tumor microenvironment by introducing a two-dimensional multiscale cellular automata model of avascular tumor growth. The spatiotemporal evolution of tumor growth and metabolic variations is modeled based on biological assumptions, physical structure, states of cells, and transition rules. Each cell is allocated to one of the following states: proliferating cancer, nonproliferating cancer, necrotic, and normal cells. According to the response of the microenvironmental conditions, each cell consumes/produces metabolic factors and updates its state based on some stochastic rules. The input parameters are compatible with cancer biology using experimental data. The effect of neighborhoods during mitosis and simulating spatial heterogeneity is studied by considering multicellular layer structure of tumor. A simple Darwinist mutation is considered by introducing a critical parameter (Nmm) that affects division probability of the proliferative tumor cells based on the microenvironmental conditions and cancer hallmarks. The results show that Nmm regulation has a significant influence on the dynamics of tumor growth, the growth fraction, necrotic fraction, and the concentration levels of the metabolic factors. The model not only is able to simulate the in vivo tumor growth quantitatively and qualitatively but also can simulate the concentration of metabolic factors, oxygen, and acidity graphically. The results show the spatial heterogeneity effects on the proliferation of cancer cells and the rest of the system. By increasing Nmm, tumor shrinkage and significant increasing in the oxygen concentration and the pH value of the tumor microenvironment are observed. The results demonstrate the model's ability, providing an essential tool for simulating different tumor evolution scenarios of a patient and reliable prediction of spatiotemporal progression of tumors for utilizing in personalized therapy.
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9
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Wang Y, Brodin E, Nishii K, Frieboes HB, Mumenthaler SM, Sparks JL, Macklin P. Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach. Sci Rep 2021; 11:1710. [PMID: 33462259 PMCID: PMC7813881 DOI: 10.1038/s41598-020-78780-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 11/24/2020] [Indexed: 12/17/2022] Open
Abstract
Colorectal cancer and other cancers often metastasize to the liver in later stages of the disease, contributing significantly to patient death. While the biomechanical properties of the liver parenchyma (normal liver tissue) are known to affect tumor cell behavior in primary and metastatic tumors, the role of these properties in driving or inhibiting metastatic inception remains poorly understood, as are the longer-term multicellular dynamics. This study adopts a multi-model approach to study the dynamics of tumor-parenchyma biomechanical interactions during metastatic seeding and growth. We employ a detailed poroviscoelastic model of a liver lobule to study how micrometastases disrupt flow and pressure on short time scales. Results from short-time simulations in detailed single hepatic lobules motivate constitutive relations and biological hypotheses for a minimal agent-based model of metastatic growth in centimeter-scale tissue over months-long time scales. After a parameter space investigation, we find that the balance of basic tumor-parenchyma biomechanical interactions on shorter time scales (adhesion, repulsion, and elastic tissue deformation over minutes) and longer time scales (plastic tissue relaxation over hours) can explain a broad range of behaviors of micrometastases, without the need for complex molecular-scale signaling. These interactions may arrest the growth of micrometastases in a dormant state and prevent newly arriving cancer cells from establishing successful metastatic foci. Moreover, the simulations indicate ways in which dormant tumors could "reawaken" after changes in parenchymal tissue mechanical properties, as may arise during aging or following acute liver illness or injury. We conclude that the proposed modeling approach yields insight into the role of tumor-parenchyma biomechanics in promoting liver metastatic growth, and advances the longer term goal of identifying conditions to clinically arrest and reverse the course of late-stage cancer.
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Affiliation(s)
- Yafei Wang
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Erik Brodin
- Department of Chemical, Paper and Biomedical Engineering, Miami University, Oxford, OH, USA
| | - Kenichiro Nishii
- Department of Chemical, Paper and Biomedical Engineering, Miami University, Oxford, OH, USA
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
- Center for Predictive Medicine, University of Louisville, Louisville, KY, USA
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica L Sparks
- Department of Chemical, Paper and Biomedical Engineering, Miami University, Oxford, OH, USA.
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
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10
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Scott JG, Maini PK, Anderson ARA, Fletcher AG. Inferring Tumor Proliferative Organization from Phylogenetic Tree Measures in a Computational Model. Syst Biol 2021; 69:623-637. [PMID: 31665523 DOI: 10.1093/sysbio/syz070] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2018] [Revised: 09/23/2019] [Accepted: 10/02/2019] [Indexed: 01/15/2023] Open
Abstract
We use a computational modeling approach to explore whether it is possible to infer a solid tumor's cellular proliferative hierarchy under the assumptions of the cancer stem cell hypothesis and neutral evolution. We work towards inferring the symmetric division probability for cancer stem cells, since this is believed to be a key driver of progression and therapeutic response. Motivated by the advent of multiregion sampling and resulting opportunities to infer tumor evolutionary history, we focus on a suite of statistical measures of the phylogenetic trees resulting from the tumor's evolution in different regions of parameter space and through time. We find strikingly different patterns in these measures for changing symmetric division probability which hinge on the inclusion of spatial constraints. These results give us a starting point to begin stratifying tumors by this biological parameter and also generate a number of actionable clinical and biological hypotheses regarding changes during therapy, and through tumor evolutionary time. [Cancer; evolution; phylogenetics.].
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Affiliation(s)
- Jacob G Scott
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK.,Departments of Translational Hematology and Oncology Research and Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Philip K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Alexander R A Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alexander G Fletcher
- School of Mathematics and Statistics, University of Sheffield, Sheffield, UK.,Bateson Centre, University of Sheffield, Sheffield, UK
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11
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Al-Huniti N, Feng Y, Yu JJ, Lu Z, Nagase M, Zhou D, Sheng J. Tumor Growth Dynamic Modeling in Oncology Drug Development and Regulatory Approval: Past, Present, and Future Opportunities. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:419-427. [PMID: 32589767 PMCID: PMC7438808 DOI: 10.1002/psp4.12542] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 06/11/2020] [Indexed: 12/29/2022]
Abstract
Model‐informed drug development (MIDD) approaches have rapidly advanced in drug development in recent years. Additionally, the Prescription Drug User Fee Act (PDUFA) VI has specific commitments to further enhance MIDD. Tumor growth dynamic (TGD) modeling, as one of the commonly utilized MIDD approaches in oncology, fulfills the purposes to accelerate the drug development, to support new drug and biologics license applications, and to guide the market access. Increasing knowledge of TGD modeling methodologies, encouraging applications in clinical setting for patients’ survival, and complementing assessment of regulatory review for submissions, together fueled promising potentials for imminent enhancement of TGD in oncology. This review is to comprehensively summarize the history of TGD, and present case examples of the recent advance of TGD modeling (mixture model and joint model), as well as the TGD impact on regulatory decisions, thus illustrating challenges and opportunities. Additionally, this review presents the future perspectives for TGD approach.
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Affiliation(s)
- Nidal Al-Huniti
- Quantitative Pharmacology, Regeneron Pharmaceuticals, New York, New York, USA
| | - Yan Feng
- Clinical Pharmacology and Pharmacometrics, Bristol-Myers Squibb, Lawrenceville, New Jersey, USA
| | - Jingyu Jerry Yu
- Division of Pharmacometrics, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Zheng Lu
- Clinical Pharmacology and Pharmacometrics, Astellas, Illinois, USA
| | - Mario Nagase
- Department of Clinical Pharmacology and Safety Science, BioPharmaceuticals R&D, AstraZeneca, Boston, Massachusetts, USA
| | - Diansong Zhou
- Department of Clinical Pharmacology and Safety Science, BioPharmaceuticals R&D, AstraZeneca, Boston, Massachusetts, USA
| | - Jennifer Sheng
- Clinical Pharmacology and Pharmacometrics, Bristol-Myers Squibb, Lawrenceville, New Jersey, USA
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12
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Kawahara D, Wu L, Watanabe Y. Optimization of irradiation interval for fractionated stereotactic radiosurgery by a cellular automata model with reoxygenation effects. Phys Med Biol 2020; 65:085008. [PMID: 32092715 DOI: 10.1088/1361-6560/ab7974] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The current study aims to determine the optimal irradiation interval of fractionated stereotactic radiosurgery (SRS) by using an improved cellular automata (CA) model. The tumor growth process was simulated by considering the amount of oxygen and the density of blood vessels, which supplied oxygen and nutrient required for cell growth. Cancer cells died by the mitotic death process due to radiation, which was quantified by the LQ-model, or the apoptosis due to the lack of nutrients. The radiation caused increased permeation of plasma protein through the blood vessel or the breakdown of the vasculature. Consequently, these changes lead to a change in radiation sensitivity of cancer cells and tumor growth rate after irradiation. The optimal model parameters were determined with experimental data of the rat tumor volume. The tumor control probability (TCP) was defined as the ratio of the number of histories in which all cancer cells died after the irradiation to the total number of the histories per simulation. The optimal irradiation interval was defined as the irradiation interval that TCP was the maximum. For one fractionation treatment, the ratio of hypoxic cells to the total number of cancer cells kept decreasing until day 16th after irradiation; whereas the number of surviving cancer cells begun increasing immediately after irradiation. This intricate relationship between the hypoxia (or reoxygenation) and the number of cancer cells lead to an optimal irradiation interval for the second irradiation. The optimal irradiation interval for two-fraction SRS was six days. The optimum intervals for the first-second irradiations and the second-third irradiations were five and two days, respectively, for three fraction SRS. For 4 and 5-fraction treatments, the optimum first-interval was five days, which was similar to three fraction treatment. The remaining intervals should be one day. We showed that the improved CA model could be used to optimize the irradiation interval by explicitly including the reoxygenation after irradiation in the model.
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Affiliation(s)
- Daisuke Kawahara
- Department of Radiation Oncology, Institute of Biomedical & Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima 734-8551, Japan
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13
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Khorrami M, Prasanna P, Gupta A, Patil P, Velu PD, Thawani R, Corredor G, Alilou M, Bera K, Fu P, Feldman M, Velcheti V, Madabhushi A. Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer. Cancer Immunol Res 2020; 8:108-119. [PMID: 31719058 PMCID: PMC7718609 DOI: 10.1158/2326-6066.cir-19-0476] [Citation(s) in RCA: 162] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 09/04/2019] [Accepted: 11/05/2019] [Indexed: 12/26/2022]
Abstract
No predictive biomarkers can robustly identify patients with non-small cell lung cancer (NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes ("delta") in the radiomic texture (DelRADx) of CT patterns both within and outside tumor nodules before and after two to three cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 patients with NSCLC at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted, and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST-derived response. Association of delta-radiomic risk score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an AUC of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, and 0.85 and 0.81 in D2 and D3 DRS was associated with OS [HR: 1.64; 95% confidence interval (CI), 1.22-2.21; P = 0.0011; C-index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in patients with NSCLC.
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Affiliation(s)
- Mohammadhadi Khorrami
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Amit Gupta
- Department of Radiology-Cardiothoracic Imaging, University Hospitals, Cleveland, Ohio
| | - Pradnya Patil
- Department of Solid Tumor Oncology, Cleveland Clinic, Cleveland, Ohio
| | - Priya D Velu
- Pathology and Laboratory Medicine, Weill Cornell Medicine Physicians, New York, New York
| | - Rajat Thawani
- Department of Internal Medicine, Maimonides Medical Center, Brooklyn, New York
| | - German Corredor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Mehdi Alilou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, CWRU, Cleveland, Ohio
| | - Michael Feldman
- Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, New York
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio
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14
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Nichol D, Robertson-Tessi M, Anderson ARA, Jeavons P. Model genotype-phenotype mappings and the algorithmic structure of evolution. J R Soc Interface 2019; 16:20190332. [PMID: 31690233 PMCID: PMC6893500 DOI: 10.1098/rsif.2019.0332] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 10/04/2019] [Indexed: 12/13/2022] Open
Abstract
Cancers are complex dynamic systems that undergo evolution and selection. Personalized medicine approaches in the clinic increasingly rely on predictions of tumour response to one or more therapies; these predictions are complicated by the inevitable evolution of the tumour. Despite enormous amounts of data on the mutational status of cancers and numerous therapies developed in recent decades to target these mutations, many of these treatments fail after a time due to the development of resistance in the tumour. The emergence of these resistant phenotypes is not easily predicted from genomic data, since the relationship between genotypes and phenotypes, termed the genotype-phenotype (GP) mapping, is neither injective nor functional. We present a review of models of this mapping within a generalized evolutionary framework that takes into account the relation between genotype, phenotype, environment and fitness. Different modelling approaches are described and compared, and many evolutionary results are shown to be conserved across studies despite using different underlying model systems. In addition, several areas for future work that remain understudied are identified, including plasticity and bet-hedging. The GP-mapping provides a pathway for understanding the potential routes of evolution taken by cancers, which will be necessary knowledge for improving personalized therapies.
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Affiliation(s)
- Daniel Nichol
- Department of Computer Science, University of Oxford, Oxford, UK
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alexander R. A. Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Peter Jeavons
- Department of Computer Science, University of Oxford, Oxford, UK
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15
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Mathematical Models of Cancer Cell Plasticity. JOURNAL OF ONCOLOGY 2019; 2019:2403483. [PMID: 31814825 PMCID: PMC6877945 DOI: 10.1155/2019/2403483] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 08/20/2019] [Accepted: 09/17/2019] [Indexed: 01/29/2023]
Abstract
Quantitative modelling is increasingly important in cancer research, helping to integrate myriad diverse experimental data into coherent pictures of the disease and able to discriminate between competing hypotheses or suggest specific experimental lines of enquiry and new approaches to therapy. Here, we review a diverse set of mathematical models of cancer cell plasticity (a process in which, through genetic and epigenetic changes, cancer cells survive in hostile environments and migrate to more favourable environments, respectively), tumour growth, and invasion. Quantitative models can help to elucidate the complex biological mechanisms of cancer cell plasticity. In this review, we discuss models of plasticity, tumour progression, and metastasis under three broadly conceived mathematical modelling techniques: discrete, continuum, and hybrid, each with advantages and disadvantages. An emerging theme from the predictions of many of these models is that cell escape from the tumour microenvironment (TME) is encouraged by a combination of physiological stress locally (e.g., hypoxia), external stresses (e.g., the presence of immune cells), and interactions with the extracellular matrix. We also discuss the value of mathematical modelling for understanding cancer more generally.
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16
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Investigation of solid tumor progression with account of proliferation/migration dichotomy via Darwinian mathematical model. J Math Biol 2019; 80:601-626. [PMID: 31576418 DOI: 10.1007/s00285-019-01434-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 06/21/2019] [Indexed: 02/06/2023]
Abstract
A new continuous spatially-distributed model of solid tumor growth and progression is presented. The model explicitly accounts for mutations/epimutations of tumor cells which take place upon their division. The tumor grows in normal tissue and its progression is driven only by competition between populations of malignant cells for limited nutrient supply. Two reasons for the motion of tumor cells in space are taken into consideration, i.e., their intrinsic motility and convective fluxes, which arise due to proliferation of tumor cells. The model is applied to investigation of solid tumor progression under phenotypic alterations that inversely affect cell proliferation rate and cell motility by increasing the value of one of the parameters at the expense of another.It is demonstrated that the crucial feature that gives evolutionary advantage to a cell population is the speed of its intergrowth into surrounding normal tissue. Of note, increase in tumor intergrowth speed in not always associated with increase in motility of tumor cells. Depending on the parameters of functions, that describe phenotypic alterations, tumor cellular composition may evolve towards: (1) maximization of cell proliferation rate, (2) maximization of cell motility, (3) non-extremum values of cell proliferation rate and motility. Scenarios are found, where after initial tendency for maximization of cell proliferation rate, the direction of tumor progression sharply switches to maximization of cell motility, which is accompanied by decrease in total speed of tumor growth.
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17
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Tzamali E, Tzedakis G, Sakkalis V. A Framework Linking Glycolytic Metabolic Capabilities and Tumor Dynamics. IEEE J Biomed Health Inform 2019; 23:1844-1854. [DOI: 10.1109/jbhi.2018.2890708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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18
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Antonopoulos M, Dionysiou D, Stamatakos G, Uzunoglu N. Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study. BMC Bioinformatics 2019; 20:442. [PMID: 31455206 PMCID: PMC6712764 DOI: 10.1186/s12859-019-2997-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 07/16/2019] [Indexed: 01/10/2023] Open
Abstract
Background Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments. Results This paper aims at developing an extensible and computationally efficient framework for in silico modeling of tumor growth in a 3-dimensional, inhomogeneous and time-varying chemical environment. The resulting model consists of a set of mathematically derived and algorithmically defined operators, each one addressing the effects of a particular biological mechanism on the state of the system. These operators may be extended or re-adjusted, in case a different set of starting assumptions or a different simulation scenario needs to be considered. Conclusion In silico modeling provides an alternative means for testing hypotheses and simulating scenarios for which exact biological knowledge remains elusive. However, finer tuning of pertinent methods presupposes qualitative and quantitative enrichment of available biological evidence. Validation in a strict sense would further require comprehensive, case-specific simulations and detailed comparisons with biomedical observations.
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Affiliation(s)
- Markos Antonopoulos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece.
| | - Dimitra Dionysiou
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Nikolaos Uzunoglu
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
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19
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Oraiopoulou ME, Tzamali E, Tzedakis G, Liapis E, Zacharakis G, Vakis A, Papamatheakis J, Sakkalis V. Integrating in vitro experiments with in silico approaches for Glioblastoma invasion: the role of cell-to-cell adhesion heterogeneity. Sci Rep 2018; 8:16200. [PMID: 30385804 PMCID: PMC6212459 DOI: 10.1038/s41598-018-34521-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 10/01/2018] [Indexed: 01/08/2023] Open
Abstract
Glioblastoma cells adopt migration strategies to invade into the brain parenchyma ranging from individual to collective mechanisms, whose role and dynamics are not yet fully understood. In this work, we explore Glioblastoma heterogeneity and recapitulate its invasive patterns both in vitro, by utilizing primary cells along with the U87MG cell line, and in silico, by adopting discrete, individual cell-based mathematics. Glioblastoma cells are cultured three-dimensionally in an ECM-like substrate. The primary Glioblastoma spheroids adopt a novel cohesive pattern, mimicking perivascular invasion in the brain, while the U87MG adopt a typical, starburst invasive pattern under the same experimental setup. Mathematically, we focus on the role of the intrinsic heterogeneity with respect to cell-to-cell adhesion. Our proposed mathematical approach mimics the invasive morphologies observed in vitro and predicts the dynamics of tumour expansion. The role of the proliferation and migration is also explored showing that their effect on tumour morphology is different per cell type. The proposed model suggests that allowing cell-to-cell adhesive heterogeneity within the tumour population is sufficient for variable invasive morphologies to emerge which remain originally undetectable by conventional imaging, indicating that exploration in pathological samples is needed to improve our understanding and reveal potential patient-specific therapeutic targets.
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Affiliation(s)
- M-E Oraiopoulou
- Department of Medicine, University of Crete, Heraklion, Crete, Greece
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
| | - E Tzamali
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
| | - G Tzedakis
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
| | - E Liapis
- Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - G Zacharakis
- Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
| | - A Vakis
- Department of Medicine, University of Crete, Heraklion, Crete, Greece
- Neurosurgery Clinic, University General Hospital of Heraklion, Crete, Greece
| | - J Papamatheakis
- Gene Expression Laboratory, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
- Department of Biology, University of Crete, Heraklion, Crete, Greece
| | - V Sakkalis
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece.
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20
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Xie H, Jiao Y, Fan Q, Hai M, Yang J, Hu Z, Yang Y, Shuai J, Chen G, Liu R, Liu L. Modeling three-dimensional invasive solid tumor growth in heterogeneous microenvironment under chemotherapy. PLoS One 2018; 13:e0206292. [PMID: 30365511 PMCID: PMC6203364 DOI: 10.1371/journal.pone.0206292] [Citation(s) in RCA: 11] [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: 12/30/2017] [Accepted: 10/10/2018] [Indexed: 01/08/2023] Open
Abstract
A systematic understanding of the evolution and growth dynamics of invasive solid tumors in response to different chemotherapy strategies is crucial for the development of individually optimized oncotherapy. Here, we develop a hybrid three-dimensional (3D) computational model that integrates pharmacokinetic model, continuum diffusion-reaction model and discrete cell automaton model to investigate 3D invasive solid tumor growth in heterogeneous microenvironment under chemotherapy. Specifically, we consider the effects of heterogeneous environment on drug diffusion, tumor growth, invasion and the drug-tumor interaction on individual cell level. We employ the hybrid model to investigate the evolution and growth dynamics of avascular invasive solid tumors under different chemotherapy strategies. Our simulations indicate that constant dosing is generally more effective in suppressing primary tumor growth than periodic dosing, due to the resulting continuous high drug concentration. In highly heterogeneous microenvironment, the malignancy of the tumor is significantly enhanced, leading to inefficiency of chemotherapies. The effects of geometrically-confined microenvironment and non-uniform drug dosing are also investigated. Our computational model, when supplemented with sufficient clinical data, could eventually lead to the development of efficient in silico tools for prognosis and treatment strategy optimization.
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Affiliation(s)
- Hang Xie
- College of Physics, Chongqing University, Chongqing, China
| | - Yang Jiao
- Materials Science and Engineering, Arizona State University, Tempe, AZ, United States of America
| | - Qihui Fan
- Key Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Science, Beijing, China
| | - Miaomiao Hai
- College of Physics, Chongqing University, Chongqing, China
| | - Jiaen Yang
- College of Physics, Chongqing University, Chongqing, China
| | - Zhijian Hu
- College of Physics, Chongqing University, Chongqing, China
| | - Yue Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University School of Oncology, Beijing Cancer Hospital and Institute, Haidian District, Beijing, China
| | - Jianwei Shuai
- Department of Physics, Xiamen University, Xiamen, China
| | - Guo Chen
- College of Physics, Chongqing University, Chongqing, China
| | - Ruchuan Liu
- College of Physics, Chongqing University, Chongqing, China
| | - Liyu Liu
- College of Physics, Chongqing University, Chongqing, China
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21
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Mathematical Modeling of the Function of Warburg Effect in Tumor Microenvironment. Sci Rep 2018; 8:8903. [PMID: 29891989 PMCID: PMC5995918 DOI: 10.1038/s41598-018-27303-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 05/22/2018] [Indexed: 12/21/2022] Open
Abstract
Tumor cells are known for their increased glucose uptake rates even in the presence of abundant oxygen. This altered metabolic shift towards aerobic glycolysis is known as the Warburg effect. Despite an enormous number of studies conducted on the causes and consequences of this phenomenon, little is known about how the Warburg effect affects tumor growth and progression. We developed a multi-scale computational model to explore the detailed effects of glucose metabolism of cancer cells on tumorigenesis behavior in a tumor microenvironment. Despite glycolytic tumors, the growth of non-glycolytic tumor is dependent on a congruous morphology without markedly interfering with glucose and acid concentrations of the tumor microenvironment. Upregulated glucose metabolism helped to retain oxygen levels above the hypoxic limit during early tumor growth, and thus obviated the need for neo-vasculature recruitment. Importantly, simulating growth of tumors within a range of glucose uptake rates showed that there exists a spectrum of glucose uptake rates within which the tumor is most aggressive, i.e. it can exert maximal acidic stress on its microenvironment and most efficiently compete for glucose supplies. Moreover, within the same spectrum, the tumor could grow to invasive morphologies while its size did not markedly shrink.
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22
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Gong C, Milberg O, Wang B, Vicini P, Narwal R, Roskos L, Popel AS. A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition. J R Soc Interface 2018; 14:rsif.2017.0320. [PMID: 28931635 PMCID: PMC5636269 DOI: 10.1098/rsif.2017.0320] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 08/30/2017] [Indexed: 12/11/2022] Open
Abstract
When the immune system responds to tumour development, patterns of immune infiltrates emerge, highlighted by the expression of immune checkpoint-related molecules such as PDL1 on the surface of cancer cells. Such spatial heterogeneity carries information on intrinsic characteristics of the tumour lesion for individual patients, and thus is a potential source for biomarkers for anti-tumour therapeutics. We developed a systems biology multiscale agent-based model to capture the interactions between immune cells and cancer cells, and analysed the emergent global behaviour during tumour development and immunotherapy. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during tumour progression, as well as three-dimensional spatial distributions of these cells. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and antigen strength, a spectrum of pretreatment spatial patterns of PDL1 expression is generated in our simulations, resembling immuno-architectures obtained via immunohistochemistry from patient biopsies. By correlating these spatial characteristics with in silico treatment results using immune checkpoint inhibitors, the model provides a framework for use to predict treatment/biomarker combinations in different cancer types based on cancer-specific experimental data.
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Affiliation(s)
- Chang Gong
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Oleg Milberg
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | | | | | | | | | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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23
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Glioma growth modeling based on the effect of vital nutrients and metabolic products. Med Biol Eng Comput 2018. [PMID: 29516334 DOI: 10.1007/s11517-018-1809-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Glioma brain tumors exhibit considerably aggressive behavior leading to high mortality rates. Mathematical modeling of tumor growth aims to explore the interactions between glioma cells and tissue microenvironment, which affect tumor evolution. Leveraging this concept, we present a three-dimensional model of glioma spatio-temporal evolution based on existing continuum approaches, yet incorporating novel factors of the phenomenon. The proposed model involves the interactions between different tumor cell phenotypes and their microenvironment, investigating how tumor growth is affected by complex biological exchanges. It focuses on the separate and combined effect of vital nutrients and cellular wastes on tumor expansion, leading to the formation of cell populations with different metabolic, proliferative, and diffusive profiles. Several simulations were performed on a virtual and a real glioma, using combinations of proliferation and diffusion rates for different evolution times. The model results were validated on a glioma model available in the literature and a real case of tumor progression. The experimental observations indicate that our model estimates quite satisfactorily the expansion of each region and the overall tumor growth. Based on the individual results, the proposed model may provide an important research tool for patient-specific simulation of different tumor evolution scenarios and reliable estimation of glioma evolution. Graphical Abstract Outline of the mathematical model functionality and application to glioma growth with indicative results.
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24
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Poleszczuk J, Macklin P, Enderling H. Agent-Based Modeling of Cancer Stem Cell Driven Solid Tumor Growth. Methods Mol Biol 2018; 1516:335-346. [PMID: 27044046 DOI: 10.1007/7651_2016_346] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Computational modeling of tumor growth has become an invaluable tool to simulate complex cell-cell interactions and emerging population-level dynamics. Agent-based models are commonly used to describe the behavior and interaction of individual cells in different environments. Behavioral rules can be informed and calibrated by in vitro assays, and emerging population-level dynamics may be validated with both in vitro and in vivo experiments. Here, we describe the design and implementation of a lattice-based agent-based model of cancer stem cell driven tumor growth.
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Affiliation(s)
- Jan Poleszczuk
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33647, USA
| | - Paul Macklin
- Center for Applied Molecular Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33647, USA.
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25
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Shahmoradi S, Rahatabad FN, Maghooli K. A stochastic cellular automata model of growth of avascular tumor with immune response and immunotherapy. INFORMATICS IN MEDICINE UNLOCKED 2018. [DOI: 10.1016/j.imu.2018.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
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26
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27
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Basanta D, Anderson ARA. Homeostasis Back and Forth: An Ecoevolutionary Perspective of Cancer. Cold Spring Harb Perspect Med 2017; 7:cshperspect.a028332. [PMID: 28289244 DOI: 10.1101/cshperspect.a028332] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The role of genetic mutations in cancer is indisputable: They are a key source of tumor heterogeneity and drive its evolution to malignancy. But, the success of these new mutant cells relies on their ability to disrupt the homeostasis that characterizes healthy tissues. Mutated clones unable to break free from intrinsic and extrinsic homeostatic controls will fail to establish a tumor. Here, we will discuss, through the lens of mathematical and computational modeling, why an evolutionary view of cancer needs to be complemented by an ecological perspective to understand why cancer cells invade and subsequently transform their environment during progression. Importantly, this ecological perspective needs to account for tissue homeostasis in the organs that tumors invade, because they perturb the normal regulatory dynamics of these tissues, often coopting them for its own gain. Furthermore, given our current lack of success in treating advanced metastatic cancers through tumor-centric therapeutic strategies, we propose that treatments that aim to restore homeostasis could become a promising venue of clinical research. This ecoevolutionary view of cancer requires mechanistic mathematical models to both integrate clinical with biological data from different scales but also to detangle the dynamic feedback between the tumor and its environment. Importantly, for these models to be useful, they need to embrace a higher degree of complexity than many mathematical modelers are traditionally comfortable with.
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Affiliation(s)
- David Basanta
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, Tampa, Florida 33612
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, Tampa, Florida 33612
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28
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Szabó A, Merks RMH. Blood vessel tortuosity selects against evolution of aggressive tumor cells in confined tissue environments: A modeling approach. PLoS Comput Biol 2017; 13:e1005635. [PMID: 28715420 PMCID: PMC5536454 DOI: 10.1371/journal.pcbi.1005635] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 07/31/2017] [Accepted: 06/13/2017] [Indexed: 12/11/2022] Open
Abstract
Cancer is a disease of cellular regulation, often initiated by genetic mutation within cells, and leading to a heterogeneous cell population within tissues. In the competition for nutrients and growth space within the tumors the phenotype of each cell determines its success. Selection in this process is imposed by both the microenvironment (neighboring cells, extracellular matrix, and diffusing substances), and the whole of the organism through for example the blood supply. In this view, the development of tumor cells is in close interaction with their increasingly changing environment: the more cells can change, the more their environment will change. Furthermore, instabilities are also introduced on the organism level: blood supply can be blocked by increased tissue pressure or the tortuosity of the tumor-neovascular vessels. This coupling between cell, microenvironment, and organism results in behavior that is hard to predict. Here we introduce a cell-based computational model to study the effect of blood flow obstruction on the micro-evolution of cells within a cancerous tissue. We demonstrate that stages of tumor development emerge naturally, without the need for sequential mutation of specific genes. Secondly, we show that instabilities in blood supply can impact the overall development of tumors and lead to the extinction of the dominant aggressive phenotype, showing a clear distinction between the fitness at the cell level and survival of the population. This provides new insights into potential side effects of recent tumor vasculature normalization approaches.
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Affiliation(s)
- András Szabó
- Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
| | - Roeland M. H. Merks
- Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- Mathematical Institute, Leiden University, Leiden, The Netherlands
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29
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McDonald JE, Kessler MM, Gardner MW, Buros AF, Ntambi JA, Waheed S, van Rhee F, Zangari M, Heuck CJ, Petty N, Schinke C, Thanendrarajan S, Mitchell A, Hoering A, Barlogie B, Morgan GJ, Davies FE. Assessment of Total Lesion Glycolysis by 18F FDG PET/CT Significantly Improves Prognostic Value of GEP and ISS in Myeloma. Clin Cancer Res 2016; 23:1981-1987. [PMID: 27698001 DOI: 10.1158/1078-0432.ccr-16-0235] [Citation(s) in RCA: 86] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 07/28/2016] [Accepted: 08/22/2016] [Indexed: 01/08/2023]
Abstract
Purpose: Fluorine-18 fluorodeoxyglucose positron emission tomography with CT attenuation correction (18F-FDG PET/CT) is useful in the detection and enumeration of focal lesions and in semiquantitative characterization of metabolic activity (glycolytic phenotype) by calculation of glucose uptake. Total lesion glycolysis (TLG) and metabolic tumor volume (MTV) have the potential to improve the value of this approach and enhance the prognostic value of disease burden measures. This study aims to determine whether TLG and MTV are associated with progression-free survival (PFS) and overall survival (OS), and whether they improve risk assessments such as International Staging System (ISS) stage and GEP70 risk.Experimental Design: 192 patients underwent whole body PET/CT in the Total Therapy 3A (TT3A) trial and were evaluated using three-dimensional region-of-interest analysis with TLG, MTV, and standard measurement parameters derived for all focal lesions with peak SUV above the background red marrow signal.Results: In multivariate analysis, baseline TLG > 620 g and MTV > 210 cm3 remained a significant factor of poor PFS and OS after adjusting for baseline myeloma variables. Combined with the GEP70 risk score, TLG > 205 g identifies a high-risk-behaving subgroup with poor expected survival. In addition, TLG > 205 g accurately divides ISS stage II patients into two subgroups with similar outcomes to ISS stage I and ISS stage III, respectively.Conclusions: TLG and MTV have significant survival implications at baseline and offer a more precise quantitation of the glycolytic phenotype of active disease. These measures can be assessed more readily than before using FDA-approved software and should be standardized and incorporated into clinical trials moving forward. Clin Cancer Res; 23(8); 1981-7. ©2016 AACR.
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Affiliation(s)
- James E McDonald
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Marcus M Kessler
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Michael W Gardner
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Amy F Buros
- Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - James A Ntambi
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Sarah Waheed
- Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Frits van Rhee
- Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Maurizio Zangari
- Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Christoph J Heuck
- Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Nathan Petty
- Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Carolina Schinke
- Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | | | - Alan Mitchell
- Cancer Research and Biostatistics, Seattle, Washington
| | - Antje Hoering
- Cancer Research and Biostatistics, Seattle, Washington
| | - Bart Barlogie
- Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Gareth J Morgan
- Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Faith E Davies
- Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
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30
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Johnston ST, Baker RE, Simpson MJ. Filling the gaps: A robust description of adhesive birth-death-movement processes. Phys Rev E 2016; 93:042413. [PMID: 27176335 DOI: 10.1103/physreve.93.042413] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Indexed: 11/07/2022]
Abstract
Existing continuum descriptions of discrete adhesive birth-death-movement processes provide accurate predictions of the average discrete behavior for limited parameter regimes. Here we present an alternative continuum description in terms of the dynamics of groups of contiguous occupied and vacant lattice sites. Our method provides more accurate predictions, is valid in parameter regimes that could not be described by previous continuum descriptions, and provides information about the spatial clustering of occupied sites. Furthermore, we present a simple analytic approximation of the spatial clustering of occupied sites at late time, when the system reaches its steady-state configuration.
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Affiliation(s)
- Stuart T Johnston
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Ruth E Baker
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Matthew J Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
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Spatial Metrics of Tumour Vascular Organisation Predict Radiation Efficacy in a Computational Model. PLoS Comput Biol 2016; 12:e1004712. [PMID: 26800503 PMCID: PMC4723304 DOI: 10.1371/journal.pcbi.1004712] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 12/16/2015] [Indexed: 02/04/2023] Open
Abstract
Intratumoural heterogeneity is known to contribute to poor therapeutic response. Variations in oxygen tension in particular have been correlated with changes in radiation response in vitro and at the clinical scale with overall survival. Heterogeneity at the microscopic scale in tumour blood vessel architecture has been described, and is one source of the underlying variations in oxygen tension. We seek to determine whether histologic scale measures of the erratic distribution of blood vessels within a tumour can be used to predict differing radiation response. Using a two-dimensional hybrid cellular automaton model of tumour growth, we evaluate the effect of vessel distribution on cell survival outcomes of simulated radiation therapy. Using the standard equations for the oxygen enhancement ratio for cell survival probability under differing oxygen tensions, we calculate average radiation effect over a range of different vessel densities and organisations. We go on to quantify the vessel distribution heterogeneity and measure spatial organization using Ripley’s L function, a measure designed to detect deviations from complete spatial randomness. We find that under differing regimes of vessel density the correlation coefficient between the measure of spatial organization and radiation effect changes sign. This provides not only a useful way to understand the differences seen in radiation effect for tissues based on vessel architecture, but also an alternate explanation for the vessel normalization hypothesis. In this paper we use a mathematical model, called a hybrid cellular automaton, to study the effect of different vessel distributions on radiation therapy outcomes at the cellular level. We show that the correlation between radiation outcome and spatial organization of vessels changes signs between relatively low and high vessel density. Specifically, that for relatively low vessel density, radiation efficacy is decreased when vessels are more homogeneously distributed, and the opposite is true, that radiation efficacy is improved, when vessel organisation is normalised in high densities. This result suggests an alteration to the vessel normalization hypothesis which states that normalisation of vascular beds should improve radio- and chemo-therapeutic response, but has failed to be validated in clinical studies. In this alteration, we show that Ripley’s L function allows discrimination between vascular architectures in different density regimes in which the standard hypothesis holds and does not hold. Further, we find that this information can be used to augment quantitative histologic analysis of tumours to aid radiation dose personalisation.
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Abstract
Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.
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Affiliation(s)
- Philipp M Altrock
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard T.H. Chan School of Public Health, 450 Brookline Avenue, Boston, Massachusetts 02115, USA
- Program for Evolutionary Dynamics, Harvard University, 1 Brattle Square, Suite 6, Cambridge, Massachusetts 02138, USA
| | - Lin L Liu
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard T.H. Chan School of Public Health, 450 Brookline Avenue, Boston, Massachusetts 02115, USA
| | - Franziska Michor
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard T.H. Chan School of Public Health, 450 Brookline Avenue, Boston, Massachusetts 02115, USA
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Papadogiorgaki M, Kounelakis MG, Koliou P, Zervakis ME. A Glycolysis-Based In Silico Model for the Solid Tumor Growth. IEEE J Biomed Health Inform 2015; 19:1106-17. [PMID: 25216488 DOI: 10.1109/jbhi.2014.2356254] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Cancer-tumor growth is a complex process depending on several biological factors, such as the chemical microenvironment of the tumor, the cellular metabolic profile, and its proliferation rate. Several mathematical models have been developed for identifying the interactions between tumor cells and tissue microenvironment, since they play an important role in tumor formation and progression. Toward this direction we propose a new continuum model of avascular glioma-tumor growth, which incorporates a new factor, namely, the glycolytic potential of cancer cells, to express the interactions of three different tumor-cell populations (proliferative, hypoxic, and necrotic) with their tissue microenvironment. The glycolytic potential engages three vital nutrients, i.e., oxygen, glucose, and lactate, which provide cells with the necessary energy for their survival and proliferation. Extensive simulations are performed for different evolution times and various proliferation rates, in order to investigate how the tumor growth is affected. According to medical experts, the experimental observations indicate that the model predicts quite satisfactorily the overall tumor growth as well as the expansion of each region separately. Following extensive evaluation, the proposed model may provide an essential tool for patient-specific tumor simulation and reliable prediction of glioma spatiotemporal expansion.
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Al-Mamun MA, Farid DM, Ravenhil L, Hossain MA, Fall C, Bass R. An in silico model to demonstrate the effects of Maspin on cancer cell dynamics. J Theor Biol 2015; 388:37-49. [PMID: 26497917 DOI: 10.1016/j.jtbi.2015.10.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Revised: 07/22/2015] [Accepted: 10/12/2015] [Indexed: 12/16/2022]
Abstract
Most cancer treatments efficacy depends on tumor metastasis suppression, where tumor suppressor genes play an important role. Maspin (Mammary Serine Protease Inhibitor), an non-inhibitory serpin has been reported as a potential tumor suppressor to influence cell migration, adhesion, proliferation and apoptosis in in vitro and in vivo experiments in last two decades. Lack of computational investigations hinders its ability to go through clinical trials. Previously, we reported first computational model for maspin effects on tumor growth using artificial neural network and cellular automata paradigm with in vitro data support. This paper extends the previous in silico model by encompassing how maspin influences cell migration and the cell-extracellular matrix interaction in subcellular level. A feedforward neural network was used to define each cell behavior (proliferation, quiescence, apoptosis) which followed a cell-cycle algorithm to show the microenvironment impacts over tumor growth. Furthermore, the model concentrates how the in silico experiments results can further confirm the fact that maspin reduces cell migration using specific in vitro data verification method. The data collected from in vitro and in silico experiments formulates an unsupervised learning problem which can be solved by using different clustering algorithms. A density based clustering technique was developed to measure the similarity between two datasets based on the number of links between instances. Our proposed clustering algorithm first finds the nearest neighbors of each instance, and then redefines the similarity between pairs of instances in terms of how many nearest neighbors share the two instances. The number of links between two instances is defined as the number of common neighbors they have. The results showed significant resemblances with in vitro experimental data. The results also offer a new insight into the dynamics of maspin and establish as a metastasis suppressor gene for further molecular research.
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Affiliation(s)
- M A Al-Mamun
- Department of Population Medicine & Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14850, USA.
| | - D M Farid
- Department of Computer Science & Engineering, United International University, Bangladesh.
| | - L Ravenhil
- Department of Applied Sciences, Faculty of Health and Life Sciences, University of Northumbria at Newcastle, UK
| | - M A Hossain
- Anglia Ruskin IT Research Institute (ARITI), Anglia Ruskin University, Cambridge, UK.
| | - C Fall
- Computational Intelligence Group, Faculty of Engineering and Environment, University of Northumbria at Newcastle, UK.
| | - R Bass
- Department of Applied Sciences, Faculty of Health and Life Sciences, University of Northumbria at Newcastle, UK; Computational Intelligence Group, Faculty of Engineering and Environment, University of Northumbria at Newcastle, UK.
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35
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Cytowski M, Szymanska Z. Large-Scale Parallel Simulations of 3D Cell Colony Dynamics: The Cellular Environment. Comput Sci Eng 2015. [DOI: 10.1109/mcse.2015.66] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Robertson-Tessi M, Gillies RJ, Gatenby RA, Anderson ARA. Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res 2015; 75:1567-79. [PMID: 25878146 DOI: 10.1158/0008-5472.can-14-1428] [Citation(s) in RCA: 186] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Histopathologic knowledge that extensive heterogeneity exists between and within tumors has been confirmed and deepened recently by molecular studies. However, the impact of tumor heterogeneity on prognosis and treatment remains as poorly understood as ever. Using a hybrid multiscale mathematical model of tumor growth in vascularized tissue, we investigated the selection pressures exerted by spatial and temporal variations in tumor microenvironment and the resulting phenotypic adaptations. A key component of this model is normal and tumor metabolism and its interaction with microenvironmental factors. The metabolic phenotype of tumor cells is plastic, and microenvironmental selection leads to increased tumor glycolysis and decreased pH. Once this phenotype emerges, the tumor dramatically changes its behavior due to acid-mediated invasion, an effect that depends on both variations in the tumor cell phenotypes and their spatial distribution within the tumor. In early stages of growth, tumors are stratified, with the most aggressive cells developing within the interior of the tumor. These cells then grow to the edge of the tumor and invade into the normal tissue using acidosis. Simulations suggest that diffusible cytotoxic treatments, such as chemotherapy, may increase the metabolic aggressiveness of a tumor due to drug-mediated selection. Chemotherapy removes the metabolic stratification of the tumor and allows more aggressive cells to grow toward blood vessels and normal tissue. Antiangiogenic therapy also selects for aggressive phenotypes due to degradation of the tumor microenvironment, ultimately resulting in a more invasive tumor. In contrast, pH buffer therapy slows down the development of aggressive tumors, but only if administered when the tumor is still stratified. Overall, findings from this model highlight the risks of cytotoxic and antiangiogenic treatments in the context of tumor heterogeneity resulting from a selection for more aggressive behaviors.
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Affiliation(s)
- Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
| | - Robert J Gillies
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Robert A Gatenby
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida. Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Alexander R A Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
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Garbey M, Rahman M, Berceli SA. A Multiscale Computational Framework to Understand Vascular Adaptation. JOURNAL OF COMPUTATIONAL SCIENCE 2015; 8:32-47. [PMID: 25977733 PMCID: PMC4426998 DOI: 10.1016/j.jocs.2015.02.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
The failure rate for vascular interventions (vein bypass grafting, arterial angioplasty/stenting) remains unacceptably high. Over the past two decades, researchers have applied a wide variety of approaches to investigate the primary failure mechanisms, neointimal hyperplasia and aberrant remodeling of the wall, in an effort to identify novel therapeutic strategies. Despite incremental progress, specific cause/effect linkages among the primary drivers of the pathology, (hemodynamic factors, inflammatory biochemical mediators, cellular effectors) and vascular occlusive phenotype remain lacking. We propose a multiscale computational framework of vascular adaptation to develop a bridge between theory and experimental observation and to provide a method for the systematic testing of relevant clinical hypotheses. Cornerstone to our model is a feedback mechanism between environmental conditions and dynamic tissue plasticity described at the cellular level with an agent based model. Our implementation (i) is modular, (ii) starts from basic mechano-biology principle at the cell level and (iii) facilitates the agile development of the model.
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Affiliation(s)
- Marc Garbey
- Dept. of Biology, University of Houston, USA ; MITIE, The Houston Methodist Hospital, Houston USA
| | | | - Scott A Berceli
- Dept. of Surgery, University of Florida, Malcom Randall VAMC, USA
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38
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Gerlee P, Kim E, Anderson ARA. Bridging scales in cancer progression: mapping genotype to phenotype using neural networks. Semin Cancer Biol 2015; 30:30-41. [PMID: 24830623 PMCID: PMC4533881 DOI: 10.1016/j.semcancer.2014.04.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2014] [Accepted: 04/28/2014] [Indexed: 12/22/2022]
Abstract
In this review we summarise our recent efforts in trying to understand the role of heterogeneity in cancer progression by using neural networks to characterise different aspects of the mapping from a cancer cells genotype and environment to its phenotype. Our central premise is that cancer is an evolving system subject to mutation and selection, and the primary conduit for these processes to occur is the cancer cell whose behaviour is regulated on multiple biological scales. The selection pressure is mainly driven by the microenvironment that the tumour is growing in and this acts directly upon the cell phenotype. In turn, the phenotype is driven by the intracellular pathways that are regulated by the genotype. Integrating all of these processes is a massive undertaking and requires bridging many biological scales (i.e. genotype, pathway, phenotype and environment) that we will only scratch the surface of in this review. We will focus on models that use neural networks as a means of connecting these different biological scales, since they allow us to easily create heterogeneity for selection to act upon and importantly this heterogeneity can be implemented at different biological scales. More specifically, we consider three different neural networks that bridge different aspects of these scales and the dialogue with the micro-environment, (i) the impact of the micro-environment on evolutionary dynamics, (ii) the mapping from genotype to phenotype under drug-induced perturbations and (iii) pathway activity in both normal and cancer cells under different micro-environmental conditions.
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Affiliation(s)
- Philip Gerlee
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive Tampa, FL 33612, USA.
| | - Eunjung Kim
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive Tampa, FL 33612, USA
| | - Alexander R A Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive Tampa, FL 33612, USA.
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39
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40
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Aristotelous AC, Durrett R. Fingering in Stochastic Growth Models. EXPERIMENTAL MATHEMATICS 2014; 23:465-474. [PMID: 26430353 PMCID: PMC4587769 DOI: 10.1080/10586458.2014.947053] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Motivated by the widespread use of hybrid-discrete cellular automata in modeling cancer, two simple growth models are studied on the two dimensional lattice that incorporate a nutrient, assumed to be oxygen. In the first model the oxygen concentration u(x, t) is computed based on the geometry of the growing blob, while in the second one u(x, t) satisfies a reaction-diffusion equation. A threshold θ value exists such that cells give birth at rate β(u(x, t) - θ)+ and die at rate δ(θ - u(x, t)+. In the first model, a phase transition was found between growth as a solid blob and "fingering" at a threshold θc = 0.5, while in the second case fingering always occurs, i.e., θc = 0.
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Affiliation(s)
| | - Richard Durrett
- Department of Mathematics, Duke U., Box 90320, Durham, NC 27708-0320
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41
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Aristotelous AC, Haider MA. Use of hybrid discrete cellular models for identification of macroscopic nutrient loss in reaction-diffusion models of tissues. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2014; 30:767-780. [PMID: 24515852 DOI: 10.1002/cnm.2628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Revised: 11/15/2013] [Accepted: 12/20/2013] [Indexed: 06/03/2023]
Abstract
Macroscopic models accounting for cellular effects in natural or engineered tissues may involve unknown constitutive terms that are highly dependent on interactions at the scale of individual cells. Hybrid discrete models, which represent cells individually, were used to develop and apply techniques for modeling diffusive nutrient transport and cellular uptake to identify a nonlinear nutrient loss term in a macroscopic reaction-diffusion model of the system. Flexible and robust numerical methods were used, based on discontinuous Galerkin finite elements in space and a Crank-Nicolson temporal discretization. Scales were bridged via averaging operations over a complete set of subdomains yielding data for identification of a macroscopic nutrient loss term that was accurately captured via a fifth-order polynomial. Accuracy of the identified macroscopic model was demonstrated by direct, quantitative comparisons of the tissue and cellular scale models in terms of three error norms computed on a mesoscale mesh.
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Affiliation(s)
- Andreas C Aristotelous
- Statistical and Applied Mathematical Sciences Institute (SAMSI), Research Triangle Park, NC 27709, USA; Department of Mathematics, Duke University, Durham, NC 27708, USA
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42
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Basanta D, Anderson ARA. Exploiting ecological principles to better understand cancer progression and treatment. Interface Focus 2014; 3:20130020. [PMID: 24511383 DOI: 10.1098/rsfs.2013.0020] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A small but growing number of people are finding interesting parallels between ecosystems as studied by ecologists (think of a savannah or the Amazon rainforest or a coral reef) and tumours. The idea of viewing cancer from an ecological perspective has many implications but, basically, it means that we should not see cancer just as a group of mutated cells. A more useful definition of cancer is to consider it a disruption in the complex balance of many interacting cellular and microenvironmental elements in a specific organ. This perspective means that organs undergoing carcinogenesis should be seen as sophisticated ecosystems in homoeostasis that cancer cells can disrupt. It also makes cancer seem even more complex but may ultimately provide insights that make it more treatable. Here, we discuss how ecological principles can be used to better understand cancer progression and treatment, using several mathematical and computational models to illustrate our argument.
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Affiliation(s)
- David Basanta
- Integrated Mathematical Oncology , Moffitt Cancer Centre , Tampa, FL 33629 , USA
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43
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Harcombe WR, Riehl WJ, Dukovski I, Granger BR, Betts A, Lang AH, Bonilla G, Kar A, Leiby N, Mehta P, Marx CJ, Segrè D. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep 2014; 7:1104-15. [PMID: 24794435 PMCID: PMC4097880 DOI: 10.1016/j.celrep.2014.03.070] [Citation(s) in RCA: 318] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Revised: 02/01/2014] [Accepted: 03/28/2014] [Indexed: 12/21/2022] Open
Abstract
The interspecies exchange of metabolites plays a key role in the spatiotemporal dynamics of microbial communities. This raises the question of whether ecosystem-level behavior of structured communities can be predicted using genome-scale metabolic models for multiple organisms. We developed a modeling framework that integrates dynamic flux balance analysis with diffusion on a lattice and applied it to engineered communities. First, we predicted and experimentally confirmed the species ratio to which a two-species mutualistic consortium converges and the equilibrium composition of a newly engineered three-member community. We next identified a specific spatial arrangement of colonies, which gives rise to what we term the "eclipse dilemma": does a competitor placed between a colony and its cross-feeding partner benefit or hurt growth of the original colony? Our experimentally validated finding that the net outcome is beneficial highlights the complex nature of metabolic interactions in microbial communities while at the same time demonstrating their predictability.
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Affiliation(s)
- William R Harcombe
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - William J Riehl
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA
| | - Ilija Dukovski
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA
| | - Brian R Granger
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA
| | - Alex Betts
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Alex H Lang
- Department of Physics, Boston University, Boston, MA 02215, USA
| | - Gracia Bonilla
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA
| | - Amrita Kar
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA
| | - Nicholas Leiby
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Systems Biology Graduate Program, Harvard University, Cambridge, MA 02138, USA
| | - Pankaj Mehta
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA; Department of Physics, Boston University, Boston, MA 02215, USA
| | - Christopher J Marx
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Faculty of Arts and Sciences Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA.
| | - Daniel Segrè
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA; Department of Biology and Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.
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Wang Z, Butner JD, Kerketta R, Cristini V, Deisboeck TS. Simulating cancer growth with multiscale agent-based modeling. Semin Cancer Biol 2014; 30:70-8. [PMID: 24793698 DOI: 10.1016/j.semcancer.2014.04.001] [Citation(s) in RCA: 129] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Revised: 03/18/2014] [Accepted: 04/04/2014] [Indexed: 01/01/2023]
Abstract
There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models.
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Affiliation(s)
- Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA.
| | - Joseph D Butner
- Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131, USA
| | - Romica Kerketta
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Vittorio Cristini
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA; Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131, USA; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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45
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Evaluation of Diffusive Transport and Cellular Uptake of Nutrients in Tissue Engineered Constructs Using a Hybrid Discrete Mathematical Model. Processes (Basel) 2014. [DOI: 10.3390/pr2020333] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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46
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Aristotelous AC, Durrett R. Chemical evolutionary games. Theor Popul Biol 2014; 93:1-13. [PMID: 24513098 DOI: 10.1016/j.tpb.2014.02.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2013] [Revised: 01/22/2014] [Accepted: 02/02/2014] [Indexed: 11/24/2022]
Abstract
Inspired by the use of hybrid cellular automata in modeling cancer, we introduce a generalization of evolutionary games in which cells produce and absorb chemicals, and the chemical concentrations dictate the death rates of cells and their fitnesses. Our long term aim is to understand how the details of the interactions in a system with n species and m chemicals translate into the qualitative behavior of the system. Here, we study two simple 2×2 games with two chemicals and revisit the two and three species versions of the one chemical colicin system studied earlier by Durrett and Levin (1997). We find that in the 2×2 examples, the behavior of our new spatial model can be predicted from that of the mean field differential equation using ideas of Durrett and Levin (1994). However, in the three species colicin model, the system with diffusion does not have the coexistence which occurs in the lattices model in which sites interact with only their nearest neighbors.
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Affiliation(s)
| | - Richard Durrett
- Department of Mathematics, Duke U., Box 90320, Durham, NC 27708-0320, United States.
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47
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Deng P, de Vargas Roditi L, van Ditmarsch D, Xavier JB. The ecological basis of morphogenesis: branching patterns in swarming colonies of bacteria. NEW JOURNAL OF PHYSICS 2014; 16:015006-15006. [PMID: 24587694 PMCID: PMC3935381 DOI: 10.1088/1367-2630/16/1/015006] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Understanding how large-scale shapes in tissues, organs and bacterial colonies emerge from local interactions among cells and how these shapes remain stable over time are two fundamental problems in biology. Here we investigate branching morphogenesis in an experimental model system, swarming colonies of the bacterium Pseudomonas aeruginosa. We combine experiments and computer simulation to show that a simple ecological model of population dispersal can describe the emergence of branching patterns. In our system, morphogenesis depends on two counteracting processes that act on different length-scales: (1) colony expansion, which increases the likelihood of colonizing a patch at a close distance and (2) colony repulsion, which decreases the colonization likelihood over a longer distance. The two processes are included in a kernel based mathematical model using an integro-differential approach borrowed from ecological theory. Computer simulations show that the model can indeed reproduce branching, but only for a narrow range of parameter values, suggesting that P. aeruginosa has a fine-tuned physiology for branching. Simulations further show that hyperswarming, a process where highly dispersive mutants reproducibly arise within the colony and disrupt branching patterns, can be interpreted as a change in the spatial kernel.
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Affiliation(s)
- Pan Deng
- Program in Computational Biology, Memorial Sloan-Kettering Cancer Center, New York NY, USA
| | - Laura de Vargas Roditi
- Program in Computational Biology, Memorial Sloan-Kettering Cancer Center, New York NY, USA
| | - Dave van Ditmarsch
- Program in Computational Biology, Memorial Sloan-Kettering Cancer Center, New York NY, USA
| | - Joao B. Xavier
- Program in Computational Biology, Memorial Sloan-Kettering Cancer Center, New York NY, USA
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48
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Yankeelov TE, Atuegwu N, Hormuth D, Weis JA, Barnes SL, Miga MI, Rericha EC, Quaranta V. Clinically relevant modeling of tumor growth and treatment response. Sci Transl Med 2013; 5:187ps9. [PMID: 23720579 DOI: 10.1126/scitranslmed.3005686] [Citation(s) in RCA: 112] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point--for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here we propose the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models. In the near future, these models could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.
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Affiliation(s)
- Thomas E Yankeelov
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37212, USA.
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Al-Mamun M, Brown L, Hossain M, Fall C, Wagstaff L, Bass R. A hybrid computational model for the effects of maspin on cancer cell dynamics. J Theor Biol 2013; 337:150-60. [DOI: 10.1016/j.jtbi.2013.08.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Revised: 08/07/2013] [Accepted: 08/15/2013] [Indexed: 01/01/2023]
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Stoll EA, Horner PJ, Rostomily RC. The impact of age on oncogenic potential: tumor-initiating cells and the brain microenvironment. Aging Cell 2013; 12:733-41. [PMID: 23711239 DOI: 10.1111/acel.12104] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/06/2013] [Indexed: 12/22/2022] Open
Abstract
Paradoxically, aging leads to both decreased regenerative capacity in the brain and an increased risk of tumorigenesis, particularly the most common adult-onset brain tumor, glioma. A shared factor contributing to both phenomena is thought to be age-related alterations in neural progenitor cells (NPCs), which function normally to produce new neurons and glia, but are also considered likely cells of origin for malignant glioma. Upon oncogenic transformation, cells acquire characteristics known as the hallmarks of cancer, including unlimited replication, altered responses to growth and anti-growth factors, increased capacity for angiogenesis, potential for invasion, genetic instability, apoptotic evasion, escape from immune surveillance, and an adaptive metabolic phenotype. The precise molecular pathogenesis and temporal acquisition of these malignant characteristics is largely a mystery. Recent studies characterizing NPCs during normal aging, however, have begun to elucidate mechanisms underlying the age-associated increase in their malignant potential. Aging cells are dependent upon multiple compensatory pathways to maintain cell cycle control, normal niche interactions, genetic stability, programmed cell death, and oxidative metabolism. A few multi-functional proteins act as 'critical nodes' in the coordination of these various cellular activities, although both intracellular signaling and elements within the brain environment are critical to maintaining a balance between senescence and tumorigenesis. Here, we provide an overview of recent progress in our understanding of how mechanisms underlying cellular aging inform on glioma pathogenesis and malignancy.
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Affiliation(s)
- Elizabeth A. Stoll
- Institute for Aging and Health; Newcastle University; Newcastle upon Tyne; UK
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