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Li H, Yang Z, Tu F, Deng L, Han Y, Fu X, Wang L, Gu D, Werner B, Huang W. Mutation divergence over space in tumour expansion. J R Soc Interface 2023; 20:20230542. [PMID: 37989227 PMCID: PMC10681009 DOI: 10.1098/rsif.2023.0542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 10/30/2023] [Indexed: 11/23/2023] Open
Abstract
Mutation accumulation in tumour evolution is one major cause of intra-tumour heterogeneity (ITH), which often leads to drug resistance during treatment. Previous studies with multi-region sequencing have shown that mutation divergence among samples within the patient is common, and the importance of spatial sampling to obtain a complete picture in tumour measurements. However, quantitative comparisons of the relationship between mutation heterogeneity and tumour expansion modes, sampling distances as well as the sampling methods are still few. Here, we investigate how mutations diverge over space by varying the sampling distance and tumour expansion modes using individual-based simulations. We measure ITH by the Jaccard index between samples and quantify how ITH increases with sampling distance, the pattern of which holds in various sampling methods and sizes. We also compare the inferred mutation rates based on the distributions of variant allele frequencies under different tumour expansion modes and sampling sizes. In exponentially fast expanding tumours, a mutation rate can always be inferred for any sampling size. However, the accuracy compared with the true value decreases when the sampling size decreases, where small sampling sizes result in a high estimate of the mutation rate. In addition, such an inference becomes unreliable when the tumour expansion is slow, such as in surface growth.
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Affiliation(s)
- Haiyang Li
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
- Evolutionary Dynamics Group, Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Zixuan Yang
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Fengyu Tu
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Lijuan Deng
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Yuqing Han
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Xing Fu
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Long Wang
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Di Gu
- The first affiliated hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Benjamin Werner
- Evolutionary Dynamics Group, Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Weini Huang
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
- School of Mathematical Sciences, Queen Mary University of London, London, UK
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2
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Luo W. Nasopharyngeal carcinoma ecology theory: cancer as multidimensional spatiotemporal "unity of ecology and evolution" pathological ecosystem. Theranostics 2023; 13:1607-1631. [PMID: 37056571 PMCID: PMC10086202 DOI: 10.7150/thno.82690] [Citation(s) in RCA: 61] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 02/26/2023] [Indexed: 03/14/2023] Open
Abstract
Nasopharyngeal carcinoma (NPC) is a particular entity of head neck cancer that is generally regarded as a genetic disease with diverse intertumor and intratumor heterogeneity. This perspective review mainly outlines the up-to-date knowledge of cancer ecology and NPC progression, and presents a number of conceptual stepping-stones. At the beginning, I explicitly advocate that the nature of NPC (cancer) is not a genetic disease but an ecological disease: a multidimensional spatiotemporal "unity of ecology and evolution" pathological ecosystem. The hallmarks of cancer is proposed to act as ecological factors of population fitness. Subsequently, NPC cells are described as invasive species and its metastasis as a multidirectional ecological dispersal. The foundational ecological principles include intraspecific relationship (e.g. communication) and interspecific relationship (e.g. competition, predation, parasitism and mutualism) are interpreted to understand NPC progression. "Mulberry-fish-ponds" model can well illustrate the dynamic reciprocity of cancer ecosystem. Tumor-host interface is the ecological transition zone of cancer, and tumor buddings should be recognized as ecological islands separated from the mainland. It should be noted that tumor-host interface has a significantly molecular and functional edge effect because of its curvature and irregularity. Selection driving factors and ecological therapy including hyperthermia for NPC patients, and future perspectives in such field as "ecological pathology", "multidimensional tumoriecology" are also discussed. I advance that "nothing in cancer evolution or ecology makes sense except in the light of the other". The cancer ecology tree is constructed to comprehensively point out the future research direction. Taken together, the establishment of NPC ecology theory and cancer ecology tree might provide a novel conceptual framework and paradigm for our understanding of cancer complex causal process and potential preventive and therapeutic applications for patients.
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Affiliation(s)
- Weiren Luo
- Cancer Research Institute, Department of Pathology, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, National Clinical Research Center for Infectious Diseases, Shenzhen, China
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3
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Spatio-temporal modelling of phenotypic heterogeneity in tumour tissues and its impact on radiotherapy treatment. J Theor Biol 2023; 556:111248. [PMID: 36150537 DOI: 10.1016/j.jtbi.2022.111248] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 08/02/2022] [Accepted: 08/17/2022] [Indexed: 11/20/2022]
Abstract
We present a mathematical model that describes how tumour heterogeneity evolves in a tissue slice that is oxygenated by a single blood vessel. Phenotype is identified with the stemness level of a cell and determines its proliferative capacity, apoptosis propensity and response to treatment. Our study is based on numerical bifurcation analysis and dynamical simulations of a system of coupled, non-local (in phenotypic "space") partial differential equations that link the phenotypic evolution of the tumour cells to local tissue oxygen levels. In our formulation, we consider a 1D geometry where oxygen is supplied by a blood vessel located on the domain boundary and consumed by the tumour cells as it diffuses through the tissue. For biologically relevant parameter values, the system exhibits multiple steady states; in particular, depending on the initial conditions, the tumour is either eliminated ("tumour-extinction") or it persists ("tumour-invasion"). We conclude by using the model to investigate tumour responses to radiotherapy, and focus on identifying radiotherapy strategies which can eliminate the tumour. Numerical simulations reveal how phenotypic heterogeneity evolves during treatment and highlight the critical role of tissue oxygen levels on the efficacy of radiation protocols that are commonly used in the clinic.
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4
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Farrokhian N, Maltas J, Dinh M, Durmaz A, Ellsworth P, Hitomi M, McClure E, Marusyk A, Kaznatcheev A, Scott JG. Measuring competitive exclusion in non-small cell lung cancer. SCIENCE ADVANCES 2022; 8:eabm7212. [PMID: 35776787 PMCID: PMC10883359 DOI: 10.1126/sciadv.abm7212] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In this study, we experimentally measure the frequency-dependent interactions between a gefitinib-resistant non-small cell lung cancer population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. Using frequency-dependent growth rate data, we then show that gefitinib treatment results in competitive exclusion of the ancestor, while the absence of treatment results in a likely, but not guaranteed, exclusion of the resistant strain. Then, using simulations, we demonstrate that incorporating ecological growth effects can influence the predicted extinction time. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and as we translate adaptive therapy regimens to the clinic.
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Affiliation(s)
| | - Jeff Maltas
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
| | - Mina Dinh
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
| | | | | | - Masahiro Hitomi
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
| | - Erin McClure
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
| | - Andriy Marusyk
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Artem Kaznatcheev
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacob G Scott
- CWRU School of Medicine, Cleveland, OH, USA
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, USA
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5
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Grimes DR, Jansen M, Macauley RJ, Scott JG, Basanta D. Evidence for hypoxia increasing the tempo of evolution in glioblastoma. Br J Cancer 2020; 123:1562-1569. [PMID: 32848201 PMCID: PMC7653934 DOI: 10.1038/s41416-020-1021-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/24/2020] [Accepted: 07/23/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Tumour hypoxia is associated with metastatic disease, and while there have been many mechanisms proposed for why tumour hypoxia is associated with metastatic disease, it remains unclear whether one precise mechanism is the key reason or several in concert. Somatic evolution drives cancer progression and treatment resistance, fuelled not only by genetic and epigenetic mutation but also by selection from interactions between tumour cells, normal cells and physical micro-environment. Ecological habitats influence evolutionary dynamics, but the impact on tempo of evolution is less clear. METHODS We explored this complex dialogue with a combined clinical-theoretical approach by simulating a proliferative hierarchy under heterogeneous oxygen availability with an agent-based model. Predictions were compared against histology samples taken from glioblastoma patients, stained to elucidate areas of necrosis and TP53 expression heterogeneity. RESULTS Results indicate that cell division in hypoxic environments is effectively upregulated, with low-oxygen niches providing avenues for tumour cells to spread. Analysis of human data indicates that cell division is not decreased under hypoxia, consistent with our results. CONCLUSIONS Our results suggest that hypoxia could be a crucible that effectively warps evolutionary velocity, making key mutations more likely. Thus, key tumour ecological niches such as hypoxic regions may alter the evolutionary tempo, driving mutations fuelling tumour heterogeneity.
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Affiliation(s)
- David Robert Grimes
- School of Physical Sciences, Dublin City University, Dublin 9, Ireland.
- Cancer Research UK/MRC Oxford Institute for Radiation Oncology, Gray Laboratory, University of Oxford, Old Road Campus Research Building, Off Roosevelt Drive, Oxford, OX3 7DQ, UK.
| | - Marnix Jansen
- Departments of Endoscopy and Pathology, University College London Hospital, London, UK
| | - Robert J Macauley
- Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jacob G Scott
- Departments of Translational Hematology and Oncology Research and Radiation Oncology, Cleveland Clinic, Cleveland, OH, USA
| | - David Basanta
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
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Hamis S, Powathil GG, Chaplain MAJ. Blackboard to Bedside: A Mathematical Modeling Bottom-Up Approach Toward Personalized Cancer Treatments. JCO Clin Cancer Inform 2020; 3:1-11. [PMID: 30742485 DOI: 10.1200/cci.18.00068] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Cancers present with high variability across patients and tumors; thus, cancer care, in terms of disease prevention, detection, and control, can highly benefit from a personalized approach. For a comprehensive personalized oncology practice, this personalization should ideally consider data gathered from various information levels, which range from the macroscale population level down to the microscale tumor level, without omission of the central patient level. Appropriate data mined from each of these levels can significantly contribute in devising personalized treatment plans tailored to the individual patient and tumor. Mathematical models of solid tumors, combined with patient-specific tumor profiles, present a unique opportunity to personalize cancer treatments after detection using a bottom-up approach. Here, we discuss how information harvested from mathematical models and from corresponding in silico experiments can be implemented in preclinical and clinical applications. To conceptually illustrate the power of these models, one such model is presented, and various pertinent tumor and treatment scenarios are demonstrated in silico. The presented model, specifically a multiscale, hybrid cellular automaton, has been fully validated in vitro using multiple cell-line-specific data. We discuss various insights provided by this model and other models like it and their role in designing predictive tools that are both patient, and tumor specific. After refinement and parametrization with appropriate data, such in silico tools have the potential to be used in a clinical setting to aid in treatment protocols and decision making.
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Affiliation(s)
- Sara Hamis
- Swansea University, Swansea, Wales, United Kingdom
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7
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Stem cell-associated heterogeneity in Glioblastoma results from intrinsic tumor plasticity shaped by the microenvironment. Nat Commun 2019; 10:1787. [PMID: 30992437 PMCID: PMC6467886 DOI: 10.1038/s41467-019-09853-z] [Citation(s) in RCA: 321] [Impact Index Per Article: 64.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 03/27/2019] [Indexed: 02/07/2023] Open
Abstract
The identity and unique capacity of cancer stem cells (CSC) to drive tumor growth and resistance have been challenged in brain tumors. Here we report that cells expressing CSC-associated cell membrane markers in Glioblastoma (GBM) do not represent a clonal entity defined by distinct functional properties and transcriptomic profiles, but rather a plastic state that most cancer cells can adopt. We show that phenotypic heterogeneity arises from non-hierarchical, reversible state transitions, instructed by the microenvironment and is predictable by mathematical modeling. Although functional stem cell properties were similar in vitro, accelerated reconstitution of heterogeneity provides a growth advantage in vivo, suggesting that tumorigenic potential is linked to intrinsic plasticity rather than CSC multipotency. The capacity of any given cancer cell to reconstitute tumor heterogeneity cautions against therapies targeting CSC-associated membrane epitopes. Instead inherent cancer cell plasticity emerges as a novel relevant target for treatment. Cancer stem cells (CSCs) comprise a putative population that can drive growth and resistance. Here, in glioblastoma models the authors show that rather than being a distinct clonal entity, the CSC population represents a plastic state adoptable by most cancer cells via reversible state transitions induced by the microenvironment.
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8
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Scott JG, Dhawan A, Hjelmeland A, Lathia J, Chumakova A, Hitomi M, Fletcher AG, Maini PK, Anderson ARA. Recasting the Cancer Stem Cell Hypothesis: Unification Using a Continuum Model of Microenvironmental Forces. CURRENT STEM CELL REPORTS 2019. [DOI: 10.1007/s40778-019-0153-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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9
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Lambert B, MacLean AL, Fletcher AG, Combes AN, Little MH, Byrne HM. Bayesian inference of agent-based models: a tool for studying kidney branching morphogenesis. J Math Biol 2018; 76:1673-1697. [PMID: 29392399 PMCID: PMC5906521 DOI: 10.1007/s00285-018-1208-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 01/02/2018] [Indexed: 12/11/2022]
Abstract
The adult mammalian kidney has a complex, highly-branched collecting duct epithelium that arises as a ureteric bud sidebranch from an epithelial tube known as the nephric duct. Subsequent branching of the ureteric bud to form the collecting duct tree is regulated by subcellular interactions between the epithelium and a population of mesenchymal cells that surround the tips of outgrowing branches. The mesenchymal cells produce glial cell-line derived neurotrophic factor (GDNF), that binds with RET receptors on the surface of the epithelial cells to stimulate several subcellular pathways in the epithelium. Such interactions are known to be a prerequisite for normal branching development, although competing theories exist for their role in morphogenesis. Here we introduce the first agent-based model of ex vivo kidney uretic branching. Through comparison with experimental data, we show that growth factor-regulated growth mechanisms can explain early epithelial cell branching, but only if epithelial cell division depends in a switch-like way on the local growth factor concentration; cell division occurring only if the driving growth factor level exceeds a threshold. We also show how a recently-developed method, "Approximate Approximate Bayesian Computation", can be used to infer key model parameters, and reveal the dependency between the parameters controlling a growth factor-dependent growth switch. These results are consistent with a requirement for signals controlling proliferation and chemotaxis, both of which are previously identified roles for GDNF.
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Affiliation(s)
- Ben Lambert
- Department of Zoology, University of Oxford, Oxford, UK.
| | - Adam L MacLean
- Mathematical Institute, University of Oxford, Andrew Wiles Building, Woodstock Road, Oxford, UK
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
| | - Alexander G Fletcher
- School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield, S3 7RH, UK
- Bateson Centre, University of Sheffield, Firth Court, Western Bank, Sheffield, S10 2TN, UK
| | - Alexander N Combes
- Department of Anatomy and Neuroscience, University of Melbourne, Melbourne, VIC, 3010, Australia
- Murdoch Childrens Research Institute, Flemington Rd, Parkville, Melbourne, VIC, 3052, Australia
| | - Melissa H Little
- Murdoch Childrens Research Institute, Flemington Rd, Parkville, Melbourne, VIC, 3052, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Helen M Byrne
- Mathematical Institute, University of Oxford, Andrew Wiles Building, Woodstock Road, Oxford, UK
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10
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Nazari F, Pearson AT, Nör JE, Jackson TL. A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted treatment. PLoS Comput Biol 2018; 14:e1005920. [PMID: 29351275 PMCID: PMC5792033 DOI: 10.1371/journal.pcbi.1005920] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 01/31/2018] [Accepted: 12/10/2017] [Indexed: 12/20/2022] Open
Abstract
Targeting key regulators of the cancer stem cell phenotype to overcome their critical influence on tumor growth is a promising new strategy for cancer treatment. Here we present a modeling framework that operates at both the cellular and molecular levels, for investigating IL-6 mediated, cancer stem cell driven tumor growth and targeted treatment with anti-IL6 antibodies. Our immediate goal is to quantify the influence of IL-6 on cancer stem cell self-renewal and survival, and to characterize the subsequent impact on tumor growth dynamics. By including the molecular details of IL-6 binding, we are able to quantify the temporal changes in fractional occupancies of bound receptors and their influence on tumor volume. There is a strong correlation between the model output and experimental data for primary tumor xenografts. We also used the model to predict tumor response to administration of the humanized IL-6R monoclonal antibody, tocilizumab (TCZ), and we found that as little as 1mg/kg of TCZ administered weekly for 7 weeks is sufficient to result in tumor reduction and a sustained deceleration of tumor growth. A small population of cancer stem cells that share many of the biological characteristics of normal adult stem cells are believed to initiate and sustain tumor growth for a wide variety of malignancies. Growth and survival of these cancer stem cells is highly influenced by tumor micro-environmental factors and molecular signaling initiated by cytokines and growth factors. This work focuses on quantifying the influence of IL-6, a pleiotropic cytokine secreted by a variety of cell types, on cancer stem cell self-renewal and survival. We present a mathematical model for IL-6 mediated, cancer stem cell driven tumor growth that operates at the following levels: (1) the molecular level—capturing cell surface dynamics of receptor-ligand binding and receptor activation that lead to intra-cellular signal transduction cascades; and (2) the cellular level—describing tumor growth, cellular composition, and response to treatments targeted against IL-6.
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Affiliation(s)
- Fereshteh Nazari
- Simon A. Levin Mathematical, Computational, and Modeling Sciences Center, School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, United States of America
| | - Alexander T. Pearson
- Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan Cancer Center, Ann Arbor, Michigan, United States of America
| | - Jacques Eduardo Nör
- Departments of Cardiology, Restorative Sciences, and Endontics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Trachette L. Jackson
- Department of Mathematics, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail:
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11
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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: 27] [Impact Index Per Article: 4.5] [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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12
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Yang J, Axelrod DE, Komarova NL. Determining the control networks regulating stem cell lineages in colonic crypts. J Theor Biol 2017; 429:190-203. [PMID: 28669884 PMCID: PMC5689466 DOI: 10.1016/j.jtbi.2017.06.033] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 05/18/2017] [Accepted: 06/25/2017] [Indexed: 12/27/2022]
Abstract
The question of stem cell control is at the center of our understanding of tissue functioning, both in healthy and cancerous conditions. It is well accepted that cellular fate decisions (such as divisions, differentiation, apoptosis) are orchestrated by a network of regulatory signals emitted by different cell populations in the lineage and the surrounding tissue. The exact regulatory network that governs stem cell lineages in a given tissue is usually unknown. Here we propose an algorithm to identify a set of candidate control networks that are compatible with (a) measured means and variances of cell populations in different compartments, (b) qualitative information on cell population dynamics, such as the existence of local controls and oscillatory reaction of the system to population size perturbations, and (c) statistics of correlations between cell numbers in different compartments. Using the example of human colon crypts, where lineages are comprised of stem cells, transit amplifying cells, and differentiated cells, we start with a theoretically known set of 32 smallest control networks compatible with tissue stability. Utilizing near-equilibrium stochastic calculus of stem cells developed earlier, we apply a series of tests, where we compare the networks' expected behavior with the observations. This allows us to exclude most of the networks, until only three, very similar, candidate networks remain, which are most compatible with the measurements. This work demonstrates how theoretical analysis of control networks combined with only static biological data can shed light onto the inner workings of stem cell lineages, in the absence of direct experimental assessment of regulatory signaling mechanisms. The resulting candidate networks are dominated by negative control loops and possess the following properties: (1) stem cell division decisions are negatively controlled by the stem cell population, (2) stem cell differentiation decisions are negatively controlled by the transit amplifying cell population.
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Affiliation(s)
- Jienian Yang
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697 USA
| | - David E Axelrod
- Department of Genetics and Cancer Institute of New Jersey, Rutgers University, Piscataway, NJ 08854-8082, USA
| | - Natalia L Komarova
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, USA.
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13
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Stability of Control Networks in Autonomous Homeostatic Regulation of Stem Cell Lineages. Bull Math Biol 2017; 80:1345-1365. [PMID: 28508298 DOI: 10.1007/s11538-017-0283-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Accepted: 04/07/2017] [Indexed: 01/02/2023]
Abstract
Design principles of biological networks have been studied extensively in the context of protein-protein interaction networks, metabolic networks, and regulatory (transcriptional) networks. Here we consider regulation networks that occur on larger scales, namely the cell-to-cell signaling networks that connect groups of cells in multicellular organisms. These are the feedback loops that orchestrate the complex dynamics of cell fate decisions and are necessary for the maintenance of homeostasis in stem cell lineages. We focus on "minimal" networks that are those that have the smallest possible numbers of controls. For such minimal networks, the number of controls must be equal to the number of compartments, and the reducibility/irreducibility of the network (whether or not it can be split into smaller independent sub-networks) is defined by a matrix comprised of the cell number increments induced by each of the controlled processes in each of the compartments. Using the formalism of digraphs, we show that in two-compartment lineages, reducible systems must contain two 1-cycles, and irreducible systems one 1-cycle and one 2-cycle; stability follows from the signs of the controls and does not require magnitude restrictions. In three-compartment systems, irreducible digraphs have a tree structure or have one 3-cycle and at least two more shorter cycles, at least one of which is a 1-cycle. With further work and proper biological validation, our results may serve as a first step toward an understanding of ways in which these networks become dysregulated in cancer.
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14
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Mathematical and Computational Modeling in Complex Biological Systems. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5958321. [PMID: 28386558 PMCID: PMC5366773 DOI: 10.1155/2017/5958321] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Revised: 12/20/2016] [Accepted: 01/16/2017] [Indexed: 12/22/2022]
Abstract
The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.
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15
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Kouidhi S, Elgaaied AB, Chouaib S. Impact of Metabolism on T-Cell Differentiation and Function and Cross Talk with Tumor Microenvironment. Front Immunol 2017; 8:270. [PMID: 28348562 PMCID: PMC5346542 DOI: 10.3389/fimmu.2017.00270] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 02/24/2017] [Indexed: 12/12/2022] Open
Abstract
The immune system and metabolism are highly integrated and multilevel interactions between metabolic system and T lymphocyte signaling and fate exist. Accumulating evidence indicates that the regulation of nutrient uptake and utilization in T cells is critically important for the control of their differentiation and manipulating metabolic pathways in these cells can shape their function and survival. This review will discuss some potential cell metabolism pathways involved in shaping T lymphocyte function and differentiation. It will also describe show subsets of T cells have specific metabolic requirements and signaling pathways that contribute to their respective function. Examples showing the apparent similarity between cancer cell metabolism and T cells during activation are illustrated and finally some mechanisms being used by tumor microenvironment to orchestrate T-cell metabolic dysregulation and the subsequent emergence of immune suppression are discussed. We believe that targeting T-cell metabolism may provide an additional opportunity to manipulate T-cell function in the development of novel therapeutics.
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Affiliation(s)
- Soumaya Kouidhi
- ISBST, Laboratory BVBGR, LR11ES31, Higher Institute of Biotechnology of Sidi Thabet, University of Manouba, Sidi Thabet, Tunisia; Laboratory of Genetics, Immunology and Human Pathology, Faculty of Sciences of Tunis, University Tunis El Manar, Tunis, Tunisia
| | - Amel Benammar Elgaaied
- Laboratory of Genetics, Immunology and Human Pathology, Faculty of Sciences of Tunis, University Tunis El Manar , Tunis , Tunisia
| | - Salem Chouaib
- Institut National de la Santé et de la Recherche Médicale (INSERM) UMR1186, Laboratory «Integrative Tumor Immunology and Genetic Oncology», Equipe Labellisée LIGUE 2015, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM), Gustave Roussy, University of Paris-Sud, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM), Gustave Roussy, Université Paris-Saclay, Villejuif, France
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16
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Abstract
OBJECTIVE Cancer stem cells (CSCs) have been hypothesized to initiate and drive tumor growth and recurrence due to their self-renewal ability. If correct, this hypothesis implies that successful therapy must focus primarily on eradication of this CSC fraction. However, recent evidence suggests stemness is niche dependent and may represent one of many phenotypic states that can be accessed by many cancer genotypes when presented with specific environmental cues. A better understanding of the relationship of stemness to niche-related phenotypic plasticity could lead to alternative treatment strategies. METHODS Here, we investigate the role of environmental context in the expression of stem-like cell properties through in-silico simulation of ductal carcinoma. We develop a two-dimensional hybrid discrete-continuum cellular automata model to describe the single-cell scale dynamics of multicellular tissue formation. Through a suite of simulations, we investigate interactions between a phenotypically heterogeneous cancer cell population and a dynamic environment. RESULTS We generate homeostatic ductal structures that consist of a mixture of stem and differentiated cells governed by both intracellular and environmental dynamics. We demonstrate that a wide spectrum of tumor-like histologies can result from these structures by varying microenvironmental parameters. CONCLUSION Niche driven phenotypic plasticity offers a simple first-principle explanation for the diverse ductal structures observed in histological sections from breast cancer. SIGNIFICANCE Conventional models of carcinogenesis largely focus on mutational events. We demonstrate that variations in the environmental niche can produce intraductal cancers independent of genetic changes in the resident cells. Therapies targeting the microenvironmental niche may offer an alternative cancer prevention strategy.
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17
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Pearson AT, Jackson TL, Nör JE. Modeling head and neck cancer stem cell-mediated tumorigenesis. Cell Mol Life Sci 2016; 73:3279-89. [PMID: 27151511 PMCID: PMC5312795 DOI: 10.1007/s00018-016-2226-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Revised: 03/29/2016] [Accepted: 04/12/2016] [Indexed: 12/22/2022]
Abstract
A large body of literature has emerged supporting the importance of cancer stem cells (CSCs) in the pathogenesis of head and neck cancers. CSCs are a subpopulation of cells within a tumor that share the properties of self-renewal and multipotency with stem cells from normal tissue. Their functional relevance to the pathobiology of cancer arises from the unique properties of tumorigenicity, chemotherapy resistance, and their ability to metastasize and invade distant tissues. Several molecular profiles have been used to discriminate a stem cell from a non-stem cell. CSCs can be grown for study and further enriched using a number of in vitro techniques. An evolving option for translational research is the use of mathematical and computational models to describe the role of CSCs in complex tumor environments. This review is focused discussing the evidence emerging from modeling approaches that have clarified the impact of CSCs to the biology of cancer.
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Affiliation(s)
- Alexander T Pearson
- Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan School of Medicine, 1500 E. Medical Center Dr., SPC 5848, Ann Arbor, MI, 48109-5848, USA.
| | - Trachette L Jackson
- Department of Mathematics, University of Michigan School of Literature, Sciences, and the Arts, Ann Arbor, MI, USA
| | - Jacques E Nör
- Department of Restorative Sciences, University of Michigan School of Dentistry, 1011 N. University Rm. 2309, Ann Arbor, MI, 48109-1078, USA.
- Department of Otolaryngology, University of Michigan School of Medicine, Ann Arbor, MI, USA.
- Department of Biomedical Engineering, University of Michigan College of Engineering, Ann Arbor, MI, USA.
- Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, USA.
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18
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Kumar S, Kulkarni R, Sen S. Cell motility and ECM proteolysis regulate tumor growth and tumor relapse by altering the fraction of cancer stem cells and their spatial scattering. Phys Biol 2016; 13:036001. [DOI: 10.1088/1478-3975/13/3/036001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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19
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Konstorum A, Hillen T, Lowengrub J. Feedback Regulation in a Cancer Stem Cell Model can Cause an Allee Effect. Bull Math Biol 2016; 78:754-785. [PMID: 27113934 DOI: 10.1007/s11538-016-0161-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 03/15/2016] [Indexed: 12/24/2022]
Abstract
The exact mechanisms of spontaneous tumor remission or complete response to treatment are phenomena in oncology that are not completely understood. We use a concept from ecology, the Allee effect, to help explain tumor extinction in a model of tumor growth that incorporates feedback regulation of stem cell dynamics, which occurs in many tumor types where certain signaling molecules, such as Wnts, are upregulated. Due to feedback and the Allee effect, a tumor may become extinct spontaneously or after therapy even when the entire tumor has not been eradicated by the end of therapy. We quantify the Allee effect using an 'Allee index' that approximates the area of the basin of attraction for tumor extinction. We show that effectiveness of combination therapy in cancer treatment may occur due to the increased probability that the system will be in the Allee region after combination treatment versus monotherapy. We identify therapies that can attenuate stem cell self-renewal, alter the Allee region and increase its size. We also show that decreased response of tumor cells to growth inhibitors can reduce the size of the Allee region and increase stem cell densities, which may help to explain why this phenomenon is a hallmark of cancer.
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Affiliation(s)
- Anna Konstorum
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA.
- Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, CT, USA.
| | - Thomas Hillen
- Centre for Mathematical Biology, University of Alberta, Edmonton, AB, Canada
| | - John Lowengrub
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA.
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA.
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20
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Yu JS, Bagheri N. Multi-class and multi-scale models of complex biological phenomena. Curr Opin Biotechnol 2016; 39:167-173. [PMID: 27115496 DOI: 10.1016/j.copbio.2016.04.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Revised: 03/28/2016] [Accepted: 04/01/2016] [Indexed: 02/06/2023]
Abstract
Computational modeling has significantly impacted our ability to analyze vast (and exponentially increasing) quantities of experimental data for a variety of applications, such as drug discovery and disease forecasting. Single-scale, single-class models persist as the most common group of models, but biological complexity often demands more sophisticated approaches. This review surveys modeling approaches that are multi-class (incorporating multiple model types) and/or multi-scale (accounting for multiple spatial or temporal scales) and describes how these models, and combinations thereof, should be used within the context of the problem statement. We end by highlighting agent-based models as an intuitive, modular, and flexible framework within which multi-scale and multi-class models can be implemented.
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Affiliation(s)
- Jessica S Yu
- Chemical & Biological Engineering, Northwestern University, Evanston, IL, United States
| | - Neda Bagheri
- Chemical & Biological Engineering, Northwestern University, Evanston, IL, United States.
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21
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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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22
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Vizirianakis IS, Mystridis GA, Avgoustakis K, Fatouros DG, Spanakis M. Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review). Oncol Rep 2016; 35:1891-904. [PMID: 26781205 DOI: 10.3892/or.2016.4575] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Accepted: 10/27/2015] [Indexed: 11/05/2022] Open
Abstract
The existing tumor heterogeneity and the complexity of cancer cell biology critically demand powerful translational tools with which to support interdisciplinary efforts aiming to advance personalized cancer medicine decisions in drug development and clinical practice. The development of physiologically based pharmacokinetic (PBPK) models to predict the effects of drugs in the body facilitates the clinical translation of genomic knowledge and the implementation of in vivo pharmacology experience with pharmacogenomics. Such a direction unequivocally empowers our capacity to also make personalized drug dosage scheme decisions for drugs, including molecularly targeted agents and innovative nanoformulations, i.e. in establishing pharmacotyping in prescription. In this way, the applicability of PBPK models to guide individualized cancer therapeutic decisions of broad clinical utility in nanomedicine in real-time and in a cost-affordable manner will be discussed. The latter will be presented by emphasizing the need for combined efforts within the scientific borderlines of genomics with nanotechnology to ensure major benefits and productivity for nanomedicine and personalized medicine interventions.
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Affiliation(s)
- Ioannis S Vizirianakis
- Laboratory of Pharmacology, Department of Pharmaceutical Sciences, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki GR‑54124, Greece
| | - George A Mystridis
- Laboratory of Pharmacology, Department of Pharmaceutical Sciences, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki GR‑54124, Greece
| | - Konstantinos Avgoustakis
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutical Sciences, University of Patras, Patras GR-26504, Greece
| | - Dimitrios G Fatouros
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutical Sciences, Aristotle University of Thessaloniki, Thessaloniki GR-54124, Greece
| | - Marios Spanakis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion GR-71110, Crete, Greece
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23
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Yang KR, Mooney SM, Zarif JC, Coffey DS, Taichman RS, Pienta KJ. Niche inheritance: a cooperative pathway to enhance cancer cell fitness through ecosystem engineering. J Cell Biochem 2015; 115:1478-85. [PMID: 24700698 PMCID: PMC4143896 DOI: 10.1002/jcb.24813] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Accepted: 04/01/2014] [Indexed: 02/03/2023]
Abstract
Cancer cells can be described as an invasive species that is able to establish itself in a new environment. The concept of niche construction can be utilized to describe the process by which cancer cells terraform their environment, thereby engineering an ecosystem that promotes the genetic fitness of the species. Ecological dispersion theory can then be utilized to describe and model the steps and barriers involved in a successful diaspora as the cancer cells leave the original host organ and migrate to new host organs to successfully establish a new metastatic community. These ecological concepts can be further utilized to define new diagnostic and therapeutic areas for lethal cancers. 115: 1478–1485, 2014. © 2014 Wiley Periodicals, Inc.
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Affiliation(s)
- Kimberline R Yang
- Cellular and Molecular Medicine Program, Johns Hopkins School of Medicine, Baltimore, Maryland
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24
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Enciso J, Mendoza L, Pelayo R. Normal vs. Malignant hematopoiesis: the complexity of acute leukemia through systems biology. Front Genet 2015; 6:290. [PMID: 26442108 PMCID: PMC4566035 DOI: 10.3389/fgene.2015.00290] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 08/31/2015] [Indexed: 01/03/2023] Open
Affiliation(s)
- Jennifer Enciso
- Oncology Research Unit, Mexican Institute for Social Security Mexico City, Mexico ; Biochemistry Sciences Program, Universidad Nacional Autónoma de México Mexico City, Mexico
| | - Luis Mendoza
- Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México Mexico City, Mexico
| | - Rosana Pelayo
- Oncology Research Unit, Mexican Institute for Social Security Mexico City, Mexico
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25
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Fornari C, Balbo G, Halawani SM, Ba-Rukab O, Ahmad AR, Calogero RA, Cordero F, Beccuti M. A versatile mathematical work-flow to explore how Cancer Stem Cell fate influences tumor progression. BMC SYSTEMS BIOLOGY 2015; 9 Suppl 3:S1. [PMID: 26050594 PMCID: PMC4464028 DOI: 10.1186/1752-0509-9-s3-s1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background Nowadays multidisciplinary approaches combining mathematical models with experimental assays are becoming relevant for the study of biological systems. Indeed, in cancer research multidisciplinary approaches are successfully used to understand the crucial aspects implicated in tumor growth. In particular, the Cancer Stem Cell (CSC) biology represents an area particularly suited to be studied through multidisciplinary approaches, and modeling has significantly contributed to pinpoint the crucial aspects implicated in this theory. More generally, to acquire new insights on a biological system it is necessary to have an accurate description of the phenomenon, such that making accurate predictions on its future behaviors becomes more likely. In this context, the identification of the parameters influencing model dynamics can be advantageous to increase model accuracy and to provide hints in designing wet experiments. Different techniques, ranging from statistical methods to analytical studies, have been developed. Their applications depend on case-specific aspects, such as the availability and quality of experimental data, and the dimension of the parameter space. Results The study of a new model on the CSC-based tumor progression has been the motivation to design a new work-flow that helps to characterize possible system dynamics and to identify those parameters influencing such behaviors. In detail, we extended our recent model on CSC-dynamics creating a new system capable of describing tumor growth during the different stages of cancer progression. Indeed, tumor cells appear to progress through lineage stages like those of normal tissues, being their division auto-regulated by internal feedback mechanisms. These new features have introduced some non-linearities in the model, making it more difficult to be studied by solely analytical techniques. Our new work-flow, based on statistical methods, was used to identify the parameters which influence the tumor growth. The effectiveness of the presented work-flow was firstly verified on two well known models and then applied to investigate our extended CSC model. Conclusions We propose a new work-flow to study in a practical and informative way complex systems, allowing an easy identification, interpretation, and visualization of the key model parameters. Our methodology is useful to investigate possible model behaviors and to establish factors driving model dynamics. Analyzing our new CSC model guided by the proposed work-flow, we found that the deregulation of CSC asymmetric proliferation contributes to cancer initiation, in accordance with several experimental evidences. Specifically, model results indicated that the probability of CSC symmetric proliferation is responsible of a switching-like behavior which discriminates between tumorigenesis and unsustainable tumor growth.
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26
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Wells DK, Chuang Y, Knapp LM, Brockmann D, Kath WL, Leonard JN. Spatial and functional heterogeneities shape collective behavior of tumor-immune networks. PLoS Comput Biol 2015; 11:e1004181. [PMID: 25905470 PMCID: PMC4408028 DOI: 10.1371/journal.pcbi.1004181] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Accepted: 02/06/2015] [Indexed: 12/31/2022] Open
Abstract
Tumor growth involves a dynamic interplay between cancer cells and host cells, which collectively form a tumor microenvironmental network that either suppresses or promotes tumor growth under different conditions. The transition from tumor suppression to tumor promotion is mediated by a tumor-induced shift in the local immune state, and despite the clinical challenge this shift poses, little is known about how such dysfunctional immune states are initiated. Clinical and experimental observations have indicated that differences in both the composition and spatial distribution of different cell types and/or signaling molecules within the tumor microenvironment can strongly impact tumor pathogenesis and ultimately patient prognosis. How such “functional” and “spatial” heterogeneities confer such effects, however, is not known. To investigate these phenomena at a level currently inaccessible by direct observation, we developed a computational model of a nascent metastatic tumor capturing salient features of known tumor-immune interactions that faithfully recapitulates key features of existing experimental observations. Surprisingly, over a wide range of model formulations, we observed that heterogeneity in both spatial organization and cell phenotype drove the emergence of immunosuppressive network states. We determined that this observation is general and robust to parameter choice by developing a systems-level sensitivity analysis technique, and we extended this analysis to generate other parameter-independent, experimentally testable hypotheses. Lastly, we leveraged this model as an in silico test bed to evaluate potential strategies for engineering cell-based therapies to overcome tumor associated immune dysfunction and thereby identified modes of immune modulation predicted to be most effective. Collectively, this work establishes a new integrated framework for investigating and modulating tumor-immune networks and provides insights into how such interactions may shape early stages of tumor formation. Over the course of tumor growth, cancer cells interact with normal cells via processes that are difficult to understand by experiment alone. This challenge is particularly pronounced at early stages of tumor formation, when experimental observation is most limited. Elucidating such interactions could inform both understanding of cancer and clinical practice. To address this need we developed a computational model capturing the current understanding of how individual metastatic tumor cells and immune cells sense and contribute to the tumor environment, which in turn enabled us to investigate the complex, collective behavior of these systems. Surprisingly, we discovered that tumor escape from immune control was enhanced by the existence of small differences (or heterogeneities) in the responses of individual immune cells to their environment, as well as by heterogeneities in the way that cells and the molecules they secrete are arranged in space. These conclusions held true over a range of model formulations, suggesting that this is a general feature of these tumor-immune networks. Finally, we used this model as a test bed to evaluate potential strategies for enhancing immunological control of early tumors, ultimately predicting that specifically modulating tumor-associated immune dysfunction may be more effective than simply enhanced tumor killing.
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Affiliation(s)
- Daniel K. Wells
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America
- Northwestern University Physical Sciences-Oncology Center, Evanston, Illinois, United States of America
| | - Yishan Chuang
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America
| | - Louis M. Knapp
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America
| | - Dirk Brockmann
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America
- Northwestern University Physical Sciences-Oncology Center, Evanston, Illinois, United States of America
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, Illinois, United States of America
- Northwestern Institute on Complex Science, Northwestern University, Evanston, Illinois, United States of America
- Institute for Theoretical Biology, Humboldt University Berlin, Berlin, Germany
| | - William L. Kath
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America
- Northwestern University Physical Sciences-Oncology Center, Evanston, Illinois, United States of America
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, Illinois, United States of America
- Northwestern Institute on Complex Science, Northwestern University, Evanston, Illinois, United States of America
- Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois, United States of America
| | - Joshua N. Leonard
- Northwestern University Physical Sciences-Oncology Center, Evanston, Illinois, United States of America
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, Illinois, United States of America
- Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois, United States of America
- * E-mail:
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27
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Poleszczuk J, Hahnfeldt P, Enderling H. Evolution and phenotypic selection of cancer stem cells. PLoS Comput Biol 2015; 11:e1004025. [PMID: 25742563 PMCID: PMC4351192 DOI: 10.1371/journal.pcbi.1004025] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 11/04/2014] [Indexed: 12/22/2022] Open
Abstract
Cells of different organs at different ages have an intrinsic set of kinetics that dictates their behavior. Transformation into cancer cells will inherit these kinetics that determine initial cell and tumor population progression dynamics. Subject to genetic mutation and epigenetic alterations, cancer cell kinetics can change, and favorable alterations that increase cellular fitness will manifest themselves and accelerate tumor progression. We set out to investigate the emerging intratumoral heterogeneity and to determine the evolutionary trajectories of the combination of cell-intrinsic kinetics that yield aggressive tumor growth. We develop a cellular automaton model that tracks the temporal evolution of the malignant subpopulation of so-called cancer stem cells(CSC), as these cells are exclusively able to initiate and sustain tumors. We explore orthogonal cell traits, including cell migration to facilitate invasion, spontaneous cell death due to genetic drift after accumulation of irreversible deleterious mutations, symmetric cancer stem cell division that increases the cancer stem cell pool, and telomere length and erosion as a mitotic counter for inherited non-stem cancer cell proliferation potential. Our study suggests that cell proliferation potential is the strongest modulator of tumor growth. Early increase in proliferation potential yields larger populations of non-stem cancer cells(CC) that compete with CSC and thus inhibit CSC division while a reduction in proliferation potential loosens such inhibition and facilitates frequent CSC division. The sub-population of cancer stem cells in itself becomes highly heterogeneous dictating population level dynamics that vary from long-term dormancy to aggressive progression. Our study suggests that the clonal diversity that is captured in single tumor biopsy samples represents only a small proportion of the total number of phenotypes. We present an in silico computational model of tumor growth and evolution according to the cancer stem cell hypothesis. Inheritable traits of cells may be genetically or epigenetically altered, and traits that confer increased fitness to the cell will be selected for on the population level. Phenotypic evolution yields aggressive tumors with large heterogeneity, prompting the notion that the cancer stem cell population per se is highly heterogeneous. Within aggressive tumors cancer stem cells with low tumorigenic potential may be isolated. Simulations of our model suggest that the cells harvested in core needle biopsies represent less than 10% of the phenotypic heterogeneity of the total tumor population. Dependent on the cells captured in the sample, xenografted tumors may exhibit aggressive growth or long-term dormancy—dynamics that may suggest opposing treatment approaches for the same tumor when translated into clinical decision-making.
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Affiliation(s)
- Jan Poleszczuk
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
- College of Inter-faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, Poland
| | - Philip Hahnfeldt
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - Heiko Enderling
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
- * E-mail:
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28
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Adorno-Cruz V, Kibria G, Liu X, Doherty M, Junk DJ, Guan D, Hubert C, Venere M, Mulkearns-Hubert E, Sinyuk M, Alvarado A, Caplan AI, Rich J, Gerson SL, Lathia J, Liu H. Cancer stem cells: targeting the roots of cancer, seeds of metastasis, and sources of therapy resistance. Cancer Res 2015; 75:924-9. [PMID: 25604264 DOI: 10.1158/0008-5472.can-14-3225] [Citation(s) in RCA: 179] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
With the goal to remove the roots of cancer, eliminate metastatic seeds, and overcome therapy resistance, the 2014 inaugural International Cancer Stem Cell (CSC) Conference at Cleveland, OH, convened together over 320 investigators, including 55 invited world-class speakers, 25 short oral presenters, and 100 poster presenters, to gain an in-depth understanding of CSCs and explore therapeutic opportunities targeting CSCs. The meeting enabled intriguing discussions on several topics including: genetics and epigenetics; cancer origin and evolution; microenvironment and exosomes; metabolism and inflammation; metastasis and therapy resistance; single cell and heterogeneity; plasticity and reprogramming; as well as other new concepts. Reports of clinical trials targeting CSCs emphasized the urgent need for strategically designing combinational CSC-targeting therapies against cancer.
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Affiliation(s)
- Valery Adorno-Cruz
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Golam Kibria
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Xia Liu
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Mary Doherty
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Damian J Junk
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Dongyin Guan
- Department of Biochemistry, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Chris Hubert
- Department of Stem Cell Biology and Regenerative Medicine, Cleveland Clinic Lerner Research Institute, Cleveland, Ohio
| | - Monica Venere
- Department of Cancer Biology, Cleveland Clinic Lerner Research Institute, Cleveland, Ohio
| | - Erin Mulkearns-Hubert
- Department of Cellular and Molecular Medicine, Cleveland Clinic Lerner Research Institute, Cleveland, Ohio
| | - Maksim Sinyuk
- Department of Cellular and Molecular Medicine, Cleveland Clinic Lerner Research Institute, Cleveland, Ohio
| | - Alvaro Alvarado
- Department of Cellular and Molecular Medicine, Cleveland Clinic Lerner Research Institute, Cleveland, Ohio
| | - Arnold I Caplan
- Department of Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio. National Center for Regenerative Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Jeremy Rich
- Department of Stem Cell Biology and Regenerative Medicine, Cleveland Clinic Lerner Research Institute, Cleveland, Ohio. National Center for Regenerative Medicine, Case Western Reserve University, Cleveland, Ohio. Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Stanton L Gerson
- National Center for Regenerative Medicine, Case Western Reserve University, Cleveland, Ohio. Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio. Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Justin Lathia
- Department of Cellular and Molecular Medicine, Cleveland Clinic Lerner Research Institute, Cleveland, Ohio. National Center for Regenerative Medicine, Case Western Reserve University, Cleveland, Ohio. Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Huiping Liu
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio. National Center for Regenerative Medicine, Case Western Reserve University, Cleveland, Ohio. Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio.
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29
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Abstract
This review discusses quantitative modeling studies of stem and non-stem cancer cell interactions and the fraction of cancer stem cells.
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Affiliation(s)
- Heiko Enderling
- Department of Integrated Mathematical Oncology
- H. Lee Moffitt Cancer Center & Research Institute
- Tampa
- USA
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