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Aguadé-Gorgorió G, Anderson ARA, Solé R. Modeling tumors as complex ecosystems. iScience 2024; 27:110699. [PMID: 39280631 PMCID: PMC11402243 DOI: 10.1016/j.isci.2024.110699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/18/2024] Open
Abstract
Many cancers resist therapeutic intervention. This is fundamentally related to intratumor heterogeneity: multiple cell populations, each with different phenotypic signatures, coexist within a tumor and its metastases. Like species in an ecosystem, cancer populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity or predict its consequences. Here, we propose that the generalized Lotka-Volterra model (GLV), a standard tool to describe species-rich ecological communities, provides a suitable framework to model the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties provide a new understanding of the disease. We discuss potential extensions of the model and their application to phenotypic plasticity, cancer-immune interactions, and metastatic growth. Our work outlines a set of questions and a road map for further research in cancer ecology.
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Affiliation(s)
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Ricard Solé
- ICREA-Complex Systems Lab, UPF-PRBB, Dr. Aiguader 80, 08003 Barcelona, Spain
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
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2
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Sadria M, Bury TM. FateNet: an integration of dynamical systems and deep learning for cell fate prediction. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae525. [PMID: 39177093 PMCID: PMC11399232 DOI: 10.1093/bioinformatics/btae525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/28/2024] [Accepted: 08/21/2024] [Indexed: 08/24/2024]
Abstract
MOTIVATION Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and cellular state dynamics from single-cell RNA sequencing data lack precision regarding decision points and broader tissue implications. Addressing this gap, we present FateNet, a computational approach integrating dynamical systems theory and deep learning to probe the cell decision-making process using scRNA-seq data. RESULTS By leveraging information about normal forms and scaling behavior near bifurcations common to many dynamical systems, FateNet predicts cell decision occurrence with higher accuracy than conventional methods and offers qualitative insights into the new state of the biological system. Also, through in-silico perturbation experiments, FateNet identifies key genes and pathways governing the differentiation process in hematopoiesis. Validated using different scRNA-seq data, FateNet emerges as a user-friendly and valuable tool for predicting critical points in biological processes, providing insights into complex trajectories. AVAILABILITY AND IMPLEMENTATION github.com/ThomasMBury/fatenet.
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Affiliation(s)
- Mehrshad Sadria
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Thomas M Bury
- Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
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3
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Patil S, Ahmed A, Viossat Y, Noble R. Preventing evolutionary rescue in cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.22.568336. [PMID: 38045391 PMCID: PMC10690287 DOI: 10.1101/2023.11.22.568336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
First-line cancer treatment frequently fails due to initially rare therapeutic resistance. An important clinical question is then how to schedule subsequent treatments to maximize the probability of tumour eradication. Here, we provide a theoretical solution to this problem by using mathematical analysis and extensive stochastic simulations within the framework of evolutionary rescue theory to determine how best to exploit the vulnerability of small tumours to stochastic extinction. Whereas standard clinical practice is to wait for evidence of relapse, we confirm a recent hypothesis that the optimal time to switch to a second treatment is when the tumour is close to its minimum size before relapse, when it is likely undetectable. This optimum can lie slightly before or slightly after the nadir, depending on tumour parameters. Given that this exact time point may be difficult to determine in practice, we study windows of high extinction probability that lie around the optimal switching point, showing that switching after the relapse has begun is typically better than switching too early. We further reveal how treatment dose and tumour demographic and evolutionary parameters influence the predicted clinical outcome, and we determine how best to schedule drugs of unequal efficacy. Our work establishes a foundation for further experimental and clinical investigation of this evolutionarily-informed "extinction therapy" strategy.
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Affiliation(s)
- Srishti Patil
- Indian Institute of Science Education and Research, Pune, India
- Department of Mathematics, City, University of London, London, UK
| | - Armaan Ahmed
- Department of Applied Math & Statistics, Johns Hopkins University, Baltimore, USA
- Department of Biophysics, Johns Hopkins University, Baltimore, USA
| | - Yannick Viossat
- Ceremade, CNRS, Université Paris-Dauphine, Université PSL, Paris,France
| | - Robert Noble
- Department of Mathematics, City, University of London, London, UK
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4
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Nuñez JG, Paulose J, Möbius W, Beller DA. Range expansions across landscapes with quenched noise. Proc Natl Acad Sci U S A 2024; 121:e2411487121. [PMID: 39136984 PMCID: PMC11348022 DOI: 10.1073/pnas.2411487121] [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: 06/08/2024] [Accepted: 07/01/2024] [Indexed: 08/15/2024] Open
Abstract
When biological populations expand into new territory, the evolutionary outcomes can be strongly influenced by genetic drift, the random fluctuations in allele frequencies. Meanwhile, spatial variability in the environment can also significantly influence the competition between subpopulations vying for space. Little is known about the interplay of these intrinsic and extrinsic sources of noise in population dynamics: When does environmental heterogeneity dominate over genetic drift or vice versa, and what distinguishes their population genetics signatures? Here, in the context of neutral evolution, we examine the interplay between a population's intrinsic, demographic noise and an extrinsic, quenched random noise provided by a heterogeneous environment. Using a multispecies Eden model, we simulate a population expanding over a landscape with random variations in local growth rates and measure how this variability affects genealogical tree structure, and thus genetic diversity. We find that, for strong heterogeneity, the genetic makeup of the expansion front is to a great extent predetermined by the set of fastest paths through the environment. The landscape-dependent statistics of these optimal paths then supersede those of the population's intrinsic noise as the main determinant of evolutionary dynamics. Remarkably, the statistics for coalescence of genealogical lineages, derived from those deterministic paths, strongly resemble the statistics emerging from demographic noise alone in uniform landscapes. This cautions interpretations of coalescence statistics and raises new challenges for inferring past population dynamics.
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Affiliation(s)
- Jimmy Gonzalez Nuñez
- Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD21218
| | - Jayson Paulose
- Department of Physics, Institute for Fundamental Science, University of Oregon, Eugene, OR97403
| | - Wolfram Möbius
- Living Systems Institute, Faculty of Health and Life Sciences, University of Exeter, ExeterEX4 4QH, United Kingdom
- Physics and Astronomy, Faculty of Environment, Science and Economy, University of Exeter, ExeterEX4 4QL, United Kingdom
| | - Daniel A. Beller
- Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD21218
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5
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Chen S, Xie D, Li Z, Wang J, Hu Z, Zhou D. Frequency-dependent selection of neoantigens fosters tumor immune escape and predicts immunotherapy response. Commun Biol 2024; 7:770. [PMID: 38918569 PMCID: PMC11199503 DOI: 10.1038/s42003-024-06460-7] [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: 09/01/2023] [Accepted: 06/14/2024] [Indexed: 06/27/2024] Open
Abstract
Cancer is an evolutionary process shaped by selective pressure from the microenvironments. However, recent studies reveal that certain tumors undergo neutral evolution where there is no detectable fitness difference amongst the cells following malignant transformation. Here, through computational modeling, we demonstrate that negative frequency-dependent selection (or NFDS), where the immune response against cancer cells depends on the clonality of neoantigens, can lead to an immunogenic landscape that is highly similar to neutral evolution. Crucially, NFDS promotes high antigenic heterogeneity and early immune evasion in hypermutable tumors, leading to poor responses to immune checkpoint blockade (ICB) therapy. Our model also reveals that NFDS is characterized by a negative association between average clonality and total burden of neoantigens. Indeed, this unique feature of NFDS is common in the whole-exome sequencing (WES) datasets (357 tumor samples from 275 patients) from four melanoma cohorts with ICB therapy and a non-small cell lung cancer (NSCLC) WES dataset (327 tumor samples from 100 patients). Altogether, our study provides quantitative evidence supporting the theory of NFDS in cancer, explaining the high prevalence of neutral-looking tumors. These findings also highlight the critical role of frequency-dependent selection in devising more efficient and predictive immunotherapies.
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Affiliation(s)
- Shaoqing Chen
- School of Mathematical Sciences, Xiamen University, Xiamen, China
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Duo Xie
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Zan Li
- Life Science Research Center, Core Research Facilities, Southern University of Science and Technology, Shenzhen, China
| | - Jiguang Wang
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
- Hong Kong Center for Neurodegenerative Diseases, InnoHK, Hong Kong SAR, China
- HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China
| | - Zheng Hu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen, China.
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
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Gourmet L, Walker-Samuel S, Mallick P. Examination of the role of mutualism in immune evasion. Front Oncol 2024; 14:1406744. [PMID: 38779085 PMCID: PMC11109368 DOI: 10.3389/fonc.2024.1406744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024] Open
Abstract
Though the earliest stages of oncogenesis, post initiation, are not well understood, it is generally appreciated that a successful transition from a collection of dysregulated cells to an aggressive tumour requires complex ecological interactions between cancer cells and their environment. One key component of tumorigenesis is immune evasion. To investigate the interplay amongst the ecological behaviour of mutualism and immune evasion, we used a computational simulation framework. Sensitivity analyses of the growth of a virtual tumour implemented as a 2D-hexagonal lattice model suggests tumour survival depends on the interplay between growth rates, mutualism and immune evasion. In 60% of simulations, cancer clones with low growth rates, but exhibiting mutualism were able to evade the immune system and continue progressing suggesting that tumours with equivalent growth rates and no mutualism are more likely to be eliminated than tumours with mutualism. Tumours with faster growth rates showed a lower dependence upon mutualism for progression. Geostatistical analysis showed decreased spatial heterogeneity over time for polyclonal tumours with a high division rate. Overall, these results suggest that in slow growing tumours, mutualism is critical for early tumorigenesis.
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Affiliation(s)
- Lucie Gourmet
- Centre for Computational Medicine, Division of Medicine, University College London, London, United Kingdom
| | - Simon Walker-Samuel
- Centre for Computational Medicine, Division of Medicine, University College London, London, United Kingdom
| | - Parag Mallick
- Canary Center for Cancer Early Detection, Stanford University, Palo Alto, CA, United States
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Aguadé-Gorgorió G, Anderson AR, Solé R. Modeling tumors as species-rich ecological communities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590504. [PMID: 38712062 PMCID: PMC11071393 DOI: 10.1101/2024.04.22.590504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.
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Affiliation(s)
| | - Alexander R.A. Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA
| | - Ricard Solé
- ICREA-Complex Systems Lab, UPF-PRBB, Dr. Aiguader 80, 08003 Barcelona, Spain
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
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8
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Song JH, Dávalos LM, MacCarthy T, Damaghi M. Evolvability of cancer-associated genes under APOBEC3A/B selection. iScience 2024; 27:109433. [PMID: 38550998 PMCID: PMC10972820 DOI: 10.1016/j.isci.2024.109433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 12/08/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Evolvability is an emergent hallmark of cancer that depends on intra-tumor heterogeneity and genetic variation. Mutations generated by APOBEC3 contribute to genetic variation and tumor evolvability. However, the influence of APOBEC3 on the evolvability of the genome and its differential impact on cancer genes versus non-cancer genes remains unclear. Analyzing over 40,000 human protein-coding transcripts, we identified distinct distribution patterns of APOBEC3A/B TC motifs between cancer and non-cancer genes, suggesting unique associations with cancer. Studying a bat species with numerous APOBEC3 genes, we found distinct motif patterns in orthologs of cancer genes compared to non-cancer genes, as in humans, suggesting APOBEC3 evolution to reduce impacts on the genome rather than the converse. Simulations confirmed that APOBEC3-induced heterogeneity enhances cancer evolution through bimodal patterns of mutations in certain classes of genes. Our results suggest the bimodal distribution of APOBEC-induced mutations can significantly increase cancer heterogeneity.
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Affiliation(s)
- Joon-Hyun Song
- Stony Brook Cancer Center, Stony Brook Medicine, Stony Brook University, Stony Brook, NY, USA
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Liliana M Dávalos
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY 11794, USA
- Consortium for Inter-Disciplinary Environmental Research, Stony Brook University, Stony Brook, NY 11794, USA
| | - Thomas MacCarthy
- Stony Brook Cancer Center, Stony Brook Medicine, Stony Brook University, Stony Brook, NY, USA
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Mehdi Damaghi
- Stony Brook Cancer Center, Stony Brook Medicine, Stony Brook University, Stony Brook, NY, USA
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
- Department of Pathology, Stony Brook Medicine, Stony Brook University, Stony Brook, NY, USA
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9
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Alejandre C, Calle-Espinosa J, Iranzo J. Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers. PLoS Comput Biol 2024; 20:e1012081. [PMID: 38687804 PMCID: PMC11087069 DOI: 10.1371/journal.pcbi.1012081] [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: 08/30/2023] [Revised: 05/10/2024] [Accepted: 04/16/2024] [Indexed: 05/02/2024] Open
Abstract
Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy.
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Affiliation(s)
- Carla Alejandre
- Centro de Astrobiología (CAB) CSIC-INTA, Torrejón de Ardoz, Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Jorge Calle-Espinosa
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Jaime Iranzo
- Centro de Astrobiología (CAB) CSIC-INTA, Torrejón de Ardoz, Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain
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10
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Maltas J, Tadele DS, Durmaz A, McFarland CD, Hinczewski M, Scott JG. Frequency-dependent ecological interactions increase the prevalence, and shape the distribution, of pre-existing drug resistance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.16.533001. [PMID: 36993678 PMCID: PMC10055114 DOI: 10.1101/2023.03.16.533001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The evolution of resistance remains one of the primary challenges for modern medicine from infectious diseases to cancers. Many of these resistance-conferring mutations often carry a substantial fitness cost in the absence of treatment. As a result, we would expect these mutants to undergo purifying selection and be rapidly driven to extinction. Nevertheless, pre-existing resistance is frequently observed from drug-resistant malaria to targeted cancer therapies in non-small cell lung cancer (NSCLC) and melanoma. Solutions to this apparent paradox have taken several forms from spatial rescue to simple mutation supply arguments. Recently, in an evolved resistant NSCLC cell line, we found that frequency-dependent ecological interactions between ancestor and resistant mutant ameliorate the cost of resistance in the absence of treatment. Here, we hypothesize that frequency-dependent ecological interactions in general play a major role in the prevalence of pre-existing resistance. We combine numerical simulations with robust analytical approximations to provide a rigorous mathematical framework for studying the effects of frequency-dependent ecological interactions on the evolutionary dynamics of pre-existing resistance. First, we find that ecological interactions significantly expand the parameter regime under which we expect to observe pre-existing resistance. Next, even when positive ecological interactions between mutants and ancestors are rare, these resistant clones provide the primary mode of evolved resistance because even weak positive interaction leads to significantly longer extinction times. We then find that even in the case where mutation supply alone is sufficient to predict pre-existing resistance, frequency-dependent ecological forces still contribute a strong evolutionary pressure that selects for increasingly positive ecological effects (negative frequency-dependent selection). Finally, we genetically engineer several of the most common clinically observed resistance mechanisms to targeted therapies in NSCLC, a treatment notorious for pre-existing resistance. We find that each engineered mutant displays a positive ecological interaction with their ancestor. As a whole, these results suggest that frequency-dependent ecological effects can play a crucial role in shaping the evolutionary dynamics of pre-existing resistance.
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Affiliation(s)
- Jeff Maltas
- Cleveland Clinic, Translational Hematology Oncology Research, Cleveland, OH
- Case Western Reserve University, School of Medicine, Cleveland, OH
| | - Dagim Shiferaw Tadele
- Cleveland Clinic, Translational Hematology Oncology Research, Cleveland, OH
- Oslo University Hospital, Ullevål, Department of Medical Genetics, Oslo, Norway
| | - Arda Durmaz
- Cleveland Clinic, Translational Hematology Oncology Research, Cleveland, OH
| | - Christopher D. McFarland
- Case Western Reserve University, School of Medicine, Cleveland, OH
- Case Comprehensive Cancer Center, Cleveland, OH
| | | | - Jacob G. Scott
- Cleveland Clinic, Translational Hematology Oncology Research, Cleveland, OH
- Case Western Reserve University, School of Medicine, Cleveland, OH
- Case Western Reserve University, Department of Physics, Cleveland, OH
- Case Comprehensive Cancer Center, Cleveland, OH
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11
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Gérard A, Owen RS, Dujon AM, Roche B, Hamede R, Thomas F, Ujvari B, Siddle HV. In vitro competition between two transmissible cancers and potential implications for their host, the Tasmanian devil. Evol Appl 2024; 17:e13670. [PMID: 38468711 PMCID: PMC10925828 DOI: 10.1111/eva.13670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/02/2024] [Accepted: 02/09/2024] [Indexed: 03/13/2024] Open
Abstract
Since the emergence of a transmissible cancer, devil facial tumour disease (DFT1), in the 1980s, wild Tasmanian devil populations have been in decline. In 2016, a second, independently evolved transmissible cancer (DFT2) was discovered raising concerns for survival of the host species. Here, we applied experimental and modelling frameworks to examine competition dynamics between the two transmissible cancers in vitro. Using representative cell lines for DFT1 and DFT2, we have found that in monoculture, DFT2 grows twice as fast as DFT1 but reaches lower maximum cell densities. Using co-cultures, we demonstrate that DFT2 outcompetes DFT1: the number of DFT1 cells decreasing over time, never reaching exponential growth. This phenomenon could not be replicated when cells were grown separated by a semi-permeable membrane, consistent with exertion of mechanical stress on DFT1 cells by DFT2. A logistic model and a Lotka-Volterra competition model were used to interrogate monoculture and co-culture growth curves, respectively, suggesting DFT2 is a better competitor than DFT1, but also showing that competition outcomes might depend on the initial number of cells, at least in the laboratory. We provide theories how the in vitro results could be translated to observations in the wild and propose that these results may indicate that although DFT2 is currently in a smaller geographic area than DFT1, it could have the potential to outcompete DFT1. Furthermore, we provide a framework for improving the parameterization of epidemiological models applied to these cancer lineages, which will inform future disease management.
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Affiliation(s)
- Anne‐Lise Gérard
- School of Life and Environmental SciencesDeakin UniversityWaurn PondsVictoriaAustralia
- CREEC/MIVEGEC, CNRS, IRDUniversité de MontpellierMontpellierFrance
| | - Rachel S. Owen
- School of Biological SciencesUniversity of SouthamptonSouthamptonUK
- Institute for Life SciencesUniversity of SouthamptonSouthamptonUK
- The Roslin InstituteThe University of EdinburghEdinburghUK
| | - Antoine M. Dujon
- School of Life and Environmental SciencesDeakin UniversityWaurn PondsVictoriaAustralia
| | - Benjamin Roche
- CREEC/MIVEGEC, CNRS, IRDUniversité de MontpellierMontpellierFrance
| | - Rodrigo Hamede
- School of Natural SciencesUniversity of TasmaniaHobartTasmaniaAustralia
| | - Frédéric Thomas
- CREEC/MIVEGEC, CNRS, IRDUniversité de MontpellierMontpellierFrance
| | - Beata Ujvari
- School of Life and Environmental SciencesDeakin UniversityWaurn PondsVictoriaAustralia
| | - Hannah V. Siddle
- School of Biological SciencesUniversity of SouthamptonSouthamptonUK
- Institute for Life SciencesUniversity of SouthamptonSouthamptonUK
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12
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Neagu AN, Whitham D, Bruno P, Arshad A, Seymour L, Morrissiey H, Hukovic AI, Darie CC. Onco-Breastomics: An Eco-Evo-Devo Holistic Approach. Int J Mol Sci 2024; 25:1628. [PMID: 38338903 PMCID: PMC10855488 DOI: 10.3390/ijms25031628] [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: 12/20/2023] [Revised: 01/21/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Known as a diverse collection of neoplastic diseases, breast cancer (BC) can be hyperbolically characterized as a dynamic pseudo-organ, a living organism able to build a complex, open, hierarchically organized, self-sustainable, and self-renewable tumor system, a population, a species, a local community, a biocenosis, or an evolving dynamical ecosystem (i.e., immune or metabolic ecosystem) that emphasizes both developmental continuity and spatio-temporal change. Moreover, a cancer cell community, also known as an oncobiota, has been described as non-sexually reproducing species, as well as a migratory or invasive species that expresses intelligent behavior, or an endangered or parasite species that fights to survive, to optimize its features inside the host's ecosystem, or that is able to exploit or to disrupt its host circadian cycle for improving the own proliferation and spreading. BC tumorigenesis has also been compared with the early embryo and placenta development that may suggest new strategies for research and therapy. Furthermore, BC has also been characterized as an environmental disease or as an ecological disorder. Many mechanisms of cancer progression have been explained by principles of ecology, developmental biology, and evolutionary paradigms. Many authors have discussed ecological, developmental, and evolutionary strategies for more successful anti-cancer therapies, or for understanding the ecological, developmental, and evolutionary bases of BC exploitable vulnerabilities. Herein, we used the integrated framework of three well known ecological theories: the Bronfenbrenner's theory of human development, the Vannote's River Continuum Concept (RCC), and the Ecological Evolutionary Developmental Biology (Eco-Evo-Devo) theory, to explain and understand several eco-evo-devo-based principles that govern BC progression. Multi-omics fields, taken together as onco-breastomics, offer better opportunities to integrate, analyze, and interpret large amounts of complex heterogeneous data, such as various and big-omics data obtained by multiple investigative modalities, for understanding the eco-evo-devo-based principles that drive BC progression and treatment. These integrative eco-evo-devo theories can help clinicians better diagnose and treat BC, for example, by using non-invasive biomarkers in liquid-biopsies that have emerged from integrated omics-based data that accurately reflect the biomolecular landscape of the primary tumor in order to avoid mutilating preventive surgery, like bilateral mastectomy. From the perspective of preventive, personalized, and participatory medicine, these hypotheses may help patients to think about this disease as a process governed by natural rules, to understand the possible causes of the disease, and to gain control on their own health.
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Affiliation(s)
- Anca-Narcisa Neagu
- Laboratory of Animal Histology, Faculty of Biology, “Alexandru Ioan Cuza” University of Iași, Carol I bvd. 20A, 700505 Iasi, Romania
| | - Danielle Whitham
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Pathea Bruno
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Aneeta Arshad
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Logan Seymour
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Hailey Morrissiey
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Angiolina I. Hukovic
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Costel C. Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
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13
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Maltas J, Killarney ST, Singleton KR, Strobl MAR, Washart R, Wood KC, Wood KB. Drug dependence in cancer is exploitable by optimally constructed treatment holidays. Nat Ecol Evol 2024; 8:147-162. [PMID: 38012363 PMCID: PMC10918730 DOI: 10.1038/s41559-023-02255-x] [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: 07/06/2022] [Accepted: 10/19/2023] [Indexed: 11/29/2023]
Abstract
Cancers with acquired resistance to targeted therapy can become simultaneously dependent on the presence of the targeted therapy drug for survival, suggesting that intermittent therapy may slow resistance. However, relatively little is known about which tumours are likely to become dependent and how to schedule intermittent therapy optimally. Here we characterized drug dependence across a panel of over 75 MAPK-inhibitor-resistant BRAFV600E mutant melanoma models at the population and single-clone levels. Melanocytic differentiated models exhibited a much greater tendency to give rise to drug-dependent progeny than their dedifferentiated counterparts. Mechanistically, acquired loss of microphthalmia-associated transcription factor in differentiated melanoma models drives ERK-JunB-p21 signalling to enforce drug dependence. We identified the optimal scheduling of 'drug holidays' using simple mathematical models that we validated across short and long timescales. Without detailed knowledge of tumour characteristics, we found that a simple adaptive therapy protocol can produce near-optimal outcomes using only measurements of total population size. Finally, a spatial agent-based model showed that optimal schedules derived from exponentially growing cells in culture remain nearly optimal in the context of tumour cell turnover and limited environmental carrying capacity. These findings may guide the implementation of improved evolution-inspired treatment strategies for drug-dependent cancers.
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Affiliation(s)
- Jeff Maltas
- Department of Biophysics, University of Michigan, Ann Arbor, MI, USA
| | - Shane T Killarney
- Department of Pharmacology and Cancer Biology, Duke University, Durham, NC, USA
| | | | - Maximilian A R Strobl
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
| | - Rachel Washart
- Department of Pharmacology and Cancer Biology, Duke University, Durham, NC, USA
| | - Kris C Wood
- Department of Pharmacology and Cancer Biology, Duke University, Durham, NC, USA.
| | - Kevin B Wood
- Department of Biophysics, University of Michigan, Ann Arbor, MI, USA.
- Department of Physics, University of Michigan, Ann Arbor, MI, USA.
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14
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Song JH, Dávalos LM, MacCarthy T, Damaghi M. Evolvability of cancer-associated genes under APOBEC3A/B selection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.27.554991. [PMID: 38106028 PMCID: PMC10723265 DOI: 10.1101/2023.08.27.554991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Evolvability is an emergent hallmark of cancer that depends on intra-tumor heterogeneity and, ultimately, genetic variation. Mutations generated by APOBEC3 cytidine deaminases can contribute to genetic variation and the consequences of APOBEC activation differ depending on the stage of cancer, with the most significant impact observed during the early stages. However, how APOBEC activity shapes evolutionary patterns of genes in the host genome and differential impacts on cancer-associated and non-cancer genes remain unclear. Analyzing over 40,000 human protein-coding transcripts, we identified distinct distribution patterns of APOBEC3A/B TC motifs between cancer-related genes and controls, suggesting unique associations with cancer. Studying a bat species with many more APOBEC3 genes, we found diverse motif patterns in orthologs of cancer genes compared to controls, similar to humans and suggesting APOBEC evolution to reduce impacts on the genome rather than the converse. Simulations confirmed that APOBEC-induced heterogeneity enhances cancer evolution, shaping clonal dynamics through bimodal introduction of mutations in certain classes of genes. Our results suggest that a major consequence of the bimodal distribution of APOBEC affects greater cancer heterogeneity.
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Affiliation(s)
- Joon-Hyun Song
- Stony Brook Cancer Center, Stony Brook Medicine, Stony Brook University, Stony Brook, NY, USA
- Department of Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Liliana M. Dávalos
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY, 11794, USA
- Consortium for Inter-Disciplinary Environmental Research, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Thomas MacCarthy
- Stony Brook Cancer Center, Stony Brook Medicine, Stony Brook University, Stony Brook, NY, USA
- Department of Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Mehdi Damaghi
- Stony Brook Cancer Center, Stony Brook Medicine, Stony Brook University, Stony Brook, NY, USA
- Department of Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
- Department of Pathology, Stony Brook Medicine, Stony Brook University, Stony Brook, NY, USA
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15
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Harkos C, Stylianopoulos T, Jain RK. Mathematical modeling of intratumoral immunotherapy yields strategies to improve the treatment outcomes. PLoS Comput Biol 2023; 19:e1011740. [PMID: 38113269 PMCID: PMC10763956 DOI: 10.1371/journal.pcbi.1011740] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 01/03/2024] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
Intratumoral injection of immunotherapy aims to maximize its activity within the tumor. However, cytokines are cleared via tumor vessels and escape from the tumor periphery into the host-tissue, reducing efficacy and causing toxicity. Thus, understanding the determinants of the tumor and immune response to intratumoral immunotherapy should lead to better treatment outcomes. In this study, we developed a mechanistic mathematical model to determine the efficacy of intratumorally-injected conjugated-cytokines, accounting for properties of the tumor microenvironment and the conjugated-cytokines. The model explicitly incorporates i) the tumor vascular density and permeability and the tumor hydraulic conductivity, ii) conjugated-cytokines size and binding affinity as well as their clearance via the blood vessels and the surrounding tissue, and iii) immune cells-cancer cells interactions. Model simulations show how the properties of the tumor and of the conjugated-cytokines determine treatment outcomes and how selection of proper parameters can optimize therapy. A high tumor tissue hydraulic permeability allows for the uniform distribution of the cytokines into the tumor, whereas uniform tumor perfusion is required for sufficient access and activation of immune cells. The permeability of the tumor vessels affects the blood clearance of the cytokines and optimal values depend on the size of the conjugates. A size >5 nm in radius was found to be optimal, whereas the binding of conjugates should be high enough to prevent clearance from the tumor into the surrounding tissue. In conclusion, development of strategies to improve vessel perfusion and tissue hydraulic conductivity by reprogramming the microenvironment along with optimal design of conjugated-cytokines can enhance intratumoral immunotherapy.
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Affiliation(s)
- Constantinos Harkos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
| | - Triantafyllos Stylianopoulos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
| | - Rakesh K. Jain
- Edwin L Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
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16
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Wang Y, Zhou JX, Pedrini E, Rubin I, Khalil M, Taramelli R, Qian H, Huang S. Cell population growth kinetics in the presence of stochastic heterogeneity of cell phenotype. J Theor Biol 2023; 575:111645. [PMID: 37863423 DOI: 10.1016/j.jtbi.2023.111645] [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: 03/03/2023] [Revised: 09/29/2023] [Accepted: 10/13/2023] [Indexed: 10/22/2023]
Abstract
Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized uniform exponential growth of the cell population. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture a departure from the uniform exponential growth model for the initial growth ("take-off"). Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth dynamics, which could be explained by the presence of inter-converting subpopulations with different growth rates, and which could last for many generations. Based on the hypothesis of existence of multiple subpopulations, we developed a branching process model that was consistent with the experimental observations.
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Affiliation(s)
- Yue Wang
- Department of Computational Medicine, University of California, Los Angeles, CA, United States of America; Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America
| | - Joseph X Zhou
- Institute for Systems Biology, Seattle, WA, United States of America
| | - Edoardo Pedrini
- Institute for Systems Biology, Seattle, WA, United States of America
| | - Irit Rubin
- Institute for Systems Biology, Seattle, WA, United States of America
| | - May Khalil
- Institute for Systems Biology, Seattle, WA, United States of America
| | - Roberto Taramelli
- Department of Theoretical and Applied Science, University of Insubria, Italy
| | - Hong Qian
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America
| | - Sui Huang
- Institute for Systems Biology, Seattle, WA, United States of America.
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17
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Shah S, Philipp LM, Giaimo S, Sebens S, Traulsen A, Raatz M. Understanding and leveraging phenotypic plasticity during metastasis formation. NPJ Syst Biol Appl 2023; 9:48. [PMID: 37803056 PMCID: PMC10558468 DOI: 10.1038/s41540-023-00309-1] [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: 02/09/2023] [Accepted: 09/15/2023] [Indexed: 10/08/2023] Open
Abstract
Cancer metastasis is the process of detrimental systemic spread and the primary cause of cancer-related fatalities. Successful metastasis formation requires tumor cells to be proliferative and invasive; however, cells cannot be effective at both tasks simultaneously. Tumor cells compensate for this trade-off by changing their phenotype during metastasis formation through phenotypic plasticity. Given the changing selection pressures and competitive interactions that tumor cells face, it is poorly understood how plasticity shapes the process of metastasis formation. Here, we develop an ecology-inspired mathematical model with phenotypic plasticity and resource competition between phenotypes to address this knowledge gap. We find that phenotypically plastic tumor cell populations attain a stable phenotype equilibrium that maintains tumor cell heterogeneity. Considering treatment types inspired by chemo- and immunotherapy, we highlight that plasticity can protect tumors against interventions. Turning this strength into a weakness, we corroborate current clinical practices to use plasticity as a target for adjuvant therapy. We present a parsimonious view of tumor plasticity-driven metastasis that is quantitative and experimentally testable, and thus potentially improving the mechanistic understanding of metastasis at the cell population level, and its treatment consequences.
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Affiliation(s)
- Saumil Shah
- Department of Theoretical Biology, Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306, Plön, Germany.
| | - Lisa-Marie Philipp
- Institute for Experimental Cancer Research, Kiel University and University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, Building U30, Entrance 1, 24105, Kiel, Germany
| | - Stefano Giaimo
- Department of Theoretical Biology, Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306, Plön, Germany
| | - Susanne Sebens
- Institute for Experimental Cancer Research, Kiel University and University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, Building U30, Entrance 1, 24105, Kiel, Germany
| | - Arne Traulsen
- Department of Theoretical Biology, Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306, Plön, Germany
| | - Michael Raatz
- Department of Theoretical Biology, Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306, Plön, Germany
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18
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Strobl MAR, Gallaher J, Robertson-Tessi M, West J, Anderson ARA. Treatment of evolving cancers will require dynamic decision support. Ann Oncol 2023; 34:867-884. [PMID: 37777307 PMCID: PMC10688269 DOI: 10.1016/j.annonc.2023.08.008] [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: 02/10/2023] [Revised: 08/01/2023] [Accepted: 08/21/2023] [Indexed: 10/02/2023] Open
Abstract
Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.
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Affiliation(s)
- M A R Strobl
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa; Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, USA
| | - J Gallaher
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - M Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - J West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - A R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa.
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19
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Moffett AS, Deng Y, Levine H. Modeling the Role of Immune Cell Conversion in the Tumor-Immune Microenvironment. Bull Math Biol 2023; 85:93. [PMID: 37658264 PMCID: PMC10474003 DOI: 10.1007/s11538-023-01201-z] [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: 03/22/2023] [Accepted: 08/17/2023] [Indexed: 09/03/2023]
Abstract
Tumors develop in a complex physical, biochemical, and cellular milieu, referred to as the tumor microenvironment. Of special interest is the set of immune cells that reciprocally interact with the tumor, the tumor-immune microenvironment (TIME). The diversity of cell types and cell-cell interactions in the TIME has led researchers to apply concepts from ecology to describe the dynamics. However, while tumor cells are known to induce immune cells to switch from anti-tumor to pro-tumor phenotypes, this type of ecological interaction has been largely overlooked. To address this gap in cancer modeling, we develop a minimal, ecological model of the TIME with immune cell conversion, to highlight this important interaction and explore its consequences. A key finding is that immune conversion increases the range of parameters supporting a co-existence phase in which the immune system and the tumor reach a stalemate. Our results suggest that further investigation of the consequences of immune cell conversion, using detailed, data-driven models, will be critical for greater understanding of TIME dynamics.
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Affiliation(s)
- Alexander S. Moffett
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115 USA
- Department of Physics, Northeastern University, Boston, MA 02115 USA
| | - Youyuan Deng
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005 USA
- Applied Physics Graduate Program, Smalley-Curl Institute, Rice University, Houston, TX 77005 USA
| | - Herbert Levine
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115 USA
- Department of Physics, Northeastern University, Boston, MA 02115 USA
- Department of Bioengineering, Northeastern University, Boston, MA 02115 USA
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20
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Reyes-Aldasoro CC. Modelling the Tumour Microenvironment, but What Exactly Do We Mean by "Model"? Cancers (Basel) 2023; 15:3796. [PMID: 37568612 PMCID: PMC10416922 DOI: 10.3390/cancers15153796] [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: 06/28/2023] [Revised: 07/19/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
The Oxford English Dictionary includes 17 definitions for the word "model" as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, "model railways" refer to replicas of railways and trains at a smaller scale and a "model student" refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, "model" can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different "models" of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word "model" related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used "models", the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use.
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21
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Proverbio D, Skupin A, Gonçalves J. Systematic analysis and optimization of early warning signals for critical transitions using distribution data. iScience 2023; 26:107156. [PMID: 37456849 PMCID: PMC10338236 DOI: 10.1016/j.isci.2023.107156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 04/21/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Abrupt shifts between alternative regimes occur in complex systems, from cell regulation to brain functions to ecosystems. Several model-free early warning signals (EWS) have been proposed to detect impending transitions, but failure or poor performance in some systems have called for better investigation of their generic applicability. Notably, there are still ongoing debates whether such signals can be successfully extracted from data in particular from biological experiments. In this work, we systematically investigate properties and performance of dynamical EWS in different deteriorating conditions, and we propose an optimized combination to trigger warnings as early as possible, eventually verified on experimental data from microbiological populations. Our results explain discrepancies observed in the literature between warning signs extracted from simulated models and from real data, provide guidance for EWS selection based on desired systems and suggest an optimized composite indicator to alert for impending critical transitions using distribution data.
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Affiliation(s)
- Daniele Proverbio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QL, UK
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- National Center for Microscopy and Imaging Research, University of California San Diego, Gilman Drive, La Jolla, CA 9500, USA
- Department of Physics and Material Science, University of Luxembourg, 162a Avenue de La Faiencerie, 1511 Luxembourg, Luxembourg
| | - Jorge Gonçalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
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22
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Perrault EN, Shireman JM, Ali ES, Lin P, Preddy I, Park C, Budhiraja S, Baisiwala S, Dixit K, James CD, Heiland DH, Ben-Sahra I, Pott S, Basu A, Miska J, Ahmed AU. Ribonucleotide reductase regulatory subunit M2 drives glioblastoma TMZ resistance through modulation of dNTP production. SCIENCE ADVANCES 2023; 9:eade7236. [PMID: 37196077 PMCID: PMC10191446 DOI: 10.1126/sciadv.ade7236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 04/13/2023] [Indexed: 05/19/2023]
Abstract
During therapy, adaptations driven by cellular plasticity are partly responsible for driving the inevitable recurrence of glioblastoma (GBM). To investigate plasticity-induced adaptation during standard-of-care chemotherapy temozolomide (TMZ), we performed in vivo single-cell RNA sequencing in patient-derived xenograft (PDX) tumors of GBM before, during, and after therapy. Comparing single-cell transcriptomic patterns identified distinct cellular populations present during TMZ therapy. Of interest was the increased expression of ribonucleotide reductase regulatory subunit M2 (RRM2), which we found to regulate dGTP and dCTP production vital for DNA damage response during TMZ therapy. Furthermore, multidimensional modeling of spatially resolved transcriptomic and metabolomic analysis in patients' tissues revealed strong correlations between RRM2 and dGTP. This supports our data that RRM2 regulates the demand for specific dNTPs during therapy. In addition, treatment with the RRM2 inhibitor 3-AP (Triapine) enhances the efficacy of TMZ therapy in PDX models. We present a previously unidentified understanding of chemoresistance through critical RRM2-mediated nucleotide production.
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Affiliation(s)
- Ella N. Perrault
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jack M. Shireman
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eunus S. Ali
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Peiyu Lin
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Isabelle Preddy
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Cheol Park
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Shreya Budhiraja
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Shivani Baisiwala
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Karan Dixit
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - C. David James
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Northwestern Medicine Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Dieter H Heiland
- Microenvironment and Immunology Research Laboratory, Medical-Center, University of Freiburg, Freiburg, Germany
- Department of Neurosurgery, Medical-Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), partner site Freiburg, Freiburg, Germany
| | - Issam Ben-Sahra
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Sebastian Pott
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Anindita Basu
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Jason Miska
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Atique U. Ahmed
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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23
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Onesto V, Forciniti S, Alemanno F, Narayanankutty K, Chandra A, Prasad S, Azzariti A, Gigli G, Barra A, De Martino A, De Martino D, del Mercato LL. Probing Single-Cell Fermentation Fluxes and Exchange Networks via pH-Sensing Hybrid Nanofibers. ACS NANO 2023; 17:3313-3323. [PMID: 36573897 PMCID: PMC9979640 DOI: 10.1021/acsnano.2c06114] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 12/19/2022] [Indexed: 05/31/2023]
Abstract
The homeostatic control of their environment is an essential task of living cells. It has been hypothesized that, when microenvironmental pH inhomogeneities are induced by high cellular metabolic activity, diffusing protons act as signaling molecules, driving the establishment of exchange networks sustained by the cell-to-cell shuttling of overflow products such as lactate. Despite their fundamental role, the extent and dynamics of such networks is largely unknown due to the lack of methods in single-cell flux analysis. In this study, we provide direct experimental characterization of such exchange networks. We devise a method to quantify single-cell fermentation fluxes over time by integrating high-resolution pH microenvironment sensing via ratiometric nanofibers with constraint-based inverse modeling. We apply our method to cell cultures with mixed populations of cancer cells and fibroblasts. We find that the proton trafficking underlying bulk acidification is strongly heterogeneous, with maximal single-cell fluxes exceeding typical values by up to 3 orders of magnitude. In addition, a crossover in time from a networked phase sustained by densely connected "hubs" (corresponding to cells with high activity) to a sparse phase dominated by isolated dipolar motifs (i.e., by pairwise cell-to-cell exchanges) is uncovered, which parallels the time course of bulk acidification. Our method addresses issues ranging from the homeostatic function of proton exchange to the metabolic coupling of cells with different energetic demands, allowing for real-time noninvasive single-cell metabolic flux analysis.
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Affiliation(s)
- Valentina Onesto
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
| | - Stefania Forciniti
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
| | - Francesco Alemanno
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
- Dipartimento
di Matematica e Fisica E. De Giorgi, University
of Salento, 73100Lecce, Italy
- Istituto
Nazionale di Fisica Nucleare (INFN), Sezione di Lecce, 73100Lecce, Italy
| | | | - Anil Chandra
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
| | - Saumya Prasad
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
| | - Amalia Azzariti
- IRCCS
Istituto Tumori Giovanni Paolo II, V.le O. Flacco, 65, 70124Bari, Italy
| | - Giuseppe Gigli
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
- Dipartimento
di Matematica e Fisica E. De Giorgi, University
of Salento, 73100Lecce, Italy
| | - Adriano Barra
- Dipartimento
di Matematica e Fisica E. De Giorgi, University
of Salento, 73100Lecce, Italy
- Istituto
Nazionale di Fisica Nucleare (INFN), Sezione di Lecce, 73100Lecce, Italy
| | - Andrea De Martino
- Politecnico
di Torino, Corso Duca degli Abruzzi, 24, I-10129Torino, Italy
- Italian Institute
for Genomic Medicine, IRCCS Candiolo, SP-142, I-10060Candiolo, Italy
| | - Daniele De Martino
- Biofisika
Institutua (UPV/EHU, CSIC) and Fundación Biofísica Bizkaia, LeioaE-48940, Spain
- Ikerbasque
Foundation, Bilbao48013, Spain
| | - Loretta L. del Mercato
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
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24
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Bukkuri A, Pienta KJ, Hockett I, Austin RH, Hammarlund EU, Amend SR, Brown JS. Modeling cancer's ecological and evolutionary dynamics. Med Oncol 2023; 40:109. [PMID: 36853375 PMCID: PMC9974726 DOI: 10.1007/s12032-023-01968-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/05/2023] [Indexed: 03/01/2023]
Abstract
In this didactic paper, we present a theoretical modeling framework, called the G-function, that integrates both the ecology and evolution of cancer to understand oncogenesis. The G-function has been used in evolutionary ecology, but has not been widely applied to problems in cancer. Here, we build the G-function framework from fundamental Darwinian principles and discuss how cancer can be seen through the lens of ecology, evolution, and game theory. We begin with a simple model of cancer growth and add on components of cancer cell competition and drug resistance. To aid in exploration of eco-evolutionary modeling with this approach, we also present a user-friendly software tool. By the end of this paper, we hope that readers will be able to construct basic G function models and grasp the usefulness of the framework to understand the games cancer plays in a biologically mechanistic fashion.
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Affiliation(s)
- Anuraag Bukkuri
- Cancer Biology and Evolution Program and Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, USA.
- Tissue Development and Evolution Research Group, Department of Laboratory Medicine, Lund University, Lund, Sweden.
| | - Kenneth J Pienta
- The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, USA
| | - Ian Hockett
- The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, USA
| | | | - Emma U Hammarlund
- Tissue Development and Evolution Research Group, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Sarah R Amend
- The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, USA
| | - Joel S Brown
- Cancer Biology and Evolution Program and Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, USA
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25
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Wang Y, Zhou JX, Pedrini E, Rubin I, Khalil M, Qian H, Huang S. Cell Population Growth Kinetics in the Presence of Stochastic Heterogeneity of Cell Phenotype. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.08.527773. [PMID: 36824755 PMCID: PMC9948979 DOI: 10.1101/2023.02.08.527773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized exponential growth. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture the departure from the exponential growth model in the initial growth phase. Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth kinetics and the presence of subpopulations with different growth rates that endured for many generations. Based on the hypothesis of existence of multiple inter-converting subpopulations, we developed a branching process model that captures the experimental observations.
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Affiliation(s)
- Yue Wang
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
| | - Joseph X. Zhou
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Edoardo Pedrini
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Irit Rubin
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - May Khalil
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Hong Qian
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
| | - Sui Huang
- Institute for Systems Biology, Seattle, Washington, United States of America
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26
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Ligasová A, Frydrych I, Koberna K. Basic Methods of Cell Cycle Analysis. Int J Mol Sci 2023; 24:ijms24043674. [PMID: 36835083 PMCID: PMC9963451 DOI: 10.3390/ijms24043674] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
Cellular growth and the preparation of cells for division between two successive cell divisions is called the cell cycle. The cell cycle is divided into several phases; the length of these particular cell cycle phases is an important characteristic of cell life. The progression of cells through these phases is a highly orchestrated process governed by endogenous and exogenous factors. For the elucidation of the role of these factors, including pathological aspects, various methods have been developed. Among these methods, those focused on the analysis of the duration of distinct cell cycle phases play important role. The main aim of this review is to guide the readers through the basic methods of the determination of cell cycle phases and estimation of their length, with a focus on the effectiveness and reproducibility of the described methods.
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27
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Invasiveness of a Growth-Migration System in a Two-dimensional Percolation cluster: A Stochastic Mathematical Approach. Bull Math Biol 2022; 84:104. [DOI: 10.1007/s11538-022-01056-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 07/20/2022] [Indexed: 11/02/2022]
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28
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Fang L, Liu K, Liu C, Wang X, Ma W, Xu W, Wu J, Sun C. Tumor accomplice: T cell exhaustion induced by chronic inflammation. Front Immunol 2022; 13:979116. [PMID: 36119037 PMCID: PMC9479340 DOI: 10.3389/fimmu.2022.979116] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/22/2022] [Indexed: 11/17/2022] Open
Abstract
The development and response to treatment of tumor are modulated by inflammation, and chronic inflammation promotes tumor progression and therapy resistance. This article summarizes the dynamic evolution of inflammation from acute to chronic in the process of tumor development, and its effect on T cells from activation to the promotion of exhaustion. We review the mechanisms by which inflammatory cells and inflammatory cytokines regulate T cell exhaustion and methods for targeting chronic inflammation to improve the efficacy of immunotherapy. It is great significance to refer to the specific state of inflammation and T cells at different stages of tumor development for accurate clinical decision-making of immunotherapy and improving the efficiency of tumor immunotherapy.
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Affiliation(s)
- Liguang Fang
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Kunjing Liu
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Cun Liu
- College of Traditional Chinese Medicine, Weifang Medical University, Weifang, China
| | - Xiaomin Wang
- Department of Inspection, The Medical Faculty of Qingdao University, Qingdao, China
| | - Wenzhe Ma
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, Macao SAR, China
| | - Wenhua Xu
- Department of Inspection, The Medical Faculty of Qingdao University, Qingdao, China
| | - Jibiao Wu
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Changgang Sun
- College of Traditional Chinese Medicine, Weifang Medical University, Weifang, China
- Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, China
- *Correspondence: Changgang Sun,
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29
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Raja Arul GL, Toruner MD, Gatenby RA, Carr RM. Ecoevolutionary biology of pancreatic ductal adenocarcinoma. Pancreatology 2022; 22:730-740. [PMID: 35821188 DOI: 10.1016/j.pan.2022.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/01/2022] [Indexed: 12/11/2022]
Abstract
Pancreatic ductal adenocarcinoma (PDAC), the most common histological subtype of pancreatic cancer, is an aggressive disease predicted to be the 2nd cause of cancer mortality in the US by 2040. While first-line therapy has improved, 5-year overall survival has only increased from 5 to ∼10%, and surgical resection is only available for ∼20% of patients as most present with advanced disease, which is invariably lethal. PDAC has well-established highly recurrent mutations in four driver genes including KRAS, TP53, CDKN2A, and SMAD4. Unfortunately, these genetic drivers are not currently therapeutically actionable. Despite extensive sequencing efforts, few additional significantly recurrent and druggable drivers have been identified. In the absence of targetable mutations, chemotherapy remains the mainstay of treatment for most patients. Further, the role of the above driver mutations on PDAC initiation and early development is well-established. However, these mutations alone cannot account for PDAC heterogeneity nor discern early from advanced disease. Taken together, management of PDAC is an example highlighting the shortcomings of the current precision medicine paradigm. PDAC, like other malignancies, represents an ecoevolutionary process. Better understanding the disease through this lens can facilitate the development of novel therapeutic strategies to better control and cure PDAC. This review aims to integrate the current understanding of PDAC pathobiology into an ecoevolutionary framework.
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Affiliation(s)
| | - Merih D Toruner
- Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Robert A Gatenby
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Ryan M Carr
- Department of Oncology, Mayo Clinic, Rochester, MN, USA.
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30
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Sun Z, Chen W, Huang D, Jiang C, Lu L. A mitochondria targeted cascade reaction nanosystem for improved therapeutic effect by overcoming cellular resistance. Biomater Sci 2022; 10:5947-5955. [PMID: 36043518 DOI: 10.1039/d2bm00956k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Mitigating cellular resistance, which could enhance the sensitivity of tumor cells to treatment, is a promising approach for obtaining better therapeutic outcomes. However, the present designs of materials generally disregard this point, or only focus on a single specific resistance. Herein, a strategy based on a series of cascade reactions aiming to suppress multiple cellular resistances is designed by integrating photothermal and chemotherapy into a mitochondria targeted nanosystem (AuBPs@TD). The intelligent nanosystem is fabricated by modifying gold nanobipyramids (AuBPs) with triphenylphosphonium (TPP) functionalized dichloroacetic acid (DCA). TPP serves as a "navigation system" and facilitates the location of AuBPs@TD in the mitochondria. Moreover, the released DCA promoted by the photothermal effect of AuBPs, as the mitochondrial kinase inhibitor, could inhibit glycolysis, and lead to a repressed expression of heat shock protein 90, which is the main resistance protein in cancer cells against photothermal therapy (PTT). Thus, the photothermal antitumor effect can be significantly improved. For the other cascade passage, the hyperthermal atmosphere depresses the expression of P-glycoprotein, a protein associated with drug resistance, and consequently prevents DCA molecules from being expelled in return. Furthermore, the retained DCA molecules elevate the concentration of intracellular hydrogen peroxide, and due to the peroxidase-like activity of AuBPs, increased intracellular reactive oxygen species could be obtained to accelerate apoptosis. As a result, these cascade reactions lead to significant inhibition of cellular resistance and greatly improve the therapeutic performance. This work paves a new way for suppressing cellular resistance to achieve the desired therapeutic effect.
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Affiliation(s)
- Zhen Sun
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.,University of Science and Technology of China, Hefei 230026, China.
| | - Weihua Chen
- Department of Chemistry, The University of Hong Kong, Hong Kong 999077, China
| | - Dianshuai Huang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.,University of Science and Technology of China, Hefei 230026, China.
| | - Chunhuan Jiang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.,University of Science and Technology of China, Hefei 230026, China.
| | - Lehui Lu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.,University of Science and Technology of China, Hefei 230026, China.
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31
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Golden A, Dukovski I, Segrè D, Korolev KS. Growth instabilities shape morphology and genetic diversity of microbial colonies. Phys Biol 2022; 19:10.1088/1478-3975/ac8514. [PMID: 35901792 PMCID: PMC11209841 DOI: 10.1088/1478-3975/ac8514] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 07/28/2022] [Indexed: 11/11/2022]
Abstract
Cellular populations assume an incredible variety of shapes ranging from circular molds to irregular tumors. While we understand many of the mechanisms responsible for these spatial patterns, little is known about how the shape of a population influences its ecology and evolution. Here, we investigate this relationship in the context of microbial colonies grown on hard agar plates. This a well-studied system that exhibits a transition from smooth circular disks to more irregular and rugged shapes as either the nutrient concentration or cellular motility is decreased. Starting from a mechanistic model of colony growth, we identify two dimensionless quantities that determine how morphology and genetic diversity of the population depend on the model parameters. Our simulations further reveal that population dynamics cannot be accurately described by the commonly-used surface growth models. Instead, one has to explicitly account for the emergent growth instabilities and demographic fluctuations. Overall, our work links together environmental conditions, colony morphology, and evolution. This link is essential for a rational design of concrete, biophysical perturbations to steer evolution in the desired direction.
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Affiliation(s)
- Alexander Golden
- Department of Physics, Graduate Program in Bioinformatics, and Biological Design Center, Boston University, Boston, MA 02215, United States of America
| | - Ilija Dukovski
- Graduate Program in Bioinformatics, and Biological Design Center, Boston University, Boston, MA 02215, United States of America
| | - Daniel Segrè
- Department of Physics, Department of Biology, Department of Biomedical Engineering, Graduate Program in Bioinformatics, and Biological Design Center, Boston University, Boston, MA 02215, United States of America
| | - Kirill S Korolev
- Department of Physics, Graduate Program in Bioinformatics, and Biological Design Center, Boston University, Boston, MA 02215, United States of America
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32
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Bukkuri A, Pienta KJ, Austin RH, Hammarlund EU, Amend SR, Brown JS. A life history model of the ecological and evolutionary dynamics of polyaneuploid cancer cells. Sci Rep 2022; 12:13713. [PMID: 35962062 PMCID: PMC9374668 DOI: 10.1038/s41598-022-18137-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/05/2022] [Indexed: 11/09/2022] Open
Abstract
Therapeutic resistance is one of the main reasons for treatment failure in cancer patients. The polyaneuploid cancer cell (PACC) state has been shown to promote resistance by providing a refuge for cancer cells from the effects of therapy and by helping them adapt to a variety of environmental stressors. This state is the result of aneuploid cancer cells undergoing whole genome doubling and skipping mitosis, cytokinesis, or both. In this paper, we create a novel mathematical framework for modeling the eco-evolutionary dynamics of state-structured populations and use this framework to construct a model of cancer populations with an aneuploid and a PACC state. Using in silico simulations, we explore how the PACC state allows cancer cells to (1) survive extreme environmental conditions by exiting the cell cycle after S phase and protecting genomic material and (2) aid in adaptation to environmental stressors by increasing the cancer cell's ability to generate heritable variation (evolvability) through the increase in genomic content that accompanies polyploidization. In doing so, we demonstrate the ability of the PACC state to allow cancer cells to persist under therapy and evolve therapeutic resistance. By eliminating cells in the PACC state through appropriately-timed PACC-targeted therapies, we show how we can prevent the emergence of resistance and promote cancer eradication.
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Affiliation(s)
- Anuraag Bukkuri
- Cancer Biology and Evolution Program, Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, USA.
| | - Kenneth J Pienta
- The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, USA
| | | | - Emma U Hammarlund
- Nordic Center for Earth Evolution, University of Southern Denmark and Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Sarah R Amend
- The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, USA
| | - Joel S Brown
- Cancer Biology and Evolution Program, Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, USA
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33
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Yung MMH, Siu MKY, Ngan HYS, Chan DW, Chan KKL. Orchestrated Action of AMPK Activation and Combined VEGF/PD-1 Blockade with Lipid Metabolic Tunning as Multi-Target Therapeutics against Ovarian Cancers. Int J Mol Sci 2022; 23:ijms23126857. [PMID: 35743298 PMCID: PMC9224484 DOI: 10.3390/ijms23126857] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/06/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
Ovarian cancer is one of the most lethal gynecological malignancies worldwide, and chemoresistance is a critical obstacle in the clinical management of the disease. Recent studies have suggested that exploiting cancer cell metabolism by applying AMP-activated protein kinase (AMPK)-activating agents and distinctive adjuvant targeted therapies can be a plausible alternative approach in cancer treatment. Therefore, the perspectives about the combination of AMPK activators together with VEGF/PD-1 blockade as a dual-targeted therapy against ovarian cancer were discussed herein. Additionally, ferroptosis, a non-apoptotic regulated cell death triggered by the availability of redox-active iron, have been proposed to be governed by multiple layers of metabolic signalings and can be synergized with immunotherapies. To this end, ferroptosis initiating therapies (FITs) and metabolic rewiring and immunotherapeutic approaches may have substantial clinical potential in combating ovarian cancer development and progression. It is hoped that the viewpoints deliberated in this review would accelerate the translation of remedial concepts into clinical trials and improve the effectiveness of ovarian cancer treatment.
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Affiliation(s)
- Mingo M. H. Yung
- Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (M.M.H.Y.); (M.K.Y.S.); (H.Y.S.N.)
| | - Michelle K. Y. Siu
- Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (M.M.H.Y.); (M.K.Y.S.); (H.Y.S.N.)
| | - Hextan Y. S. Ngan
- Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (M.M.H.Y.); (M.K.Y.S.); (H.Y.S.N.)
| | - David W. Chan
- Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (M.M.H.Y.); (M.K.Y.S.); (H.Y.S.N.)
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Correspondence: or (D.W.C.); (K.K.L.C.); Tel.: +852-3917-9367 or +852-3943-6053 (D.W.C.); +852-2255-4260 (K.K.L.C.); Fax: +852-2816-1947 or +852-2603-5123 (D.W.C.); +852-2255-0947 (K.K.L.C.)
| | - Karen K. L. Chan
- Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (M.M.H.Y.); (M.K.Y.S.); (H.Y.S.N.)
- Correspondence: or (D.W.C.); (K.K.L.C.); Tel.: +852-3917-9367 or +852-3943-6053 (D.W.C.); +852-2255-4260 (K.K.L.C.); Fax: +852-2816-1947 or +852-2603-5123 (D.W.C.); +852-2255-0947 (K.K.L.C.)
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34
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Ma Z(S, Zhang YP. Ecology of Human Medical Enterprises: From Disease Ecology of Zoonoses, Cancer Ecology Through to Medical Ecology of Human Microbiomes. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.879130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
In nature, the interaction between pathogens and their hosts is only one of a handful of interaction relationships between species, including parasitism, predation, competition, symbiosis, commensalism, and among others. From a non-anthropocentric view, parasitism has relatively fewer essential differences from the other relationships; but from an anthropocentric view, parasitism and predation against humans and their well-beings and belongings are frequently related to heinous diseases. Specifically, treating (managing) diseases of humans, crops and forests, pets, livestock, and wildlife constitute the so-termed medical enterprises (sciences and technologies) humans endeavor in biomedicine and clinical medicine, veterinary, plant protection, and wildlife conservation. In recent years, the significance of ecological science to medicines has received rising attentions, and the emergence and pandemic of COVID-19 appear accelerating the trend. The facts that diseases are simply one of the fundamental ecological relationships in nature, and the study of the relationships between species and their environment is a core mission of ecology highlight the critical importance of ecological science. Nevertheless, current studies on the ecology of medical enterprises are highly fragmented. Here, we (i) conceptually overview the fields of disease ecology of wildlife, cancer ecology and evolution, medical ecology of human microbiome-associated diseases and infectious diseases, and integrated pest management of crops and forests, across major medical enterprises. (ii) Explore the necessity and feasibility for a unified medical ecology that spans biomedicine, clinical medicine, veterinary, crop (forest and wildlife) protection, and biodiversity conservation. (iii) Suggest that a unified medical ecology of human diseases is both necessary and feasible, but laissez-faire terminologies in other human medical enterprises may be preferred. (iv) Suggest that the evo-eco paradigm for cancer research can play a similar role of evo-devo in evolutionary developmental biology. (v) Summarized 40 key ecological principles/theories in current disease-, cancer-, and medical-ecology literatures. (vi) Identified key cross-disciplinary discovery fields for medical/disease ecology in coming decade including bioinformatics and computational ecology, single cell ecology, theoretical ecology, complexity science, and the integrated studies of ecology and evolution. Finally, deep understanding of medical ecology is of obvious importance for the safety of human beings and perhaps for all living things on the planet.
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35
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Li Y, Buenzli PR, Simpson MJ. Interpreting how nonlinear diffusion affects the fate of bistable populations using a discrete modelling framework. Proc Math Phys Eng Sci 2022; 478:20220013. [PMID: 35702596 PMCID: PMC9185834 DOI: 10.1098/rspa.2022.0013] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 04/28/2022] [Indexed: 12/11/2022] Open
Abstract
Understanding whether a population will survive or become extinct is a central question in population biology. One way of exploring this question is to study population dynamics using reaction-diffusion equations, where migration is usually represented as a linear diffusion term, and birth-death is represented with a nonlinear source term. While linear diffusion is most commonly employed to study migration, there are several limitations of this approach, such as the inability of linear diffusion-based models to predict a well-defined population front. One way to overcome this is to generalize the constant diffusivity, D , to a nonlinear diffusivity function D ( C ) , where C > 0 is the population density. While the choice of D ( C ) affects long-term survival or extinction of a bistable population, working solely in a continuum framework makes it difficult to understand how the choice of D ( C ) affects survival or extinction. We address this question by working with a discrete simulation model that is easy to interpret. This approach provides clear insight into how the choice of D ( C ) either encourages or suppresses population extinction relative to the classical linear diffusion model.
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Affiliation(s)
- Yifei Li
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Pascal R. Buenzli
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Matthew J. Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
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36
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Thege FI, Cardle II, Gruber CN, Siemann MJ, Cong S, Wittmann K, Love J, Kirby BJ. Acquired chemoresistance drives spatial heterogeneity, chemoprotection and collective migration in pancreatic tumor spheroids. PLoS One 2022; 17:e0267882. [PMID: 35617275 PMCID: PMC9135276 DOI: 10.1371/journal.pone.0267882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 04/18/2022] [Indexed: 11/18/2022] Open
Abstract
Tumors display rich cellular heterogeneity and typically consist of multiple co-existing clones with distinct genotypic and phenotypic characteristics. The acquisition of resistance to chemotherapy has been shown to contribute to the development of aggressive cancer traits, such as increased migration, invasion and stemness. It has been hypothesized that collective cellular behavior and cooperation of cancer cell populations may directly contribute to disease progression and lack of response to treatment. Here we show that the spontaneous emergence of chemoresistance in a cancer cell population exposed to the selective pressure of a chemotherapeutic agent can result in the emergence of collective cell behavior, including cell-sorting, chemoprotection and collective migration. We derived several gemcitabine resistant subclones from the human pancreatic cancer cell line BxPC3 and determined that the observed chemoresistance was driven of a focal amplification of the chr11p15.4 genomic region, resulting in over-expression of the ribonucleotide reductase (RNR) subunit RRM1. Interestingly, these subclones display a rich cell-sorting behavior when cultured as mixed tumor spheroids. Furthermore, we show that chemoresistant cells are able to exert a chemoprotective effect on non-resistant cells in spheroid co-culture, whereas no protective effect is seen in conventional 2D culture. We also demonstrate that the co-culture of resistant and non-resistant cells leads to collective migration where resistant cells enable migration of otherwise non-migratory cells.
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Affiliation(s)
| | - Ian I. Cardle
- Cornell University, Ithaca, New York, United States of America
| | - Conor N. Gruber
- Cornell University, Ithaca, New York, United States of America
| | - Megan J. Siemann
- MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Sophie Cong
- Cornell University, Ithaca, New York, United States of America
| | | | - Justin Love
- Cornell University, Ithaca, New York, United States of America
| | - Brian J. Kirby
- Cornell University, Ithaca, New York, United States of America
- Weill Cornell Medicine, New York, New York, United States of America
- * E-mail:
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Deb S, Bhandary S, Sinha SK, Jolly MK, Dutta PS. Identifying critical transitions in complex diseases. J Biosci 2022. [PMID: 36210727 PMCID: PMC9018973 DOI: 10.1007/s12038-022-00258-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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38
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Autocrine signaling can explain the emergence of Allee effects in cancer cell populations. PLoS Comput Biol 2022; 18:e1009844. [PMID: 35239640 PMCID: PMC8923455 DOI: 10.1371/journal.pcbi.1009844] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 03/15/2022] [Accepted: 01/17/2022] [Indexed: 11/30/2022] Open
Abstract
In many human cancers, the rate of cell growth depends crucially on the size of the tumor cell population. Low, zero, or negative growth at low population densities is known as the Allee effect; this effect has been studied extensively in ecology, but so far lacks a good explanation in the cancer setting. Here, we formulate and analyze an individual-based model of cancer, in which cell division rates are increased by the local concentration of an autocrine growth factor produced by the cancer cells themselves. We show, analytically and by simulation, that autocrine signaling suffices to cause both strong and weak Allee effects. Whether low cell densities lead to negative (strong effect) or reduced (weak effect) growth rate depends directly on the ratio of cell death to proliferation, and indirectly on cellular dispersal. Our model is consistent with experimental observations from three patient-derived brain tumor cell lines grown at different densities. We propose that further studying and quantifying population-wide feedback, impacting cell growth, will be central for advancing our understanding of cancer dynamics and treatment, potentially exploiting Allee effects for therapy. A common feature of tumor growth is the production, by the cancer cells themselves, of hormones known as growth factors that increase the rate of cell division. This type of signalling makes the growth rate of the tumor depend on the population size in a non-linear manner, and the growth rate might become low or negative for small population sizes. This is known as the Allee effect which has been studied extensively in ecology. We have developed a computational model that can explain the Allee effect in terms of growth factor signalling, and show by mathematical analysis of the model that the magnitude of the Allee effect depends on the ratio of cell death to proliferation, as well as the properties of the growth factor. In addition we show that the model is consistent with experimental observations from three different cell lines derived from the brain tumor glioblastoma. Our findings indicate that the Allee effect can be exploited in order to improve the treatment of glioblastoma patients.
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Azimzade Y. Invasion front dynamics of interactive populations in environments with barriers. Sci Rep 2022; 12:826. [PMID: 35039586 PMCID: PMC8764055 DOI: 10.1038/s41598-022-04806-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 01/03/2022] [Indexed: 11/20/2022] Open
Abstract
Invading populations normally comprise different subpopulations that interact while trying to overcome existing barriers against their way to occupy new areas. However, the majority of studies so far only consider single or multiple population invasion into areas where there is no resistance against the invasion. Here, we developed a model to study how cooperative/competitive populations invade in the presence of a physical barrier that should be degraded during the invasion. For one dimensional (1D) environment, we found that a Langevin equation as [Formula: see text] describing invasion front position. We then obtained how [Formula: see text] and [Formula: see text] depend on population interactions and environmental barrier intensity. In two dimensional (2D) environment, for the average interface position movements we found a Langevin equation as [Formula: see text]. Similar to the 1D case, we calculate how [Formula: see text] and [Formula: see text] respond to population interaction and environmental barrier intensity. Finally, the study of invasion front morphology through dynamic scaling analysis showed that growth exponent, [Formula: see text], depends on both population interaction and environmental barrier intensity. Saturated interface width, [Formula: see text], versus width of the 2D environment (L) also exhibits scaling behavior. Our findings show revealed that competition among subpopulations leads to more rough invasion fronts. Considering the wide range of shreds of evidence for clonal diversity in cancer cell populations, our findings suggest that interactions between such diverse populations can potentially participate in the irregularities of tumor border.
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Affiliation(s)
- Youness Azimzade
- Department of Physics, University of Tehran, Tehran, 14395-547, Iran.
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40
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Slow expanders invade by forming dented fronts in microbial colonies. Proc Natl Acad Sci U S A 2022; 119:2108653119. [PMID: 34983839 PMCID: PMC8740590 DOI: 10.1073/pnas.2108653119] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2021] [Indexed: 12/19/2022] Open
Abstract
Living organisms never cease to evolve, so there is a significant interest in predicting and controlling evolution in all branches of life sciences. The most basic question is whether a trait should increase or decrease in a given environment. The answer seems to be trivial for traits such as the growth rate in a bioreactor or the expansion rate of a tumor. Yet, it has been suggested that such traits can decrease, rather than increase, during evolution. Here, we report a mutant that outcompeted the ancestor despite having a slower expansion velocity when in isolation. To explain this observation, we developed and validated a theory that describes spatial competition between organisms with different expansion rates and arbitrary competitive interactions. Most organisms grow in space, whether they are viruses spreading within a host tissue or invasive species colonizing a new continent. Evolution typically selects for higher expansion rates during spatial growth, but it has been suggested that slower expanders can take over under certain conditions. Here, we report an experimental observation of such population dynamics. We demonstrate that mutants that grow slower in isolation nevertheless win in competition, not only when the two types are intermixed, but also when they are spatially segregated into sectors. The latter was thought to be impossible because previous studies focused exclusively on the global competitions mediated by expansion velocities, but overlooked the local competitions at sector boundaries. Local competition, however, can enhance the velocity of either type at the sector boundary and thus alter expansion dynamics. We developed a theory that accounts for both local and global competitions and describes all possible sector shapes. In particular, the theory predicted that a slower on its own, but more competitive, mutant forms a dented V-shaped sector as it takes over the expansion front. Such sectors were indeed observed experimentally, and their shapes matched quantitatively with the theory. In simulations, we further explored several mechanisms that could provide slow expanders with a local competitive advantage and showed that they are all well-described by our theory. Taken together, our results shed light on previously unexplored outcomes of spatial competition and establish a universal framework to understand evolutionary and ecological dynamics in expanding populations.
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41
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Nousi A, Søgaard MT, Audoin M, Jauffred L. Single-cell tracking reveals super-spreading brain cancer cells with high persistence. Biochem Biophys Rep 2021; 28:101120. [PMID: 34541340 PMCID: PMC8435994 DOI: 10.1016/j.bbrep.2021.101120] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/12/2021] [Accepted: 08/26/2021] [Indexed: 01/06/2023] Open
Abstract
Cell migration is a fundamental characteristic of vital processes such as tissue morphogenesis, wound healing and immune cell homing to lymph nodes and inflamed or infected sites. Therefore, various brain defect diseases, chronic inflammatory diseases as well as tumor formation and metastasis are associated with aberrant or absent cell migration. We embedded multicellular brain cancer spheroids in Matrigel™ and utilized single-particle tracking to extract the paths of cells migrating away from the spheroids. We found that - in contrast to local invasion - single cell migration is independent of Matrigel™ concentration and is characterized by high directionality and persistence. Furthermore, we identified a subpopulation of super-spreading cells with >200-fold longer persistence times than the majority of cells. These results highlight yet another aspect of cell heterogeneity in tumors.
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Affiliation(s)
| | - Maria Tangen Søgaard
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, DK-2100, Copenhagen O, Denmark
| | | | - Liselotte Jauffred
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, DK-2100, Copenhagen O, Denmark
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42
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Campbell NR, Rao A, Hunter MV, Sznurkowska MK, Briker L, Zhang M, Baron M, Heilmann S, Deforet M, Kenny C, Ferretti LP, Huang TH, Perlee S, Garg M, Nsengimana J, Saini M, Montal E, Tagore M, Newton-Bishop J, Middleton MR, Corrie P, Adams DJ, Rabbie R, Aceto N, Levesque MP, Cornell RA, Yanai I, Xavier JB, White RM. Cooperation between melanoma cell states promotes metastasis through heterotypic cluster formation. Dev Cell 2021; 56:2808-2825.e10. [PMID: 34529939 DOI: 10.1016/j.devcel.2021.08.018] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 07/07/2021] [Accepted: 08/20/2021] [Indexed: 02/08/2023]
Abstract
Melanomas can have multiple coexisting cell states, including proliferative (PRO) versus invasive (INV) subpopulations that represent a "go or grow" trade-off; however, how these populations interact is poorly understood. Using a combination of zebrafish modeling and analysis of patient samples, we show that INV and PRO cells form spatially structured heterotypic clusters and cooperate in the seeding of metastasis, maintaining cell state heterogeneity. INV cells adhere tightly to each other and form clusters with a rim of PRO cells. Intravital imaging demonstrated cooperation in which INV cells facilitate dissemination of less metastatic PRO cells. We identified the TFAP2 neural crest transcription factor as a master regulator of clustering and PRO/INV states. Isolation of clusters from patients with metastatic melanoma revealed a subset with heterotypic PRO-INV clusters. Our data suggest a framework for the co-existence of these two divergent cell populations, in which heterotypic clusters promote metastasis via cell-cell cooperation.
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Affiliation(s)
- Nathaniel R Campbell
- Weill Cornell/Rockefeller Memorial Sloan Kettering Tri-Institutional MD-PhD Program, New York, NY 10065, USA; Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Anjali Rao
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Miranda V Hunter
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Magdalena K Sznurkowska
- Department of Biology, Institute of Molecular Health Sciences, Swiss Federal Institute of Technology (ETH) Zurich, 8093 Zurich, Switzerland
| | - Luzia Briker
- Department of Dermatology, University of Zürich Hospital, University of Zürich, Zurich, Switzerland
| | - Maomao Zhang
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Maayan Baron
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Silja Heilmann
- Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Maxime Deforet
- Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Colin Kenny
- Department of Anatomy and Cell Biology, University of Iowa, Iowa City, IA 52242, USA
| | - Lorenza P Ferretti
- Department of Dermatology, University of Zürich Hospital, University of Zürich, Zurich, Switzerland; Department of Molecular Mechanisms of Disease, University of Zürich, Zurich, Switzerland
| | - Ting-Hsiang Huang
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sarah Perlee
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Manik Garg
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, UK
| | - Jérémie Nsengimana
- Leeds Institute of Medical Research at St. James's, University of Leeds School of Medicine, Leeds, UK
| | - Massimo Saini
- Department of Biology, Institute of Molecular Health Sciences, Swiss Federal Institute of Technology (ETH) Zurich, 8093 Zurich, Switzerland
| | - Emily Montal
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Mohita Tagore
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Julia Newton-Bishop
- Leeds Institute of Medical Research at St. James's, University of Leeds School of Medicine, Leeds, UK
| | - Mark R Middleton
- Oxford NIHR Biomedical Research Centre and Department of Oncology, University of Oxford, Oxford, UK
| | - Pippa Corrie
- Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - David J Adams
- Experimental Cancer Genetics, the Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Roy Rabbie
- Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Experimental Cancer Genetics, the Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Nicola Aceto
- Department of Biology, Institute of Molecular Health Sciences, Swiss Federal Institute of Technology (ETH) Zurich, 8093 Zurich, Switzerland
| | - Mitchell P Levesque
- Department of Dermatology, University of Zürich Hospital, University of Zürich, Zurich, Switzerland
| | - Robert A Cornell
- Department of Anatomy and Cell Biology, University of Iowa, Iowa City, IA 52242, USA
| | - Itai Yanai
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Joao B Xavier
- Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
| | - Richard M White
- Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
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43
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Lang J, Nie Q, Li C. Landscape and kinetic path quantify critical transitions in epithelial-mesenchymal transition. Biophys J 2021; 120:4484-4500. [PMID: 34480928 PMCID: PMC8553640 DOI: 10.1016/j.bpj.2021.08.043] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/04/2021] [Accepted: 08/30/2021] [Indexed: 01/11/2023] Open
Abstract
Epithelial-mesenchymal transition (EMT), a basic developmental process that might promote cancer metastasis, has been studied from various perspectives. Recently, the early warning theory has been used to anticipate critical transitions in EMT from mathematical modeling. However, the underlying mechanisms of EMT involving complex molecular networks remain to be clarified. Especially, how to quantify the global stability and stochastic transition dynamics of EMT and what the underlying mechanism for early warning theory in EMT is remain to be fully clarified. To address these issues, we constructed a comprehensive gene regulatory network model for EMT and quantified the corresponding potential landscape. The landscape for EMT displays multiple stable attractors, which correspond to E, M, and some other intermediate states. Based on the path-integral approach, we identified the most probable transition paths of EMT, which are supported by experimental data. Correspondingly, the results of transition actions demonstrated that intermediate states can accelerate EMT, consistent with recent studies. By integrating the landscape and path with early warning concept, we identified the potential barrier height from the landscape as a global and more accurate measure for early warning signals to predict critical transitions in EMT. The landscape results also provide an intuitive and quantitative explanation for the early warning theory. Overall, the landscape and path results advance our mechanistic understanding of dynamical transitions and roles of intermediate states in EMT, and the potential barrier height provides a new, to our knowledge, measure for critical transitions and quantitative explanations for the early warning theory.
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Affiliation(s)
- Jintong Lang
- Institute of Science and Technology for Brain-Inspired Intelligence, Shanghai, China; Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, California
| | - Chunhe Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Shanghai, China; Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China; School of Mathematical Sciences, Fudan University, Shanghai, China.
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44
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Kareva I, Luddy KA, O’Farrelly C, Gatenby RA, Brown JS. Predator-Prey in Tumor-Immune Interactions: A Wrong Model or Just an Incomplete One? Front Immunol 2021; 12:668221. [PMID: 34531851 PMCID: PMC8438324 DOI: 10.3389/fimmu.2021.668221] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 08/05/2021] [Indexed: 01/05/2023] Open
Abstract
Tumor-immune interactions are often framed as predator-prey. This imperfect analogy describes how immune cells (the predators) hunt and kill immunogenic tumor cells (the prey). It allows for evaluation of tumor cell populations that change over time during immunoediting and it also considers how the immune system changes in response to these alterations. However, two aspects of predator-prey type models are not typically observed in immuno-oncology. The first concerns the conversion of prey killed into predator biomass. In standard predator-prey models, the predator relies on the prey for nutrients, while in the tumor microenvironment the predator and prey compete for resources (e.g. glucose). The second concerns oscillatory dynamics. Standard predator-prey models can show a perpetual cycling in both prey and predator population sizes, while in oncology we see increases in tumor volume and decreases in infiltrating immune cell populations. Here we discuss the applicability of predator-prey models in the context of cancer immunology and evaluate possible causes for discrepancies. Key processes include "safety in numbers", resource availability, time delays, interference competition, and immunoediting. Finally, we propose a way forward to reconcile differences between model predictions and empirical observations. The immune system is not just predator-prey. Like natural food webs, the immune-tumor community of cell types forms an immune-web of different and identifiable interactions.
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Affiliation(s)
- Irina Kareva
- EMD Serono, Merck KGaA, Billerica, MA, United States
| | - Kimberly A. Luddy
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland
| | - Cliona O’Farrelly
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland
| | - Robert A. Gatenby
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, United States
| | - Joel S. Brown
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, United States
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45
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Davern M, Donlon NE, Power R, Hayes C, King R, Dunne MR, Reynolds JV. The tumour immune microenvironment in oesophageal cancer. Br J Cancer 2021; 125:479-494. [PMID: 33903730 PMCID: PMC8368180 DOI: 10.1038/s41416-021-01331-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 01/16/2021] [Accepted: 02/17/2021] [Indexed: 02/02/2023] Open
Abstract
Oesophageal cancer (OC) is an inflammation-associated malignancy linked to gastro-oesophageal reflux disease, obesity and tobacco use. Knowledge of the microenvironment of oesophageal tumours is relevant to our understanding of the development of OC and its biology, and has major implications for understanding the response to standard therapies and immunotherapies, as well as for uncovering novel targets. In this context, we discuss what is known about the TME in OC from tumour initiation to development and progression, and how this is relevant to therapy sensitivity and resistance in the two major types of OC. We provide an immunological characterisation of the OC TME and discuss its prognostic implications with specific comparison with the Immunoscore and immune-hot, -cold, altered-immunosuppressed and -altered-excluded models. Targeted therapeutics for the TME under pre-clinical and clinical investigation in OCs are also summarised. A deeper understanding of the TME will enable the development of combination approaches to concurrently target the tumour cells and TME delivering precision medicine to OC patients.
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Affiliation(s)
- Maria Davern
- Department of Surgery, School of Medicine, Trinity College Dublin, Dublin, Ireland
- Trinity St James's Cancer Institute, St James's Hospital, Dublin, Ireland
| | - Noel E Donlon
- Department of Surgery, School of Medicine, Trinity College Dublin, Dublin, Ireland
- Trinity St James's Cancer Institute, St James's Hospital, Dublin, Ireland
| | - Robert Power
- Department of Surgery, School of Medicine, Trinity College Dublin, Dublin, Ireland
- Trinity St James's Cancer Institute, St James's Hospital, Dublin, Ireland
| | - Conall Hayes
- Department of Surgery, School of Medicine, Trinity College Dublin, Dublin, Ireland
- Trinity St James's Cancer Institute, St James's Hospital, Dublin, Ireland
| | - Ross King
- Department of Surgery, School of Medicine, Trinity College Dublin, Dublin, Ireland
- Trinity St James's Cancer Institute, St James's Hospital, Dublin, Ireland
| | - Margaret R Dunne
- Department of Surgery, School of Medicine, Trinity College Dublin, Dublin, Ireland
- Trinity St James's Cancer Institute, St James's Hospital, Dublin, Ireland
| | - John V Reynolds
- Department of Surgery, School of Medicine, Trinity College Dublin, Dublin, Ireland.
- Trinity St James's Cancer Institute, St James's Hospital, Dublin, Ireland.
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46
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Wrenn E, Huang Y, Cheung K. Collective metastasis: coordinating the multicellular voyage. Clin Exp Metastasis 2021; 38:373-399. [PMID: 34254215 PMCID: PMC8346286 DOI: 10.1007/s10585-021-10111-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 06/14/2021] [Indexed: 12/16/2022]
Abstract
The metastatic process is arduous. Cancer cells must escape the confines of the primary tumor, make their way into and travel through the circulation, then survive and proliferate in unfavorable microenvironments. A key question is how cancer cells overcome these multiple barriers to orchestrate distant organ colonization. Accumulating evidence in human patients and animal models supports the hypothesis that clusters of tumor cells can complete the entire metastatic journey in a process referred to as collective metastasis. Here we highlight recent studies unraveling how multicellular coordination, via both physical and biochemical coupling of cells, induces cooperative properties advantageous for the completion of metastasis. We discuss conceptual challenges and unique mechanisms arising from collective dissemination that are distinct from single cell-based metastasis. Finally, we consider how the dissection of molecular transitions regulating collective metastasis could offer potential insight into cancer therapy.
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Affiliation(s)
- Emma Wrenn
- Translational Research Program, Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, 98195, USA
| | - Yin Huang
- Translational Research Program, Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Kevin Cheung
- Translational Research Program, Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.
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47
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Cassidy T, Nichol D, Robertson-Tessi M, Craig M, Anderson ARA. The role of memory in non-genetic inheritance and its impact on cancer treatment resistance. PLoS Comput Biol 2021; 17:e1009348. [PMID: 34460809 PMCID: PMC8432806 DOI: 10.1371/journal.pcbi.1009348] [Citation(s) in RCA: 8] [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: 03/01/2021] [Revised: 09/10/2021] [Accepted: 08/11/2021] [Indexed: 12/24/2022] Open
Abstract
Intra-tumour heterogeneity is a leading cause of treatment failure and disease progression in cancer. While genetic mutations have long been accepted as a primary mechanism of generating this heterogeneity, the role of phenotypic plasticity is becoming increasingly apparent as a driver of intra-tumour heterogeneity. Consequently, understanding the role of this plasticity in treatment resistance and failure is a key component of improving cancer therapy. We develop a mathematical model of stochastic phenotype switching that tracks the evolution of drug-sensitive and drug-tolerant subpopulations to clarify the role of phenotype switching on population growth rates and tumour persistence. By including cytotoxic therapy in the model, we show that, depending on the strategy of the drug-tolerant subpopulation, stochastic phenotype switching can lead to either transient or permanent drug resistance. We study the role of phenotypic heterogeneity in a drug-resistant, genetically homogeneous population of non-small cell lung cancer cells to derive a rational treatment schedule that drives population extinction and avoids competitive release of the drug-tolerant sub-population. This model-informed therapeutic schedule results in increased treatment efficacy when compared against periodic therapy, and, most importantly, sustained tumour decay without the development of resistance.
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Affiliation(s)
- Tyler Cassidy
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Daniel Nichol
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Morgan Craig
- Département de mathématiques et de statistique, Université de Montréal, Montreal, Canada
- CHU Sainte-Justine, Montreal, Canada
| | - Alexander R. A. Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida, United States of America
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48
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Bukkuri A, Adler FR. Viewing Cancer Through the Lens of Corruption: Using Behavioral Ecology to Understand Cancer. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.678533] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
All biological systems depend on signals for coordination: signals which pass information among agents that run the gamut from cells to organisms. However, their very importance makes signals vulnerable to subversion. How can a receiver know whether a signal is honest or deceptive? In other words, are signals necessarily a reliable indicator of agent quality or need? By drawing parallels to ecological phenomena ranging from begging by nestlings to social insects, we investigate the role of signal degradation in cancer. We thus think of cancer as a form of corruption, in which cells command huge resource investment through relatively cheap signals, just as relatively small bribes can leverage large profits. We discuss various mechanisms which prevent deceptive signaling in the natural world and within tissues. We show how cancers evolve ways to escape these controls and relate these back to evasion mechanisms in ecology. We next introduce two related concepts, co-option and collusion, and show how they play critical roles in ecology and cancer. Drawing on public policy, we propose new approaches to view treatment based on taxation, changing the incentive structure, and the recognition of corrupted signaling networks.
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49
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Fiandaca G, Delitala M, Lorenzi T. A Mathematical Study of the Influence of Hypoxia and Acidity on the Evolutionary Dynamics of Cancer. Bull Math Biol 2021; 83:83. [PMID: 34129102 PMCID: PMC8205926 DOI: 10.1007/s11538-021-00914-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 05/25/2021] [Indexed: 10/31/2022]
Abstract
Hypoxia and acidity act as environmental stressors promoting selection for cancer cells with a more aggressive phenotype. As a result, a deeper theoretical understanding of the spatio-temporal processes that drive the adaptation of tumour cells to hypoxic and acidic microenvironments may open up new avenues of research in oncology and cancer treatment. We present a mathematical model to study the influence of hypoxia and acidity on the evolutionary dynamics of cancer cells in vascularised tumours. The model is formulated as a system of partial integro-differential equations that describe the phenotypic evolution of cancer cells in response to dynamic variations in the spatial distribution of three abiotic factors that are key players in tumour metabolism: oxygen, glucose and lactate. The results of numerical simulations of a calibrated version of the model based on real data recapitulate the eco-evolutionary spatial dynamics of tumour cells and their adaptation to hypoxic and acidic microenvironments. Moreover, such results demonstrate how nonlinear interactions between tumour cells and abiotic factors can lead to the formation of environmental gradients which select for cells with phenotypic characteristics that vary with distance from intra-tumour blood vessels, thus promoting the emergence of intra-tumour phenotypic heterogeneity. Finally, our theoretical findings reconcile the conclusions of earlier studies by showing that the order in which resistance to hypoxia and resistance to acidity arise in tumours depend on the ways in which oxygen and lactate act as environmental stressors in the evolutionary dynamics of cancer cells.
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Affiliation(s)
- Giada Fiandaca
- Department of Mathematical Sciences "G. L. Lagrange", Politecnico di Torino, Corso Duca degli Abruzzi, 24, Torino, Italy
| | - Marcello Delitala
- Department of Mathematical Sciences "G. L. Lagrange", Politecnico di Torino, Corso Duca degli Abruzzi, 24, Torino, Italy
| | - Tommaso Lorenzi
- Department of Mathematical Sciences "G. L. Lagrange", Politecnico di Torino, Corso Duca degli Abruzzi, 24, Torino, Italy.
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Murugan A, Husain K, Rust MJ, Hepler C, Bass J, Pietsch JMJ, Swain PS, Jena SG, Toettcher JE, Chakraborty AK, Sprenger KG, Mora T, Walczak AM, Rivoire O, Wang S, Wood KB, Skanata A, Kussell E, Ranganathan R, Shih HY, Goldenfeld N. Roadmap on biology in time varying environments. Phys Biol 2021; 18:10.1088/1478-3975/abde8d. [PMID: 33477124 PMCID: PMC8652373 DOI: 10.1088/1478-3975/abde8d] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [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: 01/21/2021] [Indexed: 02/02/2023]
Abstract
Biological organisms experience constantly changing environments, from sudden changes in physiology brought about by feeding, to the regular rising and setting of the Sun, to ecological changes over evolutionary timescales. Living organisms have evolved to thrive in this changing world but the general principles by which organisms shape and are shaped by time varying environments remain elusive. Our understanding is particularly poor in the intermediate regime with no separation of timescales, where the environment changes on the same timescale as the physiological or evolutionary response. Experiments to systematically characterize the response to dynamic environments are challenging since such environments are inherently high dimensional. This roadmap deals with the unique role played by time varying environments in biological phenomena across scales, from physiology to evolution, seeking to emphasize the commonalities and the challenges faced in this emerging area of research.
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Affiliation(s)
- Arvind Murugan
- James Franck Institute, Department of Physics, University of Chicago, Chicago, IL 60637, United States of America
| | - Kabir Husain
- James Franck Institute, Department of Physics, University of Chicago, Chicago, IL 60637, United States of America
| | - Michael J Rust
- Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 60637, United States of America
- Department of Physics, University of Chicago, Chicago, IL 60637, United States of America
| | - Chelsea Hepler
- Department of Medicine, Feinberg School of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University, Chicago, IL 60611, United States of America
| | - Joseph Bass
- Department of Medicine, Feinberg School of Medicine, Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University, Chicago, IL 60611, United States of America
| | - Julian M J Pietsch
- SynthSys: Centre for Synthetic and Systems Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
| | - Peter S Swain
- SynthSys: Centre for Synthetic and Systems Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
| | - Siddhartha G Jena
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544, United States of America
| | - Jared E Toettcher
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544, United States of America
| | - Arup K Chakraborty
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
- Ragon Institute of the Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Cambridge, MA 02139, United States of America
| | - Kayla G Sprenger
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
- Ragon Institute of the Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Cambridge, MA 02139, United States of America
| | - T Mora
- Laboratoire de physique, Ecole normale supérieure, CNRS, PSL Research University, Paris, France
| | - A M Walczak
- Laboratoire de physique, Ecole normale supérieure, CNRS, PSL Research University, Paris, France
| | - O Rivoire
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL Research University, Paris, France
| | - Shenshen Wang
- Department of Physics and Astronomy, University of California, Los Angeles, Los Angeles, CA 90095, United States of America
| | - Kevin B Wood
- Departments of Biophysics and Physics, University of Michigan, Ann Arbor, MI 48109-1055, United States of America
| | - Antun Skanata
- Center for Genomics and Systems Biology, New York University, 12 Waverly Place, Rm. 206, New York, NY 10003, United States of America
| | - Edo Kussell
- Center for Genomics and Systems Biology, New York University, 12 Waverly Place, Rm. 206, New York, NY 10003, United States of America
| | - Rama Ranganathan
- Center for Physics of Evolving Systems, Biochemistry & Molecular Biology, and the Pritzker School for Molecular Engineering, University of Chicago, Chicago IL 60637, United States of America
| | - Hong-Yan Shih
- Department of Physics, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, United States of America
- Institute of Physics, Academia Sinica, Taipei 11529, Taiwan
| | - Nigel Goldenfeld
- Department of Physics, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, United States of America
- Carl R Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, United States of America
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