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Del Pino Herrera A, Ferrall-Fairbanks MC. A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies. Front Genet 2024; 15:1383676. [PMID: 38873108 PMCID: PMC11169904 DOI: 10.3389/fgene.2024.1383676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 04/26/2024] [Indexed: 06/15/2024] Open
Abstract
Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.
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Affiliation(s)
- Adriana Del Pino Herrera
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Meghan C. Ferrall-Fairbanks
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
- University of Florida Health Cancer Center, University of Florida, Gainesville, FL, United States
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2
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Choi J. Spatial simulation of autologous cell defection for cancer treatment. Evol Med Public Health 2023; 11:461-471. [PMID: 38111808 PMCID: PMC10727474 DOI: 10.1093/emph/eoad042] [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: 08/09/2023] [Revised: 11/02/2023] [Indexed: 12/20/2023] Open
Abstract
Cancer cells are highly cooperative in a nepotistic way and evolutionarily dynamic. Present cancer treatments often overlook these aspects, inducing the selection of resistant cancer cells and the corresponding relapse. As an alternative method of cancer elimination, autologous cell defection (ACD) was suggested by which modified cancer cells parasitically reliant on other cancer cells are implemented to the cancer cluster. Specifically, modified cancer cells should not produce costly growth factors that promote the growth of other cancer cells while receiving the benefit of exposure to such growth factors. Analytical models and rudimentary experiments up to date provide the medical feasibility of this method. In this study, I built comprehensive spatial simulation models by embracing the effects of the multiple growth factors, the Warburg effect, mutations and immunity. The simulation results based on planar spatial structures indicate that implementation of the defective modified tumours may replace the existing cancer cluster and defective cells would later collapse by themselves. Furthermore, I built a mathematical model that compares the fitness of the cells adjacent to the hypertumour-cancer interface. I also calculated whether anticancer drugs that reduce the effects of the growth factors promote or demote the utility of ACD under diverse fitness functions. The computational examination implies that anticancer drugs may impede the therapeutic effect of ACD when there is a strong concavity in the fitness function. The analysis results could work as a general guidance for effective ACD that may expand the paradigm of cancer treatment.
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Affiliation(s)
- Jibeom Choi
- Department of Applied Mathematics, College of Applied Sciences, Kyung Hee University, Yongin 17104, Republic of Korea
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Capp J, Thomas F, Marusyk A, M. Dujon A, Tissot S, Gatenby R, Roche B, Ujvari B, DeGregori J, Brown JS, Nedelcu AM. The paradox of cooperation among selfish cancer cells. Evol Appl 2023; 16:1239-1256. [PMID: 37492150 PMCID: PMC10363833 DOI: 10.1111/eva.13571] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 05/19/2023] [Accepted: 06/06/2023] [Indexed: 07/27/2023] Open
Abstract
It is traditionally assumed that during cancer development, tumor cells abort their initially cooperative behavior (i.e., cheat) in favor of evolutionary strategies designed solely to enhance their own fitness (i.e., a "selfish" life style) at the expense of that of the multicellular organism. However, the growth and progress of solid tumors can also involve cooperation among these presumed selfish cells (which, by definition, should be noncooperative) and with stromal cells. The ultimate and proximate reasons behind this paradox are not fully understood. Here, in the light of current theories on the evolution of cooperation, we discuss the possible evolutionary mechanisms that could explain the apparent cooperative behaviors among selfish malignant cells. In addition to the most classical explanations for cooperation in cancer and in general (by-product mutualism, kin selection, direct reciprocity, indirect reciprocity, network reciprocity, group selection), we propose the idea that "greenbeard" effects are relevant to explaining some cooperative behaviors in cancer. Also, we discuss the possibility that malignant cooperative cells express or co-opt cooperative traits normally expressed by healthy cells. We provide examples where considerations of these processes could help understand tumorigenesis and metastasis and argue that this framework provides novel insights into cancer biology and potential strategies for cancer prevention and treatment.
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Affiliation(s)
- Jean‐Pascal Capp
- Toulouse Biotechnology InstituteUniversity of Toulouse, INSA, CNRS, INRAEToulouseFrance
| | - Frédéric Thomas
- CREEC, MIVEGECUniversity of Montpellier, CNRS, IRDMontpellierFrance
| | - Andriy Marusyk
- Department of Cancer PhysiologyH Lee Moffitt Cancer Center and Research InstituteTampaFloridaUSA
| | - Antoine M. Dujon
- Centre for Integrative Ecology, School of Life and Environmental SciencesDeakin UniversityGeelongVictoriaAustralia
| | - Sophie Tissot
- Department of Biochemistry and Molecular GeneticsUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Robert Gatenby
- Department of Cancer PhysiologyH Lee Moffitt Cancer Center and Research InstituteTampaFloridaUSA
| | - Benjamin Roche
- Department of Biochemistry and Molecular GeneticsUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Beata Ujvari
- Centre for Integrative Ecology, School of Life and Environmental SciencesDeakin UniversityGeelongVictoriaAustralia
| | - James DeGregori
- Department of Biochemistry and Molecular GeneticsUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Joel S. Brown
- Department of Cancer PhysiologyH Lee Moffitt Cancer Center and Research InstituteTampaFloridaUSA
| | - Aurora M. Nedelcu
- Department of BiologyUniversity of New BrunswickFrederictonNew BrunswickCanada
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Laruelle A, Rocha A, Manini C, López JI, Inarra E. Effects of Heterogeneity on Cancer: A Game Theory Perspective. Bull Math Biol 2023; 85:72. [PMID: 37336793 DOI: 10.1007/s11538-023-01178-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 06/13/2023] [Indexed: 06/21/2023]
Abstract
In this study, we explore interactions between cancer cells by using the hawk-dove game. We analyze the heterogeneity of tumors by considering games with populations composed of 2 or 3 types of cell. We determine what strategies are evolutionarily stable in the 2-type and 3-type population games and what the corresponding expected payoffs are. Our results show that the payoff of the best-off cell in the 2-type population game is higher than that of the best-off cell in the 3-type population game. When these mathematical findings are transferred to the field of oncology they suggest that a tumor with low intratumor heterogeneity pursues a more aggressive course than one with high intratumor heterogeneity. Some histological and genomic data on clear cell renal cell carcinomas is consistent with these results. We underline the importance of identifying intratumor heterogeneity in routine practice and suggest that therapeutic strategies that preserve heterogeneity may be promising as they may slow down cancer growth.
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Affiliation(s)
- Annick Laruelle
- Department of Economic Analysis (ANEKO), University of the Basque Country (UPV/EHU), Avenida Lehendakari Aguirre, 83, 48015, Bilbao, Spain.
- IKERBASQUE, Basque Foundation of Science, 48011, Bilbao, Spain.
| | - André Rocha
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente 225, Gávea, Rio de Janeiro, RJ, CEP 22451-900, Brazil
| | - Claudia Manini
- Department of Pathology, San Giovanni Bosco Hospital, 10154, Turin, Italy
- Department of Sciences of Public Health and Pediatrics, University of Turin, 10124, Turin, Italy
| | - José I López
- Department of Pathology, Cruces University Hospital, 48903, Barakaldo, Spain
- Biomarkers in Cancer Group, Biocruces-Bizkaia Research Institute, 48903, Barakaldo, Spain
| | - Elena Inarra
- Department of Economic Analysis (ANEKO), University of the Basque Country (UPV/EHU), Avenida Lehendakari Aguirre, 83, 48015, Bilbao, Spain
- Institute of Public Economics, University of the Basque Country (UPV/EHU), Avenida Lehendakari Aguirre, 83, 48015, Bilbao, Spain
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Lee ND, Kaveh K, Bozic I. Clonal interactions in cancer: integrating quantitative models with experimental and clinical data. Semin Cancer Biol 2023; 92:61-73. [PMID: 37023969 DOI: 10.1016/j.semcancer.2023.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/16/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023]
Abstract
Tumors consist of different genotypically distinct subpopulations-or subclones-of cells. These subclones can influence neighboring clones in a process called "clonal interaction." Conventionally, research on driver mutations in cancer has focused on their cell-autonomous effects that lead to an increase in fitness of the cells containing the driver. Recently, with the advent of improved experimental and computational technologies for investigating tumor heterogeneity and clonal dynamics, new studies have shown the importance of clonal interactions in cancer initiation, progression, and metastasis. In this review we provide an overview of clonal interactions in cancer, discussing key discoveries from a diverse range of approaches to cancer biology research. We discuss common types of clonal interactions, such as cooperation and competition, its mechanisms, and the overall effect on tumorigenesis, with important implications for tumor heterogeneity, resistance to treatment, and tumor suppression. Quantitative models-in coordination with cell culture and animal model experiments-have played a vital role in investigating the nature of clonal interactions and the complex clonal dynamics they generate. We present mathematical and computational models that can be used to represent clonal interactions and provide examples of the roles they have played in identifying and quantifying the strength of clonal interactions in experimental systems. Clonal interactions have proved difficult to observe in clinical data; however, several very recent quantitative approaches enable their detection. We conclude by discussing ways in which researchers can further integrate quantitative methods with experimental and clinical data to elucidate the critical-and often surprising-roles of clonal interactions in human cancers.
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Affiliation(s)
- Nathan D Lee
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America
| | - Kamran Kaveh
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America
| | - Ivana Bozic
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America; Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
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Coggan H, Page KM. The role of evolutionary game theory in spatial and non-spatial models of the survival of cooperation in cancer: a review. JOURNAL OF THE ROYAL SOCIETY, INTERFACE 2022; 19:20220346. [PMID: 35975562 PMCID: PMC9382458 DOI: 10.1098/rsif.2022.0346] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Evolutionary game theory (EGT) is a branch of mathematics which considers populations of individuals interacting with each other to receive pay-offs. An individual’s pay-off is dependent on the strategy of its opponent(s) as well as on its own, and the higher its pay-off, the higher its reproductive fitness. Its offspring generally inherit its interaction strategy, subject to random mutation. Over time, the composition of the population shifts as different strategies spread or are driven extinct. In the last 25 years there has been a flood of interest in applying EGT to cancer modelling, with the aim of explaining how cancerous mutations spread through healthy tissue and how intercellular cooperation persists in tumour-cell populations. This review traces this body of work from theoretical analyses of well-mixed infinite populations through to more realistic spatial models of the development of cooperation between epithelial cells. We also consider work in which EGT has been used to make experimental predictions about the evolution of cancer, and discuss work that remains to be done before EGT can make large-scale contributions to clinical treatment and patient outcomes.
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Affiliation(s)
- Helena Coggan
- Department of Mathematics, University College London, London, UK
| | - Karen M Page
- Department of Mathematics, University College London, London, UK
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