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Axenie C, López-Corona O, Makridis MA, Akbarzadeh M, Saveriano M, Stancu A, West J. Antifragility as a complex system's response to perturbations, volatility, and time. ARXIV 2023:arXiv:2312.13991v1. [PMID: 38196741 PMCID: PMC10775345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Antifragility characterizes the benefit of a dynamical system derived from the variability in environmental perturbations. Antifragility carries a precise definition that quantifies a system's output response to input variability. Systems may respond poorly to perturbations (fragile) or benefit from perturbations (antifragile). In this manuscript, we review a range of applications of antifragility theory in technical systems (e.g., traffic control, robotics) and natural systems (e.g., cancer therapy, antibiotics). While there is a broad overlap in methods used to quantify and apply antifragility across disciplines, there is a need for precisely defining the scales at which antifragility operates. Thus, we provide a brief general introduction to the properties of antifragility in applied systems and review relevant literature for both natural and technical systems' antifragility. We frame this review within three scales common to technical systems: intrinsic (input-output nonlinearity), inherited (extrinsic environmental signals), and interventional (feedback control), with associated counterparts in biological systems: ecological (homogeneous systems), evolutionary (heterogeneous systems), and interventional (control). We use the common noun in designing systems that exhibit antifragile behavior across scales and guide the reader along the spectrum of fragility-adaptiveness-resilience-robustness-antifragility, the principles behind it, and its practical implications.
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Affiliation(s)
- Cristian Axenie
- Department of Computer Science and Center for Artificial Intelligence, Nuremberg Institute of Technology Georg Simon Ohm, Nuremberg, Germany
| | - Oliver López-Corona
- Investigadores por México (IxM) at Instituto de Investigaciones en Matemáticas Aplicadas y Sistemas (IIMAS), Universidad Nacional Autónoma de México (UNAM), Ciudad Universitaria, CDMX, México
| | | | - Meisam Akbarzadeh
- Department of Transportation Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Matteo Saveriano
- Department of Industrial Engineering, University of Trento, Trento, Italy
| | - Alexandru Stancu
- Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK
| | - Jeffrey West
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
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Kolben Y, Azmanov H, Gelman R, Dror D, Ilan Y. Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections. Ann Med 2023; 55:311-318. [PMID: 36594558 PMCID: PMC9815249 DOI: 10.1080/07853890.2022.2163053] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. Numerous methods are used to overcome this problem with moderate success. Besides efforts of antimicrobial stewards, several artificial intelligence (AI)-based technologies are being explored for preventing resistance development. These first-generation systems mainly focus on improving patients' adherence. Chronobiology is inherent in all biological systems. Host response to infections and pathogens activity are assumed to be affected by the circadian clock. This paper describes the problem of antimicrobial resistance and reviews some of the current AI technologies. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. An algorithm-controlled regimen that improves the long-term effectiveness of antimicrobial agents is being developed based on the implementation of variability in dosing and drug administration times. The method provides a means for ensuring a sustainable response and improved outcomes. Ongoing clinical trials determine the effectiveness of this second-generation system in chronic infections. Data from these studies are expected to shed light on a new aspect of resistance mechanisms and suggest methods for overcoming them.IMPORTANCE SECTIONThe paper presents the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.Key messagesAntimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents.We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.
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Affiliation(s)
- Yotam Kolben
- Department of Medicine, Faculty of Medicine, Hadassah Medical Center, Hebrew University, Jerusalem, Israel
| | - Henny Azmanov
- Department of Medicine, Faculty of Medicine, Hadassah Medical Center, Hebrew University, Jerusalem, Israel
| | - Ram Gelman
- Department of Medicine, Faculty of Medicine, Hadassah Medical Center, Hebrew University, Jerusalem, Israel
| | - Danna Dror
- Department of Clinical Microbiology and Infectious Diseases, Faculty of Medicine, Hadassah Medical Center, Hebrew University, Jerusalem, Israel
| | - Yaron Ilan
- Department of Medicine, Faculty of Medicine, Hadassah Medical Center, Hebrew University, Jerusalem, Israel
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Sun YH, Wu YL, Liao BY. Phenotypic heterogeneity in human genetic diseases: ultrasensitivity-mediated threshold effects as a unifying molecular mechanism. J Biomed Sci 2023; 30:58. [PMID: 37525275 PMCID: PMC10388531 DOI: 10.1186/s12929-023-00959-7] [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: 04/01/2023] [Accepted: 07/26/2023] [Indexed: 08/02/2023] Open
Abstract
Phenotypic heterogeneity is very common in genetic systems and in human diseases and has important consequences for disease diagnosis and treatment. In addition to the many genetic and non-genetic (e.g., epigenetic, environmental) factors reported to account for part of the heterogeneity, we stress the importance of stochastic fluctuation and regulatory network topology in contributing to phenotypic heterogeneity. We argue that a threshold effect is a unifying principle to explain the phenomenon; that ultrasensitivity is the molecular mechanism for this threshold effect; and discuss the three conditions for phenotypic heterogeneity to occur. We suggest that threshold effects occur not only at the cellular level, but also at the organ level. We stress the importance of context-dependence and its relationship to pleiotropy and edgetic mutations. Based on this model, we provide practical strategies to study human genetic diseases. By understanding the network mechanism for ultrasensitivity and identifying the critical factor, we may manipulate the weak spot to gently nudge the system from an ultrasensitive state to a stable non-disease state. Our analysis provides a new insight into the prevention and treatment of genetic diseases.
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Affiliation(s)
- Y Henry Sun
- Institute of Molecular and Genomic Medicine, National Health Research Institute, Zhunan, Miaoli, Taiwan.
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan.
| | - Yueh-Lin Wu
- Institute of Molecular and Genomic Medicine, National Health Research Institute, Zhunan, Miaoli, Taiwan
- Division of Nephrology, Department of Internal Medicine, Wei-Gong Memorial Hospital, Miaoli, Taiwan
- Division of Nephrology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei City, Taiwan
| | - Ben-Yang Liao
- Institute of Population Health Sciences, National Health Research Institute, Zhunan, Miaoli, Taiwan
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Gopal P, Petty A, Rogacki K, Bera T, Bareja R, Peacock CD, Abazeed ME. Multivalent state transitions shape the intratumoral composition of small cell lung carcinoma. SCIENCE ADVANCES 2022; 8:eabp8674. [PMID: 36516249 PMCID: PMC9750150 DOI: 10.1126/sciadv.abp8674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 10/26/2022] [Indexed: 06/17/2023]
Abstract
Studies to date have not resolved how diverse transcriptional programs contribute to the intratumoral heterogeneity of small cell lung carcinoma (SCLC), an aggressive tumor associated with a dismal prognosis. Here, we identify distinct and commutable transcriptional states that confer discrete functional attributes in individual SCLC tumors. We combine an integrative approach comprising the transcriptomes of 52,975 single cells, high-resolution measurement of cell state dynamics at the single-cell level, and functional and correlative studies using treatment naïve xenografts with associated clinical outcomes. We show that individual SCLC tumors contain distinctive proportions of stable cellular states that are governed by bidirectional cell state transitions. Using drugs that target the epigenome, we reconfigure tumor state composition in part by altering individual state transition rates. Our results reveal new insights into how single-cell transition behaviors promote cell state equilibrium in SCLC and suggest that facile plasticity underlies its resistance to therapy and lethality.
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Affiliation(s)
- Priyanka Gopal
- Department of Radiation Oncology, Northwestern University, Feinberg School of Medicine, 251 E. Huron St., Galter Pavilion LC-178, Chicago, IL 60611, USA
| | - Aaron Petty
- Department of Translational Hematology Oncology Research, Cleveland Clinic, 2111 East 96th St./NE-6, Cleveland, OH 44195, USA
| | - Kevin Rogacki
- Department of Radiation Oncology, Northwestern University, Feinberg School of Medicine, 251 E. Huron St., Galter Pavilion LC-178, Chicago, IL 60611, USA
| | - Titas Bera
- Department of Radiation Oncology, Northwestern University, Feinberg School of Medicine, 251 E. Huron St., Galter Pavilion LC-178, Chicago, IL 60611, USA
| | - Rohan Bareja
- Institute for Computational Biomedicine, Weill Cornell Medicine, 1305 York Ave., New York, NY 10021, USA
| | - Craig D. Peacock
- Department of Genetics and Genome Sciences, Case Western Reserve University, 2109 Adelbert Road, Biomedical Research Building 647B, Cleveland, OH 44106, USA
| | - Mohamed E. Abazeed
- Department of Radiation Oncology, Northwestern University, Feinberg School of Medicine, 251 E. Huron St., Galter Pavilion LC-178, Chicago, IL 60611, USA
- Robert H. Lurie Cancer Center, Northwestern University, 303 E. Superior St./Lurie 7, Chicago, IL 60611, USA
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5
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Brutovský B. Scales of Cancer Evolution: Selfish Genome or Cooperating Cells? Cancers (Basel) 2022; 14:cancers14133253. [PMID: 35805025 PMCID: PMC9264996 DOI: 10.3390/cancers14133253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 11/25/2022] Open
Abstract
Simple Summary Cancer continuously evolves its ability to survive in time-varying microenvironment, which results, regarding the therapeutic context, in its therapeutic resistance. As it is accepted that the development of resistance is the direct consequence of intratumour heterogeneity, its evolutionary etiology is intensively studied. Models of carinogenesis are often assessed accordingly to how well they fit into the evolutionary scenario. In the paper, the relevant observations and concepts in cancer research, such as intratumour heterogeneity, cell plasticity, and Markov cell state dynamics, are reviewed and integrated into an evolutionary model. The possibility that the interaction between cancer cells can be interpreted as cooperation is proposed. Abstract The exploitation of the evolutionary modus operandi of cancer to steer its progression towards drug sensitive cancer cells is a challenging research topic. Integrating evolutionary principles into cancer therapy requires properly identified selection level, the relevant timescale, and the respective fitness of the principal selection unit on that timescale. Interpretation of some features of cancer progression, such as increased heterogeneity of isogenic cancer cells, is difficult from the most straightforward evolutionary view with the cancer cell as the principal selection unit. In the paper, the relation between the two levels of intratumour heterogeneity, genetic, due to genetic instability, and non-genetic, due to phenotypic plasticity, is reviewed and the evolutionary role of the latter is outlined. In analogy to the evolutionary optimization in a changing environment, the cell state dynamics in cancer clones are interpreted as the risk diversifying strategy bet hedging, optimizing the balance between the exploitation and exploration of the cell state space.
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Affiliation(s)
- Branislav Brutovský
- Department of Biophysics, Faculty of Science, P. J. Šafárik University, Jesenná 5, 041 54 Košice, Slovakia
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6
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Loria R, Vici P, Di Lisa FS, Soddu S, Maugeri-Saccà M, Bon G. Cross-Resistance Among Sequential Cancer Therapeutics: An Emerging Issue. Front Oncol 2022; 12:877380. [PMID: 35814399 PMCID: PMC9259985 DOI: 10.3389/fonc.2022.877380] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/04/2022] [Indexed: 11/13/2022] Open
Abstract
Over the past two decades, cancer treatment has benefited from having a significant increase in the number of targeted drugs approved by the United States Food and Drug Administration. With the introduction of targeted therapy, a great shift towards a new era has taken place that is characterized by reduced cytotoxicity and improved clinical outcomes compared to traditional chemotherapeutic drugs. At present, targeted therapies and other systemic anti-cancer therapies available (immunotherapy, cytotoxic, endocrine therapies and others) are used alone or in combination in different settings (neoadjuvant, adjuvant, and metastatic). As a result, it is not uncommon for patients affected by an advanced malignancy to receive subsequent anti-cancer therapies. In this challenging complexity of cancer treatment, the clinical pathways of real-life patients are often not as direct as predicted by standard guidelines and clinical trials, and cross-resistance among sequential anti-cancer therapies represents an emerging issue. In this review, we summarize the main cross-resistance events described in the diverse tumor types and provide insight into the molecular mechanisms involved in this process. We also discuss the current challenges and provide perspectives for the research and development of strategies to overcome cross-resistance and proceed towards a personalized approach.
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Affiliation(s)
- Rossella Loria
- Cellular Network and Molecular Therapeutic Target Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Patrizia Vici
- Unit of Phase IV Trials, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Sofia Di Lisa
- Unit of Phase IV Trials, IRCCS Regina Elena National Cancer Institute, Rome, Italy
- Medical Oncology A, Department of Radiological, Oncological, and Anatomo-Pathological Sciences, Umberto I University Hospital, University Sapienza, Rome, Italy
| | - Silvia Soddu
- Cellular Network and Molecular Therapeutic Target Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Marcello Maugeri-Saccà
- Division of Medical Oncology 2, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Giulia Bon
- Cellular Network and Molecular Therapeutic Target Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
- *Correspondence: Giulia Bon,
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Mathé-Hubert H, Amia R, Martin M, Gaffé J, Schneider D. Evolution of Bacterial Persistence to Antibiotics during a 50,000-Generation Experiment in an Antibiotic-Free Environment. Antibiotics (Basel) 2022; 11:antibiotics11040451. [PMID: 35453204 PMCID: PMC9028194 DOI: 10.3390/antibiotics11040451] [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] [Received: 02/25/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 02/05/2023] Open
Abstract
Failure of antibiotic therapies causes > 700,000 deaths yearly and involves both bacterial resistance and persistence. Persistence results in the relapse of infections by producing a tiny fraction of pathogen survivors that stay dormant during antibiotic exposure. From an evolutionary perspective, persistence is either a ‘bet-hedging strategy’ that helps to cope with stochastically changing environments or an unavoidable minimal rate of ‘cellular errors’ that lock the cells in a low activity state. Here, we analyzed the evolution of persistence over 50,000 bacterial generations in a stable environment by improving a published method that estimates the number of persister cells based on the growth of the reviving population. Our results challenged our understanding of the factors underlying persistence evolution. In one case, we observed a substantial decrease in persistence proportion, suggesting that the naturally observed persistence level is not an unavoidable minimal rate of ‘cellular errors’. However, although there was no obvious environmental stochasticity, in 11 of the 12 investigated populations, the persistence level was maintained during 50,000 bacterial generations.
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Foo J, Basanta D, Rockne RC, Strelez C, Shah C, Ghaffarian K, Mumenthaler SM, Mitchell K, Lathia JD, Frankhouser D, Branciamore S, Kuo YH, Marcucci G, Vander Velde R, Marusyk A, Hang S, Hari K, Jolly MK, Hatzikirou H, Poels K, Spilker M, Shtylla B, Robertson-Tessi M, Anderson ARA. Roadmap on plasticity and epigenetics in cancer. Phys Biol 2022; 19. [PMID: 35078159 PMCID: PMC9190291 DOI: 10.1088/1478-3975/ac4ee2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/25/2022] [Indexed: 11/22/2022]
Abstract
The role of plasticity and epigenetics in shaping cancer evolution and response to therapy has taken center stage with recent technological advances including single cell sequencing. This roadmap article is focused on state-of-the-art mathematical and experimental approaches to interrogate plasticity in cancer, and addresses the following themes and questions: is there a formal overarching framework that encompasses both non-genetic plasticity and mutation-driven somatic evolution? How do we measure and model the role of the microenvironment in influencing/controlling non-genetic plasticity? How can we experimentally study non-genetic plasticity? Which mathematical techniques are required or best suited? What are the clinical and practical applications and implications of these concepts?
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Affiliation(s)
- Jasmine Foo
- University of Minnesota System, School of Mathematics, Minneapolis, Minnesota, 55455-2020, UNITED STATES
| | - David Basanta
- Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Center Inc, H Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, MRC-3 West/IMO, Tampa, Florida 33612USA, Tampa, Florida, 33612-9416, UNITED STATES
| | - Russell C Rockne
- Computational and Quantitative Medicine; Division of Mathematical Oncology, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Carly Strelez
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Curran Shah
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Kimya Ghaffarian
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Kelly Mitchell
- Department of Cardiovascular & Metabolic Sciences, Cleveland Clinic, Lerner Research Institute, Cleveland, Ohio, 44195-5243, UNITED STATES
| | - Justin D Lathia
- Department of Cardiovascular & Metabolic Sciences, Cleveland Clinic, Lerner Research Institute, Cleveland, Ohio, 44195-5243, UNITED STATES
| | - David Frankhouser
- Computational and Quantitative Medicine; Division of Mathematical Oncology, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Sergio Branciamore
- Computational and Quantitative Medicine; Division of Mathematical Oncology, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Ya-Huei Kuo
- Hematologic Malignancies Translational Science, City of Hope National Medical Center, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Guido Marcucci
- Hematologic Malignancies Translational Science, City of Hope National Medical Center, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Robert Vander Velde
- Department of Cancer Physiology, H Lee Moffitt Cancer Center and Research Center Inc, H Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, MRC-3 West/IMO, Tampa, Florida 33612USA, Tampa, Florida, 33612-9416, UNITED STATES
| | - Andriy Marusyk
- Cancer Physiology, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, Florida, 33612, UNITED STATES
| | - Sui Hang
- Institute for Systems Biology, Systems Biology, WA , WA 98109, UNITED STATES
| | - Kishore Hari
- Indian Institute of Science, 560012 Bangalore, Bangalore, 560012, INDIA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering,, Indian Institute of Science, 560012 Bangalore, Bangalore, 560012, INDIA
| | - Haralampos Hatzikirou
- Khalifa University, P.O. Box: 127788, Abu Dhabi, Abu Dhabi, NA, UNITED ARAB EMIRATES
| | - Kamrine Poels
- Early Clinical Development, Pfizer Global Research and Development, Early Clinical Development, Groton, Connecticut, 06340, UNITED STATES
| | - Mary Spilker
- Medicine Design, Pfizer Global Research and Development, Medicine Design, Groton, Connecticut, 06340, UNITED STATES
| | - Blerta Shtylla
- Early Clinical Development, Pfizer Global Research and Development, Early Clinical Development, Groton, Connecticut, 06340, UNITED STATES
| | - Mark Robertson-Tessi
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, Florida, 33612, UNITED STATES
| | - Alexander R A Anderson
- Integrated Mathematical Oncology, Moffitt Cancer Center, Co-Director of Integrated Mathematical Oncology, 12902 Magnolia Drive, SRB 4 Rm 24000H, Tampa, Florida 33612, Tampa, 33612, UNITED STATES
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Tate AT, Van Cleve J. Bet-hedging in innate and adaptive immune systems. Evol Med Public Health 2022; 10:256-265. [PMID: 35712085 PMCID: PMC9195227 DOI: 10.1093/emph/eoac021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
Immune system evolution is shaped by the fitness costs and trade-offs associated with mounting an immune response. Costs that arise mainly as a function of the magnitude of investment, including energetic and immunopathological costs, are well-represented in studies of immune system evolution. Less well considered, however, are the costs of immune cell plasticity and specialization. Hosts in nature encounter a large diversity of microbes and parasites that require different and sometimes conflicting immune mechanisms for defense, but it takes precious time to recognize and correctly integrate signals for an effective polarized response. In this perspective, we propose that bet-hedging can be a viable alternative to plasticity in immune cell effector function, discuss conditions under which bet-hedging is likely to be an advantageous strategy for different arms of the immune system, and present cases from both innate and adaptive immune systems that suggest bet-hedging at play.
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Affiliation(s)
- Ann T Tate
- Department of Biological Sciences, Vanderbilt University , 465 21st Ave S. , Nashville, TN 37232, USA
- Vanderbilt Institute for Infection, Immunology, and Inflammation , Nashville, TN, USA
- Evolutionary Studies Institute, Vanderbilt University , Nashville, TN, USA
| | - Jeremy Van Cleve
- Department of Biology, University of Kentucky , 101 T.H. Morgan Building , Lexington, KY 40506, USA
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Protein conformational dynamics and phenotypic switching. Biophys Rev 2021; 13:1127-1138. [PMID: 35059032 PMCID: PMC8724335 DOI: 10.1007/s12551-021-00858-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 10/18/2021] [Indexed: 12/14/2022] Open
Abstract
Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure but exist as conformational ensembles. Because of their structural plasticity, they can interact with multiple partners. The protein interactions between IDPs and their partners form scale-free protein interaction networks (PINs) that facilitate information flow in the cell. Because of their plasticity, IDPs typically occupy hub positions in cellular PINs. Furthermore, their conformational dynamics and propensity for post-translational modifications contribute to "conformational" noise which is distinct from the well-recognized transcriptional noise. Therefore, upregulation of IDPs in response to a specific input, such as stress, contributes to increased noise and, hence, an increase in stochastic, "promiscuous" interactions. These interactions lead to activation of latent pathways or can induce "rewiring" of the PIN to yield an optimal output underscoring the critical role of IDPs in regulating information flow. We have used PAGE4, a highly intrinsically disordered stress-response protein as a paradigm. Employing a variety of experimental and computational techniques, we have elucidated the role of PAGE4 in phenotypic switching of prostate cancer cells at a systems level. These cumulative studies over the past decade provide a conceptual framework to better understand how IDP conformational dynamics and conformational noise might facilitate cellular decision-making.
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11
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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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12
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Bhattacharya S, Mohanty A, Achuthan S, Kotnala S, Jolly MK, Kulkarni P, Salgia R. Group Behavior and Emergence of Cancer Drug Resistance. Trends Cancer 2021; 7:323-334. [PMID: 33622644 PMCID: PMC8500356 DOI: 10.1016/j.trecan.2021.01.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/25/2021] [Accepted: 01/26/2021] [Indexed: 02/06/2023]
Abstract
Drug resistance is a major impediment in cancer. Although it is generally thought that acquired drug resistance is due to genetic mutations, emerging evidence indicates that nongenetic mechanisms also play an important role. Resistance emerges through a complex interplay of clonal groups within a heterogeneous tumor and the surrounding microenvironment. Traits such as phenotypic plasticity, intercellular communication, and adaptive stress response, act in concert to ensure survival of intermediate reversible phenotypes, until permanent, resistant clones can emerge. Understanding the role of group behavior, and the underlying nongenetic mechanisms, can lead to more efficacious treatment designs and minimize or delay emergence of resistance.
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Affiliation(s)
- Supriyo Bhattacharya
- Translational Bioinformatics, Center for Informatics, Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Atish Mohanty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Srisairam Achuthan
- Center for Informatics, Division of Research Informatics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Sourabh Kotnala
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Mohit Kumar Jolly
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Prakash Kulkarni
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA.
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Jones MC, Koh JM, Cheong KH. Synaptic Pruning in Schizophrenia: Does Minocycline Modulate Psychosocial Brain Development? Bioessays 2021; 42:e2000046. [PMID: 33448432 DOI: 10.1002/bies.202000046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 05/29/2020] [Indexed: 02/06/2023]
Abstract
Recent studies suggest that the tetracycline antibiotic minocycline, or its cousins, hold therapeutic potential for affective and psychotic disorders. This is proposed on the basis of a direct effect on microglia-mediated frontocortical synaptic pruning (FSP) during adolescence, perhaps in genetically susceptible individuals harboring risk alleles in the complement component cascade that is involved in this normal process of CNS circuit refinement. In reviewing this field, it is argued that minocycline is actually probing and modulating a deeply evolved and intricate system wherein psychosocial stimuli sculpt the circuitry of the "social brain" underlying adult behavior and personality. Furthermore, this system can generate psychiatric morbidity that is not dependent on genetic variation. This view has important ramifications for understanding "pathologies" of human social behavior and cognition as well as providing long-sought potential mechanistic links between social experience and susceptibility to mental and physical disease.
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Affiliation(s)
- Michael C Jones
- Science, Mathematics & Technology Cluster, Singapore University of Technology and Design (SUTD), 8 Somapah Road, S487372, Singapore
| | - Jin Ming Koh
- California Institute of Technology, Pasadena, CA, 91125, USA
| | - Kang Hao Cheong
- Science, Mathematics & Technology Cluster, Singapore University of Technology and Design (SUTD), 8 Somapah Road, S487372, Singapore.,SUTD-Massachusetts Institute of Technology International Design Centre, S487372, Singapore
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14
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Browning AP, Sharp JA, Mapder T, Baker CM, Burrage K, Simpson MJ. Persistence as an Optimal Hedging Strategy. Biophys J 2020; 120:133-142. [PMID: 33253635 DOI: 10.1016/j.bpj.2020.11.2260] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 10/07/2020] [Accepted: 11/05/2020] [Indexed: 02/02/2023] Open
Abstract
Bacteria invest in a slow-growing subpopulation, called persisters, to ensure survival in the face of uncertainty. This hedging strategy is remarkably similar to financial hedging, where diversifying an investment portfolio protects against economic uncertainty. We provide a new, to our knowledge, theoretical foundation for understanding cellular hedging by unifying the study of biological population dynamics and the mathematics of financial risk management through optimal control theory. Motivated by the widely accepted role of volatility in the emergence of persistence, we consider several models of environmental volatility described by continuous-time stochastic processes. This allows us to study an emergent cellular hedging strategy that maximizes the expected per capita growth rate of the population. Analytical and simulation results probe the optimal persister strategy, revealing results that are consistent with experimental observations and suggest new opportunities for experimental investigation and design. Overall, we provide a new, to our knowledge, way of conceptualizing and modeling cellular decision making in volatile environments by explicitly unifying theory from mathematical biology and finance.
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Affiliation(s)
- Alexander P Browning
- School of Mathematical Sciences, Queensland University of Technology, Queensland, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Queensland, Australia.
| | - Jesse A Sharp
- School of Mathematical Sciences, Queensland University of Technology, Queensland, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Queensland, Australia
| | - Tarunendu Mapder
- School of Mathematical Sciences, Queensland University of Technology, Queensland, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Queensland, Australia; Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Christopher M Baker
- School of Mathematical Sciences, Queensland University of Technology, Queensland, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Queensland, Australia; School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Kevin Burrage
- School of Mathematical Sciences, Queensland University of Technology, Queensland, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Queensland, Australia; Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Matthew J Simpson
- School of Mathematical Sciences, Queensland University of Technology, Queensland, Australia
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15
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Ilan Y, Spigelman Z. Establishing patient-tailored variability-based paradigms for anti-cancer therapy: Using the inherent trajectories which underlie cancer for overcoming drug resistance. Cancer Treat Res Commun 2020; 25:100240. [PMID: 33246316 DOI: 10.1016/j.ctarc.2020.100240] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/30/2020] [Accepted: 11/16/2020] [Indexed: 06/11/2023]
Abstract
Drug resistance is a major obstacle for successful therapy of many malignancies and is affecting the loss of response to chemotherapy and immunotherapy. Tumor-related compensatory adaptation mechanisms contribute to the development of drug resistance. Variability is inherent to biological systems and altered patterns of variability are associated with disease conditions. The marked intra and inter patient tumor heterogeneity, and the diverse mechanism contributing to drug resistance in different subjects, which may change over time even in the same patient, necessitate the development of personalized dynamic approaches for overcoming drug resistance. Altered dosing regimens, the potential role of chronotherapy, and drug holidays are effective in cancer therapy and immunotherapy. In the present review we describe the difficulty of overcoming drug resistance in a dynamic system and present the use of the inherent trajectories which underlie cancer development for building therapeutic regimens which can overcome resistance. The establishment of a platform wherein patient-tailored variability signatures are used for overcoming resistance for ensuing long term sustainable improved responses is presented.
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Affiliation(s)
- Yaron Ilan
- Department of Medicine, Hebrew University-Hadassah Medical Center, Jerusalem, Israel.
| | - Zachary Spigelman
- Department of Hematology and Oncology, Lahey Hospital and Beth Israel Medical Center, MA, USA
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16
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Genotype networks of 80 quantitative Arabidopsis thaliana phenotypes reveal phenotypic evolvability despite pervasive epistasis. PLoS Comput Biol 2020; 16:e1008082. [PMID: 32790763 PMCID: PMC7447023 DOI: 10.1371/journal.pcbi.1008082] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 08/25/2020] [Accepted: 06/22/2020] [Indexed: 12/23/2022] Open
Abstract
We study the genotype-phenotype maps of 80 quantitative phenotypes in the model plant Arabidopsis thaliana, by representing the genotypes affecting each phenotype as a genotype network. In such a network, each vertex or node corresponds to an individual's genotype at all those genomic loci that affect a given phenotype. Two vertices are connected by an edge if the associated genotypes differ in exactly one nucleotide. The 80 genotype networks we analyze are based on data from genome-wide association studies of 199 A. thaliana accessions. They form connected graphs whose topography differs substantially among phenotypes. We focus our analysis on the incidence of epistasis (non-additive interactions among mutations) because a high incidence of epistasis can reduce the accessibility of evolutionary paths towards high or low phenotypic values. We find epistatic interactions in 67 phenotypes, and in 51 phenotypes every pairwise mutant interaction is epistatic. Moreover, we find phenotype-specific differences in the fraction of accessible mutational paths to maximum phenotypic values. However, even though epistasis affects the accessibility of maximum phenotypic values, the relationships between genotypic and phenotypic change of our analyzed phenotypes are sufficiently smooth that some evolutionary paths remain accessible for most phenotypes, even where epistasis is pervasive. The genotype network representation we use can complement existing approaches to understand the genetic architecture of polygenic traits in many different organisms.
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17
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Ardaševa A, Gatenby RA, Anderson ARA, Byrne HM, Maini PK, Lorenzi T. A Mathematical Dissection of the Adaptation of Cell Populations to Fluctuating Oxygen Levels. Bull Math Biol 2020; 82:81. [PMID: 32556703 DOI: 10.1007/s11538-020-00754-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 05/27/2020] [Indexed: 12/17/2022]
Abstract
The disordered network of blood vessels that arises from tumour angiogenesis results in variations in the delivery of oxygen into the tumour tissue. This brings about regions of chronic hypoxia (i.e. sustained low oxygen levels) and regions with alternating periods of low and relatively higher oxygen levels, and makes it necessary for cancer cells to adapt to fluctuating environmental conditions. We use a phenotype-structured model to dissect the evolutionary dynamics of cell populations exposed to fluctuating oxygen levels. In this model, the phenotypic state of every cell is described by a continuous variable that provides a simple representation of its metabolic phenotype, ranging from fully oxidative to fully glycolytic, and cells are grouped into two competing populations that undergo heritable, spontaneous phenotypic variations at different rates. Model simulations indicate that, depending on the rate at which oxygen is consumed by the cells, dynamic nonlinear interactions between cells and oxygen can stimulate chronic hypoxia and cycling hypoxia. Moreover, the model supports the idea that under chronic-hypoxic conditions lower rates of phenotypic variation lead to a competitive advantage, whereas higher rates of phenotypic variation can confer a competitive advantage under cycling-hypoxic conditions. In the latter case, the numerical results obtained show that bet-hedging evolutionary strategies, whereby cells switch between oxidative and glycolytic phenotypes, can spontaneously emerge. We explain how these results can shed light on the evolutionary process that may underpin the emergence of phenotypic heterogeneity in vascularised tumours.
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Affiliation(s)
- Aleksandra Ardaševa
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK
| | - Robert A Gatenby
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Alexander R A Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Helen M Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK
| | - Philip K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK
| | - Tommaso Lorenzi
- School of Mathematics and Statistics, University of St Andrews, St Andrews, KY16 9SS, UK. .,Department of Mathematical Sciences "G. L. Lagrange", Dipartimento di Eccellenza, 2018-2022, Politecnico di Torino, 10129, Turin, Italy.
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18
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Dhar R. Role of Mitochondria in Generation of Phenotypic Heterogeneity in Yeast. J Indian Inst Sci 2020. [DOI: 10.1007/s41745-020-00176-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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19
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Acar A, Nichol D, Fernandez-Mateos J, Cresswell GD, Barozzi I, Hong SP, Trahearn N, Spiteri I, Stubbs M, Burke R, Stewart A, Caravagna G, Werner B, Vlachogiannis G, Maley CC, Magnani L, Valeri N, Banerji U, Sottoriva A. Exploiting evolutionary steering to induce collateral drug sensitivity in cancer. Nat Commun 2020; 11:1923. [PMID: 32317663 PMCID: PMC7174377 DOI: 10.1038/s41467-020-15596-z] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 03/18/2020] [Indexed: 12/17/2022] Open
Abstract
Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased fecundity or increased sensitivity to another drug. These evolutionary trade-offs can be exploited using 'evolutionary steering' to control the tumour population and delay resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here, we present an approach for evolutionary steering based on a combination of single-cell barcoding, large populations of 108-109 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary steering in a lung cancer model, showing that it shifts the clonal composition of the tumour in our favour, leading to collateral sensitivity and proliferative costs. Genomic profiling revealed some of the mechanisms that drive evolved sensitivity. This approach allows modelling evolutionary steering strategies that can potentially control treatment resistance.
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Affiliation(s)
- Ahmet Acar
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Department of Biological Sciences, Middle East Technical University, Ankara, Turkey
| | - Daniel Nichol
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Javier Fernandez-Mateos
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - George D Cresswell
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Iros Barozzi
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Sung Pil Hong
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Nicholas Trahearn
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Inmaculada Spiteri
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Mark Stubbs
- CRUK Cancer Therapeutics Unit, The Institute of Cancer Research, London, UK
| | - Rosemary Burke
- CRUK Cancer Therapeutics Unit, The Institute of Cancer Research, London, UK
| | - Adam Stewart
- Clinical Pharmacology-Adaptive Therapy Group, Division of Cancer Therapeutics and Clinical Studies, The Institute of Cancer Research, London, UK
| | - Giulio Caravagna
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Benjamin Werner
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Georgios Vlachogiannis
- Gastrointestinal Cancer Biology and Genomics Team, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Carlo C Maley
- Arizona Cancer Evolution Center, Biodesign Institute, Arizona State University, Tempe, USA
| | - Luca Magnani
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Nicola Valeri
- Gastrointestinal Cancer Biology and Genomics Team, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Department of Medicine, The Royal Marsden NHS Foundation Trust, London, UK
| | - Udai Banerji
- CRUK Cancer Therapeutics Unit, The Institute of Cancer Research, London, UK.
- Clinical Pharmacology-Adaptive Therapy Group, Division of Cancer Therapeutics and Clinical Studies, The Institute of Cancer Research, London, UK.
- Drug Development Unit, The Institute of Cancer Research and The Royal Marsden Hospital NHS Foundation Trust, London, UK.
| | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
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20
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Ardaševa A, Gatenby RA, Anderson ARA, Byrne HM, Maini PK, Lorenzi T. Evolutionary dynamics of competing phenotype-structured populations in periodically fluctuating environments. J Math Biol 2020; 80:775-807. [PMID: 31641842 PMCID: PMC7028828 DOI: 10.1007/s00285-019-01441-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 08/14/2019] [Indexed: 12/20/2022]
Abstract
Living species, ranging from bacteria to animals, exist in environmental conditions that exhibit spatial and temporal heterogeneity which requires them to adapt. Risk-spreading through spontaneous phenotypic variations is a known concept in ecology, which is used to explain how species may survive when faced with the evolutionary risks associated with temporally varying environments. In order to support a deeper understanding of the adaptive role of spontaneous phenotypic variations in fluctuating environments, we consider a system of non-local partial differential equations modelling the evolutionary dynamics of two competing phenotype-structured populations in the presence of periodically oscillating nutrient levels. The two populations undergo heritable, spontaneous phenotypic variations at different rates. The phenotypic state of each individual is represented by a continuous variable, and the phenotypic landscape of the populations evolves in time due to variations in the nutrient level. Exploiting the analytical tractability of our model, we study the long-time behaviour of the solutions to obtain a detailed mathematical depiction of the evolutionary dynamics. The results suggest that when nutrient levels undergo small and slow oscillations, it is evolutionarily more convenient to rarely undergo spontaneous phenotypic variations. Conversely, under relatively large and fast periodic oscillations in the nutrient levels, which bring about alternating cycles of starvation and nutrient abundance, higher rates of spontaneous phenotypic variations confer a competitive advantage. We discuss the implications of our results in the context of cancer metabolism.
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Affiliation(s)
- Aleksandra Ardaševa
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG UK
| | - Robert A. Gatenby
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, FL USA
| | | | - Helen M. Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG UK
| | - Philip K. Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG UK
| | - Tommaso Lorenzi
- School of Mathematics and Statistics, University of St Andrews, St Andrews, KY16 9SS UK
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21
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Brutovsky B, Horvath D. In Silico implementation of evolutionary paradigm in therapy design: Towards anti-cancer therapy as Darwinian process. J Theor Biol 2020; 485:110038. [PMID: 31580834 DOI: 10.1016/j.jtbi.2019.110038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 09/24/2019] [Accepted: 09/30/2019] [Indexed: 02/02/2023]
Abstract
In here presented in silico study we suggest a way how to implement the evolutionary principles into anti-cancer therapy design. We hypothesize that instead of its ongoing supervised adaptation, the therapy may be constructed as a self-sustaining evolutionary process in a dynamic fitness landscape established implicitly by evolving cancer cells, microenvironment and the therapy itself. For these purposes, we replace a unified therapy with the 'therapy species', which is a population of heterogeneous elementary therapies, and propose a way how to turn the toxicity of the elementary therapy into its fitness in a way conforming to evolutionary causation. As a result, not only the therapies govern the evolution of different cell phenotypes, but the cells' resistances govern the evolution of the therapies as well. We illustrate the approach by the minimalistic ad hoc evolutionary model. Its results indicate that the resistant cells could bias the evolution towards more toxic elementary therapies by inhibiting the less toxic ones. As the evolutionary causation of cancer drug resistance has been intensively studied for a few decades, we refer to cancer as a special case to illustrate purely theoretical analysis.
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Affiliation(s)
- B Brutovsky
- Department of Biophysics, Faculty of Science, Jesenna 5, P. J. Safarik University, Jesenna 5, Kosice 04154, Slovakia.
| | - D Horvath
- Technology and Innovation Park, Center of Interdisciplinary Biosciences, P. J. Safarik University, Jesenna 5, Kosice 04154, Slovakia
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22
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Understanding the role of phenotypic switching in cancer drug resistance. J Theor Biol 2020; 490:110162. [PMID: 31953135 DOI: 10.1016/j.jtbi.2020.110162] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 12/19/2019] [Accepted: 01/13/2020] [Indexed: 12/17/2022]
Abstract
The emergence of acquired drug resistance in cancer represents a major barrier to treatment success. While research has traditionally focused on genetic sources of resistance, recent findings suggest that cancer cells can acquire transient resistant phenotypes via epigenetic modifications and other non-genetic mechanisms. Although these resistant phenotypes are eventually relinquished by individual cells, they can temporarily 'save' the tumor from extinction and enable the emergence of more permanent resistance mechanisms. These observations have generated interest in the potential of epigenetic therapies for long-term tumor control or eradication. In this work, we develop a mathematical model to study how phenotypic switching at the single-cell level affects resistance evolution in cancer. We highlight unique features of non-genetic resistance, probe the evolutionary consequences of epigenetic drugs and explore potential therapeutic strategies. We find that even short-term epigenetic modifications and stochastic fluctuations in gene expression can drive long-term drug resistance in the absence of any bona fide resistance mechanisms. We also find that an epigenetic drug that slightly perturbs the average retention of the resistant phenotype can turn guaranteed treatment failure into guaranteed success. Lastly, we find that combining an epigenetic drug with an anti-cancer agent can significantly outperform monotherapy, and that treatment outcome is heavily affected by drug sequencing.
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23
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Nichol D, Robertson-Tessi M, Anderson ARA, Jeavons P. Model genotype-phenotype mappings and the algorithmic structure of evolution. J R Soc Interface 2019; 16:20190332. [PMID: 31690233 PMCID: PMC6893500 DOI: 10.1098/rsif.2019.0332] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 10/04/2019] [Indexed: 12/13/2022] Open
Abstract
Cancers are complex dynamic systems that undergo evolution and selection. Personalized medicine approaches in the clinic increasingly rely on predictions of tumour response to one or more therapies; these predictions are complicated by the inevitable evolution of the tumour. Despite enormous amounts of data on the mutational status of cancers and numerous therapies developed in recent decades to target these mutations, many of these treatments fail after a time due to the development of resistance in the tumour. The emergence of these resistant phenotypes is not easily predicted from genomic data, since the relationship between genotypes and phenotypes, termed the genotype-phenotype (GP) mapping, is neither injective nor functional. We present a review of models of this mapping within a generalized evolutionary framework that takes into account the relation between genotype, phenotype, environment and fitness. Different modelling approaches are described and compared, and many evolutionary results are shown to be conserved across studies despite using different underlying model systems. In addition, several areas for future work that remain understudied are identified, including plasticity and bet-hedging. The GP-mapping provides a pathway for understanding the potential routes of evolution taken by cancers, which will be necessary knowledge for improving personalized therapies.
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Affiliation(s)
- Daniel Nichol
- Department of Computer Science, University of Oxford, Oxford, UK
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alexander R. A. Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Peter Jeavons
- Department of Computer Science, University of Oxford, Oxford, UK
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24
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Kiskowski M, Glimm T, Moreno N, Gamble T, Chiari Y. Isolating and quantifying the role of developmental noise in generating phenotypic variation. PLoS Comput Biol 2019; 15:e1006943. [PMID: 31009449 PMCID: PMC6497311 DOI: 10.1371/journal.pcbi.1006943] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 05/02/2019] [Accepted: 03/11/2019] [Indexed: 02/07/2023] Open
Abstract
Genotypic variation, environmental variation, and their interaction may produce variation in the developmental process and cause phenotypic differences among individuals. Developmental noise, which arises during development from stochasticity in cellular and molecular processes when genotype and environment are fixed, also contributes to phenotypic variation. While evolutionary biology has long focused on teasing apart the relative contribution of genes and environment to phenotypic variation, our understanding of the role of developmental noise has lagged due to technical difficulties in directly measuring the contribution of developmental noise. The influence of developmental noise is likely underestimated in studies of phenotypic variation due to intrinsic mechanisms within organisms that stabilize phenotypes and decrease variation. Since we are just beginning to appreciate the extent to which phenotypic variation due to stochasticity is potentially adaptive, the contribution of developmental noise to phenotypic variation must be separated and measured to fully understand its role in evolution. Here, we show that variation in the component of the developmental process corresponding to environmental and genetic factors (here treated together as a unit called the LALI-type) versus the contribution of developmental noise, can be distinguished for leopard gecko (Eublepharis macularius) head color patterns using mathematical simulations that model the role of random variation (corresponding to developmental noise) in patterning. Specifically, we modified the parameters of simulations corresponding to variation in the LALI-type to generate the full range of phenotypic variation in color pattern seen on the heads of eight leopard geckos. We observed that over the range of these parameters, variation in color pattern due to LALI-type variation exceeds that due to developmental noise in the studied gecko cohort. However, the effect of developmental noise on patterning is also substantial. Our approach addresses one of the major goals of evolutionary biology: to quantify the role of stochasticity in shaping phenotypic variation.
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Affiliation(s)
- Maria Kiskowski
- University of South Alabama, Department of Mathematics and Statistics, Mobile, AL, United States of America
- * E-mail:
| | - Tilmann Glimm
- Western Washington University, Department of Mathematics, Bellingham, WA, United States of America
| | - Nickolas Moreno
- University of South Alabama, Department of Biology, Mobile, AL, United States of America
| | - Tony Gamble
- Department of Biological Sciences, Marquette University, Milwaukee, WI, United States of America
- Bell Museum of Natural History, University of Minnesota, St. Paul, MN, United States of America
- Milwaukee Public Museum, Milwaukee, WI, United States of America
| | - Ylenia Chiari
- University of South Alabama, Department of Biology, Mobile, AL, United States of America
- George Mason University, Department of Biology, Science & Technology Campus, Manassas, VA, United States of America
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25
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Gillies RJ, Brown JS, Anderson ARA, Gatenby RA. Eco-evolutionary causes and consequences of temporal changes in intratumoural blood flow. Nat Rev Cancer 2018; 18:576-585. [PMID: 29891961 PMCID: PMC6441333 DOI: 10.1038/s41568-018-0030-7] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Temporal changes in blood flow are commonly observed in malignant tumours, but the evolutionary causes and consequences are rarely considered. We propose that stochastic temporal variations in blood flow and microenvironmental conditions arise from the eco-evolutionary dynamics of tumour angiogenesis in which cancer cells, as individual units of selection, can influence and respond only to local environmental conditions. This leads to new vessels arising from the closest available vascular structure regardless of the size or capacity of this parental vessel. These dynamics produce unstable vascular networks with unpredictable spatial and temporal variations in blood flow and microenvironmental conditions. Adaptations of evolving populations to temporally varying environments in nature include increased diversity, greater motility and invasiveness, and highly plastic phenotypes, allowing for broad metabolic adaptability and rapid shifts to high rates of proliferation and profound quiescence. These adaptive strategies, when adopted in cancer cells, promote many commonly observed phenotypic properties including those found in the stem phenotype and in epithelial-to-mesenchymal transition. Temporal variations in intratumoural blood flow, which occur through the promotion of cancer cell phenotypes that facilitate both metastatic spread and resistance to therapy, may have substantial clinical consequences.
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Affiliation(s)
- Robert J Gillies
- Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Joel S Brown
- Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Robert A Gatenby
- Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL, USA.
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26
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Gallaher JA, Enriquez-Navas PM, Luddy KA, Gatenby RA, Anderson ARA. Spatial Heterogeneity and Evolutionary Dynamics Modulate Time to Recurrence in Continuous and Adaptive Cancer Therapies. Cancer Res 2018; 78:2127-2139. [PMID: 29382708 PMCID: PMC5899666 DOI: 10.1158/0008-5472.can-17-2649] [Citation(s) in RCA: 134] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 12/05/2017] [Accepted: 01/24/2018] [Indexed: 12/31/2022]
Abstract
Treatment of advanced cancers has benefited from new agents that supplement or bypass conventional therapies. However, even effective therapies fail as cancer cells deploy a wide range of resistance strategies. We propose that evolutionary dynamics ultimately determine survival and proliferation of resistant cells. Therefore, evolutionary strategies should be used with conventional therapies to delay or prevent resistance. Using an agent-based framework to model spatial competition among sensitive and resistant populations, we applied antiproliferative drug treatments to varying ratios of sensitive and resistant cells. We compared a continuous maximum-tolerated dose schedule with an adaptive schedule aimed at tumor control via competition between sensitive and resistant cells. Continuous treatment cured mostly sensitive tumors, but with any resistant cells, recurrence was inevitable. We identified two adaptive strategies that control heterogeneous tumors: dose modulation controls most tumors with less drug, while a more vacation-oriented schedule can control more invasive tumors. These findings offer potential modifications to treatment regimens that may improve outcomes and reduce resistance and recurrence.Significance: By using drug dose modulation or treatment vacations, adaptive therapy strategies control the emergence of tumor drug resistance by spatially suppressing less fit resistant populations in favor of treatment sensitive ones. Cancer Res; 78(8); 2127-39. ©2018 AACR.
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Affiliation(s)
- Jill A Gallaher
- H. Lee Moffitt Cancer Center, Integrated Mathematical Oncology, Tampa, Florida
| | | | - Kimberly A Luddy
- H. Lee Moffitt Cancer Center, Cancer Imaging and Metabolism, Tampa, Florida
| | - Robert A Gatenby
- H. Lee Moffitt Cancer Center, Integrated Mathematical Oncology, Tampa, Florida
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Mechanisms and clinical implications of tumor heterogeneity and convergence on recurrent phenotypes. J Mol Med (Berl) 2017; 95:1167-1178. [PMID: 28871446 DOI: 10.1007/s00109-017-1587-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 08/11/2017] [Accepted: 08/20/2017] [Indexed: 10/18/2022]
Abstract
Tumor heterogeneity has been identified at various -omic levels. The tumor genome, transcriptome, proteome, and phenome can vary widely across cells in patient tumors and are influenced by tumor cell interactions with heterogeneous physical conditions and cellular components of the tumor microenvironment. Here, we explore the concept that while variation exists at multiple -omic levels, changes at each of these levels converge on the same pathways and lead to convergent phenotypes in tumors that can provide common drug targets. These phenotypes include cellular growth and proliferation, sustained oncogenic signaling, and immune avoidance, among others. Tumor heterogeneity complicates treatment of patient cancers as it leads to varied response to therapies. Identification of convergent cellular phenotypes arising in patient cancers and targeted therapies that reverse them has the potential to transform the way clinicians treat these cancers and to improve patient outcome.
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28
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Goldman A, Kohandel M, Clairambault J. Integrating Biological and Mathematical Models to Explain and Overcome Drug Resistance in Cancer, Part 2: from Theoretical Biology to Mathematical Models. CURRENT STEM CELL REPORTS 2017. [DOI: 10.1007/s40778-017-0098-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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29
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Dhawan A, Nichol D, Kinose F, Abazeed ME, Marusyk A, Haura EB, Scott JG. Collateral sensitivity networks reveal evolutionary instability and novel treatment strategies in ALK mutated non-small cell lung cancer. Sci Rep 2017; 7:1232. [PMID: 28450729 PMCID: PMC5430816 DOI: 10.1038/s41598-017-00791-8] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 03/13/2017] [Indexed: 12/31/2022] Open
Abstract
Drug resistance remains an elusive problem in cancer therapy, particularly for novel targeted therapies. Much work is focused upon the development of an arsenal of targeted therapies, towards oncogenic driver genes such as ALK-EML4, to overcome the inevitable resistance that develops over time. Currently, after failure of first line ALK TKI therapy, another ALK TKI is administered, though collateral sensitivity is not considered. To address this, we evolved resistance in an ALK rearranged non-small cell lung cancer line (H3122) to a panel of 4 ALK TKIs, and performed a collateral sensitivity analysis. All ALK inhibitor resistant cell lines displayed significant cross-resistance to all other ALK inhibitors. We then evaluated ALK-inhibitor sensitivities after drug holidays of varying length (1-21 days), and observed dynamic patterns of resistance. This unpredictability led us to an expanded search for treatment options, where we tested 6 further anti-cancer agents for collateral sensitivity among resistant cells, uncovering possibilities for further treatment, including cross-sensitivity to standard cytotoxic therapies, as well as Hsp90 inhibitors. Taken together, these results imply that resistance to targeted therapy in non-small cell lung cancer is highly dynamic, and also one where there are many opportunities to re-establish sensitivities where there was once resistance. Drug resistance in cancer inevitably emerges during treatment; particularly with novel targeted therapies, designed to inhibit specific molecules. A clinically-relevant example of this phenomenon occurs in ALK-positive non-small cell lung cancer, where targeted therapies are used to inhibit the ALK-EML4 fusion protein. A potential solution to this may lie in finding drug sensitivities in the resistant population, termed collateral sensitivities, and then using these as second-line agents. This study shows how the evolution of resistance in ALK-positive lung cancer is a dynamic process through time, one in which patterns of drug resistance and collateral sensitivity change substantially, and therefore one where temporal regimens, such as drug cycling and drug holidays may have great benefit.
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Affiliation(s)
- Andrew Dhawan
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA.,Department of Oncology, University of Oxford, Oxford, UK
| | - Daniel Nichol
- Department of Computer Science, University of Oxford, Oxford, UK.,Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Fumi Kinose
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Mohamed E Abazeed
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA.,Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, USA
| | - Andriy Marusyk
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Eric B Haura
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Jacob G Scott
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA. .,Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, USA.
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30
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Sottoriva A, Barnes CP, Graham TA. Catch my drift? Making sense of genomic intra-tumour heterogeneity. Biochim Biophys Acta Rev Cancer 2017; 1867:95-100. [PMID: 28069394 PMCID: PMC5446319 DOI: 10.1016/j.bbcan.2016.12.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Revised: 12/24/2016] [Accepted: 12/27/2016] [Indexed: 01/08/2023]
Abstract
The cancer genome is shaped by three components of the evolutionary process: mutation, selection and drift. While many studies have focused on the first two components, the role of drift in cancer evolution has received little attention. Drift occurs when all individuals in the population have the same likelihood of producing surviving offspring, and so by definition a drifting population is one that is evolving neutrally. Here we focus on how neutral evolution is manifested in the cancer genome. We discuss how neutral passenger mutations provide a magnifying glass that reveals the evolutionary dynamics underpinning cancer development, and outline how statistical inference can be used to quantify these dynamics from sequencing data. We argue that only after we understand the impact of neutral drift on the genome can we begin to make full sense of clonal selection. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer? Edited by Dr. Robert A. Gatenby.
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Affiliation(s)
- Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK.
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK.
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Barts Cancer Institute, Charterhouse Sq, Queen Mary University of London, EC1M 6BQ, UK.
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