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França GS, Baron M, King BR, Bossowski JP, Bjornberg A, Pour M, Rao A, Patel AS, Misirlioglu S, Barkley D, Tang KH, Dolgalev I, Liberman DA, Avital G, Kuperwaser F, Chiodin M, Levine DA, Papagiannakopoulos T, Marusyk A, Lionnet T, Yanai I. Cellular adaptation to cancer therapy along a resistance continuum. Nature 2024; 631:876-883. [PMID: 38987605 DOI: 10.1038/s41586-024-07690-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 06/07/2024] [Indexed: 07/12/2024]
Abstract
Advancements in precision oncology over the past decades have led to new therapeutic interventions, but the efficacy of such treatments is generally limited by an adaptive process that fosters drug resistance1. In addition to genetic mutations2, recent research has identified a role for non-genetic plasticity in transient drug tolerance3 and the acquisition of stable resistance4,5. However, the dynamics of cell-state transitions that occur in the adaptation to cancer therapies remain unknown and require a systems-level longitudinal framework. Here we demonstrate that resistance develops through trajectories of cell-state transitions accompanied by a progressive increase in cell fitness, which we denote as the 'resistance continuum'. This cellular adaptation involves a stepwise assembly of gene expression programmes and epigenetically reinforced cell states underpinned by phenotypic plasticity, adaptation to stress and metabolic reprogramming. Our results support the notion that epithelial-to-mesenchymal transition or stemness programmes-often considered a proxy for phenotypic plasticity-enable adaptation, rather than a full resistance mechanism. Through systematic genetic perturbations, we identify the acquisition of metabolic dependencies, exposing vulnerabilities that can potentially be exploited therapeutically. The concept of the resistance continuum highlights the dynamic nature of cellular adaptation and calls for complementary therapies directed at the mechanisms underlying adaptive cell-state transitions.
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Affiliation(s)
- Gustavo S França
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY, USA
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Maayan Baron
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY, USA
| | - Benjamin R King
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
- Bristol-Myers Squibb Company, Lawrenceville, NJ, USA
| | - Jozef P Bossowski
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Alicia Bjornberg
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Maayan Pour
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY, USA
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Anjali Rao
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY, USA
| | - Ayushi S Patel
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY, USA
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Selim Misirlioglu
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York, NY, USA
| | - Dalia Barkley
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY, USA
| | - Kwan Ho Tang
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York, NY, USA
- Translational Medicine, Oncology R&D, AstraZeneca, Boston, MA, USA
| | - Igor Dolgalev
- Applied Bioinformatics Laboratories, NYU Grossman School of Medicine, New York, NY, USA
| | - Deborah A Liberman
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY, USA
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Gal Avital
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY, USA
| | - Felicia Kuperwaser
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY, USA
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Marta Chiodin
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY, USA
| | - Douglas A Levine
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York, NY, USA
- Merck & Co., Rahway, NJ, USA
| | - Thales Papagiannakopoulos
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York, NY, USA
- Bristol-Myers Squibb Company, Lawrenceville, NJ, USA
| | - Andriy Marusyk
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Timothée Lionnet
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
- Department of Cell Biology, NYU Grossman School of Medicine, New York, NY, USA
| | - Itai Yanai
- Institute for Computational Medicine, NYU Grossman School of Medicine, New York, NY, USA.
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA.
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York, NY, USA.
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, USA.
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2
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Vibishan B, B V H, Dey S. A resource-based mechanistic framework for castration-resistant prostate cancer (CRPC). J Theor Biol 2024; 587:111806. [PMID: 38574968 DOI: 10.1016/j.jtbi.2024.111806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 02/04/2024] [Accepted: 03/25/2024] [Indexed: 04/06/2024]
Abstract
Cancer therapy often leads to the selective elimination of drug-sensitive cells from the tumour. This can favour the growth of cells resistant to the therapeutic agent, ultimately causing a tumour relapse. Castration-resistant prostate cancer (CRPC) is a well-characterised instance of this phenomenon. In CRPC, after systemic androgen deprivation therapy (ADT), a subset of drug-resistant cancer cells autonomously produce testosterone, thus enabling tumour regrowth. A previous theoretical study has shown that such a tumour relapse can be delayed by inhibiting the growth of drug-resistant cells using biotic competition from drug-sensitive cells. In this context, the centrality of resource dynamics to intra-tumour competition in the CRPC system indicates clear scope for the construction of theoretical models that can explicitly incorporate the underlying mechanisms of tumour ecology. In the current study, we use a modified logistic framework to model cell-cell interactions in terms of the production and consumption of resources. Our results show that steady state composition of CRPC can be understood as a composite function of the availability and utilisation efficiency of two resources-oxygen and testosterone. In particular, we show that the effect of changing resource availability or use efficiency is conditioned by their general abundance regimes. Testosterone typically functions in trace amounts and thus affects steady state behaviour of the CRPC system differently from oxygen, which is usually available at higher levels. Our data thus indicate that explicit consideration of resource dynamics can produce novel and useful mechanistic understanding of CRPC. Furthermore, such a modelling approach also incorporates variables into the system's description that can be directly measured in a clinical context. This is therefore a promising avenue of research in cancer ecology that could lead to therapeutic approaches that are more clearly rooted in the biology of CRPC.
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Affiliation(s)
- B Vibishan
- Department of Biology, Indian Institute of Science Education and Research (IISER) Pune, Pune, Maharashtra, India.
| | - Harshavardhan B V
- Department of Biology, Indian Institute of Science Education and Research (IISER) Pune, Pune, Maharashtra, India; IISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka, India.
| | - Sutirth Dey
- Department of Biology, Indian Institute of Science Education and Research (IISER) Pune, Pune, Maharashtra, India.
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3
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Strobl MAR, Martin AL, West J, Gallaher J, Robertson-Tessi M, Gatenby R, Wenham R, Maini PK, Damaghi M, Anderson ARA. To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy. Cell Syst 2024; 15:510-525.e6. [PMID: 38772367 PMCID: PMC11190943 DOI: 10.1016/j.cels.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 02/27/2024] [Accepted: 04/17/2024] [Indexed: 05/23/2024]
Abstract
Toxicity and emerging drug resistance pose important challenges in poly-adenosine ribose polymerase inhibitor (PARPi) maintenance therapy of ovarian cancer. We propose that adaptive therapy, which dynamically reduces treatment based on the tumor dynamics, might alleviate both issues. Utilizing in vitro time-lapse microscopy and stepwise model selection, we calibrate and validate a differential equation mathematical model, which we leverage to test different plausible adaptive treatment schedules. Our model indicates that adjusting the dosage, rather than skipping treatments, is more effective at reducing drug use while maintaining efficacy due to a delay in cell kill and a diminishing dose-response relationship. In vivo pilot experiments confirm this conclusion. Although our focus is toxicity mitigation, reducing drug use may also delay resistance. This study enhances our understanding of PARPi treatment scheduling and illustrates the first steps in developing adaptive therapies for new treatment settings. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Maximilian A R Strobl
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA; Department of Translational Hematology & Oncology Research, Cleveland Clinic, Cleveland, OH, USA.
| | - Alexandra L Martin
- Department of Obstetrics and Gynecology, University of Tennessee Health Science Center, Memphis, TN, USA; Division of Gynecologic Oncology, West Cancer Center and Research Institute, Memphis, TN, USA
| | - Jeffrey West
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Jill Gallaher
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Robert Gatenby
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA; Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Robert Wenham
- Gynecologic Oncology Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Philip K Maini
- Wolfson Centre for Mathematical Biology, University of Oxford, Oxford, UK.
| | - Mehdi Damaghi
- Department of Pathology, Stony Brook Medicine, SUNY, Brookhaven, NY, USA; Stony Brook Cancer Center, Stony Brook Medicine, SUNY, Brookhaven, NY, USA.
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4
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Gallagher K, Strobl MA, Park DS, Spoendlin FC, Gatenby RA, Maini PK, Anderson AR. Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy. Cancer Res 2024; 84:1929-1941. [PMID: 38569183 PMCID: PMC11148552 DOI: 10.1158/0008-5472.can-23-2040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 01/05/2024] [Accepted: 03/21/2024] [Indexed: 04/05/2024]
Abstract
Standard-of-care treatment regimens have long been designed for maximal cell killing, yet these strategies often fail when applied to metastatic cancers due to the emergence of drug resistance. Adaptive treatment strategies have been developed as an alternative approach, dynamically adjusting treatment to suppress the growth of treatment-resistant populations and thereby delay, or even prevent, tumor progression. Promising clinical results in prostate cancer indicate the potential to optimize adaptive treatment protocols. Here, we applied deep reinforcement learning (DRL) to guide adaptive drug scheduling and demonstrated that these treatment schedules can outperform the current adaptive protocols in a mathematical model calibrated to prostate cancer dynamics, more than doubling the time to progression. The DRL strategies were robust to patient variability, including both tumor dynamics and clinical monitoring schedules. The DRL framework could produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing optimal treatment strategies. The DRL framework had no knowledge of the underlying mathematical tumor model, demonstrating the capability of DRL to help develop treatment strategies in novel or complex settings. Finally, a proposed five-step pathway, which combined mechanistic modeling with the DRL framework and integrated conventional tools to improve interpretability compared with traditional "black-box" DRL models, could allow translation of this approach to the clinic. Overall, the proposed framework generated personalized treatment schedules that consistently outperformed clinical standard-of-care protocols. SIGNIFICANCE Generation of interpretable and personalized adaptive treatment schedules using a deep reinforcement framework that interacts with a virtual patient model overcomes the limitations of standardized strategies caused by heterogeneous treatment responses.
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Affiliation(s)
- Kit Gallagher
- Wolfson Centre for Mathematical Biology, Mathematical Institute, Oxford, United Kingdom
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
| | | | - Derek S. Park
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Fabian C. Spoendlin
- Wolfson Centre for Mathematical Biology, Mathematical Institute, Oxford, United Kingdom
| | - Robert A. Gatenby
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Philip K. Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, Oxford, United Kingdom
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5
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Marzban S, Srivastava S, Kartika S, Bravo R, Safriel R, Zarski A, Anderson A, Chung CH, Amelio AL, West J. Spatial interactions modulate tumor growth and immune infiltration. RESEARCH SQUARE 2024:rs.3.rs-3962451. [PMID: 38826398 PMCID: PMC11142313 DOI: 10.21203/rs.3.rs-3962451/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Lenia, a cellular automata framework used in artificial life, provides a natural setting to implement mathematical models of cancer incorporating features such as morphogenesis, homeostasis, motility, reproduction, growth, stimuli response, evolvability, and adaptation. Historically, agent-based models of cancer progression have been constructed with rules that govern birth, death and migration, with attempts to map local rules to emergent global growth dynamics. In contrast, Lenia provides a flexible framework for considering a spectrum of local (cell-scale) to global (tumor-scale) dynamics by defining an interaction kernel governing density-dependent growth dynamics. Lenia can recapitulate a range of cancer model classifications including local or global, deterministic or stochastic, non-spatial or spatial, single or multi-population, and off or on-lattice. Lenia is subsequently used to develop data-informed models of 1) single-population growth dynamics, 2) multi-population cell-cell competition models, and 3) cell migration or chemotaxis. Mathematical modeling provides important mechanistic insights. First, short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects. Next, we find that asymmetric interaction tumor-immune kernels lead to poor immune response. Finally, modeling recapitulates immune-ECM interactions where patterns of collagen formation provide immune protection, indicated by an emergent inverse relationship between disease stage and immune coverage.
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Affiliation(s)
- Sadegh Marzban
- Integrated Mathematical Oncology Dept., H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Sonal Srivastava
- Dept. of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Sharon Kartika
- Dept. of Biological Sciences, Indian Institute of Science Education and Research Kolkata
| | - Rafael Bravo
- Integrated Mathematical Oncology Dept., H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Rachel Safriel
- High School Internship Program, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Aidan Zarski
- High School Internship Program, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Alexander Anderson
- Integrated Mathematical Oncology Dept., H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Christine H. Chung
- Dept. of Head and Neck-Endocrine Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Antonio L. Amelio
- Dept. of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
- Dept. of Head and Neck-Endocrine Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Jeffrey West
- Integrated Mathematical Oncology Dept., H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
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6
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Colyer B, Bak M, Basanta D, Noble R. A seven-step guide to spatial, agent-based modelling of tumour evolution. Evol Appl 2024; 17:e13687. [PMID: 38707992 PMCID: PMC11064804 DOI: 10.1111/eva.13687] [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: 02/07/2024] [Revised: 03/27/2024] [Accepted: 03/29/2024] [Indexed: 05/07/2024] Open
Abstract
Spatial agent-based models are frequently used to investigate the evolution of solid tumours subject to localized cell-cell interactions and microenvironmental heterogeneity. As spatial genomic, transcriptomic and proteomic technologies gain traction, spatial computational models are predicted to become ever more necessary for making sense of complex clinical and experimental data sets, for predicting clinical outcomes, and for optimizing treatment strategies. Here we present a non-technical step by step guide to developing such a model from first principles. Stressing the importance of tailoring the model structure to that of the biological system, we describe methods of increasing complexity, from the basic Eden growth model up to off-lattice simulations with diffusible factors. We examine choices that unavoidably arise in model design, such as implementation, parameterization, visualization and reproducibility. Each topic is illustrated with examples drawn from recent research studies and state of the art modelling platforms. We emphasize the benefits of simpler models that aim to match the complexity of the phenomena of interest, rather than that of the entire biological system. Our guide is aimed at both aspiring modellers and other biologists and oncologists who wish to understand the assumptions and limitations of the models on which major cancer studies now so often depend.
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Affiliation(s)
- Blair Colyer
- Department of MathematicsCity, University of LondonLondonUK
| | - Maciej Bak
- Department of MathematicsCity, University of LondonLondonUK
| | - David Basanta
- Department of Integrated Mathematical OncologyH. Lee Moffitt Cancer Center and Research InstituteTampaFloridaUSA
| | - Robert Noble
- Department of MathematicsCity, University of LondonLondonUK
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7
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Palakollu VN, Veera Manohara Reddy Y, Shekh MI, Vattikuti SVP, Shim J, Karpoormath R. Electrochemical immunosensing of tumor markers. Clin Chim Acta 2024; 557:117882. [PMID: 38521164 DOI: 10.1016/j.cca.2024.117882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 03/25/2024]
Abstract
The rising incidence and mortality rates of cancer have led to a growing need for precise and prompt early diagnostic approaches to effectively combat this disease. However, traditional methods employed for detecting tumor cells, such as histopathological and immunological techniques, are often associated with complex procedures, high analytical expenses, elevated false positive rates, and a dependence on experienced personnel. Tracking tumor markers is recognized as one of the most effective approaches for early detection and prognosis of cancer. While onco-biomarkers can also be produced in normal circumstances, their concentration is significantly elevated when tumors are present. By monitoring the levels of these markers, healthcare professionals can obtain valuable insights into the presence, progression, and response to treatment of cancer, aiding in timely diagnosis and effective management. This review aims to provide researchers with a comprehensive overview of the recent advancements in tumor markers using electrochemical immunosensors. By highlighting the latest developments in this field, researchers can gain a general understanding of the progress made in the utilization of electrochemical immunosensors for detecting tumor markers. Furthermore, this review also discusses the current limitations associated with electrochemical immunosensors and offers insights into paving the way for further improvements and advancements in this area of research.
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Affiliation(s)
- Venkata Narayana Palakollu
- Department of Chemistry, School of Applied Sciences, REVA University, Bengaluru 560064, India; Department of Pharmaceutical Chemistry, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa.
| | - Y Veera Manohara Reddy
- Department of Chemistry, Sri Venkateswara College, University of Delhi, New Delhi 110021, India
| | - Mehdihasan I Shekh
- College of Materials Science and Engineering, Shenzhen University, Shenzhen 518055, PR China
| | | | - Jaesool Shim
- School of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Rajshekhar Karpoormath
- Department of Pharmaceutical Chemistry, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa
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Bishop RT, Miller AK, Froid M, Nerlakanti N, Li T, Frieling JS, Nasr MM, Nyman KJ, Sudalagunta PR, Canevarolo RR, Silva AS, Shain KH, Lynch CC, Basanta D. The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous drug resistant disease. Nat Commun 2024; 15:2458. [PMID: 38503736 PMCID: PMC10951361 DOI: 10.1038/s41467-024-46594-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 03/04/2024] [Indexed: 03/21/2024] Open
Abstract
Multiple myeloma (MM) is an osteolytic malignancy that is incurable due to the emergence of treatment resistant disease. Defining how, when and where myeloma cell intrinsic and extrinsic bone microenvironmental mechanisms cause relapse is challenging with current biological approaches. Here, we report a biology-driven spatiotemporal hybrid agent-based model of the MM-bone microenvironment. Results indicate MM intrinsic mechanisms drive the evolution of treatment resistant disease but that the protective effects of bone microenvironment mediated drug resistance (EMDR) significantly enhances the probability and heterogeneity of resistant clones arising under treatment. Further, the model predicts that targeting of EMDR deepens therapy response by eliminating sensitive clones proximal to stroma and bone, a finding supported by in vivo studies. Altogether, our model allows for the study of MM clonal evolution over time in the bone microenvironment and will be beneficial for optimizing treatment efficacy so as to significantly delay disease relapse.
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Affiliation(s)
- Ryan T Bishop
- Department of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Anna K Miller
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Matthew Froid
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
- The Cancer Biology Ph.D. Program, University of South Florida, Tampa, FL, USA
| | - Niveditha Nerlakanti
- Department of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
- The Cancer Biology Ph.D. Program, University of South Florida, Tampa, FL, USA
| | - Tao Li
- Department of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Jeremy S Frieling
- Department of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Mostafa M Nasr
- Department of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
- The Cancer Biology Ph.D. Program, University of South Florida, Tampa, FL, USA
| | - Karl J Nyman
- Department of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
- The Cancer Biology Ph.D. Program, University of South Florida, Tampa, FL, USA
| | - Praneeth R Sudalagunta
- Department of Metabolism and Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Rafael R Canevarolo
- Department of Metabolism and Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Ariosto Siqueira Silva
- Department of Metabolism and Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Kenneth H Shain
- Department of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
- Department of Malignant Hematology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Conor C Lynch
- Department of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.
| | - David Basanta
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.
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Marzban S, Srivastava S, Kartika S, Bravo R, Safriel R, Zarski A, Anderson A, Chung CH, Amelio AL, West J. Spatial interactions modulate tumor growth and immune infiltration. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.575036. [PMID: 38370722 PMCID: PMC10871273 DOI: 10.1101/2024.01.10.575036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Direct observation of immune cell trafficking patterns and tumor-immune interactions is unlikely in human tumors with currently available technology, but computational simulations based on clinical data can provide insight to test hypotheses. It is hypothesized that patterns of collagen formation evolve as a mechanism of immune escape, but the exact nature of the interaction between immune cells and collagen is poorly understood. Spatial data quantifying the degree of collagen fiber alignment in squamous cell carcinomas indicates that late stage disease is associated with highly aligned fibers. Here, we introduce a computational modeling framework (called Lenia) to discriminate between two hypotheses: immune cell migration that moves 1) parallel or 2) perpendicular to collagen fiber orientation. The modeling recapitulates immune-ECM interactions where collagen patterns provide immune protection, leading to an emergent inverse relationship between disease stage and immune coverage. We also illustrate the capabilities of Lenia to model the evolution of tumor progression and immune predation. Lenia provides a flexible framework for considering a spectrum of local (cell-scale) to global (tumor-scale) dynamics by defining a kernel cell-cell interaction function that governs tumor growth dynamics under immune predation with immune cell migration. Mathematical modeling provides important mechanistic insights into cell interactions. Short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects, while asymmetric tumor-immune interaction kernels lead to poor immune response. Thus, the length scale of tumor-immune interactions drives tumor growth and infiltration.
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Affiliation(s)
- Sadegh Marzban
- Integrated Mathematical Oncology Dept., H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Sonal Srivastava
- Dept. of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Sharon Kartika
- Dept. of Biological Sciences, Indian Institute of Science Education and Research Kolkata
| | - Rafael Bravo
- Integrated Mathematical Oncology Dept., H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Rachel Safriel
- High School Internship Program, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Aidan Zarski
- High School Internship Program, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Alexander Anderson
- Integrated Mathematical Oncology Dept., H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Christine H. Chung
- Dept. of Head and Neck-Endocrine Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Antonio L. Amelio
- Dept. of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
- Dept. of Head and Neck-Endocrine Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Jeffrey West
- Integrated Mathematical Oncology Dept., H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
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10
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Alvarez FE, Viossat Y. Tumor containment: a more general mathematical analysis. J Math Biol 2024; 88:41. [PMID: 38446165 DOI: 10.1007/s00285-024-02062-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 02/07/2024] [Accepted: 02/11/2024] [Indexed: 03/07/2024]
Abstract
Clinical and pre-clinical data suggest that treating some tumors at a mild, patient-specific dose might delay resistance to treatment and increase survival time. A recent mathematical model with sensitive and resistant tumor cells identified conditions under which a treatment aiming at tumor containment rather than eradication is indeed optimal. This model however neglected mutations from sensitive to resistant cells, and assumed that the growth-rate of sensitive cells is non-increasing in the size of the resistant population. The latter is not true in standard models of chemotherapy. This article shows how to dispense with this assumption and allow for mutations from sensitive to resistant cells. This is achieved by a novel mathematical analysis comparing tumor sizes across treatments not as a function of time, but as a function of the resistant population size.
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Affiliation(s)
- Frank Ernesto Alvarez
- CEREMADE, CNRS, Université Paris-Dauphine, Université PSL, Place du Maréchal De Lattre De Tassigny, 75016, Paris, France.
- GMM, INSA Toulouse, 135 Avenue de Rangueil, 31000, Toulouse, France.
| | - Yannick Viossat
- CEREMADE, CNRS, Université Paris-Dauphine, Université PSL, Place du Maréchal De Lattre De Tassigny, 75016, Paris, France
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11
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Tan RZ. Tumour Growth Mechanisms Determine Effectiveness of Adaptive Therapy in Glandular Tumours. Interdiscip Sci 2024; 16:73-90. [PMID: 37776475 DOI: 10.1007/s12539-023-00586-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 09/03/2023] [Accepted: 09/06/2023] [Indexed: 10/02/2023]
Abstract
In cancer treatment, adaptive therapy holds promise for delaying the onset of recurrence through regulating the competition between drug-sensitive and drug-resistant cells. Adaptive therapy has been studied in well-mixed models assuming free mixing of all cells and spatial models considering the interactions of single cells with their immediate adjacent cells. Both models do not reflect the spatial structure in glandular tumours where intra-gland cellular interaction is high, while inter-gland interaction is limited. Here, we use mathematical modelling to study the effects of adaptive therapy on glandular tumours that expand using either glandular fission or invasive growth. A two-dimensional, lattice-based model of sites containing sensitive and resistant cells within individual glands is developed to study the evolution of glandular tumour cells under continuous and adaptive therapies. We found that although both growth models benefit from adaptive therapy's ability to prevent recurrence, invasive growth benefits more from it than fission growth. This difference is due to the migration of daughter cells into neighboring glands that is absent in fission but present in invasive growth. The migration resulted in greater mixing of cells, enhancing competition induced by adaptive therapy. By varying the initial spatial spread and location of the resistant cells within the tumour, we found that modifying the conditions within the resistant cells containing glands affect both fission and invasive growth. However, modifying the conditions surrounding these glands affect invasive growth only. Our work reveals the interplay between growth mechanism and tumour topology in modulating the effectiveness of cancer therapy.
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Affiliation(s)
- Rui Zhen Tan
- Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore, 138683, Singapore.
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12
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Zhou P, Xiao Y, Zhou X, Fang J, Zhang J, Liu J, Guo L, Zhang J, Zhang N, Chen K, Zhao C. Mapping Spatiotemporal Heterogeneity in Multifocal Breast Tumor Progression by Noninvasive Ultrasound Elastography-Guided Mass Spectrometry Imaging Strategy. JACS AU 2024; 4:465-475. [PMID: 38425919 PMCID: PMC10900218 DOI: 10.1021/jacsau.3c00589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 01/10/2024] [Accepted: 01/24/2024] [Indexed: 03/02/2024]
Abstract
Spatiotemporal heterogeneity of tumors provides an escape mechanism for breast cancer cells, which can obstruct the investigation of tumor progression. While molecular profiling obtained from mass spectrometry imaging (MSI) is rich in biochemical information, it lacks the capacity for in vivo analysis. Ultrasound diagnosis has a high diagnostic accuracy but low chemical specificity. Here, we describe a noninvasive ultrasound elastography (UE)-guided MSI strategy (UEg-MSI) that integrates physical and biochemical characteristics of tumors acquired from both in vivo and in vitro imaging. Using UEg-MSI, both elasticity histopathology metabolism "fingerprints" and reciprocal crosstalk are revealed, indicating the intact, multifocal spatiotemporal heterogeneity of spontaneous tumorigenesis of the breast from early, middle, and late stages. Our results demonstrate a gradual increase in malignant degree of primary focus in cervical and thoracic mammary glands. This progression is characterized by increased stiffness according to elasticity scores, histopathological changes from hyperplasia to increased nests of neoplastic cells and necrotic areas, and regional metabolic heterogeneity and reprogramming at the spatiotemporal level. De novo fatty acid (FA) synthesis focused on independent (such as ω-9 FAs) and dependent (such as ω-6 FAs) dietary FA intake in the core cancerous nest areas in the middle and late stages of tumor or in the peripheral microareas in the early stage of the tumor. SM-Cer signaling pathway and GPs biosynthesis and degradation, as well as glycerophosphoinositol intensity, changed in multiple characteristic microareas. The UEg-MSI strategy holds the potential to expand MSI applications and enhance ultrasound-mediated cancer diagnosis. It offers new insight into early cancer discovery and the occurrence of metastasis.
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Affiliation(s)
- Peng Zhou
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Yu Xiao
- Department
of Thyroid and Breast department, First Affiliated Hospital of Shenzhen
University, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Xin Zhou
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jinghui Fang
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Jingwen Zhang
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
| | - Jianjun Liu
- Shenzhen
Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline
of Health Toxicology (2020-2024), Shenzhen
Center for Disease Control and Prevention, 518054, Shenzhen, China
| | - Ling Guo
- Shenzhen
Key Laboratory of Epigenetics and Precision Medicine for Cancers,
National Cancer Center/National Clinical Research Center for Cancer/Cancer
Hospital & Shenzhen Hospital, Chinese
Academic of Medical Sciences & Peking Union Medical College, Shenzhen 518172, China
| | - Jiuhong Zhang
- Shenzhen
Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline
of Health Toxicology (2020-2024), Shenzhen
Center for Disease Control and Prevention, 518054, Shenzhen, China
| | - Ning Zhang
- College
of Chemistry and Chemical Engineering, Dezhou
University, Dezhou 253026, Shandong, China
| | - Ke Chen
- Key
Laboratory of Resources Conversion and Pollution Control of the State
Ethnic Affairs Commission, College of Resources and Environmental
Science, South-Central Minzu University, Wuhan 430074, China
| | - Chao Zhao
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Department
of Ultrasound, First Affiliated Hospital of Shenzhen University Health
Science Center, Shenzhen Second People’s
Hospital, Shenzhen 518009, China
- Shenzhen
Key Laboratory of Precision Diagnosis and Treatment of Depression, Shenzhen Institute of Advanced Technology, Chinese
Academy of Sciences, Shenzhen 518055, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
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13
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Uppal G, Vural DC. On the possibility of engineering social evolution in microfluidic environments. Biophys J 2024; 123:407-419. [PMID: 38204167 PMCID: PMC10870175 DOI: 10.1016/j.bpj.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/18/2023] [Accepted: 01/04/2024] [Indexed: 01/12/2024] Open
Abstract
Many species of microbes cooperate by producing public goods from which they collectively benefit. However, these populations are under the risk of being taken over by cheating mutants that do not contribute to the pool of public goods. Here we present theoretical findings that address how the social evolution of microbes can be manipulated by external perturbations to inhibit or promote the fixation of cheaters. To control social evolution, we determine the effects of fluid-dynamical properties such as flow rate or domain geometry. We also study the social evolutionary consequences of introducing beneficial or harmful chemicals at steady state and in a time-dependent fashion. We show that by modulating the flow rate and by applying pulsed chemical signals, we can modulate the spatial structure and dynamics of the population in a way that can select for more or less cooperative microbial populations.
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Affiliation(s)
- Gurdip Uppal
- Harvard Medical School, Boston, Massachusetts; Division of Computational Pathology, Brigham and Women's hospital, Boston, Massachusetts
| | - Dervis Can Vural
- Department of Physics, University of Notre Dame, Notre Dame, Indiana.
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14
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Bukkuri A. Modeling stress-induced responses: plasticity in continuous state space and gradual clonal evolution. Theory Biosci 2024; 143:63-77. [PMID: 38289469 DOI: 10.1007/s12064-023-00410-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 12/13/2023] [Indexed: 03/01/2024]
Abstract
Mathematical models of cancer and bacterial evolution have generally stemmed from a gene-centric framework, assuming clonal evolution via acquisition of resistance-conferring mutations and selection of their corresponding subpopulations. More recently, the role of phenotypic plasticity has been recognized and models accounting for phenotypic switching between discrete cell states (e.g., epithelial and mesenchymal) have been developed. However, seldom do models incorporate both plasticity and mutationally driven resistance, particularly when the state space is continuous and resistance evolves in a continuous fashion. In this paper, we develop a framework to model plastic and mutational mechanisms of acquiring resistance in a continuous gradual fashion. We use this framework to examine ways in which cancer and bacterial populations can respond to stress and consider implications for therapeutic strategies. Although we primarily discuss our framework in the context of cancer and bacteria, it applies broadly to any system capable of evolving via plasticity and genetic evolution.
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Affiliation(s)
- Anuraag Bukkuri
- Cancer Biology and Evolution Program and Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, USA.
- Tissue Development and Evolution Research Group, Department of Laboratory Medicine, Lund University, Lund, Sweden.
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15
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Khan MZ, Tahir D, Asim M, Israr M, Haider A, Xu DD. Revolutionizing Cancer Care: Advances in Carbon-Based Materials for Diagnosis and Treatment. Cureus 2024; 16:e52511. [PMID: 38371088 PMCID: PMC10874252 DOI: 10.7759/cureus.52511] [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] [Accepted: 01/16/2024] [Indexed: 02/20/2024] Open
Abstract
Cancer involves intricate pathological mechanisms marked by complexities such as cytotoxicity, drug resistance, stem cell proliferation, and inadequate specificity in current chemotherapy approaches. Cancer therapy has embraced diverse nanomaterials renowned for their unique magnetic, electrical, and optical properties to address these challenges. Despite the expanding corpus of knowledge in this area, there has been less advancement in approving nano drugs for use in clinical settings. Nanotechnology, and more especially the development of intelligent nanomaterials, has had a profound impact on cancer research and treatment in recent years. Due to their large surface area, nanoparticles can adeptly encapsulate diverse compounds. Furthermore, the modification of nanoparticles is achievable through a broad spectrum of bio-based substrates, including DNA, aptamers, RNA, and antibodies. This functionalization substantially enhances their theranostic capabilities. Nanomaterials originating from biological sources outperform their conventionally created counterparts, offering advantages such as reduced toxicity, lower manufacturing costs, and enhanced efficiency. This review uses carbon nanomaterials, including graphene-based materials, carbon nanotubes (CNTs) based nanomaterials, and carbon quantum dots (CQDs), to give a complete overview of various methods used in cancer theranostics. We also discussed their advantages and limitations in cancer diagnosis and treatment settings. Carbon nanomaterials might significantly improve cancer theranostics and pave the way for fresh tumor diagnosis and treatment approaches. More study is needed to determine whether using nano-carriers for targeted medicine delivery may increase material utilization. More insight is required to explore the correlation between heightened cytotoxicity and retention resulting from increased permeability.
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Affiliation(s)
| | - Danial Tahir
- Internal Medicine, Nazareth Hospital, Philadelphia, USA
| | - Muhammad Asim
- Internal Medicine, Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, GBR
| | | | - Ali Haider
- Department of Allied Health Sciences, The University of Lahore, Gujrat Campus, Gujrat, PAK
| | - Dan Dan Xu
- Integrative Medicine, Shandong University of Traditional Chinese Medicine, Jinan, CHN
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16
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Lan H, Jamil M, Ke G, Dong N. The role of nanoparticles and nanomaterials in cancer diagnosis and treatment: a comprehensive review. Am J Cancer Res 2023; 13:5751-5784. [PMID: 38187049 PMCID: PMC10767363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 11/28/2023] [Indexed: 01/09/2024] Open
Abstract
Cancer's pathological processes are complex and present several challenges for current chemotherapy methods. These challenges include cytotoxicity, multidrug resistance, the proliferation of cancer stem cells, and a lack of specificity. To address these issues, researchers have turned to nanomaterials, which possess distinct optical, magnetic, and electrical properties due to their size range of 1-100 nm. Nanomaterials have been engineered to improve cancer treatment by mitigating cytotoxicity, enhancing specificity, increasing drug payload capacity, and improving drug bioavailability. Despite a growing corpus of research on this subject, there has been limited progress in permitting nanodrugs for medical use. The advent of nanotechnology, particularly advances in intelligent nanomaterials, has transformed the field of cancer diagnosis and therapy. Nanoparticles' large surface area allows them to successfully encapsulate a large number of molecules. Nanoparticles can be functionalized with various bio-based substrates like RNA, DNA, aptamers, and antibodies, enhancing their theranostic capabilities. Biologically derived nanomaterials offer economical, easily producible, and less toxic alternatives to conventionally manufactured ones. This review offers a comprehensive overview of cancer theranostics methodologies, focusing on intelligent nanomaterials such as metal, polymeric, and carbon-based nanoparticles. I have also critically discussed their benefits and challenges in cancer therapy and diagnostics. Utilizing intelligent nanomaterials holds promise for advancing cancer theranostics, and improving tumor detection and treatment. Further research should optimize nanocarriers for targeted drug delivery and explore enhanced permeability, cytotoxicity, and retention effects.
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Affiliation(s)
- Hongwen Lan
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan 430022, Hubei, China
| | - Muhammad Jamil
- PARC Arid Zone Research CenterDera Ismail Khan 29050, Pakistan
| | - Gaotan Ke
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan 430030, Hubei, China
| | - Nianguo Dong
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan 430022, Hubei, China
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17
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Zhang L, Ma J, Liu L, Li G, Li H, Hao Y, Zhang X, Ma X, Chen Y, Wu J, Wang X, Yang S, Xu S. Adaptive therapy: a tumor therapy strategy based on Darwinian evolution theory. Crit Rev Oncol Hematol 2023; 192:104192. [PMID: 37898477 DOI: 10.1016/j.critrevonc.2023.104192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 04/07/2023] [Accepted: 10/22/2023] [Indexed: 10/30/2023] Open
Abstract
Cancer progression is a dynamic process of continuous evolution, in which genetic diversity and heterogeneity are generated by clonal and subclonal amplification based on random mutations. Traditional cancer treatment strategies have a great challenge, which often leads to treatment failure due to drug resistance. Integrating evolutionary dynamics into treatment regimens may be an effective way to overcome the problem of drug resistance. In particular, a potential treatment is adaptive therapy, which strategy advocates containment strategies that adjust the treatment cycles according to tumor evolution to control the growth of treatment-resistant cells. In this review, we first summarize the shortcomings of traditional tumor treatment methods in evolution and then introduce the theoretical basis and research status of adaptive therapy. By analyzing the limitations of adaptive therapy and exploring possible solutions, we can broaden people's understanding of adaptive therapy and provide new insights and strategies for tumor treatment.
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Affiliation(s)
- Lei Zhang
- Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Jianli Ma
- Department of Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Lei Liu
- Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Guozheng Li
- Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Hui Li
- Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Yi Hao
- Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Xin Zhang
- Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Xin Ma
- Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Yihai Chen
- Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Jiale Wu
- Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Xinheng Wang
- Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Shuai Yang
- Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Shouping Xu
- Harbin Medical University Cancer Hospital, Harbin, 150040, China.
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18
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Li H, Yang Z, Tu F, Deng L, Han Y, Fu X, Wang L, Gu D, Werner B, Huang W. Mutation divergence over space in tumour expansion. J R Soc Interface 2023; 20:20230542. [PMID: 37989227 PMCID: PMC10681009 DOI: 10.1098/rsif.2023.0542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 10/30/2023] [Indexed: 11/23/2023] Open
Abstract
Mutation accumulation in tumour evolution is one major cause of intra-tumour heterogeneity (ITH), which often leads to drug resistance during treatment. Previous studies with multi-region sequencing have shown that mutation divergence among samples within the patient is common, and the importance of spatial sampling to obtain a complete picture in tumour measurements. However, quantitative comparisons of the relationship between mutation heterogeneity and tumour expansion modes, sampling distances as well as the sampling methods are still few. Here, we investigate how mutations diverge over space by varying the sampling distance and tumour expansion modes using individual-based simulations. We measure ITH by the Jaccard index between samples and quantify how ITH increases with sampling distance, the pattern of which holds in various sampling methods and sizes. We also compare the inferred mutation rates based on the distributions of variant allele frequencies under different tumour expansion modes and sampling sizes. In exponentially fast expanding tumours, a mutation rate can always be inferred for any sampling size. However, the accuracy compared with the true value decreases when the sampling size decreases, where small sampling sizes result in a high estimate of the mutation rate. In addition, such an inference becomes unreliable when the tumour expansion is slow, such as in surface growth.
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Affiliation(s)
- Haiyang Li
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
- Evolutionary Dynamics Group, Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Zixuan Yang
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Fengyu Tu
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Lijuan Deng
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Yuqing Han
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Xing Fu
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Long Wang
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
| | - Di Gu
- The first affiliated hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Benjamin Werner
- Evolutionary Dynamics Group, Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Weini Huang
- Group of Theoretical Biology, The State Key Laboratory of Bio-control, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, People’s Republic of China
- School of Mathematical Sciences, Queen Mary University of London, London, UK
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19
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Strobl MAR, Gallaher J, Robertson-Tessi M, West J, Anderson ARA. Treatment of evolving cancers will require dynamic decision support. Ann Oncol 2023; 34:867-884. [PMID: 37777307 PMCID: PMC10688269 DOI: 10.1016/j.annonc.2023.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/01/2023] [Accepted: 08/21/2023] [Indexed: 10/02/2023] Open
Abstract
Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.
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Affiliation(s)
- M A R Strobl
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa; Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, USA
| | - J Gallaher
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - M Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - J West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - A R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa.
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20
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Mielnicka A, Kołodziej T, Dziob D, Lasota S, Sroka J, Rajfur Z. Impact of elastic substrate on the dynamic heterogeneity of WC256 Walker carcinosarcoma cells. Sci Rep 2023; 13:15743. [PMID: 37735532 PMCID: PMC10514059 DOI: 10.1038/s41598-023-35313-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 05/16/2023] [Indexed: 09/23/2023] Open
Abstract
Cellular heterogeneity is a phenomenon in which cell populations are composed of subpopulations that vary in their behavior. Heterogeneity is particularly pronounced in cancer cells and can affect the efficacy of oncological therapies. Previous studies have considered heterogeneity dynamics to be indicative of evolutionary changes within subpopulations; however, these studies do not consider the short-time morphological plasticity of cells. Physical properties of the microenvironment elasticity have also been poorly investigated within the context of cellular heterogeneity, despite its role in determining cellular behavior. This article demonstrates that cellular heterogeneity can be highly dynamic and dependent on the micromechanical properties of the substrate. During observation, migrating Walker carcinosarcoma WC256 cells were observed to belong to different subpopulations, in which their morphologies and migration strategies differed. Furthermore, the application of an elastic substrate (E = 40 kPa) modified three aspects of cellular heterogeneity: the occurrence of subpopulations, the occurrence of transitions between subpopulations, and cellular migration and morphology. These findings provide a new perspective in the analysis of cellular heterogeneity, whereby it may not be a static feature of cancer cell populations, instead varying over time. This helps further the understanding of cancer cell behavior, including their phenotype and migration strategy, which may help to improve cancer therapies by extending their suitability to investigate tumor heterogeneity.
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Affiliation(s)
- Aleksandra Mielnicka
- Department of Molecular and Interfacial Biophysics, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, ul. Lojasiewicza 11, 30-348, Kraków, Poland
- BRAINCITY, Laboratory of Neurobiology, The Nencki Institute of Experimental Biology, PAS, ul. Ludwika Pasteura 3, 02-093, Warsaw, Poland
| | - Tomasz Kołodziej
- Department of Molecular and Interfacial Biophysics, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, ul. Lojasiewicza 11, 30-348, Kraków, Poland
- Department of Pharmaceutical Biophysics, Faculty of Pharmacy, Jagiellonian University Medical College, ul. Medyczna 9, 30-688, Kraków, Poland
| | - Daniel Dziob
- Department of Pharmaceutical Biophysics, Faculty of Pharmacy, Jagiellonian University Medical College, ul. Medyczna 9, 30-688, Kraków, Poland
| | - Sławomir Lasota
- Department of Cell Biology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, ul. Gronostajowa 7, 30-387, Kraków, Poland
| | - Jolanta Sroka
- Department of Cell Biology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, ul. Gronostajowa 7, 30-387, Kraków, Poland
| | - Zenon Rajfur
- Department of Molecular and Interfacial Biophysics, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, ul. Lojasiewicza 11, 30-348, Kraków, Poland.
- Jagiellonian Center of Biomedical Imaging, Jagiellonian University, 30-348, Kraków, Poland.
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21
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Pendleton KE, Wang K, Echeverria GV. Rewiring of mitochondrial metabolism in therapy-resistant cancers: permanent and plastic adaptations. Front Cell Dev Biol 2023; 11:1254313. [PMID: 37779896 PMCID: PMC10534013 DOI: 10.3389/fcell.2023.1254313] [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: 07/06/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
Deregulation of tumor cell metabolism is widely recognized as a "hallmark of cancer." Many of the selective pressures encountered by tumor cells, such as exposure to anticancer therapies, navigation of the metastatic cascade, and communication with the tumor microenvironment, can elicit further rewiring of tumor cell metabolism. Furthermore, phenotypic plasticity has been recently appreciated as an emerging "hallmark of cancer." Mitochondria are dynamic organelles and central hubs of metabolism whose roles in cancers have been a major focus of numerous studies. Importantly, therapeutic approaches targeting mitochondria are being developed. Interestingly, both plastic (i.e., reversible) and permanent (i.e., stable) metabolic adaptations have been observed following exposure to anticancer therapeutics. Understanding the plastic or permanent nature of these mechanisms is of crucial importance for devising the initiation, duration, and sequential nature of metabolism-targeting therapies. In this review, we compare permanent and plastic mitochondrial mechanisms driving therapy resistance. We also discuss experimental models of therapy-induced metabolic adaptation, therapeutic implications for targeting permanent and plastic metabolic states, and clinical implications of metabolic adaptations. While the plasticity of metabolic adaptations can make effective therapeutic treatment challenging, understanding the mechanisms behind these plastic phenotypes may lead to promising clinical interventions that will ultimately lead to better overall care for cancer patients.
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Affiliation(s)
- Katherine E. Pendleton
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, United States
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, United States
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States
| | - Karen Wang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, United States
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, United States
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States
- Department of BioSciences, Rice University, Houston, TX, United States
| | - Gloria V. Echeverria
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, United States
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, United States
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States
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22
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Park J, Newton PK. Stochastic competitive release and adaptive chemotherapy. Phys Rev E 2023; 108:034407. [PMID: 37849192 DOI: 10.1103/physreve.108.034407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 07/10/2023] [Indexed: 10/19/2023]
Abstract
We develop a finite-cell model of tumor natural selection dynamics to investigate the stochastic fluctuations associated with multiple rounds of adaptive chemotherapy. The adaptive cycles are designed to avoid chemoresistance in the tumor by managing the ecological mechanism of competitive release of a resistant subpopulation. Our model is based on a three-component evolutionary game played among healthy (H), sensitive (S), and resistant (R) populations of N cells, with a chemotherapy control parameter, C(t), which we use to dynamically impose selection pressure on the sensitive subpopulation to slow tumor growth and manage competitive release of the resistant population. The adaptive chemoschedule is designed based on the deterministic (N→∞) adjusted replicator dynamical system, then implemented using the finite-cell stochastic frequency dependent Moran process model (N=10K-50K) to ascertain the cumulative effect of the stochastic fluctuations on the efficacy of the adaptive schedules over multiple rounds. We quantify the stochastic fixation probability regions of the R and S populations in the HSR trilinear phase plane as a function of the control parameter C∈[0,1], showing that the size of the R region increases with increasing C. We then implement an adaptive time-dependent schedule C(t) for the stochastic model and quantify the variances (using principal component coordinates) associated with the evolutionary cycles over multiple rounds of adaptive therapy. The variances increase subquadratically through several rounds before the evolutionary cycle begins to break down. Despite this, we show the stochastic adaptive schedules are more effective at delaying resistance than standard maximum tolerated dose and low-dose metronomic schedules. The simplified low-dimensional model provides some insights on how well multiple rounds of adaptive therapies are likely to perform over a range of tumor sizes (i.e., different values of N) if the goal is to maintain a sustained balance among competing subpopulations of cells to avoid chemoresistance via competitive release in a stochastic environment.
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Affiliation(s)
- J Park
- Department of Mathematics, University of Southern California, Los Angeles, California 90089-1191, USA
| | - P K Newton
- Department of Aerospace & Mechanical Engineering, Department of Mathematics, and Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California 90089-1191, USA
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23
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Gallaher J, Strobl M, West J, Gatenby R, Zhang J, Robertson-Tessi M, Anderson AR. Intermetastatic and Intrametastatic Heterogeneity Shapes Adaptive Therapy Cycling Dynamics. Cancer Res 2023; 83:2775-2789. [PMID: 37205789 PMCID: PMC10425736 DOI: 10.1158/0008-5472.can-22-2558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 03/11/2023] [Accepted: 05/17/2023] [Indexed: 05/21/2023]
Abstract
Adaptive therapies that alternate between drug applications and drug-free vacations can exploit competition between sensitive and resistant cells to maximize the time to progression. However, optimal dosing schedules depend on the properties of metastases, which are often not directly measurable in clinical practice. Here, we proposed a framework for estimating features of metastases through tumor response dynamics during the first adaptive therapy treatment cycle. Longitudinal prostate-specific antigen (PSA) levels in 16 patients with metastatic castration-resistant prostate cancer undergoing adaptive androgen deprivation treatment were analyzed to investigate relationships between cycle dynamics and clinical variables such as Gleason score, the change in the number of metastases over a cycle, and the total number of cycles over the course of treatment. The first cycle of adaptive therapy, which consists of a response period (applying therapy until 50% PSA reduction), and a regrowth period (removing treatment until reaching initial PSA levels), delineated several features of the computational metastatic system: larger metastases had longer cycles; a higher proportion of drug-resistant cells slowed the cycles; and a faster cell turnover rate sped up drug response time and slowed regrowth time. The number of metastases did not affect cycle times, as response dynamics were dominated by the largest tumors rather than the aggregate. In addition, systems with higher intermetastasis heterogeneity responded better to continuous therapy and correlated with dynamics from patients with high or low Gleason scores. Conversely, systems with higher intrametastasis heterogeneity responded better to adaptive therapy and correlated with dynamics from patients with intermediate Gleason scores. SIGNIFICANCE Multiscale mathematical modeling combined with biomarker dynamics during adaptive therapy helps identify underlying features of metastatic cancer to inform treatment decisions.
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Affiliation(s)
- Jill Gallaher
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Maximilian Strobl
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Jeffrey West
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Robert Gatenby
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
- Department of Radiology, Moffitt Cancer Center, Tampa, Florida
| | - Jingsong Zhang
- Department of Genitourinary Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
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24
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Masud MA, Kim JY, Kim E. Modeling the effect of acquired resistance on cancer therapy outcomes. Comput Biol Med 2023; 162:107035. [PMID: 37276754 DOI: 10.1016/j.compbiomed.2023.107035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/17/2023] [Accepted: 05/11/2023] [Indexed: 06/07/2023]
Abstract
Adaptive therapy (AT) is an evolution-based treatment strategy that exploits cell-cell competition. Acquired resistance can change the competitive nature of cancer cells in a tumor, impacting AT outcomes. We aimed to determine if adaptive therapy can still be effective with cell's acquiring resistance. We developed an agent-based model for spatial tumor growth considering three different types of acquired resistance: random genetic mutations during cell division, drug-induced reversible (plastic) phenotypic changes, and drug-induced irreversible phenotypic changes. These three resistance mechanisms lead to different spatial distributions of resistant cells. To quantify the spatial distribution, we propose an extension of Ripley's K-function, Sampled Ripley's K-function (SRKF), which calculates the non-randomness of the resistance distribution over the tumor domain. Our model predicts that the emergent spatial distribution of resistance can determine the time to progression under both adaptive and continuous therapy (CT). Notably, a high rate of random genetic mutations leads to quicker progression under AT than CT due to the emergence of many small clumps of resistant cells. Drug-induced phenotypic changes accelerate tumor progression irrespective of the treatment strategy. Low-rate switching to a sensitive state reduces the benefits of AT compared to CT. Furthermore, we also demonstrated that drug-induced resistance necessitates aggressive treatment under CT, regardless of the presence of cancer-associated fibroblasts. However, there is an optimal dose that can most effectively delay tumor relapse under AT by suppressing resistance. In conclusion, this study demonstrates that diverse resistance mechanisms can shape the distribution of resistance and thus determine the efficacy of adaptive therapy.
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Affiliation(s)
- M A Masud
- Natural Product Informatics Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Republic of Korea.
| | - Jae-Young Kim
- Graduate School of Analytical Science and Technology (GRAST), Chungnam National University, Daejeon 34134, Republic of Korea.
| | - Eunjung Kim
- Natural Product Informatics Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Republic of Korea.
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25
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Gatenbee CD, Baker AM, Prabhakaran S, Swinyard O, Slebos RJC, Mandal G, Mulholland E, Andor N, Marusyk A, Leedham S, Conejo-Garcia JR, Chung CH, Robertson-Tessi M, Graham TA, Anderson ARA. Virtual alignment of pathology image series for multi-gigapixel whole slide images. Nat Commun 2023; 14:4502. [PMID: 37495577 PMCID: PMC10372014 DOI: 10.1038/s41467-023-40218-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 07/13/2023] [Indexed: 07/28/2023] Open
Abstract
Interest in spatial omics is on the rise, but generation of highly multiplexed images remains challenging, due to cost, expertise, methodical constraints, and access to technology. An alternative approach is to register collections of whole slide images (WSI), generating spatially aligned datasets. WSI registration is a two-part problem, the first being the alignment itself and the second the application of transformations to huge multi-gigapixel images. To address both challenges, we developed Virtual Alignment of pathoLogy Image Series (VALIS), software which enables generation of highly multiplexed images by aligning any number of brightfield and/or immunofluorescent WSI, the results of which can be saved in the ome.tiff format. Benchmarking using publicly available datasets indicates VALIS provides state-of-the-art accuracy in WSI registration and 3D reconstruction. Leveraging existing open-source software tools, VALIS is written in Python, providing a free, fast, scalable, robust, and easy-to-use pipeline for registering multi-gigapixel WSI, facilitating downstream spatial analyses.
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Affiliation(s)
- Chandler D Gatenbee
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA.
| | - Ann-Marie Baker
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Sandhya Prabhakaran
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA
| | - Ottilie Swinyard
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Robbert J C Slebos
- Department of Head and Neck-Endocrine Oncology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, CSB 6, Tampa, FL, USA
| | - Gunjan Mandal
- Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, MRC, Tampa, FL, 336122, USA
| | - Eoghan Mulholland
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX37BN, UK
| | - Noemi Andor
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA
| | - Andriy Marusyk
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, USA
| | - Simon Leedham
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX37BN, UK
| | - Jose R Conejo-Garcia
- Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, MRC, Tampa, FL, 336122, USA
| | - Christine H Chung
- Department of Head and Neck-Endocrine Oncology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, CSB 6, Tampa, FL, USA
| | - Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Alexander R A Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, SRB 4, Tampa, FL, 336122, USA.
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26
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Klotz DM, Schwarz FM, Dubrovska A, Schuster K, Theis M, Krüger A, Kutz O, Link T, Wimberger P, Drukewitz S, Buchholz F, Thomale J, Kuhlmann JD. Establishment and Molecular Characterization of an In Vitro Model for PARPi-Resistant Ovarian Cancer. Cancers (Basel) 2023; 15:3774. [PMID: 37568590 PMCID: PMC10417418 DOI: 10.3390/cancers15153774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/18/2023] [Accepted: 07/22/2023] [Indexed: 08/13/2023] Open
Abstract
Overcoming PARPi resistance is a high clinical priority. We established and characterized comparative in vitro models of acquired PARPi resistance, derived from either a BRCA1-proficient or BRCA1-deficient isogenic background by long-term exposure to olaparib. While parental cell lines already exhibited a certain level of intrinsic activity of multidrug resistance (MDR) proteins, resulting PARPi-resistant cells from both models further converted toward MDR. In both models, the PARPi-resistant phenotype was shaped by (i) cross-resistance to other PARPis (ii) impaired susceptibility toward the formation of DNA-platinum adducts upon exposure to cisplatin, which could be reverted by the drug efflux inhibitors verapamil or diphenhydramine, and (iii) reduced PARP-trapping activity. However, the signature and activity of ABC-transporter expression and the cross-resistance spectra to other chemotherapeutic drugs considerably diverged between the BRCA1-proficient vs. BRCA1-deficient models. Using dual-fluorescence co-culture experiments, we observed that PARPi-resistant cells had a competitive disadvantage over PARPi-sensitive cells in a drug-free medium. However, they rapidly gained clonal dominance under olaparib selection pressure, which could be mitigated by the MRP1 inhibitor MK-751. Conclusively, we present a well-characterized in vitro model, which could be instrumental in dissecting mechanisms of PARPi resistance from HR-proficient vs. HR-deficient background and in studying clonal dynamics of PARPi-resistant cells in response to experimental drugs, such as novel olaparib-sensitizers.
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Affiliation(s)
- Daniel Martin Klotz
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (D.M.K.); (F.M.S.); (K.S.); (O.K.); (T.L.); (P.W.)
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Franziska Maria Schwarz
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (D.M.K.); (F.M.S.); (K.S.); (O.K.); (T.L.); (P.W.)
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Anna Dubrovska
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, 01328 Dresden, Germany
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden and Helmholtz-Zentrum Dresden-Rossendorf, 01307 Dresden, Germany
| | - Kati Schuster
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (D.M.K.); (F.M.S.); (K.S.); (O.K.); (T.L.); (P.W.)
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Mirko Theis
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- UCC Section Medical Systems Biology, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Alexander Krüger
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- Core Unit for Molecular Tumor Diagnostics (CMTD), National Center for Tumor Diseases (NCT), Partner Site Dresden, German Cancer Consortium (DKTK), Dresden, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Oliver Kutz
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (D.M.K.); (F.M.S.); (K.S.); (O.K.); (T.L.); (P.W.)
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Theresa Link
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (D.M.K.); (F.M.S.); (K.S.); (O.K.); (T.L.); (P.W.)
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Pauline Wimberger
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (D.M.K.); (F.M.S.); (K.S.); (O.K.); (T.L.); (P.W.)
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Stephan Drukewitz
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- Core Unit for Molecular Tumor Diagnostics (CMTD), National Center for Tumor Diseases (NCT), Partner Site Dresden, German Cancer Consortium (DKTK), Dresden, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Institute of Human Genetics, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Frank Buchholz
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- UCC Section Medical Systems Biology, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
| | - Jürgen Thomale
- Institute of Cell Biology (Cancer Research), University of Duisburg-Essen Medical School, 45147 Essen, Germany;
| | - Jan Dominik Kuhlmann
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (D.M.K.); (F.M.S.); (K.S.); (O.K.); (T.L.); (P.W.)
- National Center for Tumor Diseases (NCT), 01307 Dresden, Germany; German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01307 Dresden, Germany; (A.D.); (M.T.); (A.K.); (S.D.); (F.B.)
- German Cancer Consortium (DKTK), Partner Site Dresden and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
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27
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Hockings H, Lakatos E, Huang W, Mossner M, Khan MA, Metcalf S, Nicolini F, Smith K, Baker AM, Graham TA, Lockley M. Adaptive therapy achieves long-term control of chemotherapy resistance in high grade ovarian cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.549688. [PMID: 37546942 PMCID: PMC10401956 DOI: 10.1101/2023.07.21.549688] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Drug resistance results in poor outcomes for most patients with metastatic cancer. Adaptive Therapy (AT) proposes to address this by exploiting presumed fitness costs incurred by drug-resistant cells when drug is absent, and prescribing dose reductions to allow fitter, sensitive cells to re-grow and re-sensitise the tumour. However, empirical evidence for treatment-induced fitness change is lacking. We show that fitness costs in chemotherapy-resistant ovarian cancer cause selective decline and apoptosis of resistant populations in low-resource conditions. Moreover, carboplatin AT caused fluctuations in sensitive/resistant tumour population size in vitro and significantly extended survival of tumour-bearing mice. In sequential blood-derived cell-free DNA and tumour samples obtained longitudinally from ovarian cancer patients during treatment, we inferred resistant cancer cell population size through therapy and observed it correlated strongly with disease burden. These data have enabled us to launch a multicentre, phase 2 randomised controlled trial (ACTOv) to evaluate AT in ovarian cancer.
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Affiliation(s)
- Helen Hockings
- Centre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Eszter Lakatos
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
- Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Weini Huang
- School of Mathematical Sciences, Queen Mary University of London, London, UK
| | - Maximilian Mossner
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Mohammed Ateeb Khan
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Stephen Metcalf
- Centre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Francesco Nicolini
- Centre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Kane Smith
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Ann-Marie Baker
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Trevor A. Graham
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Michelle Lockley
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
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28
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Sharon S, Daher-Ghanem N, Zaid D, Gough MJ, Kravchenko-Balasha N. The immunogenic radiation and new players in immunotherapy and targeted therapy for head and neck cancer. FRONTIERS IN ORAL HEALTH 2023; 4:1180869. [PMID: 37496754 PMCID: PMC10366623 DOI: 10.3389/froh.2023.1180869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023] Open
Abstract
Although treatment modalities for head and neck cancer have evolved considerably over the past decades, survival rates have plateaued. The treatment options remained limited to definitive surgery, surgery followed by fractionated radiotherapy with optional chemotherapy, and a definitive combination of fractionated radiotherapy and chemotherapy. Lately, immunotherapy has been introduced as the fourth modality of treatment, mainly administered as a single checkpoint inhibitor for recurrent or metastatic disease. While other regimens and combinations of immunotherapy and targeted therapy are being tested in clinical trials, adapting the appropriate regimens to patients and predicting their outcomes have yet to reach the clinical setting. Radiotherapy is mainly regarded as a means to target cancer cells while minimizing the unwanted peripheral effect. Radiotherapy regimens and fractionation are designed to serve this purpose, while the systemic effect of radiation on the immune response is rarely considered a factor while designing treatment. To bridge this gap, this review will highlight the effect of radiotherapy on the tumor microenvironment locally, and the immune response systemically. We will review the methodology to identify potential targets for therapy in the tumor microenvironment and the scientific basis for combining targeted therapy and radiotherapy. We will describe a current experience in preclinical models to test these combinations and propose how challenges in this realm may be faced. We will review new players in targeted therapy and their utilization to drive immunogenic response against head and neck cancer. We will outline the factors contributing to head and neck cancer heterogeneity and their effect on the response to radiotherapy. We will review in-silico methods to decipher intertumoral and intratumoral heterogeneity and how these algorithms can predict treatment outcomes. We propose that (a) the sequence of surgery, radiotherapy, chemotherapy, and targeted therapy should be designed not only to annul cancer directly, but to prime the immune response. (b) Fractionation of radiotherapy and the extent of the irradiated field should facilitate systemic immunity to develop. (c) New players in targeted therapy should be evaluated in translational studies toward clinical trials. (d) Head and neck cancer treatment should be personalized according to patients and tumor-specific factors.
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Affiliation(s)
- Shay Sharon
- Department of Oral and Maxillofacial Surgery, Hadassah Medical Center, Faculty of Dental Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
- The Institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Oral and Maxillofacial Surgery, Boston University and Boston Medical Center, Boston, MA, United States
| | - Narmeen Daher-Ghanem
- The Institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Deema Zaid
- The Institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michael J. Gough
- Earle A. Chiles Research Institute, Robert W. Franz Cancer Center, Providence Portland Medical Center, Portland, OR, United States
| | - Nataly Kravchenko-Balasha
- The Institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, Jerusalem, Israel
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29
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Masud MA, Kim JY, Kim E. Effective dose window for containing tumor burden under tolerable level. NPJ Syst Biol Appl 2023; 9:17. [PMID: 37221258 DOI: 10.1038/s41540-023-00279-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 05/05/2023] [Indexed: 05/25/2023] Open
Abstract
A maximum-tolerated dose (MTD) reduces the drug-sensitive cell population, though it may result in the competitive release of drug resistance. Alternative treatment strategies such as adaptive therapy (AT) or dose modulation aim to impose competitive stress on drug-resistant cell populations by maintaining a sufficient number of drug-sensitive cells. However, given the heterogeneous treatment response and tolerable tumor burden level of individual patients, determining an effective dose that can fine-tune competitive stress remains challenging. This study presents a mathematical model-driven approach that determines the plausible existence of an effective dose window (EDW) as a range of doses that conserve sufficient sensitive cells while maintaining the tumor volume below a threshold tolerable tumor volume (TTV). We use a mathematical model that explains intratumor cell competition. Analyzing the model, we derive an EDW determined by TTV and the competitive strength. By applying a fixed endpoint optimal control model, we determine the minimal dose to contain cancer at a TTV. As a proof of concept, we study the existence of EDW for a small cohort of melanoma patients by fitting the model to longitudinal tumor response data. We performed identifiability analysis, and for the patients with uniquely identifiable parameters, we deduced patient-specific EDW and minimal dose. The tumor volume for a patient could be theoretically contained at the TTV either using continuous dose or AT strategy with doses belonging to EDW. Further, we conclude that the lower bound of the EDW approximates the minimum effective dose (MED) for containing tumor volume at the TTV.
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Affiliation(s)
- M A Masud
- Natural Product Informatics Research Center, Korea Institute of Science and Technology (KIST), Gangneung, 25451, Republic of Korea
| | - Jae-Young Kim
- Graduate School of Analytical Science and Technology (GRAST), Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Eunjung Kim
- Natural Product Informatics Research Center, Korea Institute of Science and Technology (KIST), Gangneung, 25451, Republic of Korea.
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30
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Lin-Rahardja K, Weaver DT, Scarborough JA, Scott JG. Evolution-Informed Strategies for Combating Drug Resistance in Cancer. Int J Mol Sci 2023; 24:6738. [PMID: 37047714 PMCID: PMC10095117 DOI: 10.3390/ijms24076738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/01/2023] [Accepted: 04/03/2023] [Indexed: 04/14/2023] Open
Abstract
The ever-changing nature of cancer poses the most difficult challenge oncologists face today. Cancer's remarkable adaptability has inspired many to work toward understanding the evolutionary dynamics that underlie this disease in hopes of learning new ways to fight it. Eco-evolutionary dynamics of a tumor are not accounted for in most standard treatment regimens, but exploiting them would help us combat treatment-resistant effectively. Here, we outline several notable efforts to exploit these dynamics and circumvent drug resistance in cancer.
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Affiliation(s)
- Kristi Lin-Rahardja
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Davis T. Weaver
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jessica A. Scarborough
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jacob G. Scott
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
- Department of Translational Hematology & Oncology, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44106, USA
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31
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West J, Robertson-Tessi M, Anderson ARA. Agent-based methods facilitate integrative science in cancer. Trends Cell Biol 2023; 33:300-311. [PMID: 36404257 PMCID: PMC10918696 DOI: 10.1016/j.tcb.2022.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/25/2022] [Accepted: 10/31/2022] [Indexed: 11/19/2022]
Abstract
In this opinion, we highlight agent-based modeling as a key tool for exploration of cell-cell and cell-environment interactions that drive cancer progression, therapeutic resistance, and metastasis. These biological phenomena are particularly suited to be captured at the cell-scale resolution possible only within agent-based or individual-based mathematical models. These modeling approaches complement experimental work (in vitro and in vivo systems) through parameterization and data extrapolation but also feed forward to drive new experiments that test model-generated predictions.
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Affiliation(s)
- Jeffrey West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Mark Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
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32
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Strobl M, Martin AL, West J, Gallaher J, Robertson-Tessi M, Gatenby R, Wenham R, Maini P, Damaghi M, Anderson A. Adaptive therapy for ovarian cancer: An integrated approach to PARP inhibitor scheduling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.22.533721. [PMID: 36993591 PMCID: PMC10055330 DOI: 10.1101/2023.03.22.533721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Toxicity and emerging drug resistance are important challenges in PARP inhibitor (PARPi) treatment of ovarian cancer. Recent research has shown that evolutionary-inspired treatment algorithms which adapt treatment to the tumor's treatment response (adaptive therapy) can help to mitigate both. Here, we present a first step in developing an adaptive therapy protocol for PARPi treatment by combining mathematical modelling and wet-lab experiments to characterize the cell population dynamics under different PARPi schedules. Using data from in vitro Incucyte Zoom time-lapse microscopy experiments and a step-wise model selection process we derive a calibrated and validated ordinary differential equation model, which we then use to test different plausible adaptive treatment schedules. Our model can accurately predict the in vitro treatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumour growth, even in the absence of any resistance. This is because our model predicts that multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Pilot experiments in vivo confirm this conclusion. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis and showcases some of the challenges involved in developing adaptive therapies for new treatment settings.
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Affiliation(s)
- Maximilian Strobl
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Alexandra L. Martin
- Department of Obstetrics and Gynecology, University of Tennessee Health Science Center, Memphis, TN, USA
- Division of Gynecologic Oncology, West Cancer Center and Research Institute, Memphis, TN, USA
| | - Jeffrey West
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Jill Gallaher
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Robert Gatenby
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
- Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Robert Wenham
- Gynecologic Oncology Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Philip Maini
- Wolfson Centre for Mathematical Biology, University of Oxford, Oxford, UK
| | - Mehdi Damaghi
- Department of Pathology, Stony Brook Medicine, SUNY, NY, USA
- Stony Brook Cancer Center, Stony Brook Medicine, SUNY, NY, USA
| | - Alexander Anderson
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
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33
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West J, Adler F, Gallaher J, Strobl M, Brady-Nicholls R, Brown J, Roberson-Tessi M, Kim E, Noble R, Viossat Y, Basanta D, Anderson ARA. A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation. eLife 2023; 12:e84263. [PMID: 36952376 PMCID: PMC10036119 DOI: 10.7554/elife.84263] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/27/2023] [Indexed: 03/24/2023] Open
Abstract
Adaptive therapy is a dynamic cancer treatment protocol that updates (or 'adapts') treatment decisions in anticipation of evolving tumor dynamics. This broad term encompasses many possible dynamic treatment protocols of patient-specific dose modulation or dose timing. Adaptive therapy maintains high levels of tumor burden to benefit from the competitive suppression of treatment-sensitive subpopulations on treatment-resistant subpopulations. This evolution-based approach to cancer treatment has been integrated into several ongoing or planned clinical trials, including treatment of metastatic castrate resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma. In the previous few decades, experimental and clinical investigation of adaptive therapy has progressed synergistically with mathematical and computational modeling. In this work, we discuss 11 open questions in cancer adaptive therapy mathematical modeling. The questions are split into three sections: (1) integrating the appropriate components into mathematical models (2) design and validation of dosing protocols, and (3) challenges and opportunities in clinical translation.
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Affiliation(s)
- Jeffrey West
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Fred Adler
- Department of Mathematics, University of UtahSalt Lake CityUnited States
- School of Biological Sciences, University of UtahSalt Lake CityUnited States
| | - Jill Gallaher
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Maximilian Strobl
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Renee Brady-Nicholls
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Joel Brown
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Mark Roberson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Eunjung Kim
- Natural Product Informatics Research Center, Korea Institute of Science and TechnologyGangneungRepublic of Korea
| | - Robert Noble
- Department of Mathematics, University of LondonLondonUnited Kingdom
| | - Yannick Viossat
- Ceremade, Université Paris-Dauphine, Université Paris Sciences et LettresParisFrance
| | - David Basanta
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Alexander RA Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
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34
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Haughey MJ, Bassolas A, Sousa S, Baker AM, Graham TA, Nicosia V, Huang W. First passage time analysis of spatial mutation patterns reveals sub-clonal evolutionary dynamics in colorectal cancer. PLoS Comput Biol 2023; 19:e1010952. [PMID: 36913406 PMCID: PMC10035892 DOI: 10.1371/journal.pcbi.1010952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 03/23/2023] [Accepted: 02/14/2023] [Indexed: 03/14/2023] Open
Abstract
The signature of early cancer dynamics on the spatial arrangement of tumour cells is poorly understood, and yet could encode information about how sub-clones grew within the expanding tumour. Novel methods of quantifying spatial tumour data at the cellular scale are required to link evolutionary dynamics to the resulting spatial architecture of the tumour. Here, we propose a framework using first passage times of random walks to quantify the complex spatial patterns of tumour cell population mixing. First, using a simple model of cell mixing we demonstrate how first passage time statistics can distinguish between different pattern structures. We then apply our method to simulated patterns of mutated and non-mutated tumour cell population mixing, generated using an agent-based model of expanding tumours, to explore how first passage times reflect mutant cell replicative advantage, time of emergence and strength of cell pushing. Finally, we explore applications to experimentally measured human colorectal cancer, and estimate parameters of early sub-clonal dynamics using our spatial computational model. We infer a wide range of sub-clonal dynamics, with mutant cell division rates varying between 1 and 4 times the rate of non-mutated cells across our sample set. Some mutated sub-clones emerged after as few as 100 non-mutant cell divisions, and others only after 50,000 divisions. The majority were consistent with boundary driven growth or short-range cell pushing. By analysing multiple sub-sampled regions in a small number of samples, we explore how the distribution of inferred dynamics could inform about the initial mutational event. Our results demonstrate the efficacy of first passage time analysis as a new methodology in spatial analysis of solid tumour tissue, and suggest that patterns of sub-clonal mixing can provide insights into early cancer dynamics.
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Affiliation(s)
- Magnus J. Haughey
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Aleix Bassolas
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Sandro Sousa
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Ann-Marie Baker
- Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Trevor A. Graham
- Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Vincenzo Nicosia
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Weini Huang
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
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35
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Patkulkar P, Subbalakshmi AR, Jolly MK, Sinharay S. Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis. ACS OMEGA 2023; 8:6126-6138. [PMID: 36844580 PMCID: PMC9948167 DOI: 10.1021/acsomega.2c06659] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 01/05/2023] [Indexed: 05/14/2023]
Abstract
Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics, and spatial transcriptomics allow recording of patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multiscale dynamics of its evolution. Here, we review the latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed burgeoning growth in the recent past in terms of mapping heterogeneity within tumor cell types as well as the stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor and a more systematic investigation of the implications of heterogeneity for patient outcomes.
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36
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Cotner M, Meng S, Jost T, Gardner A, De Santiago C, Brock A. Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics. Am J Physiol Cell Physiol 2023; 324:C247-C262. [PMID: 36503241 PMCID: PMC9886359 DOI: 10.1152/ajpcell.00185.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 12/15/2022]
Abstract
Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.
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Affiliation(s)
- Michael Cotner
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
| | - Sarah Meng
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
| | - Tyler Jost
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
| | - Andrea Gardner
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
| | - Carolina De Santiago
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas
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37
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Evolutionary rescue of resistant mutants is governed by a balance between radial expansion and selection in compact populations. Nat Commun 2022; 13:7916. [PMID: 36564390 PMCID: PMC9789051 DOI: 10.1038/s41467-022-35484-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
Mutation-mediated treatment resistance is one of the primary challenges for modern antibiotic and anti-cancer therapy. Yet, many resistance mutations have a substantial fitness cost and are subject to purifying selection. How emerging resistant lineages may escape purifying selection via subsequent compensatory mutations is still unclear due to the difficulty of tracking such evolutionary rescue dynamics in space and time. Here, we introduce a system of fluorescence-coupled synthetic mutations to show that the probability of evolutionary rescue, and the resulting long-term persistence of drug resistant mutant lineages, is dramatically increased in dense microbial populations. By tracking the entire evolutionary trajectory of thousands of resistant lineages in expanding yeast colonies we uncover an underlying quasi-stable equilibrium between the opposing forces of radial expansion and natural selection, a phenomenon we term inflation-selection balance. Tailored computational models and agent-based simulations corroborate the fundamental nature of the observed effects and demonstrate the potential impact on drug resistance evolution in cancer. The described phenomena should be considered when predicting multi-step evolutionary dynamics in any mechanically compact cellular population, including pathogenic microbial biofilms and solid tumors. The insights gained will be especially valuable for the quantitative understanding of response to treatment, including emerging evolution-based therapy strategies.
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38
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Faisal Hamdi AI, How SH, Islam MK, Lim JCW, Stanslas J. Adaptive therapy to circumvent drug resistance to tyrosine kinase inhibitors in cancer: is it clinically relevant? Expert Rev Anticancer Ther 2022; 22:1309-1323. [PMID: 36376248 DOI: 10.1080/14737140.2022.2147671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Cancer is highly adaptable and is constantly evolving against current targeted therapies such as tyrosine kinase inhibitors. Despite advances in recent decades, the emergence of drug resistance to tyrosine kinase inhibitors constantly hampers therapeutic efficacy of cancer treatment. Continuous therapy versus intermittent clinical regimen has been a debate in drug administration of cancer patients. An ecologically-inspired shift in cancer treatment known as 'adaptive therapy' intends to improve the drug administration of drugs to cancer patients that can delay emergence of drug resistance. AREAS COVERED We discuss improved understanding of the concept of drug resistance, the basis of continuous therapy, intermittent clinical regimens, and adaptive therapy will be reviewed. In addition, we discuss how adaptive therapy provides guidance for future cancer treatment. EXPERT OPINION The current understanding of drug resistance in cancer leads to poor prognosis and limited treatment options in patients. Fighting drug resistance mutants is constantly followed by new forms of resistance. In most reported cases, continuous therapy leads to drug resistance and an intermittent clinical regimen vaguely delays it. However, adaptive therapy, conceptually, exploits multiple parameters that can suppress the growth of drug resistance and provides safe treatment for cancer patients in the future.
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Affiliation(s)
- Amir Imran Faisal Hamdi
- Pharmacotherapeutics Unit, Department of Medicine, Universiti Putra MalaysiaMedicine, 43400, Serdang, Malaysia
| | - Soon Hin How
- Kuliyyah of Medicine, International Islamic University Malaysia, Kuantan Campus, Kuliyyah of Medicine, 25200, Kuantan, Malaysia
| | | | - Jonathan Chee Woei Lim
- Pharmacotherapeutics Unit, Department of Medicine, Universiti Putra MalaysiaMedicine, 43400, Serdang, Malaysia
| | - Johnson Stanslas
- Pharmacotherapeutics Unit, Department of Medicine, Universiti Putra MalaysiaMedicine, 43400, Serdang, Malaysia
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39
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Deb D, Zhu S, LeBlanc MJ, Danino T. Assessing chemotherapy dosing strategies in a spatial cell culture model. Front Oncol 2022; 12:980770. [PMID: 36505801 PMCID: PMC9729937 DOI: 10.3389/fonc.2022.980770] [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: 06/28/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
Predicting patient responses to chemotherapy regimens is a major challenge in cancer treatment. Experimental model systems coupled with quantitative mathematical models to calculate optimal dose and frequency of drugs can enable improved chemotherapy regimens. Here we developed a simple approach to track two-dimensional cell colonies composed of chemo-sensitive and resistant cell populations via fluorescence microscopy and coupled this to computational model predictions. Specifically, we first developed multiple 4T1 breast cancer cell lines resistant to varying concentrations of doxorubicin, and demonstrated how heterogeneous populations expand in a two-dimensional colony. We subjected cell populations to varied dose and frequency of chemotherapy and measured colony growth. We then built a mathematical model to describe the dynamics of both chemosensitive and chemoresistant populations, where we determined which number of doses can produce the smallest tumor size based on parameters in the system. Finally, using an in vitro model we demonstrated multiple doses can decrease overall colony growth as compared to a single dose at the same total dose. In the future, this system can be adapted to optimize dosing strategies in the setting of heterogeneous cell types or patient derived cells with varied chemoresistance.
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Affiliation(s)
- Dhruba Deb
- Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Shu Zhu
- Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Michael J. LeBlanc
- Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Tal Danino
- Department of Biomedical Engineering, Columbia University, New York, NY, United States,Data Science Institute, Columbia University, New York, NY, United States,Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, United States,*Correspondence: Tal Danino,
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40
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Househam J, Heide T, Cresswell GD, Spiteri I, Kimberley C, Zapata L, Lynn C, James C, Mossner M, Fernandez-Mateos J, Vinceti A, Baker AM, Gabbutt C, Berner A, Schmidt M, Chen B, Lakatos E, Gunasri V, Nichol D, Costa H, Mitchinson M, Ramazzotti D, Werner B, Iorio F, Jansen M, Caravagna G, Barnes CP, Shibata D, Bridgewater J, Rodriguez-Justo M, Magnani L, Sottoriva A, Graham TA. Phenotypic plasticity and genetic control in colorectal cancer evolution. Nature 2022; 611:744-753. [PMID: 36289336 PMCID: PMC9684078 DOI: 10.1038/s41586-022-05311-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 09/01/2022] [Indexed: 12/12/2022]
Abstract
Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather 'plastic'. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.
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Affiliation(s)
- Jacob Househam
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Timon Heide
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - George D Cresswell
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Inmaculada Spiteri
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Chris Kimberley
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Luis Zapata
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Claire Lynn
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Chela James
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Maximilian Mossner
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | | | | | - Ann-Marie Baker
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Calum Gabbutt
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Alison Berner
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Melissa Schmidt
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Bingjie Chen
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Eszter Lakatos
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Vinaya Gunasri
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Daniel Nichol
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Helena Costa
- UCL Cancer Institute, University College London, London, UK
| | - Miriam Mitchinson
- Histopathology Department, University College London Hospitals NHS Foundation Trust, London, UK
| | - Daniele Ramazzotti
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Benjamin Werner
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Francesco Iorio
- Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - Marnix Jansen
- UCL Cancer Institute, University College London, London, UK
| | - Giulio Caravagna
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Darryl Shibata
- Department of Pathology, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | | | | | - Luca Magnani
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- Computational Biology Research Centre, Human Technopole, Milan, Italy.
| | - Trevor A Graham
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, UK.
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Brady-Nicholls R, Enderling H. Range-Bounded Adaptive Therapy in Metastatic Prostate Cancer. Cancers (Basel) 2022; 14:5319. [PMID: 36358738 PMCID: PMC9657943 DOI: 10.3390/cancers14215319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 03/31/2024] Open
Abstract
Adaptive therapy with abiraterone acetate (AA), whereby treatment is cycled on and off, has been presented as an alternative to continuous therapy for metastatic castration resistant prostate cancer (mCRPC). It is hypothesized that cycling through treatment allows sensitive cells to competitively suppress resistant cells, thereby increasing the amount of time that treatment is effective. It has been proposed that there exists a subset of patients for whom this competition can be enhanced through slight modifications. Here, we investigate how adaptive AA can be modified to extend time to progression using a simple mathematical model of stem cell, non-stem cell, and prostate-specific antigen (PSA) dynamics. The model is calibrated to longitudinal PSA data from 16 mCRPC patients undergoing adaptive AA in a pilot clinical study at Moffitt Cancer Center. Model parameters are then used to simulate range-bounded adaptive therapy (RBAT) whereby treatment is modulated to maintain PSA levels between pre-determined patient-specific bounds. Model simulations of RBAT are compared to the clinically applied adaptive therapy and show that RBAT can further extend time to progression, while reducing the cumulative dose patients received in 11/16 patients. Simulations also show that the cumulative dose can be reduced by up to 40% under RBAT. Through small modifications to the conventional adaptive therapy design, our study demonstrates that RBAT offers the opportunity to improve patient care, particularly in those patients who do not respond well to conventional adaptive therapy.
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Affiliation(s)
- Renee Brady-Nicholls
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
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42
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Zhang J, Gallaher J, Cunningham JJ, Choi JW, Ionescu F, Chatwal MS, Jain R, Kim Y, Wang L, Brown JS, Anderson AR, Gatenby RA. A Phase 1b Adaptive Androgen Deprivation Therapy Trial in Metastatic Castration Sensitive Prostate Cancer. Cancers (Basel) 2022; 14:5225. [PMID: 36358643 PMCID: PMC9656891 DOI: 10.3390/cancers14215225] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/19/2022] [Accepted: 10/21/2022] [Indexed: 11/16/2022] Open
Abstract
Background: We hypothesize that cancer survival can be improved through adapting treatment strategies to cancer evolutionary dynamics and conducted a phase 1b study in metastatic castration sensitive prostate cancer (mCSPC). Methods: Men with asymptomatic mCSPC were enrolled and proceeded with a treatment break after achieving > 75% PSA decline with LHRH analog plus an NHA. ADT was restarted at the time of PSA or radiographic progression and held again after achieving >50% PSA decline. This on-off cycling of ADT continued until on treatment imaging progression. Results: At data cut off in August 2022, only 2 of the 16 evaluable patients were off study due to imaging progression at 28 months from first dose of LHRH analog for mCSPC. Two additional patients showed PSA progression at 12.4 and 20.5 months and remain on trial. Since none of the 16 patients developed imaging progression at 12 months, the study succeeded in its primary objective of feasibility. The secondary endpoints of median time to PSA progression and median time to radiographic progression have not been reached at a median follow up of 26 months. Conclusions: It is feasible to use an individual’s PSA response and testosterone levels to guide intermittent ADT in mCSPC.
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Affiliation(s)
- Jingsong Zhang
- Department of Genitourinary Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Jill Gallaher
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | | | - Jung W. Choi
- Department of Radiology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Filip Ionescu
- Department of Oncological Science, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Monica S. Chatwal
- Department of Genitourinary Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Rohit Jain
- Department of Genitourinary Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Youngchul Kim
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Liang Wang
- Department of Tumor Biology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Joel S. Brown
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Alexander R. Anderson
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Robert A. Gatenby
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
- Department of Radiology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
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43
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Enzyme-Enhanced Codelivery of Doxorubicin and Bcl-2 Inhibitor by Electrospun Nanofibers for Synergistic Inhibition of Prostate Cancer Recurrence. Pharmaceuticals (Basel) 2022; 15:ph15101244. [PMID: 36297356 PMCID: PMC9610395 DOI: 10.3390/ph15101244] [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: 09/20/2022] [Revised: 10/01/2022] [Accepted: 10/05/2022] [Indexed: 11/17/2022] Open
Abstract
One of the great challenges of postoperative prostate cancer management is tumor recurrence. Although postoperative chemotherapy presents benefits to inhibit unexpected recurrence, it is still limited due to the drug resistance or intolerable complications of some patients. Electrospun nanofiber, as a promising drug carrier, demonstrating sustained drug release behavior, can be implanted into the tumor resection site during surgery and is conductive to tumor inhibition. Herein, we fabricated electrospun nanofibers loaded with doxorubicin (DOX) and ABT199 to synergistically prevent postoperative tumor recurrence. Enzymatic degradation of the biodegradable electrospun nanofibers facilitated the release of the two drugs. The primarily released DOX from the electrospun nanofibers effectively inhibited tumor recurrence. However, the sustained release of DOX led to drug resistance of the tumor cells, yielding unsatisfactory eradication of the residual tumor. Remarkably, the combined administration of DOX and ABT199, simultaneously released from the nanofibers, not only prolonged the chemotherapy by DOX but also overcame the drug resistance via inhibiting the Bcl-2 activation and thereby enhancing the apoptosis of tumor cells by ABT199. This dual-drug-loaded implant system, combining efficient chemotherapy and anti-drug resistance, offers a prospective strategy for the potent inhibition of postoperative tumor recurrence.
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44
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Leighow SM, Landry B, Lee MJ, Peyton SR, Pritchard JR. Agent-Based Models Help Interpret Patterns of Clinical Drug Resistance by Contextualizing Competition Between Distinct Drug Failure Modes. Cell Mol Bioeng 2022; 15:521-533. [PMID: 36444351 PMCID: PMC9700548 DOI: 10.1007/s12195-022-00748-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 10/26/2022] [Indexed: 11/16/2022] Open
Abstract
Introduction Modern targeted cancer therapies are carefully crafted small molecules. These exquisite technologies exhibit an astonishing diversity of observed failure modes (drug resistance mechanisms) in the clinic. This diversity is surprising because back of the envelope calculations and classic modeling results in evolutionary dynamics suggest that the diversity in the modes of clinical drug resistance should be considerably smaller than what is observed. These same calculations suggest that the outgrowth of strong pre-existing genetic resistance mutations within a tumor should be ubiquitous. Yet, clinically relevant drug resistance occurs in the absence of obvious resistance conferring genetic alterations. Quantitatively, understanding the underlying biological mechanisms of failure mode diversity may improve the next generation of targeted anticancer therapies. It also provides insights into how intratumoral heterogeneity might shape interpatient diversity during clinical relapse. Materials and Methods We employed spatial agent-based models to explore regimes where spatial constraints enable wild type cells (that encounter beneficial microenvironments) to compete against genetically resistant subclones in the presence of therapy. In order to parameterize a model of microenvironmental resistance, BT20 cells were cultured in the presence and absence of fibroblasts from 16 different tissues. The degree of resistance conferred by cancer associated fibroblasts in the tumor microenvironment was quantified by treating mono- and co-cultures with letrozole and then measuring the death rates. Results and Discussion Our simulations indicate that, even when a mutation is more drug resistant, its outgrowth can be delayed by abundant, low magnitude microenvironmental resistance across large regions of a tumor that lack genetic resistance. These observations hold for different modes of microenvironmental resistance, including juxtacrine signaling, soluble secreted factors, and remodeled ECM. This result helps to explain the remarkable diversity of resistance mechanisms observed in solid tumors, which subverts the presumption that the failure mode that causes the quantitatively fastest growth in the presence of drug should occur most often in the clinic. Conclusion Our model results demonstrate that spatial effects can interact with low magnitude of resistance microenvironmental effects to successfully compete against genetic resistance that is orders of magnitude larger. Clinical outcomes of solid tumors are intrinsically connected to their spatial structure, and the tractability of spatial agent-based models like the ones presented here enable us to understand this relationship more completely.
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Affiliation(s)
- Scott M. Leighow
- Department of Biomedical Engineering, 211 Wartik Laboratory, Pennsylvania State University, University Park, State College, PA 16802 USA
| | - Ben Landry
- Department of Systems Biology, University of Massachusetts Medical School, Worcester, MA USA
| | - Michael J. Lee
- Department of Systems Biology, University of Massachusetts Medical School, Worcester, MA USA
| | - Shelly R. Peyton
- Department of Chemical Engineering, University of Massachusetts, Amherst, MA USA
| | - Justin R. Pritchard
- Department of Biomedical Engineering, 211 Wartik Laboratory, Pennsylvania State University, University Park, State College, PA 16802 USA
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45
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Cárdenas SD, Reznik CJ, Ranaweera R, Song F, Chung CH, Fertig EJ, Gevertz JL. Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer. NPJ Syst Biol Appl 2022; 8:32. [PMID: 36075912 PMCID: PMC9458753 DOI: 10.1038/s41540-022-00244-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 08/05/2022] [Indexed: 11/09/2022] Open
Abstract
The promise of precision medicine has been limited by the pervasive resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We ask if resistance mechanisms can be determined from this data alone, and if not, what data would be needed to deduce the underlying mode(s) of resistance. To answer these questions, we propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member(s) of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.
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Affiliation(s)
- Santiago D Cárdenas
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA
| | - Constance J Reznik
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA.,Datacor, Inc., Florham Park, NJ, USA
| | - Ruchira Ranaweera
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Feifei Song
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Christine H Chung
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Elana J Fertig
- Convergence Institute, Department of Oncology, Department of Biomedical Engineering, Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
| | - Jana L Gevertz
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA.
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46
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Raja Arul GL, Toruner MD, Gatenby RA, Carr RM. Ecoevolutionary biology of pancreatic ductal adenocarcinoma. Pancreatology 2022; 22:730-740. [PMID: 35821188 DOI: 10.1016/j.pan.2022.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/01/2022] [Indexed: 12/11/2022]
Abstract
Pancreatic ductal adenocarcinoma (PDAC), the most common histological subtype of pancreatic cancer, is an aggressive disease predicted to be the 2nd cause of cancer mortality in the US by 2040. While first-line therapy has improved, 5-year overall survival has only increased from 5 to ∼10%, and surgical resection is only available for ∼20% of patients as most present with advanced disease, which is invariably lethal. PDAC has well-established highly recurrent mutations in four driver genes including KRAS, TP53, CDKN2A, and SMAD4. Unfortunately, these genetic drivers are not currently therapeutically actionable. Despite extensive sequencing efforts, few additional significantly recurrent and druggable drivers have been identified. In the absence of targetable mutations, chemotherapy remains the mainstay of treatment for most patients. Further, the role of the above driver mutations on PDAC initiation and early development is well-established. However, these mutations alone cannot account for PDAC heterogeneity nor discern early from advanced disease. Taken together, management of PDAC is an example highlighting the shortcomings of the current precision medicine paradigm. PDAC, like other malignancies, represents an ecoevolutionary process. Better understanding the disease through this lens can facilitate the development of novel therapeutic strategies to better control and cure PDAC. This review aims to integrate the current understanding of PDAC pathobiology into an ecoevolutionary framework.
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Affiliation(s)
| | - Merih D Toruner
- Schulze Center for Novel Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Robert A Gatenby
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Ryan M Carr
- Department of Oncology, Mayo Clinic, Rochester, MN, USA.
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47
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Kareva I. Different costs of therapeutic resistance in cancer: Short- and long-term impact of population heterogeneity. Math Biosci 2022; 352:108891. [PMID: 35998834 DOI: 10.1016/j.mbs.2022.108891] [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: 02/22/2022] [Revised: 06/28/2022] [Accepted: 08/13/2022] [Indexed: 11/29/2022]
Abstract
Therapeutic resistance continues to undercut long-term success of many promising cancer treatments. At times, development of therapeutic resistance can come at a fitness cost for the cancer cell population, which could potentially be leveraged to the patient's advantage. A mathematical formulation of such a situation was proposed by Pressley et al. (2020), who discussed two scenarios, namely, when developing therapeutic resistance can come at a cost to proliferative capacity (such as when a drug targets a growth receptor), or to the total tumor carrying capacity (such as when a drug targets neovascularization). Here we expand the analysis of the two models and evaluate both short- and long-term dynamics of a population heterogeneous with respect to resistance. We analyze the four initial distributions with respect to resistance at the time of treatment initiation: uniform, bell-shaped, exponential, and U-shaped. We show that final population composition is invariant to the initial distribution, with a single clone eventually dominating within the population; the value of the resistance parameter of the final clone depends on other system parameters but not on the initial distribution. Transitional behaviors, however, which may have more significant implications for immediate treatment decisions, depend critically on the initial distribution. Furthermore, we show that depending on the mechanism for the cost of resistance (i.e., proliferation vs carrying capacity), increase in natural cell death rate has opposite effects, with higher natural death rate selecting for less resistant cell clones in the long term for proliferation-dependent model, and selecting for more resistant cell clones for carrying capacity-dependent model, a prediction that may have implications for combination therapy with cytotoxic agents. We conclude with a discussion of strengths and limitations of using modeling for understanding treatment trajectory, as well as the promise of model-informed evolutionary steering for improved long-term therapeutic outcomes.
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Affiliation(s)
- Irina Kareva
- Department of Biomedical Engineering, Northeastern University, Boston, MA, USA.
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48
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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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49
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Boguszewicz Ł. Predictive Biomarkers for Response and Toxicity of Induction Chemotherapy in Head and Neck Cancers. Front Oncol 2022; 12:900903. [PMID: 35875133 PMCID: PMC9299243 DOI: 10.3389/fonc.2022.900903] [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: 03/21/2022] [Accepted: 05/24/2022] [Indexed: 01/17/2023] Open
Abstract
This review focuses on the molecular biology of head and neck squamous cell carcinomas and presents current and emerging biomarkers of the response of patients to induction chemotherapy. The usefulness of genes, proteins, and parameters from diagnostic clinical imaging as well as other clinicopathological parameters is thoroughly discussed. The role of induction chemotherapy before radiotherapy or before chemo-radiotherapy is still debated, as the data on its efficacy are somehow confusing. Despite the constant improvement of treatment protocols and the introduction of new cytostatics, there is still no consensus regarding the use of induction chemotherapy in the treatment of head and neck cancer, with the possible exception of larynx preservation. Such difficulties indicate that potential future treatment strategies should be personalized. Personalized medicine, in which individual tumor genetics drive the selection of targeted therapies and treatment plans for each patient, has recently emerged as the next generation of cancer therapy. Early prediction of treatment outcome or its toxicity may be highly beneficial for those who are at risk of the development of severe toxicities or treatment failure—a different treatment strategy may be applied to these patients, sparing them unnecessary pain. The literature search was carried out in the PubMed and ScienceDirect databases as well as in the selected conference proceedings repositories. Of the 265 articles and abstracts found, only 30 met the following inclusion criteria: human studies, analyzing prediction of induction chemotherapy outcome or toxicity based on the pretreatment (or after the first cycle, if more cycles of induction were administered) data, published after the year 2015. The studies regarding metastatic and recurrent cancers as well as the prognosis of overall survival or the outcome of consecutive treatment were not taken into consideration. As revealed from the systematic inspection of the papers, there are over 100 independent parameters analyzed for their suitability as prognostic markers in HNSCC patients undergoing induction chemotherapy. Some of them are promising, but usually they lack important features such as high specificity and sensitivity, low cost, high positive predictive value, clinical relevance, short turnaround time, etc. Subsequent studies are necessary to confirm the usability of the biomarkers for personal medicine.
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Affiliation(s)
- Łukasz Boguszewicz
- Department of Medical Physics, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Warszawa, Poland
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50
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Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model. PLoS Comput Biol 2022; 18:e1010254. [PMID: 35867773 PMCID: PMC9348712 DOI: 10.1371/journal.pcbi.1010254] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 08/03/2022] [Accepted: 05/26/2022] [Indexed: 12/23/2022] Open
Abstract
Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front.
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