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Cogno N, Axenie C, Bauer R, Vavourakis V. Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation. Cancer Biol Ther 2024; 25:2344600. [PMID: 38678381 PMCID: PMC11057625 DOI: 10.1080/15384047.2024.2344600] [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: 10/30/2023] [Accepted: 04/15/2024] [Indexed: 04/29/2024] Open
Abstract
Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.
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Affiliation(s)
- Nicolò Cogno
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Institute for Condensed Matter Physics, Technische Universit¨at Darmstadt, Darmstadt, Germany
| | - Cristian Axenie
- Computer Science Department and Center for Artificial Intelligence, Technische Hochschule Nürnberg Georg Simon Ohm, Nuremberg, Germany
| | - Roman Bauer
- Nature Inspired Computing and Engineering Research Group, Computer Science Research Centre, University of Surrey, Guildford, UK
| | - Vasileios Vavourakis
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
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2
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Kolokotroni E, Abler D, Ghosh A, Tzamali E, Grogan J, Georgiadi E, Büchler P, Radhakrishnan R, Byrne H, Sakkalis V, Nikiforaki K, Karatzanis I, McFarlane NJB, Kaba D, Dong F, Bohle RM, Meese E, Graf N, Stamatakos G. A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin. J Pers Med 2024; 14:475. [PMID: 38793058 PMCID: PMC11122096 DOI: 10.3390/jpm14050475] [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: 03/03/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/26/2024] Open
Abstract
The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.
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Affiliation(s)
- Eleni Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
| | - Daniel Abler
- Department of Oncology, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland;
- Department of Oncology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Alokendra Ghosh
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Eleftheria Tzamali
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - James Grogan
- Irish Centre for High End Computing, University of Galway, H91 TK33 Galway, Ireland;
| | - Eleni Georgiadi
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
- Biomedical Engineering Department, University of West Attica, 12243 Egaleo, Greece
| | | | - Ravi Radhakrishnan
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Helen Byrne
- Mathematical Institute, University of Oxford, Oxford OX1 2JD, UK;
| | - Vangelis Sakkalis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Katerina Nikiforaki
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Ioannis Karatzanis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | | | - Djibril Kaba
- Department of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK;
| | - Feng Dong
- Department of Computer & Information Sciences, University of Strathclyde, Glasgow G1 1XH, UK;
| | - Rainer M. Bohle
- Department of Pathology, Saarland University, 66421 Homburg, Germany;
| | - Eckart Meese
- Department of Human Genetics, Saarland University, 66421 Homburg, Germany;
| | - Norbert Graf
- Department of Paediatric Oncology and Haematology, Saarland University, 66421 Homburg, Germany;
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
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3
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Jayathilake PG, Victori P, Pavillet CE, Lee CH, Voukantsis D, Miar A, Arora A, Harris AL, Morten KJ, Buffa FM. Metabolic symbiosis between oxygenated and hypoxic tumour cells: An agent-based modelling study. PLoS Comput Biol 2024; 20:e1011944. [PMID: 38489376 DOI: 10.1371/journal.pcbi.1011944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/27/2024] [Accepted: 02/24/2024] [Indexed: 03/17/2024] Open
Abstract
Deregulated metabolism is one of the hallmarks of cancer. It is well-known that tumour cells tend to metabolize glucose via glycolysis even when oxygen is available and mitochondrial respiration is functional. However, the lower energy efficiency of aerobic glycolysis with respect to mitochondrial respiration makes this behaviour, namely the Warburg effect, counter-intuitive, although it has now been recognized as source of anabolic precursors. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from glycolysis. We employed a multi-scale approach that integrates multi-agent modelling, diffusion-reaction, stoichiometric equations, and Boolean networks to study metabolic cooperation between hypoxic and oxygenated cells exposed to varying oxygen, nutrient, and inhibitor concentrations. The results show that the cooperation reduces the depletion of environmental glucose, resulting in an overall advantage of using aerobic glycolysis. In addition, the oxygen level was found to be decreased by symbiosis, promoting a further shift towards anaerobic glycolysis. However, the oxygenated and hypoxic populations may gradually reach quasi-equilibrium. A sensitivity analysis using Latin hypercube sampling and partial rank correlation shows that the symbiotic dynamics depends on properties of the specific cell such as the minimum glucose level needed for glycolysis. Our results suggest that strategies that block glucose transporters may be more effective to reduce tumour growth than those blocking lactate intake transporters.
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Affiliation(s)
| | - Pedro Victori
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Clara E Pavillet
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
- MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
- Department of Computing Sciences and Institute for Data Science and Analytics, Bocconi University, Milan, Italy
| | - Chang Heon Lee
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Dimitrios Voukantsis
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Ana Miar
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Anjali Arora
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Adrian L Harris
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Karl J Morten
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Francesca M Buffa
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
- Department of Computing Sciences and Institute for Data Science and Analytics, Bocconi University, Milan, Italy
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Yankeelov TE, Hormuth DA, Lima EA, Lorenzo G, Wu C, Okereke LC, Rauch GM, Venkatesan AM, Chung C. Designing clinical trials for patients who are not average. iScience 2024; 27:108589. [PMID: 38169893 PMCID: PMC10758956 DOI: 10.1016/j.isci.2023.108589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Abstract
The heterogeneity inherent in cancer means that even a successful clinical trial merely results in a therapeutic regimen that achieves, on average, a positive result only in a subset of patients. The only way to optimize an intervention for an individual patient is to reframe their treatment as their own, personalized trial. Toward this goal, we formulate a computational framework for performing personalized trials that rely on four mathematical techniques. First, mathematical models that can be calibrated with patient-specific data to make accurate predictions of response. Second, digital twins built on these models capable of simulating the effects of interventions. Third, optimal control theory applied to the digital twins to optimize outcomes. Fourth, data assimilation to continually update and refine predictions in response to therapeutic interventions. In this perspective, we describe each of these techniques, quantify their "state of readiness", and identify use cases for personalized clinical trials.
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Affiliation(s)
- Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Division of Diagnostic Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ernesto A.B.F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Texas Advanced Computer Center, The University of Texas at Austin, Austin, TX 78712, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Civil Engineering and Architecture, University of Pavia, 27100 Pavia, Italy
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Lois C. Okereke
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Gaiane M. Rauch
- Department of Abdominal Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Aradhana M. Venkatesan
- Department of Abdominal Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
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Chaudhuri A, Pash G, Hormuth DA, Lorenzo G, Kapteyn M, Wu C, Lima EABF, Yankeelov TE, Willcox K. Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas. Front Artif Intell 2023; 6:1222612. [PMID: 37886348 PMCID: PMC10598726 DOI: 10.3389/frai.2023.1222612] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 09/07/2023] [Indexed: 10/28/2023] Open
Abstract
We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data. The calibrated digital twin is used to propose optimal radiotherapy treatment regimens by solving a multi-objective risk-based optimization under uncertainty problem. The solution leads to a suite of patient-specific optimal radiotherapy treatment regimens exhibiting varying levels of trade-off between the two competing clinical objectives: (i) maximizing tumor control (characterized by minimizing the risk of tumor volume growth) and (ii) minimizing the toxicity from radiotherapy. The proposed digital twin framework is illustrated by generating an in silico cohort of 100 patients with high-grade glioma growth and response properties typically observed in the literature. For the same total radiation dose as the SOC, the personalized treatment regimens lead to median increase in tumor time to progression of around six days. Alternatively, for the same level of tumor control as the SOC, the digital twin provides optimal treatment options that lead to a median reduction in radiation dose by 16.7% (10 Gy) compared to SOC total dose of 60 Gy. The range of optimal solutions also provide options with increased doses for patients with aggressive cancer, where SOC does not lead to sufficient tumor control.
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Affiliation(s)
- Anirban Chaudhuri
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Graham Pash
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, United States
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Michael Kapteyn
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Ernesto A. B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX, United States
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, United States
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, United States
- Department of Oncology, The University of Texas at Austin, Austin, TX, United States
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States
| | - Karen Willcox
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
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Rabah N, Ait Mohand FE, Kravchenko-Balasha N. Understanding Glioblastoma Signaling, Heterogeneity, Invasiveness, and Drug Delivery Barriers. Int J Mol Sci 2023; 24:14256. [PMID: 37762559 PMCID: PMC10532387 DOI: 10.3390/ijms241814256] [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: 08/29/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
The most prevalent and aggressive type of brain cancer, namely, glioblastoma (GBM), is characterized by intra- and inter-tumor heterogeneity and strong spreading capacity, which makes treatment ineffective. A true therapeutic answer is still in its infancy despite various studies that have made significant progress toward understanding the mechanisms behind GBM recurrence and its resistance. The primary causes of GBM recurrence are attributed to the heterogeneity and diffusive nature; therefore, monitoring the tumor's heterogeneity and spreading may offer a set of therapeutic targets that could improve the clinical management of GBM and prevent tumor relapse. Additionally, the blood-brain barrier (BBB)-related poor drug delivery that prevents effective drug concentrations within the tumor is discussed. With a primary emphasis on signaling heterogeneity, tumor infiltration, and computational modeling of GBM, this review covers typical therapeutic difficulties and factors contributing to drug resistance development and discusses potential therapeutic approaches.
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Affiliation(s)
| | | | - Nataly Kravchenko-Balasha
- The Institute of Biomedical and Oral Research, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (N.R.); (F.-E.A.M.)
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7
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Dean JA, Tanguturi SK, Cagney D, Shin KY, Youssef G, Aizer A, Rahman R, Hammoudeh L, Reardon D, Lee E, Dietrich J, Tamura K, Aoyagi M, Wickersham L, Wen PY, Catalano P, Haas-Kogan D, Alexander BM, Michor F. Phase I study of a novel glioblastoma radiation therapy schedule exploiting cell-state plasticity. Neuro Oncol 2023; 25:1100-1112. [PMID: 36402744 PMCID: PMC10237407 DOI: 10.1093/neuonc/noac253] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2024] Open
Abstract
BACKGROUND Glioblastomas comprise heterogeneous cell populations with dynamic, bidirectional plasticity between treatment-resistant stem-like and treatment-sensitive differentiated states, with treatment influencing this process. However, current treatment protocols do not account for this plasticity. Previously, we generated a mathematical model based on preclinical experiments to describe this process and optimize a radiation therapy fractionation schedule that substantially increased survival relative to standard fractionation in a murine glioblastoma model. METHODS We developed statistical models to predict the survival benefit of interventions to glioblastoma patients based on the corresponding survival benefit in the mouse model used in our preclinical study. We applied our mathematical model of glioblastoma radiation response to optimize a radiation therapy fractionation schedule for patients undergoing re-irradiation for glioblastoma and developed a first-in-human trial (NCT03557372) to assess the feasibility and safety of administering our schedule. RESULTS Our statistical modeling predicted that the hazard ratio when comparing our novel radiation schedule with a standard schedule would be 0.74. Our mathematical modeling suggested that a practical, near-optimal schedule for re-irradiation of recurrent glioblastoma patients was 3.96 Gy × 7 (1 fraction/day) followed by 1.0 Gy × 9 (3 fractions/day). Our optimized schedule was successfully administered to 14/14 (100%) patients. CONCLUSIONS A novel radiation therapy schedule based on mathematical modeling of cell-state plasticity is feasible and safe to administer to glioblastoma patients.
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Affiliation(s)
- Jamie A Dean
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- UCL Cancer Institute, University College London, London, UK
| | - Shyam K Tanguturi
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Daniel Cagney
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Kee-Young Shin
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Gilbert Youssef
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
- Center for Neuro-Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ayal Aizer
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Rifaquat Rahman
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Lubna Hammoudeh
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - David Reardon
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Eudocia Lee
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Jorg Dietrich
- Center for Neuro-Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kaoru Tamura
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masaru Aoyagi
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Lacey Wickersham
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Paul Catalano
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Daphne Haas-Kogan
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Brian M Alexander
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Franziska Michor
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- The Ludwig Center at Harvard, Boston, Massachusetts, USA
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Pedersen RK, Andersen M, Skov V, Kjær L, Hasselbalch HC, Ottesen JT, Stiehl T. HSC Niche Dynamics in Regeneration, Pre-malignancy, and Cancer: Insights From Mathematical Modeling. Stem Cells 2023; 41:260-270. [PMID: 36371719 PMCID: PMC10020982 DOI: 10.1093/stmcls/sxac079] [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: 09/07/2021] [Accepted: 09/28/2022] [Indexed: 11/15/2022]
Abstract
The hematopoietic stem cell (HSC) niche is a crucial driver of regeneration and malignancy. Its interaction with hematopoietic and malignant stem cells is highly complex and direct experimental observations are challenging. We here develop a mathematical model which helps relate processes in the niche to measurable changes of stem and non-stem cell counts. HSC attached to the niche are assumed to be quiescent. After detachment HSC become activated and divide or differentiate. To maintain their stemness, the progeny originating from division must reattach to the niche. We use mouse data from literature to parametrize the model. By combining mathematical analysis and computer simulations, we systematically investigate the impact of stem cell proliferation, differentiation, niche attachment, and detachment on clinically relevant scenarios. These include bone marrow transplantation, clonal competition, and eradication of malignant cells. According to our model, sampling of blood or bulk marrow provides only limited information about cellular interactions in the niche and the clonal composition of the stem cell population. Furthermore, we investigate how interference with processes in the stem cell niche could help to increase the effect of low-dose chemotherapy or to improve the homing of genetically engineered cells.
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Affiliation(s)
- Rasmus Kristoffer Pedersen
- IMFUFA, Department of Science and Environment, Roskilde University, Roskilde, Denmark
- Centre for Mathematical Modeling - Human Health and Disease, Roskilde University, Roskilde, Denmark
| | - Morten Andersen
- IMFUFA, Department of Science and Environment, Roskilde University, Roskilde, Denmark
- Centre for Mathematical Modeling - Human Health and Disease, Roskilde University, Roskilde, Denmark
| | - Vibe Skov
- Department of Hematology, Zealand University Hospital, Roskilde, Denmark
| | - Lasse Kjær
- Department of Hematology, Zealand University Hospital, Roskilde, Denmark
| | - Hans C Hasselbalch
- Department of Hematology, Zealand University Hospital, Roskilde, Denmark
| | - Johnny T Ottesen
- IMFUFA, Department of Science and Environment, Roskilde University, Roskilde, Denmark
- Centre for Mathematical Modeling - Human Health and Disease, Roskilde University, Roskilde, Denmark
| | - Thomas Stiehl
- Corresponding author: Dr. rer. nat. Thomas Stiehl, Aachen University, Pauwelsstr. 19, 52074 Aachen, Germany. E-mail:
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9
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Verdugo E, Puerto I, Medina MÁ. An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment. CANCER COMMUNICATIONS (LONDON, ENGLAND) 2022; 42:1083-1111. [PMID: 36129048 DOI: 10.1002/cac2.12361] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 08/07/2022] [Accepted: 09/05/2022] [Indexed: 11/08/2022]
Abstract
Glioblastoma multiforme (GBM) is the most aggressive and common malignant primary brain tumor. Patients with GBM often have poor prognoses, with a median survival of ∼15 months. Enhanced understanding of the molecular biology of central nervous system tumors has led to modifications in their classifications, the most recent of which classified these tumors into new categories and made some changes in their nomenclature and grading system. This review aims to give a panoramic view of the last 3 years' findings in glioblastoma characterization, its heterogeneity, and current advances in its treatment. Several molecular parameters have been used to achieve an accurate and personalized characterization of glioblastoma in patients, including epigenetic, genetic, transcriptomic and metabolic features, as well as age- and sex-related patterns and the involvement of several noncoding RNAs in glioblastoma progression. Astrocyte-like neural stem cells and outer radial glial-like cells from the subventricular zone have been proposed as agents involved in GBM of IDH-wildtype origin, but this remains controversial. Glioblastoma metabolism is characterized by upregulation of the PI3K/Akt/mTOR signaling pathway, promotion of the glycolytic flux, maintenance of lipid storage, and other features. This metabolism also contributes to glioblastoma's resistance to conventional therapies. Tumor heterogeneity, a hallmark of GBM, has been shown to affect the genetic expression, modulation of metabolic pathways, and immune system evasion. GBM's aggressive invasion potential is modulated by cell-to-cell crosstalk within the tumor microenvironment and altered expressions of specific genes, such as ANXA2, GBP2, FN1, PHIP, and GLUT3. Nevertheless, the rising number of active clinical trials illustrates the efforts to identify new targets and drugs to treat this malignancy. Immunotherapy is still relevant for research purposes, given the amount of ongoing clinical trials based on this strategy to treat GBM, and neoantigen and nucleic acid-based vaccines are gaining importance due to their antitumoral activity by inducing the immune response. Furthermore, there are clinical trials focused on the PI3K/Akt/mTOR axis, angiogenesis, and tumor heterogeneity for developing molecular-targeted therapies against GBM. Other strategies, such as nanodelivery and computational models, may improve the drug pharmacokinetics and the prognosis of patients with GBM.
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Affiliation(s)
- Elena Verdugo
- Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Málaga, E-29071, Spain
| | - Iker Puerto
- Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Málaga, E-29071, Spain
| | - Miguel Ángel Medina
- Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Málaga, E-29071, Spain.,Biomedical Research Institute of Málaga (IBIMA-Plataforma Bionand), Málaga, Málaga, E-29071, Spain.,Spanish Biomedical Research Network Center for Rare Diseases (CIBERER), Spanish Health Institute Carlos III (ISCIII), Málaga, Málaga, E-29071, Spain
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10
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Hormuth DA, Farhat M, Christenson C, Curl B, Chad Quarles C, Chung C, Yankeelov TE. Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 2022; 187:114367. [PMID: 35654212 DOI: 10.1016/j.addr.2022.114367] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/25/2022] [Accepted: 05/25/2022] [Indexed: 11/01/2022]
Abstract
Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.
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Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Maguy Farhat
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Chase Christenson
- Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Brandon Curl
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - C Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Caroline Chung
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77230, USA
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11
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Jenner AL, Smalley M, Goldman D, Goins WF, Cobbs CS, Puchalski RB, Chiocca EA, Lawler S, Macklin P, Goldman A, Craig M. Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy. iScience 2022; 25:104395. [PMID: 35637733 PMCID: PMC9142563 DOI: 10.1016/j.isci.2022.104395] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/18/2022] [Accepted: 04/08/2022] [Indexed: 11/26/2022] Open
Abstract
Oncolytic viruses (OVs) are emerging cancer immunotherapy. Despite notable successes in the treatment of some tumors, OV therapy for central nervous system cancers has failed to show efficacy. We used an ex vivo tumor model developed from human glioblastoma tissue to evaluate the infiltration of herpes simplex OV rQNestin (oHSV-1) into glioblastoma tumors. We next leveraged our data to develop a computational, model of glioblastoma dynamics that accounts for cellular interactions within the tumor. Using our computational model, we found that low stromal density was highly predictive of oHSV-1 therapeutic success, suggesting that the efficacy of oHSV-1 in glioblastoma may be determined by stromal-to-tumor cell regional density. We validated these findings in heterogenous patient samples from brain metastatic adenocarcinoma. Our integrated modeling strategy can be applied to suggest mechanisms of therapeutic responses for central nervous system cancers and to facilitate the successful translation of OVs into the clinic.
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Affiliation(s)
- Adrianne L. Jenner
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, QC, Canada
| | - Munisha Smalley
- Division of Engineering in Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | | | - William F. Goins
- Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Charles S. Cobbs
- Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
| | - Ralph B. Puchalski
- Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
| | - E. Antonio Chiocca
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Sean Lawler
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Aaron Goldman
- Division of Engineering in Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, QC, Canada
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12
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Corti A, Colombo M, Migliavacca F, Rodriguez Matas JF, Casarin S, Chiastra C. Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models. Front Bioeng Biotechnol 2021; 9:744560. [PMID: 34796166 PMCID: PMC8593007 DOI: 10.3389/fbioe.2021.744560] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 10/04/2021] [Indexed: 12/20/2022] Open
Abstract
The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.
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Affiliation(s)
- Anna Corti
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Monika Colombo
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy.,Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Jose Felix Rodriguez Matas
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Stefano Casarin
- Department of Surgery, Houston Methodist Hospital, Houston, TX, United States.,Center for Computational Surgery, Houston Methodist Research Institute, Houston, TX, United States.,Houston Methodist Academic Institute, Houston, TX, United States
| | - Claudio Chiastra
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy.,PoliToMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
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