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Soares Dos Santos MP, Bernardo RMC, Vidal J, Moreira A, Torres DFM, Herdeiro CAR, Santos HA, Gonçalves G. Next-generation chemotherapy treatments based on black hole algorithms: From cancer remission to chronic disease management. Comput Biol Med 2024; 180:108961. [PMID: 39106673 DOI: 10.1016/j.compbiomed.2024.108961] [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: 03/15/2024] [Revised: 07/10/2024] [Accepted: 07/26/2024] [Indexed: 08/09/2024]
Abstract
PROBLEM Therapeutic planning strategies have been developed to enhance the effectiveness of cancer drugs. Nevertheless, their performance is highly limited by the inefficient biological representativeness of predictive tumor growth models, which hinders their translation to clinical practice. OBJECTIVE This study proposes a disruptive approach to oncology based on nature-inspired control using realistic Black Hole physical laws, in which tumor masses are trapped to experience attraction dynamics on their path to complete remission or to become a chronic disease. This control method is designed to operate independently of individual patient idiosyncrasies, including high tumor heterogeneities and highly uncertain tumor dynamics, making it a promising avenue for advancing beyond the limitations of the traditional survival probabilistic paradigm. DESIGN Here, we provide a multifaceted study of chemotherapy therapeutic planning that includes: (1) the design of a pioneering controller algorithm based on physical laws found in the Black Holes; (2) investigation of the ability of this controller algorithm to ensure stable equilibrium treatments; and (3) simulation tests concerning tumor volume dynamics using drugs with significantly different pharmacokinetics (Cyclophosphamide and Atezolizumab), tumor volumes (200 mm3 and 12 732 mm3) and modeling characterizations (Gompertzian and Logistic tumor growth models). RESULTS Our results highlight the ability of this new astrophysical-inspired control algorithm to perform effective chemotherapy treatments for multiple tumor-treatment scenarios, including tumor resistance to chemotherapy, clinical scenarios modelled by time-dependent parameters, and highly uncertain tumor dynamics. CONCLUSIONS Our findings provide strong evidence that cancer therapy inspired by phenomena found in black holes can emerge as a disruptive paradigm. This opens new high-impacting research directions, exploring synergies between astrophysical-inspired control algorithms and Artificial Intelligence applied to advanced personalized cancer therapeutics.
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Affiliation(s)
- Marco P Soares Dos Santos
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal; Intelligent Systems Associate Laboratory (LASI), Guimarães, Portugal.
| | - Rodrigo M C Bernardo
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal
| | - JoãoV Vidal
- Department of Physics and Aveiro Institute of Materials (CICECO), University of Aveiro, Aveiro, Portugal; Department of Physics and Institute for Nanostructures, Nanomodelling and Nanofabrication (I3N), University of Aveiro, Aveiro, Portugal
| | - Ana Moreira
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, 3810-193, Aveiro, Portugal
| | - Delfim F M Torres
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, Aveiro, Portugal
| | - Carlos A R Herdeiro
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, Aveiro, Portugal
| | - Hélder A Santos
- Department of Biomaterials and Biomedical Technology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Gil Gonçalves
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal; Intelligent Systems Associate Laboratory (LASI), Guimarães, Portugal
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Lorenzo G, Ahmed SR, Hormuth DA, Vaughn B, Kalpathy-Cramer J, Solorio L, Yankeelov TE, Gomez H. Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data. Annu Rev Biomed Eng 2024; 26:529-560. [PMID: 38594947 DOI: 10.1146/annurev-bioeng-081623-025834] [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] [Indexed: 04/11/2024]
Abstract
Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.
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Affiliation(s)
- Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Syed Rakin Ahmed
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
- Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard University, Cambridge, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David A Hormuth
- Livestrong Cancer Institutes, University of Texas, Austin, Texas, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
| | - Brenna Vaughn
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | | | - Luis Solorio
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | - Thomas E Yankeelov
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, Texas, USA
- Department of Biomedical Engineering, Department of Oncology, and Department of Diagnostic Medicine, University of Texas, Austin, Texas, USA
- Livestrong Cancer Institutes, University of Texas, Austin, Texas, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
| | - Hector Gomez
- School of Mechanical Engineering and Purdue Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
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3
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Ravichandran A, Honrao S, Xie S, Fonseca E, Lawson JW. Computational Design of Low Melting Eutectics of Molten Salts: A Combined Machine Learning and Thermodynamic Modeling Approach. J Phys Chem Lett 2024; 15:121-126. [PMID: 38147653 DOI: 10.1021/acs.jpclett.3c02888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
We develop a computational framework combining thermodynamic and machine learning models to predict the melting temperatures of molten salt eutectic mixtures (Teut). The model shows an accuracy of ∼6% (mean absolute percentage error) over the entire data set. Using this approach, we screen millions of combinatorial eutectics ranging from binary to hexanary, predict new mixtures, and propose design rules that lead to low Teut. We show that heterogeneity in molecular sizes, quantified by the molecular volume of the components, and mixture configurational entropy, quantified by the number of mixture components, are important factors that can be exploited to design low Teut mixtures. While predicting eutectic composition with existing techniques had proved challenging, we provide some preliminary models for estimating the compositions. The high-throughput screening technique presented here is essential to design novel mixtures for target applications and efficiently navigate the vast design space of the eutectic mixtures.
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Affiliation(s)
- Ashwin Ravichandran
- KBR Inc., Intelligent Systems Division, NASA Ames Research Center, Moffett Field, California 94035, United States
| | - Shreyas Honrao
- KBR Inc., Intelligent Systems Division, NASA Ames Research Center, Moffett Field, California 94035, United States
| | - Stephen Xie
- KBR Inc., Intelligent Systems Division, NASA Ames Research Center, Moffett Field, California 94035, United States
| | - Eric Fonseca
- Intelligent Systems Division, NASA Ames Research Center, Moffett Field, California 94035, United States
| | - John W Lawson
- Intelligent Systems Division, NASA Ames Research Center, Moffett Field, California 94035, United States
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4
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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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Brummer AB, Xella A, Woodall R, Adhikarla V, Cho H, Gutova M, Brown CE, Rockne RC. Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables. Front Immunol 2023; 14:1115536. [PMID: 37256133 PMCID: PMC10226275 DOI: 10.3389/fimmu.2023.1115536] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/27/2023] [Indexed: 06/01/2023] Open
Abstract
In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to a real biological system in order discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. We show how this data-driven model-discovery based approach provides unique insight into CAR T-cell dynamics when compared to an established model-first approach. These results demonstrate the potential for SINDy to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.
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Affiliation(s)
- Alexander B. Brummer
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
- Department of Physics and Astronomy, College of Charleston, Charleston, SC, United States
| | - Agata Xella
- Department of Hemtaology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Ryan Woodall
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Vikram Adhikarla
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Heyrim Cho
- Department of Mathematics, University of California, Riverside, Riverside, CA, United States
| | - Margarita Gutova
- Department of Stem Cell Biology and Regenerative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Christine E. Brown
- Department of Hemtaology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Russell C. Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
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6
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Plaszczynski S, Grammaticos B, Pallud J, Campagne JE, Badoual M. Predicting regrowth of low-grade gliomas after radiotherapy. PLoS Comput Biol 2023; 19:e1011002. [PMID: 37000852 PMCID: PMC10128962 DOI: 10.1371/journal.pcbi.1011002] [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] [Revised: 04/25/2023] [Accepted: 03/04/2023] [Indexed: 04/03/2023] Open
Abstract
Diffuse low grade gliomas are invasive and incurable brain tumors that inevitably transform into higher grade ones. A classical treatment to delay this transition is radiotherapy (RT). Following RT, the tumor gradually shrinks during a period of typically 6 months to 4 years before regrowing. To improve the patient’s health-related quality of life and help clinicians build personalized follow-ups, one would benefit from predictions of the time during which the tumor is expected to decrease. The challenge is to provide a reliable estimate of this regrowth time shortly after RT (i.e. with few data), although patients react differently to the treatment. To this end, we analyze the tumor size dynamics from a batch of 20 high-quality longitudinal data, and propose a simple and robust analytical model, with just 4 parameters. From the study of their correlations, we build a statistical constraint that helps determine the regrowth time even for patients for which we have only a few measurements of the tumor size. We validate the procedure on the data and predict the regrowth time at the moment of the first MRI after RT, with precision of, typically, 6 months. Using virtual patients, we study whether some forecast is still possible just three months after RT. We obtain some reliable estimates of the regrowth time in 75% of the cases, in particular for all “fast-responders”. The remaining 25% represent cases where the actual regrowth time is large and can be safely estimated with another measurement a year later. These results show the feasibility of making personalized predictions of the tumor regrowth time shortly after RT.
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Affiliation(s)
- Stéphane Plaszczynski
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
- Université Paris-Cité, IJCLab, Orsay, France
- * E-mail:
| | - Basile Grammaticos
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
- Université Paris-Cité, IJCLab, Orsay, France
| | - Johan Pallud
- Department of Neurosurgery, GHU Paris Sainte-Anne Hospital, Paris, France
- Université de Paris, Sorbonne Paris Cité, Paris, France
- Inserm, U1266, IMA-Brain, Institut de Psychiatrie et Neurosciences de Paris, Paris, France
| | - Jean-Eric Campagne
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
- Université Paris-Cité, IJCLab, Orsay, France
| | - Mathilde Badoual
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
- Université Paris-Cité, IJCLab, Orsay, France
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7
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Hussain A, Muthuvalu MS, Faye I, Zafar M, Inc M, Afzal F, Iqbal MS. Numerical investigation of treated brain glioma model using a two-stage successive over-relaxation method. Comput Biol Med 2023; 153:106429. [PMID: 36587570 DOI: 10.1016/j.compbiomed.2022.106429] [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: 09/20/2022] [Revised: 11/17/2022] [Accepted: 12/13/2022] [Indexed: 12/31/2022]
Abstract
A brain tumor is a dynamic system in which cells develop rapidly and abnormally, as is the case with most cancers. Cancer develops in the brain or inside the skull when aberrant and odd cells proliferate in the brain. By depriving the healthy cells of leisure, nutrition, and oxygen, these aberrant cells eventually cause the healthy cells to perish. This article investigated the development of glioma cells in treating brain tumors. Mathematically, reaction-diffusion models have been developed for brain glioma growth to quantify the diffusion and proliferation of the tumor cells within brain tissues. This study presents the formulation the two-stage successive over-relaxation (TSSOR) algorithm based on the finite difference approximation for solving the treated brain glioma model to predict glioma cells in treating the brain tumor. Also, the performance of TSSOR method is compared to the Gauss-Seidel (GS) and two-stage Gauss-Seidel (TSGS) methods in terms of the number of iterations, the amount of time it takes to process the data, and the rate at which glioma cells grow the fastest. The implementation of the TSSOR, TSGS, and GS methods predicts the growth of tumor cells under the treatment protocol. The results show that the number of glioma cells decreased initially and then increased gradually by the next day. The computational complexity analysis is also used and concludes that the TSSOR method is faster compared to the TSGS and GS methods. According to the results of the treated glioma development model, the TSSOR approach reduced the number of iterations by between 8.0 and 71.95%. In terms of computational time, the TSSOR approach is around 1.18-76.34% faster than the TSGS and GS methods.
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Affiliation(s)
- Abida Hussain
- Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia; Centre for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia
| | - Mohana Sundaram Muthuvalu
- Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia
| | - Ibrahima Faye
- Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia; Centre for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia
| | - Mudasar Zafar
- Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia; Centre for Research in Enhanced Oil Recovery, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia
| | - Mustafa Inc
- Firat University, Science Faculty, Department of Mathematics, 23119, Elazig, Turkey; Department of Medical Research, China Medical University, Taichung, Taiwan.
| | - Farkhanda Afzal
- Department of Humanities and Basic Sciences, MCS, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Muhammad Sajid Iqbal
- Department of Humanities and Basic Sciences, MCS, National University of Sciences and Technology (NUST), Islamabad, Pakistan
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8
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Movilla N, Gonçalves IG, Borau C, García-Aznar JM. A novel integrated experimental and computational approach to unravel fibroblast motility in response to chemical gradients in 3D collagen matrices. Integr Biol (Camb) 2022; 14:212-227. [PMID: 36756930 DOI: 10.1093/intbio/zyad002] [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: 02/13/2022] [Revised: 01/06/2023] [Accepted: 01/07/2023] [Indexed: 02/10/2023]
Abstract
Fibroblasts play an essential role in tissue repair and regeneration as they migrate to wounded areas to secrete and remodel the extracellular matrix. Fibroblasts recognize chemical substances such as growth factors, which enhance their motility towards the wounded tissues through chemotaxis. Although several studies have characterized single-cell fibroblast motility before, the migration patterns of fibroblasts in response to external factors have not been fully explored in 3D environments. We present a study that combines experimental and computational efforts to characterize the effect of chemical stimuli on the invasion of 3D collagen matrices by fibroblasts. Experimentally, we used microfluidic devices to create chemical gradients using collagen matrices of distinct densities. We evaluated how cell migration patterns were affected by the presence of growth factors and the mechanical properties of the matrix. Based on these results, we present a discrete-based computational model to simulate cell motility, which we calibrated through the quantitative comparison of experimental and computational data via Bayesian optimization. By combining these approaches, we predict that fibroblasts respond to both the presence of chemical factors and their spatial location. Furthermore, our results show that the presence of these chemical gradients could be reproduced by our computational model through increases in the magnitude of cell-generated forces and enhanced cell directionality. Although these model predictions require further experimental validation, we propose that our framework can be applied as a tool that takes advantage of experimental data to guide the calibration of models and predict which mechanisms at the cellular level may justify the experimental findings. Consequently, these new insights may also guide the design of new experiments, tailored to validate the variables of interest identified by the model.
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Affiliation(s)
- Nieves Movilla
- Department of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza 50018, Spain
| | - Inês G Gonçalves
- Department of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza 50018, Spain
| | - Carlos Borau
- Department of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza 50018, Spain
| | - Jose Manuel García-Aznar
- Department of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza 50018, Spain
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Shojaee P, Mornata F, Deutsch A, Locati M, Hatzikirou H. The impact of tumor associated macrophages on tumor biology under the lens of mathematical modelling: A review. Front Immunol 2022; 13:1050067. [PMID: 36439180 PMCID: PMC9685623 DOI: 10.3389/fimmu.2022.1050067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 10/18/2022] [Indexed: 09/10/2023] Open
Abstract
In this article, we review the role of mathematical modelling to elucidate the impact of tumor-associated macrophages (TAMs) in tumor progression and therapy design. We first outline the biology of TAMs, and its current application in tumor therapies, and their experimental methods that provide insights into tumor cell-macrophage interactions. We then focus on the mechanistic mathematical models describing the role of macrophages as drug carriers, the impact of macrophage polarized activation on tumor growth, and the role of tumor microenvironment (TME) parameters on the tumor-macrophage interactions. This review aims to identify the synergies between biological and mathematical approaches that allow us to translate knowledge on fundamental TAMs biology in addressing current clinical challenges.
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Affiliation(s)
- Pejman Shojaee
- Centre for Information Services and High Performance Computing, Technische Universität (TU) Dresden, Dresden, Germany
| | - Federica Mornata
- Leukocyte Biology Lab, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Andreas Deutsch
- Centre for Information Services and High Performance Computing, Technische Universität (TU) Dresden, Dresden, Germany
| | - Massimo Locati
- Leukocyte Biology Lab, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Medical Biotechnologies and Translational Medicine, Universitàdegli Studi di Milano, Milan, Italy
| | - Haralampos Hatzikirou
- Centre for Information Services and High Performance Computing, Technische Universität (TU) Dresden, Dresden, Germany
- Mathematics Department, Khalifa University, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Centre (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
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10
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Coggan H, Andres Terre H, Liò P. A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth. Front Big Data 2022; 5:941451. [PMID: 36172548 PMCID: PMC9510846 DOI: 10.3389/fdata.2022.941451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
Recent years have seen an increase in the application of machine learning to the analysis of physical and biological systems, including cancer progression. A fundamental downside to these tools is that their complexity and nonlinearity makes it almost impossible to establish a deterministic, a priori relationship between their input and output, and thus their predictions are not wholly accountable. We begin with a series of proofs establishing that this holds even for the simplest possible model of a neural network; the effects of specific loss functions are explored more fully in Appendices. We return to first principles and consider how to construct a physics-inspired model of tumor growth without resorting to stochastic gradient descent or artificial nonlinearities. We derive an algorithm which explores the space of possible parameters in a model of tumor growth and identifies candidate equations much faster than a simulated annealing approach. We test this algorithm on synthetic tumor-growth trajectories and show that it can efficiently and reliably narrow down the area of parameter space where the correct values are located. This approach has the potential to greatly improve the speed and reliability with which patient-specific models of cancer growth can be identified in a clinical setting.
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Affiliation(s)
- Helena Coggan
- Department of Mathematics, University College London, London, United Kingdom
- *Correspondence: Helena Coggan
| | - Helena Andres Terre
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Pietro Liò
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
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11
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Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning. Comput Biol Med 2022; 146:105511. [DOI: 10.1016/j.compbiomed.2022.105511] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 12/11/2022]
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12
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Foo J, Basanta D, Rockne RC, Strelez C, Shah C, Ghaffarian K, Mumenthaler SM, Mitchell K, Lathia JD, Frankhouser D, Branciamore S, Kuo YH, Marcucci G, Vander Velde R, Marusyk A, Hang S, Hari K, Jolly MK, Hatzikirou H, Poels K, Spilker M, Shtylla B, Robertson-Tessi M, Anderson ARA. Roadmap on plasticity and epigenetics in cancer. Phys Biol 2022; 19. [PMID: 35078159 PMCID: PMC9190291 DOI: 10.1088/1478-3975/ac4ee2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/25/2022] [Indexed: 11/22/2022]
Abstract
The role of plasticity and epigenetics in shaping cancer evolution and response to therapy has taken center stage with recent technological advances including single cell sequencing. This roadmap article is focused on state-of-the-art mathematical and experimental approaches to interrogate plasticity in cancer, and addresses the following themes and questions: is there a formal overarching framework that encompasses both non-genetic plasticity and mutation-driven somatic evolution? How do we measure and model the role of the microenvironment in influencing/controlling non-genetic plasticity? How can we experimentally study non-genetic plasticity? Which mathematical techniques are required or best suited? What are the clinical and practical applications and implications of these concepts?
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Affiliation(s)
- Jasmine Foo
- University of Minnesota System, School of Mathematics, Minneapolis, Minnesota, 55455-2020, UNITED STATES
| | - David Basanta
- Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Center Inc, H Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, MRC-3 West/IMO, Tampa, Florida 33612USA, Tampa, Florida, 33612-9416, UNITED STATES
| | - Russell C Rockne
- Computational and Quantitative Medicine; Division of Mathematical Oncology, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Carly Strelez
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Curran Shah
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Kimya Ghaffarian
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Kelly Mitchell
- Department of Cardiovascular & Metabolic Sciences, Cleveland Clinic, Lerner Research Institute, Cleveland, Ohio, 44195-5243, UNITED STATES
| | - Justin D Lathia
- Department of Cardiovascular & Metabolic Sciences, Cleveland Clinic, Lerner Research Institute, Cleveland, Ohio, 44195-5243, UNITED STATES
| | - David Frankhouser
- Computational and Quantitative Medicine; Division of Mathematical Oncology, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Sergio Branciamore
- Computational and Quantitative Medicine; Division of Mathematical Oncology, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Ya-Huei Kuo
- Hematologic Malignancies Translational Science, City of Hope National Medical Center, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Guido Marcucci
- Hematologic Malignancies Translational Science, City of Hope National Medical Center, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Robert Vander Velde
- Department of Cancer Physiology, H Lee Moffitt Cancer Center and Research Center Inc, H Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, MRC-3 West/IMO, Tampa, Florida 33612USA, Tampa, Florida, 33612-9416, UNITED STATES
| | - Andriy Marusyk
- Cancer Physiology, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, Florida, 33612, UNITED STATES
| | - Sui Hang
- Institute for Systems Biology, Systems Biology, WA , WA 98109, UNITED STATES
| | - Kishore Hari
- Indian Institute of Science, 560012 Bangalore, Bangalore, 560012, INDIA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering,, Indian Institute of Science, 560012 Bangalore, Bangalore, 560012, INDIA
| | - Haralampos Hatzikirou
- Khalifa University, P.O. Box: 127788, Abu Dhabi, Abu Dhabi, NA, UNITED ARAB EMIRATES
| | - Kamrine Poels
- Early Clinical Development, Pfizer Global Research and Development, Early Clinical Development, Groton, Connecticut, 06340, UNITED STATES
| | - Mary Spilker
- Medicine Design, Pfizer Global Research and Development, Medicine Design, Groton, Connecticut, 06340, UNITED STATES
| | - Blerta Shtylla
- Early Clinical Development, Pfizer Global Research and Development, Early Clinical Development, Groton, Connecticut, 06340, UNITED STATES
| | - Mark Robertson-Tessi
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, Florida, 33612, UNITED STATES
| | - Alexander R A Anderson
- Integrated Mathematical Oncology, Moffitt Cancer Center, Co-Director of Integrated Mathematical Oncology, 12902 Magnolia Drive, SRB 4 Rm 24000H, Tampa, Florida 33612, Tampa, 33612, UNITED STATES
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Combining dynamic modeling with machine learning can be the key for the integration of mathematical and clinical oncology. Phys Life Rev 2022; 40:1-2. [DOI: 10.1016/j.plrev.2022.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 01/17/2022] [Indexed: 11/17/2022]
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