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Ganesh SR, Roth CM, Parekkadan B. Simulating Interclonal Interactions in Diffuse Large B-Cell Lymphoma. Bioengineering (Basel) 2023; 10:1360. [PMID: 38135951 PMCID: PMC10740451 DOI: 10.3390/bioengineering10121360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 12/24/2023] Open
Abstract
Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of cancers, accounting for 37% of B-cell tumor cases globally. DLBCL is known to be a heterogeneous disease, resulting in variable clinical presentations and the development of drug resistance. One underexplored aspect of drug resistance is the evolving dynamics between parental and drug-resistant clones within the same microenvironment. In this work, the effects of interclonal interactions between two cell populations-one sensitive to treatment and the other resistant to treatment-on tumor growth behaviors were explored through a mathematical model. In vitro cultures of mixed DLBCL populations demonstrated cooperative interactions and revealed the need for modifying the model to account for complex interactions. Multiple best-fit models derived from in vitro data indicated a difference in steady-state behaviors based on therapy administrations in simulations. The model and methods may serve as a tool for understanding the behaviors of heterogeneous tumors and identifying the optimal therapeutic regimen to eliminate cancer cell populations using computer-guided simulations.
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Affiliation(s)
- Siddarth R. Ganesh
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA; (S.R.G.); (C.M.R.)
| | - Charles M. Roth
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA; (S.R.G.); (C.M.R.)
| | - Biju Parekkadan
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA; (S.R.G.); (C.M.R.)
- Department of Medicine, Rutgers Biomedical Health Sciences, New Brunswick, NJ 08852, USA
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Pérez-Aliacar M, Ayensa-Jiménez J, Doblaré M. Modelling cell adaptation using internal variables: Accounting for cell plasticity in continuum mathematical biology. Comput Biol Med 2023; 164:107291. [PMID: 37586203 DOI: 10.1016/j.compbiomed.2023.107291] [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: 05/04/2023] [Revised: 07/20/2023] [Accepted: 07/28/2023] [Indexed: 08/18/2023]
Abstract
Cellular adaptation is the ability of cells to change in response to different stimuli and environmental conditions. It occurs via phenotypic plasticity, that is, changes in gene expression derived from changes in the physiological environment. This phenomenon is important in many biological processes, in particular in cancer evolution and its treatment. Therefore, it is crucial to understand the mechanisms behind it. Specifically, the emergence of the cancer stem cell phenotype, showing enhanced proliferation and invasion rates, is an essential process in tumour progression. We present a mathematical framework to simulate phenotypic heterogeneity in different cell populations as a result of their interaction with chemical species in their microenvironment, through a continuum model using the well-known concept of internal variables to model cell phenotype. The resulting model, derived from conservation laws, incorporates the relationship between the phenotype and the history of the stimuli to which cells have been subjected, together with the inheritance of that phenotype. To illustrate the model capabilities, it is particularised for glioblastoma adaptation to hypoxia. A parametric analysis is carried out to investigate the impact of each model parameter regulating cellular adaptation, showing that it permits reproducing different trends reported in the scientific literature. The framework can be easily adapted to any particular problem of cell plasticity, with the main limitation of having enough cells to allow working with continuum variables. With appropriate calibration and validation, it could be useful for exploring the underlying processes of cellular adaptation, as well as for proposing favourable/unfavourable conditions or treatments.
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Affiliation(s)
- Marina Pérez-Aliacar
- Mechanical Engineering Department, School of Engineering and Architecture, University of Zaragoza, C/ Maria de Luna, Zaragoza, 50018, Spain; Engineering Research Institute of Aragón (I3A), University of Zaragoza, C/ Mariano Esquillor, Zaragoza, 50018, Spain.
| | - Jacobo Ayensa-Jiménez
- Engineering Research Institute of Aragón (I3A), University of Zaragoza, C/ Mariano Esquillor, Zaragoza, 50018, Spain; Aragón Health Research Institute (IISAragón), Avda. San Juan Bosco, Zaragoza, 50009, Spain.
| | - Manuel Doblaré
- Engineering Research Institute of Aragón (I3A), University of Zaragoza, C/ Mariano Esquillor, Zaragoza, 50018, Spain; Aragón Health Research Institute (IISAragón), Avda. San Juan Bosco, Zaragoza, 50009, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), Avda. Monforte de Lemos, Madrid, 28029, Spain; Nanjing Tech University, South Puzhu Road, Nanging, 211800, China.
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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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Yang EY, Howard GR, Brock A, Yankeelov TE, Lorenzo G. Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin. Front Mol Biosci 2022; 9:972146. [PMID: 36172049 PMCID: PMC9510895 DOI: 10.3389/fmolb.2022.972146] [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: 06/17/2022] [Accepted: 08/17/2022] [Indexed: 11/20/2022] Open
Abstract
The development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the cytotoxic effects of the standard chemotherapeutic drug doxorubicin on the MCF-7 breast cancer cell line. We assume that treatment with doxorubicin induces a compartmentalization of the breast cancer cell population into surviving cells, which continue proliferating after treatment, and irreversibly damaged cells, which gradually transition from proliferating to treatment-induced death. The model is fit to experimental data including variations in drug concentration, inter-treatment interval, and number of doses. Our model recapitulates tumor cell dynamics in all these scenarios (as quantified by the concordance correlation coefficient, CCC > 0.95). In particular, superior tumor control is observed with higher doxorubicin concentrations, shorter inter-treatment intervals, and a higher number of doses (p < 0.05). Longer inter-treatment intervals require adapting the model parameterization after each doxorubicin dose, suggesting the promotion of chemoresistance. Additionally, we propose promising empirical formulas to describe the variation of model parameters as functions of doxorubicin concentration (CCC > 0.78). Thus, we conclude that our mathematical model could deepen our understanding of the cytotoxic effects of doxorubicin and could be used to explore practical drug regimens achieving optimal tumor control.
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Affiliation(s)
- Emily Y. Yang
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Grant R. Howard
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, United States
- Interdisciplinary Life Sciences Program, 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
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States
- Livestrong Cancer Institutes, Dell Medical School, 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, The University of Texas MD Anderson Cancer Center, Houston, 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
- *Correspondence: Guillermo Lorenzo, ,
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The MYC oncogene - the grand orchestrator of cancer growth and immune evasion. Nat Rev Clin Oncol 2022; 19:23-36. [PMID: 34508258 PMCID: PMC9083341 DOI: 10.1038/s41571-021-00549-2] [Citation(s) in RCA: 272] [Impact Index Per Article: 136.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2021] [Indexed: 02/08/2023]
Abstract
The MYC proto-oncogenes encode a family of transcription factors that are among the most commonly activated oncoproteins in human neoplasias. Indeed, MYC aberrations or upregulation of MYC-related pathways by alternate mechanisms occur in the vast majority of cancers. MYC proteins are master regulators of cellular programmes. Thus, cancers with MYC activation elicit many of the hallmarks of cancer required for autonomous neoplastic growth. In preclinical models, MYC inactivation can result in sustained tumour regression, a phenomenon that has been attributed to oncogene addiction. Many therapeutic agents that directly target MYC are under development; however, to date, their clinical efficacy remains to be demonstrated. In the past few years, studies have demonstrated that MYC signalling can enable tumour cells to dysregulate their microenvironment and evade the host immune response. Herein, we discuss how MYC pathways not only dictate cancer cell pathophysiology but also suppress the host immune response against that cancer. We also propose that therapies targeting the MYC pathway will be key to reversing cancerous growth and restoring antitumour immune responses in patients with MYC-driven cancers.
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Roy SM, Garg V, Barman S, Ghosh C, Maity AR, Ghosh SK. Kinetics of Nanomedicine in Tumor Spheroid as an In Vitro Model System for Efficient Tumor-Targeted Drug Delivery With Insights From Mathematical Models. Front Bioeng Biotechnol 2021; 9:785937. [PMID: 34926430 PMCID: PMC8671936 DOI: 10.3389/fbioe.2021.785937] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 10/27/2021] [Indexed: 12/25/2022] Open
Abstract
Numerous strategies have been developed to treat cancer conventionally. Most importantly, chemotherapy shows its huge promise as a better treatment modality over others. Nonetheless, the very complex behavior of the tumor microenvironment frequently impedes successful drug delivery to the tumor sites that further demands very urgent and effective distribution mechanisms of anticancer drugs specifically to the tumor sites. Hence, targeted drug delivery to tumor sites has become a major challenge to the scientific community for cancer therapy by assuring drug effects to selective tumor tissue and overcoming undesired toxic side effects to the normal tissues. The application of nanotechnology to the drug delivery system pays heed to the design of nanomedicine for specific cell distribution. Aiming to limit the use of traditional strategies, the adequacy of drug-loaded nanocarriers (i.e., nanomedicine) proves worthwhile. After systemic blood circulation, a typical nanomedicine follows three levels of disposition to tumor cells in order to exhibit efficient pharmacological effects induced by the drug candidates residing within it. As a result, nanomedicine propounds the assurance towards the improved bioavailability of anticancer drug candidates, increased dose responses, and enhanced targeted efficiency towards delivery and distribution of effective therapeutic concentration, limiting toxic concentration. These aspects emanate the proficiency of drug delivery mechanisms. Understanding the potential tumor targeting barriers and limiting conditions for nanomedicine extravasation, tumor penetration, and final accumulation of the anticancer drug to tumor mass, experiments with in vivo animal models for nanomedicine screening are a key step before it reaches clinical translation. Although the study with animals is undoubtedly valuable, it has many associated ethical issues. Moreover, individual experiments are very expensive and take a longer time to conclude. To overcome these issues, nowadays, multicellular tumor spheroids are considered a promising in vitro model system that proposes better replication of in vivo tumor properties for the future development of new therapeutics. In this review, we will discuss how tumor spheroids could be used as an in vitro model system to screen nanomedicine used in targeted drug delivery, aiming for better therapeutic benefits. In addition, the recent proliferation of mathematical modeling approaches gives profound insight into the underlying physical principles and produces quantitative predictions. The hierarchical tumor structure is already well decorous to be treated mathematically. To study targeted drug delivery, mathematical modeling of tumor architecture, its growth, and the concentration gradient of oxygen are the points of prime focus. Not only are the quantitative models circumscribed to the spheroid, but also the role of modeling for the nanoparticle is equally inevitable. Abundant mathematical models have been set in motion for more elaborative and meticulous designing of nanomedicine, addressing the question regarding the objective of nanoparticle delivery to increase the concentration and the augmentative exposure of the therapeutic drug molecule to the core. Thus, to diffuse the dichotomy among the chemistry involved, biological data, and the underlying physics, the mathematical models play an indispensable role in assisting the experimentalist with further evaluation by providing the admissible quantitative approach that can be validated. This review will provide an overview of the targeted drug delivery mechanism for spheroid, using nanomedicine as an advantageous tool.
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Affiliation(s)
| | - Vrinda Garg
- Department of Physics, National Institute of Technology, Warangal, India
| | - Sourav Barman
- Amity Institute of Biotechnology, Amity University, Kolkata, India
| | - Chitrita Ghosh
- Department of Pharmacology, Burdwan Medical College and Hospital, Burdwan, India
| | | | - Surya K Ghosh
- Department of Physics, National Institute of Technology, Warangal, India
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