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Xue L, Zhang H, Zheng X, Sun W, Lei J. Treatment of melanoma with dendritic cell vaccines and immune checkpoint inhibitors: A mathematical modeling study. J Theor Biol 2023; 568:111489. [PMID: 37054970 DOI: 10.1016/j.jtbi.2023.111489] [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: 07/01/2022] [Revised: 12/13/2022] [Accepted: 04/05/2023] [Indexed: 04/15/2023]
Abstract
Dendritic cell (DC) vaccines and immune checkpoint inhibitors (ICIs) play critical roles in shaping the immune responses of tumor cells (TCs) and are widely used in cancer immunotherapies. Quantitatively evaluating the effectiveness of these therapies are essential for the optimization of treatment strategies. Here, based on the combined therapy of melanoma with DC vaccines and ICIs, we formulated a mathematical model to investigate the dynamic interactions between TCs and the immune system and understand the underlying mechanisms of immunotherapy. First, we obtained a threshold parameter for the growth of TCs, which is given by the ratio of spontaneous proliferation to immune inhibition. Next, we proved the existence and locally asymptotic stability of steady states of tumor-free, tumor-dominant, and tumor-immune coexistent equilibrium, and identified the existence of Hopf bifurcation of the proposed model. Furthermore, global sensitivity analysis showed that the growth of TCs strongly correlates with the injection rate of DC vaccines, the activation rate of CTLs, and the killing rate of TCs. Finally, we tested the efficacy of multiple monotherapies and combined therapies with model simulations. Our results indicate that DC vaccines can decelerate the growth of TCs, and ICIs can inhibit the growth of TCs. Besides, both therapies can prolong the lifetime of patients, and the combined therapy of DC vaccines and ICIs can effectively eradicate TCs.
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Affiliation(s)
- Ling Xue
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang, 150001, China; College of Mathematical Sciences, Harbin Engineering University, Harbin, Heilongjiang, 150001, China
| | - Hongyu Zhang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang, 150001, China; College of Mathematical Sciences, Harbin Engineering University, Harbin, Heilongjiang, 150001, China
| | - Xiaoming Zheng
- Department of Mathematics, Central Michigan University, Mount Pleasant, MI 48859, United States of America
| | - Wei Sun
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang, 150001, China; College of Mathematical Sciences, Harbin Engineering University, Harbin, Heilongjiang, 150001, China.
| | - Jinzhi Lei
- School of Mathematical Sciences, Center for Applied Mathematics, Tiangong University, Tianjin, 300387, China.
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Kamran M, Bhattacharya U, Omar M, Marchionni L, Ince TA. ZNF92, an unexplored transcription factor with remarkably distinct breast cancer over-expression associated with prognosis and cell-of-origin. NPJ Breast Cancer 2022; 8:99. [PMID: 36038558 PMCID: PMC9424319 DOI: 10.1038/s41523-022-00474-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 08/09/2022] [Indexed: 11/10/2022] Open
Abstract
Tumor phenotype is shaped both by transforming genomic alterations and the normal cell-of-origin. We identified a cell-of-origin associated prognostic gene expression signature, ET-9, that correlates with remarkably shorter overall and relapse free breast cancer survival, 8.7 and 6.2 years respectively. The genes associated with the ET-9 signature are regulated by histone deacetylase 7 (HDAC7) partly through ZNF92, a previously unexplored transcription factor with a single PubMed citation since its cloning in 1990s. Remarkably, ZNF92 is distinctively over-expressed in breast cancer compared to other tumor types, on a par with the breast cancer specificity of the estrogen receptor. Importantly, ET-9 signature appears to be independent of proliferation, and correlates with outcome in lymph-node positive, HER2+, post-chemotherapy and triple-negative breast cancers. These features distinguish ET-9 from existing breast cancer prognostic signatures that are generally related to proliferation and correlate with outcome in lymph-node negative, ER-positive, HER2-negative breast cancers. Our results suggest that ET-9 could be also utilized as a predictive signature to select patients for HDAC inhibitor treatment.
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Talaei K, Garan SA, Quintela BDM, Olufsen MS, Cho J, Jahansooz JR, Bhullar PK, Suen EK, Piszker WJ, Martins NRB, Moreira de Paula MA, Dos Santos RW, Lobosco M. A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus. Front Cell Infect Microbiol 2021; 11:711153. [PMID: 34869049 PMCID: PMC8633844 DOI: 10.3389/fcimb.2021.711153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 10/14/2021] [Indexed: 11/13/2022] Open
Abstract
Cell-based mathematical models have previously been developed to simulate the immune system in response to pathogens. Mathematical modeling papers which study the human immune response to pathogens have predicted concentrations of a variety of cells, including activated and resting macrophages, plasma cells, and antibodies. This study aims to create a comprehensive mathematical model that can predict cytokine levels in response to a gram-positive bacterium, S. aureus by coupling previous models. To accomplish this, the cytokines Tumor Necrosis Factor Alpha (TNF-α), Interleukin 6 (IL-6), Interleukin 8 (IL-8), and Interleukin 10 (IL-10) are included to quantify the relationship between cytokine release from macrophages and the concentration of the pathogen, S. aureus, ex vivo. Partial differential equations (PDEs) are used to model cellular response and ordinary differential equations (ODEs) are used to model cytokine response, and interactions between both components produce a more robust and more complete systems-level understanding of immune activation. In the coupled cellular and cytokine model outlined in this paper, a low concentration of S. aureus is used to stimulate the measured cellular response and cytokine expression. Results show that our cellular activation and cytokine expression model characterizing septic conditions can predict ex vivo mechanisms in response to gram-negative and gram-positive bacteria. Our simulations provide new insights into how the human immune system responds to infections from different pathogens. Novel applications of these insights help in the development of more powerful tools and protocols in infection biology.
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Affiliation(s)
- Kian Talaei
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Lawrence Berkeley National Laboratory, Berkeley, CA, United States.,Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, United States
| | - Steven A Garan
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | | | - Mette S Olufsen
- Department of Mathematics, North Carolina State University, Raleigh, NC, United States
| | - Joshua Cho
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Lawrence Berkeley National Laboratory, Berkeley, CA, United States.,College of Chemistry, University of California, Berkeley, Berkeley, CA, United States
| | - Julia R Jahansooz
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, United States
| | - Puneet K Bhullar
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Mayo Clinic Alix School of Medicine, Scottsdale, AZ, United States
| | - Elliott K Suen
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, United States
| | - Walter J Piszker
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States.,College of Chemistry, University of California, Berkeley, Berkeley, CA, United States
| | - Nuno R B Martins
- Center for Research and Education in Aging, University of California, Berkeley, Berkeley, CA, United States
| | | | | | - Marcelo Lobosco
- Department of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, Brazil
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Masurel L, Bianca C, Lemarchand A. Space-velocity thermostatted kinetic theory model of tumor growth. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:5525-5551. [PMID: 34517499 DOI: 10.3934/mbe.2021279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The competition between cancer cells and immune system cells in inhomogeneous conditions is described at cell scale within the framework of the thermostatted kinetic theory. Cell learning is reproduced by increased cell activity during favorable interactions. The cell activity fluctuations are controlled by a thermostat. The direction of cell velocity is changed according to stochastic rules mimicking a dense fluid. We develop a kinetic Monte Carlo algorithm inspired from the direct simulation Monte Carlo (DSMC) method initially used for dilute gases. The simulations generate stochastic trajectories sampling the kinetic equations for the distributions of the different cell types. The evolution of an initially localized tumor is analyzed. Qualitatively different behaviors are observed as the field regulating activity fluctuations decreases. For high field values, i.e. efficient thermalization, cancer is controlled. For small field values, cancer rapidly and monotonously escapes from immunosurveillance. For the critical field value separating these two domains, the 3E's of immunotherapy are reproduced, with an apparent initial elimination of cancer, a long quasi-equilibrium period followed by large fluctuations, and the final escape of cancer, even for a favored production of immune system cells. For field values slightly smaller than the critical value, more regular oscillations of the number of immune system cells are spontaneously observed in agreement with clinical observations. The antagonistic effects that the stimulation of the immune system may have on oncogenesis are reproduced in the model by activity-weighted rate constants for the autocatalytic productions of immune system cells and cancer cells. Local favorable conditions for the launching of the oscillations are met in the fluctuating inhomogeneous system, able to generate a small cluster of immune system cells with larger activities than those of the surrounding cancer cells.
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Affiliation(s)
- Léon Masurel
- Laboratoire de Physique Théorique de la Matière Condensée, Sorbonne Université, CNRS, 4 place Jussieu, case courrier 121, 75252 Paris Cedex 05, France
| | - Carlo Bianca
- École Supérieure d'Ingénieurs en Génie Électrique, Productique et Management Industriel, Laboratoire Quartz EA 7393, Laboratoire de Recherche en Eco-innovation Industrielle et Energétique, 13 Boulevard de l'Hautil, 95092 Cergy Pontoise Cedex, France
| | - Annie Lemarchand
- Laboratoire de Physique Théorique de la Matière Condensée, Sorbonne Université, CNRS, 4 place Jussieu, case courrier 121, 75252 Paris Cedex 05, France
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Bushnell GG, Deshmukh AP, den Hollander P, Luo M, Soundararajan R, Jia D, Levine H, Mani SA, Wicha MS. Breast cancer dormancy: need for clinically relevant models to address current gaps in knowledge. NPJ Breast Cancer 2021; 7:66. [PMID: 34050189 PMCID: PMC8163741 DOI: 10.1038/s41523-021-00269-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 04/08/2021] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is the most commonly diagnosed cancer in the USA. Although advances in treatment over the past several decades have significantly improved the outlook for this disease, most women who are diagnosed with estrogen receptor positive disease remain at risk of metastatic relapse for the remainder of their life. The cellular source of late relapse in these patients is thought to be disseminated tumor cells that reactivate after a long period of dormancy. The biology of these dormant cells and their natural history over a patient's lifetime is largely unclear. We posit that research on tumor dormancy has been significantly limited by the lack of clinically relevant models. This review will discuss existing dormancy models, gaps in biological understanding, and propose criteria for future models to enhance their clinical relevance.
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Affiliation(s)
- Grace G Bushnell
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Abhijeet P Deshmukh
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Petra den Hollander
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ming Luo
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Rama Soundararajan
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dongya Jia
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics and Departments of Physics and Bioengineering, Northeastern University, Boston, MA, USA.
| | - Sendurai A Mani
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Max S Wicha
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
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