2027 Volume 17 Issue 2
Article Contents

Renxiang Shi. THE INFLUENCE OF DELAYS AND INJECTION TREATMENTS ON THE DYNAMICS OF TUMOR-IMMUNE SYSTEM[J]. Journal of Applied Analysis & Computation, 2027, 17(2): 1312-1350. doi: 10.11948/20260107
Citation: Renxiang Shi. THE INFLUENCE OF DELAYS AND INJECTION TREATMENTS ON THE DYNAMICS OF TUMOR-IMMUNE SYSTEM[J]. Journal of Applied Analysis & Computation, 2027, 17(2): 1312-1350. doi: 10.11948/20260107

THE INFLUENCE OF DELAYS AND INJECTION TREATMENTS ON THE DYNAMICS OF TUMOR-IMMUNE SYSTEM

  • Corresponding author: Email: srxahu@aliyun.com(R. Shi)
  • Fund Project: This work was supported by the research project of Jiangsu Commission of Health (No. Z2024011), the cultivation project of original scientific research of Nanjing University of Chinese Medicine (No. 012062111003-3), the 2025-key research and development program-disease control of Xianyang, Shaanxi Province (No. L2025-ZDYF-JBFZ-025)
  • We propose a tumor-CD4+-cytokine-host system with two delays and injection treatments, where two delays denote activation delays for CD4+ T cells and cytokines respectively and host cells compete with tumor cells. First we prove the positivity and boundedness of solutions without injection treatments, the positivity means that tumor cells always exist, the boundedness means that immune cells can control the increase of tumor cells, then we discuss the influence of two delays on the dynamics of system, that is as any of the delays is sufficiently small, the corresponding interior positive equilibrium point is locally stable, as the delay increases to critical value, Hopf bifurcation occurs and periodic solution bifurcates from the equilibrium point, which means the relapse of tumor cells occurs, furthermore base on center manifold and normal form, we study the property of Hopf bifurcation, at last, in absence of delay, we study the influence of relative parameters on the volume of tumor cells and feasibility of injection treatments of CD4+ T cells and cytokines on eradication of tumor cells, that is the tumor cells can be eradicated if the dose of injection treatment of CD4+ T cells or cytokines is sufficiently large. All the results are supported by simulations.

    MSC: 92B05, 37N25, 37G15
  • 加载中
  • [1] R. Amin and P. Yu, Dynamical analysis of a tumor model with immunotherapy treatment, Chaos Sol. Fract., 2026, 202, 117607. doi: 10.1016/j.chaos.2025.117607

    CrossRef Google Scholar

    [2] L. Anderson, S. Jang and J.-L. Yu, Qualitative behavior of system of tumor-CD4+-cytokine interactions with treatments, Math. Meth. Appl. Sci., 2015, 38(17), 4330–4344. doi: 10.1002/mma.3370

    CrossRef Google Scholar

    [3] J. C. Arciero, T. L. Jackson and D. E. Kirschner, A mathematical model of tumor-immune evasion and siRNA treatment, Dis. Con. Dyn, Sys. Ser.-B, 2004, 4(1), 39–58. doi: 10.3934/dcdsb.2004.4.39

    CrossRef Google Scholar

    [4] K. Dehingia, P. Das, R. K. Upadhyay, A. K. Misra, F. A. Rihan and K. Hosseini, Modelling and analysis of delayed tumor-immune system with hunting T-cells, Math. Compu. Simu., 2023, 203, 669–684. doi: 10.1016/j.matcom.2022.07.009

    CrossRef Google Scholar

    [5] A. Perez-Diez, N. T. Joncker, K. Choi, W. F. N. Chan, C. C. Anderson, O. Lantz and P. Matzinger, CD4 cells can be more efficient at tumor rejection than CD8 cells, Blood, 2007, 109, 5346–5354. doi: 10.1182/blood-2006-10-051318

    CrossRef Google Scholar

    [6] Y. Dong, G. Huang, R. Miyazaki and Y. Takeuchi, Dynamics in a tumor immune system with time delays, Appl. Math. Compu., 2015, 252, 99–113.

    Google Scholar

    [7] R. Eftimie, J. L. Bramson and D. J. D. Earn, Interaction between the immune system and cancer: A brief review of non-spatial mathematical model, Bull. Math. Biol., 2011, 73, 2–32. doi: 10.1007/s11538-010-9526-3

    CrossRef Google Scholar

    [8] R. Eftimie, J. L. Bramson and D. J. D. Earn, Modeling anti-tumor Th1 and Th2 immunity in the rejection of melanoma, J. Theor. Biol., 2010, 265(3), 467–480. doi: 10.1016/j.jtbi.2010.04.030

    CrossRef Google Scholar

    [9] Y. Fujita, Flower power as human cancer cells compete with normal cells, Nature, 2019, 572, 181–182. doi: 10.1038/d41586-019-02161-y

    CrossRef Google Scholar

    [10] D. Hanahan and R. A. Weinberg, The Hallmarks of cancer, Cell, 2000, 100, 57–70. doi: 10.1016/S0092-8674(00)81683-9

    CrossRef Google Scholar

    [11] B. Hassard, N. Kazarinof and Y. Wan, Theory and Application of Hopf Bifurcation, Cambridge University Press, Cambridge, 1981.

    Google Scholar

    [12] F. He, H. Zhu, J. Lin and Y. Ou, Global dynamics of a tumor-immune model with an immune checkpoint inhibitor, Int. J. Biomath, 2023, 2350048, 1–24.

    Google Scholar

    [13] X. Hu and S. R.-J. Jang, Dynamics of tumor-CD4+-cytokine-host cells interactions with treatments, Appl. Math. Compu., 2018, 321, 700–720.

    Google Scholar

    [14] S. Khajanchi, The impact of immunotherapy on a glioma immune interaction model, Chaos Sol. Fract., 2021, 152, 111346. doi: 10.1016/j.chaos.2021.111346

    CrossRef Google Scholar

    [15] S. Khajanchi, S. Bera and T. K. Kar, An Optimal Control Problem for HTLV-Ⅰ Infection Model, Opt. Con. Appl. Method., 2025, 46(2), 798–810. doi: 10.1002/oca.3232

    CrossRef Google Scholar

    [16] S. Khajanchi, J. Mondal and P. K. Tiwari, Optimal treatment strategies using dendritic cell vaccination for a tumor model with parameter identifiability, J. Bio. Systems, 2023, 31(2), 487–516. doi: 10.1142/S0218339023500171

    CrossRef Google Scholar

    [17] S. Khajanchi and J. J. Nieto, Spatiotemporal dynamics of a glioma immune interaction model, Sci. Reports, 2021, 11, 22385.

    Google Scholar

    [18] D. Kirschner and J. C. Panetta, Modeling immunotherapy of the tumor-immune interaction, J. Math. Biol., 1998, 37, 235–252. doi: 10.1007/s002850050127

    CrossRef Google Scholar

    [19] J. Li, Y. Chen, H. Cao, D. Zhang and P. Zhang, A simple model of Tumor-Immune interaction: The effect of antigen delay, Int. J. Bifur. Chaos, 2023, 33(11), 2350129, 1–20. doi: 10.1142/S0218127423501298

    CrossRef Google Scholar

    [20] J. Li, F. Liu, Y. Chen and D. Zhang, Dynamical analysis of a model on tumor-immune system with regulation of PD-1/PD-L1 and stimulation delay of tumor antigen, Qua. Theo. Dyn. Systems, 2022, 21(90), 1–32.

    Google Scholar

    [21] J. Li and X. Ma, Complex dynamic behaviors of a tumor-immune system with two delays in tumor actions, Dis. Con. Dyn. Sys. Ser.-B, 2022, 27(12), 7065–7087. doi: 10.3934/dcdsb.2022033

    CrossRef Google Scholar

    [22] A. G. Lopez, J. M. Seoane and M. A. F. Sanjuan, A validated mathematical model of tumor growth including tumor-host interaction, cell-mediated immune response and chemotherapy, Bull. Math. Biol., 2014, 76, 2884–2906. doi: 10.1007/s11538-014-0037-5

    CrossRef Google Scholar

    [23] W. Mari, How normal cells can win the battle for survival against cancer cells, Plos, Biol., 2010, 8(7), e1000423. doi: 10.1371/journal.pbio.1000423

    CrossRef Google Scholar

    [24] T. Maruyama and Y. Fujita, Cell competition in mammals-novel homeostatic machinery for embryonic development and cancer prevention, Curr. Opin. Cell Biol., 2017, 48, 106–112. doi: 10.1016/j.ceb.2017.06.007

    CrossRef Google Scholar

    [25] J. Mattes, M. Hulett, W. Xie, S. Hogan, M. E. Rothenberg, P. Foster and C. Parish, Immunotherapy of cytotoxic T cell-resistant tumors by T helper 2 cells: An eotaxin and STAT6-dependent process, J. Exp. Med., 2003, 197, 387–393. doi: 10.1084/jem.20021683

    CrossRef Google Scholar

    [26] L. G. de Pillis and A. Radunskaya, The dynamics of an optimally controlled tumor model: A case study, Math. Com. Model., 2003, 37, 1221–1244. doi: 10.1016/S0895-7177(03)00133-X

    CrossRef Google Scholar

    [27] G. E. Plautz, R. M. Bukowski, A. C. Novick, E. A. Klein, E. D. Kursh, T. E. Olencki, R. J. Yetman, A. Pienkny, K. Sandstorm and S. Shu, T-cell adoptive immunotherapy of metastatic renal carcinoma, Adu. Urol., 1999, 54, 617–623.

    Google Scholar

    [28] S. A. Rosenberg, B. S. Packard, P. M. Aebersold, D. Solomon, S. L. Topalian, S. T. Toy, P. Simon, M. T. Lotze, J. C. Yang, C. A. Seipp, et al., Use of tumor-infiltrating lymphocytes and interleukin-2 in the immunotherapy of patients with metastatic melanoma, New Eng. J. Med., 1998, 319(25), 1676–1680.

    Google Scholar

    [29] M. Sardar, S. Biswas and S. Khajanchi, The impact of distributed time delay in a tumor-immune interaction system, Chaos Sol. Fract., 2021, 142, 110483. doi: 10.1016/j.chaos.2020.110483

    CrossRef Google Scholar

    [30] M. Sardar, S. Biswas and S. Khajanchi, Modeling the dynamics of mixed immunotherapy and chemotherapy for the treatment of immunogenic tumor, Eur. Phys. J. Plus, 2024, 139, 228. doi: 10.1140/epjp/s13360-024-05004-6

    CrossRef Google Scholar

    [31] M. Sardar, S. Khajanchi and B. Ahmad, A tumor-immune interaction model with the effect of impulse therapy, Comm. Non. Sci. Numer. Simul., 2023, 126, 107430. doi: 10.1016/j.cnsns.2023.107430

    CrossRef Google Scholar

    [32] M. Sardar, S. Khajanchi, S. Biswas, S. F. Abdelwahab and K. S. Nisar, Exploring the dynamics of a tumor-immune interplay with time delay, Alexandria Eng. J., 2021, 60(5), 4875–4888. doi: 10.1016/j.aej.2021.03.041

    CrossRef Google Scholar

    [33] M. Sardar, S. Khajanchi, S. Biswas and S. Ghosh, A mathematical model for tumor-immune competitive system with multiple time delays, Chaos Sol. Fract., 2024, 179, 114397. doi: 10.1016/j.chaos.2023.114397

    CrossRef Google Scholar

    [34] S. Shi, J. Huang, Y. Kuang and S. Ruan, Stability and Hopf bifurcation of a tumor-immune system interaction model with an immune checkpoint inhibitor, Comm. Non. Sci. Numer. Simul., 2023, 118, 106996. doi: 10.1016/j.cnsns.2022.106996

    CrossRef Google Scholar

    [35] R. A. Weinberg, The Biology of Cancer, Second Ed. , Garland Science, London, UK, 2013.

    Google Scholar

    [36] J.-L. Yu and S. R.-J. Jang, A mathematical model of tumor-immune interactions with an immune checkpoint inhibitor, Appl. Math. Compu., 2019, 362, 124523.

    Google Scholar

    [37] J.-L. Yu, H.-C. Wei and S. R.-J. Jang, A model of tumor-immune system interactions with healthy cells and immunotherapies, Math. Meth. Appl. Sci., 2022, 45, 2852–2870. doi: 10.1002/mma.7958

    CrossRef Google Scholar

    [38] S. Zhang, D. Bernard, W. I. Khan, M. H. Kaplan, J. L. Bramson and Y. Wan, CD4+ T-cell-mediated anti-tumor immunity can be uncoupled from autoimmunity via the STAT4/STAT6 signaling axis, Eur. J. Immunol., 2009, 39, 1252–1259. doi: 10.1002/eji.200839152

    CrossRef Google Scholar

    [39] Y. Zhao, Q. Deng, Z. Qiu, T. Guo and S. Ruan, Modeling the interaction of cytotoxic T-Lymphocytes and oncolytic viruses in a tumor microenvironment, SIAM. J. Appl. Math., 2025, 85(2), 983–1005. doi: 10.1137/23M1613608

    CrossRef Google Scholar

Figures(34)  /  Tables(1)

Article Metrics

Article views(39) PDF downloads(8) Cited by(0)

Access History

Other Articles By Authors

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint