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Volume 12 Issue 3
Mar.  2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  >  12(3): 575-584
J. Sun, D. Li, H. Zhang, L. Liu, and  W. Zhao,  “Consensus control strategy for the treatment of tumour with neuroadaptive cellular immunotherapy,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 575–584, Mar. 2025. doi: 10.1109/JAS.2024.124941
Citation: J. Sun, D. Li, H. Zhang, L. Liu, and  W. Zhao,  “Consensus control strategy for the treatment of tumour with neuroadaptive cellular immunotherapy,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 575–584, Mar. 2025. doi: 10.1109/JAS.2024.124941
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Consensus Control Strategy for the Treatment of Tumour With Neuroadaptive Cellular Immunotherapy

doi: 10.1109/JAS.2024.124941
Funds:  This work was supported in part by the National Natural Science Foundation of China (62203097), the National High-Level Talents Special Support Program (Youth Talent of Technological Innovation of Ten-Thousands Talents Program) (QNBJ-2023-12), and the Fundamental Research Funds for the Central Universities (N2404018)
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    Abstract
  • This paper presents a novel neuro-adaptive cellular immunotherapy control strategy that leverages the high efficiency and applicability of chimeric antigen receptor-engineered T (CAR-T) cells in treating cancer. The proposed real-time control strategy aims to maximize tumor regression while ensuring the safety of the treatment. A dynamic growth model of cancer cells under the influence of cellular immunotherapy is established for the first time, which aligns with clinical experimental results. Utilizing the backstepping method, a novel consensus reference model is designed to consider the characteristics of cancer cell changes during the treatment process and conform to clinical rules. The model is segmented and continuous, with cancer cells expected to decrease in a step-like manner. Furthermore, a prescribed performance mechanism is constructed to maintain the therapeutic effect of the proposed scheme while ensuring the transient performance of the system. Through the analysis of Lyapunov stability, all signals within the closed-loop system are proven to be semiglobally uniformly ultimately bounded (SGUUB). Simulation results demonstrate the effectiveness of the proposed control strategy, highlighting its potential for clinical application in cancer treatment.

     

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  • [1]
    S. Sharma and G. Samanta, “Analysis of the dynamics of a tumor-immune system with chemotherapy and immunotherapy and quadratic optimal control,” Differential Equations and Dynamical Systems, vol. 24, pp. 149–171, 2016. doi: 10.1007/s12591-015-0250-1
    [2]
    C. Wu, T. Rakhshandehroo, H. I. Wettersten, et al., “Pancreatic cancer cells upregulate LPAR4 in response to isolation stress to promote an ecm-enriched niche and support tumour initiation,” Nature Cell Biology, vol. 25, no. 2, pp. 309–322, 2023.
    [3]
    X. Li, Y. Zhong, X. Zhang, A. K. Sood, and J. Liu, “Spatiotemporal view of malignant histogenesis and macroevolution via formation of polyploid giant cancer cells,” Oncogene, vol. 42, no. 9, pp. 665–678, 2023.
    [4]
    G. Schett, A. Mackensen, and D. Mougiakakos, “CAR T-cell therapy in autoimmune diseases,” The Lancet, vol. 402, no. 10416, pp. 2034–2044, 2023.
    [5]
    S. M. Albelda, “CAR T cell therapy for patients with solid tumours: Key lessons to learn and unlearn,” Nature Reviews Clinical Oncology, vol. 21, no. 1, pp. 47–66, 2024. doi: 10.1038/s41571-023-00832-4
    [6]
    Z. Wang, Z. Wu, Y. Liu, and W. Han, “New development in CAR-T cell therapy,” J. Hematology & Oncology, vol. 10, no. 1, pp. 1–11, 2017.
    [7]
    L. Wang, L. Zhang, L. C. Dunmall, Y. Y. Wang, Z. Fan, Z. Cheng, and Y. Wang, “The dilemmas and possible solutions for CAR-T cell therapy application in solid tumors,” Cancer Letters, vol. 591, p. 216871, 2024. doi: 10.1016/j.canlet.2024.216871
    [8]
    L. Tang, S. Pan, X. Wei, X. Xu, and Q. Wei, “Arming CAR-T cells with cytokines and more: Innovations in the fourth-generation CAR-T development,” Molecular Therapy, vol. 31, no. 11, pp. 3146–3162, 2023. doi: 10.1016/j.ymthe.2023.09.021
    [9]
    S. Pandit, P. Agarwalla, F. Song, A. Jansson, G. Dotti, and Y. Brudno, “Implantable CAR T cell factories enhance solid tumor treatment,” Biomaterials, vol. 308, p. 122580, 2024. doi: 10.1016/j.biomaterials.2024.122580
    [10]
    G. C. Russell, Y. Hamzaoui, D. Rho, G. Sutrave, J. S. Choi, D. S. Missan, G. A. Reckard, M. P. Gustafson, and G. B. Kim, “Synthetic biology approaches for enhancing safety and specificity of CAR-T cell therapies for solid cancers,” Cytotherapy, vol. 26, no. 8, pp. 842–857, 2024.
    [11]
    J. Pan, Y. Tan, G. Wang, et al., “Donor-derived CD7 chimeric antigen receptor T cells for T-cell acute lymphoblastic leukemia: first-in-human, phase I trial,” J. Clinical Oncology, vol. 39, no. 30, pp. 3340–3351, 2021. doi: 10.1200/JCO.21.00389
    [12]
    M. Al-Haideri, S. B. Tondok, S. H. Safa, et al., “CAR-T cell combination therapy: The next revolution in cancer treatment,” Cancer Cell Int., vol. 22, no. 1, p. 365, 2022. doi: 10.1186/s12935-022-02778-6
    [13]
    N. Singh and M. V. Maus, “Synthetic manipulation of the cancer-immunity cycle: CAR-T cell therapy,” Immunity, vol. 56, no. 10, pp. 2296–2310, 2023.
    [14]
    R. Rezaei, H. E. G. Ghaleh, M. Farzanehpour, R. Dorostkar, R. Ranjbar, M. Bolandian, M. M. Nodooshan, and A. G. Alvanegh, “Combination therapy with CAR T cells and oncolytic viruses: A new era in cancer immunotherapy,” Cancer Gene Therapy, vol. 29, no. 6, pp. 647–660, 2022. doi: 10.1038/s41417-021-00359-9
    [15]
    N. Dullerud and V. D. Jonsson, “Cellular immunotherapy treatment scheduling to address antigen escape,” in Proc. 59th IEEE Conf. Decision and Control, 2020, pp. 4634–4639.
    [16]
    S. Dey and H. Xu, “Distributed adaptive flocking control for large-scale multiagent systems,” IEEE Trans. Neural Networks and Learning Systems, 2024. DOI: 10.1109/TNNLS.2023.3343666.
    [17]
    Z. Song, L. Gao, Z. Wang, and P. Li, “Adaptive neural control of constrained MIMO nonlinear systems with asymmetric input saturation and dead zone,” IEEE Trans. Neural Networks and Learning Systems, 2023. DOI: 10.1109/TNNLS.2023.3321596.
    [18]
    X. Wang, C. Hua, and Y. Qiu, “Event-triggered model-free adaptive control for nonlinear multiagent systems under jamming attacks,” IEEE Trans. Neural Networks and Learning Systems, 2023. DOI: 10.1109/TNNLS.2023.3279144.
    [19]
    M. Kumar, K. Rajagopal, S. N. Balakrishnan, and N. T. Nguyen, “Reinforcement learning based controller synthesis for flexible aircraft wings,” IEEE/CAA J. Autom. Sinica, vol. 1, no. 4, pp. 435–448, 2014. doi: 10.1109/JAS.2014.7004670
    [20]
    M. Ha, D. Wang, and D. Liu, “Discounted iterative adaptive critic designs with novel stability analysis for tracking control,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1262–1272, 2022.
    [21]
    Z. Gao and G. Guo, “Command filtered finite/fixed-time heading tracking control of surface vehicles,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1667–1676, 2021. doi: 10.1109/JAS.2021.1004135
    [22]
    Y. Jiang, D. Shi, J. Fan, T. Chai, and T. Chen, “Event-triggered model reference adaptive control for linear partially time-variant continuous-time systems with nonlinear parametric uncertainty,” IEEE Trans. Autom. Control, vol. 68, no. 3, pp. 1878–1885, 2023. doi: 10.1109/TAC.2022.3169847
    [23]
    H. Wang, K. Xu, and H. Zhang, “Adaptive finite-time tracking control of nonlinear systems with dynamics uncertainties,” IEEE Trans. Autom. Control, vol. 68, no. 9, pp. 5737–5744, 2023. doi: 10.1109/TAC.2022.3226703
    [24]
    J. Yu, S. Cheng, P. Shi, and C. Lin, “Command-filtered neuroadaptive output-feedback control for stochastic nonlinear systems with input constraint,” IEEE Trans. Cybern., vol. 53, no. 4, pp. 2301–2310, 2023.
    [25]
    G. Chen and J. Dong, “Approximate optimal adaptive prescribed performance control for uncertain nonlinear systems with feature information,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 54, no. 4, pp. 2298–2308, 2024. doi: 10.1109/TSMC.2023.3342854
    [26]
    L. Zhang, W. Che, C. Deng, and Z. Wu, “Prescribed performance control for multiagent systems via fuzzy adaptive event-triggered strategy,” IEEE Trans. Fuzzy Systems, vol. 30, no. 12, pp. 5078–5090, 2022. doi: 10.1109/TFUZZ.2022.3165629
    [27]
    C. Zhang, X. Ren, J. Na, and D. Zheng, “Safe dual-layer nested adaptive prescribed performance control of nonlinear systems with discontinuous reference,” IEEE Trans. Industrial Electronics, vol. 71, no. 8, pp. 9128–9138, 2024. doi: 10.1109/TIE.2023.3317842
    [28]
    X. Liu, H. Zhang, J. Sun, and X. Guo, “Dynamic threshold finite-time prescribed performance control for nonlinear systems with dead-zone output,” IEEE Trans. Cybern., vol. 54, no. 1, pp. 655–664, 2024. doi: 10.1109/TCYB.2023.3279841
    [29]
    Z. Xu, C. Sun, and Q. Liu, “Output-feedback prescribed performance control for the full-state constrained nonlinear systems and its application to DC motor system,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 53, no. 7, pp. 3898–3907, 2023. doi: 10.1109/TSMC.2022.3216119
    [30]
    H. Ren, Z. Cheng, J. Qin, and R. Lu, “Deception attacks on event-triggered distributed consensus estimation for nonlinear systems,” Automatica, vol. 154, p. 111100, 2023.
    [31]
    H. Ren, H. Ma, H. Li, and Z. Wang, “Adaptive fixed-time control of nonlinear MASs with actuator faults,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1252–1262, 2023. doi: 10.1109/JAS.2023.123558
    [32]
    A. Zou, Y. Liu, Z. Hou, and Z. Hu, “Practical predefined-time output-feedback consensus tracking control for multiagent systems,” IEEE Trans. Cybern., vol. 53, no. 8, pp. 5311–5322, 2023. doi: 10.1109/TCYB.2022.3207325
    [33]
    J. Sun, Y. Yan, H. Zhang, and M. Shao, “Consensus-fuzzy ecological joint therapy for multi-tumor populations,” IEEE Trans. Fuzzy Systems, vol. 52, no. 3, pp. 699–709, 2024.
    [34]
    G. Cui, J. Yu, and P. Shi, “Observer-based finite-time adaptive fuzzy control with prescribed performance for nonstrict-feedback nonlinear systems,” IEEE Trans. Fuzzy Systems, vol. 30, no. 3, pp. 767–778, 2020.
    [35]
    H. Liang, D. Li, Y. Pan, and T. Li, “Adaptive predictor-based event-triggered tracking control for nonlinear multiagent systems with fault detect-switch-compensate mechanism,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 54, no. 2, pp. 1276–1287, 2024.
    [36]
    H. Li, Z. Wang, C. Lan, P. Wu, and N. Zeng, “A novel dynamic multiobjective optimization algorithm with non-inductive transfer learning based on multi-strategy adaptive selection,” IEEE Trans. Neural Networks and Learning Systems, vol. 35, no. 11, pp. 16533–16547, 2024. doi: 10.1109/TNNLS.2023.3295461
    [37]
    H. Li, Z. Fang, L. Hu, H. Liu, P. Wu, and N. Zeng, “A novel population robustness-based switching response framework for solving dynamic multi-objective problems,” Neurocomputing, vol. 583, p. 127601, 2024. doi: 10.1016/j.neucom.2024.127601
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    Highlights

    • The dynamic change model of cancer cells constructed can be simplified and considered as a second-order system model, which is convenient for theoretical analysis and simulation verification
    • A novel model of expected cancer cell change trajectory is designed in this paper, which ensures the expectation descends in a step-like way
    • The cellular immunotherapy scheduling is considered under the backstepping method for the first time, which broadens the application scope of the backstepping method

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