Zhang Zhuoyu, Liu Lunan, Ma Chao, Cui Xin, Lam Raymond H W, Chen Weiqiang
Department of Mechanical and Aerospace Engineering, New York University, Brooklyn, NY, 11201, USA.
Department of Biomedical Engineering, Jinan University, Guangzhou, China.
Small Methods. 2021 Jun 15;5(6). doi: 10.1002/smtd.202100197. Epub 2021 Apr 22.
The PD-1 immune checkpoint-based therapy has emerged as a promising therapy strategy for treating the malignant brain tumor glioblastoma (GBM). However, patient response varies in clinical trials due in large to the tumor heterogeneity and immunological resistance in the tumor microenvironment. To further understand how mechanistically the niche interplay and competition drive anti-PD-1 resistance, we established an model to quantitatively describe the biological rationale of critical GBM-immune interactions, such as tumor growth and apoptosis, T cell activation and cytotoxicity, and tumor-associated macrophage (TAM) mediated immunosuppression. Such an experimentation and predictive model, based on the microfluidic chip-measured end-point data and patient-specific immunological characteristics, allowed for a comprehensive and dynamic analysis of multiple TAM-associated immunosuppression mechanisms against the anti-PD-1 immunotherapy. Our computational model demonstrated that the TAM-associated immunosuppression varied in severity across different GBM subtypes, which resulted in distinct tumor responses. Our prediction results indicated that a combination therapy co-targeting of PD-1 checkpoint and TAM-associated CSF-1R signaling could enhance the immune responses of GBM patients, especially those patients with mesenchymal GBM who are irresponsive to the single anti-PD-1 therapy. The development of a patient-specific GBM model would help navigate and personalize immunotherapies for GBM patients.
基于PD-1免疫检查点的疗法已成为治疗恶性脑肿瘤胶质母细胞瘤(GBM)的一种有前景的治疗策略。然而,在临床试验中患者的反应差异很大,这主要归因于肿瘤异质性和肿瘤微环境中的免疫抗性。为了进一步从机制上理解生态位相互作用和竞争如何驱动抗PD-1抗性,我们建立了一个模型来定量描述关键的GBM-免疫相互作用的生物学原理,如肿瘤生长和凋亡、T细胞活化和细胞毒性,以及肿瘤相关巨噬细胞(TAM)介导的免疫抑制。这种基于微流控芯片测量的终点数据和患者特异性免疫特征的实验和预测模型,能够对多种针对抗PD-1免疫疗法的TAM相关免疫抑制机制进行全面动态分析。我们的计算模型表明,TAM相关的免疫抑制在不同GBM亚型中的严重程度不同,这导致了不同的肿瘤反应。我们的预测结果表明,联合靶向PD-1检查点和TAM相关的CSF-1R信号通路的疗法可以增强GBM患者的免疫反应,尤其是那些对单一抗PD-1疗法无反应的间充质GBM患者。建立患者特异性GBM模型将有助于为GBM患者制定和个性化免疫疗法。