Matalon Omri, Perissinotto Andrea, Baruch Kuti, Braiman Shai, Geiger Maor Anat, Yoles Eti, Wilczynski Ella, Nevo Uri, Priel Avner
ImmunoBrain, Ltd., Rehovot, Israel.
Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.
PLoS One. 2025 Jun 9;20(6):e0324618. doi: 10.1371/journal.pone.0324618. eCollection 2025.
Targeting immune checkpoint pathways to evoke an immune response against tumors has revolutionized clinical oncology over the last decade. Antibodies that block the PD-1/PD-L1 pathway have demonstrated effective antitumor activity in cancer patients and are approved for treatment of several different types of cancer. However, many patients do not experience durable beneficial clinical responses. The ability to predict response to immunotherapy is a clinical need with immediate implications on the optimization of oncologic treatments. In this work we developed and tested the ability of an Agent-Based Model (ABM) to predict the ex vivo immune response of memory T cells to anti-PD-L1 blocking antibody, based on personalized immune-phenotypes. We performed mixed lymphocyte reaction (MLR) experiments on blood samples of healthy volunteers to model the dose-response kinetics of the immune response to anti-PD-L1 antibody. Additionally, immunophenotype of peripheral lymphocyte and monocyte populations was used for modeling and prediction. In silico MLR experiments were conducted using the ABM-based Cell Studio Platform, and the results of ex vivo vs. in silico experiments were compared. Our ABM accurately recapitulates MLR-derived immune responses, achieving >80% predictive accuracy. Notably, given the relatively small cohort tested, such results are typically impossible to model with methods based solely on statistical or data-driven approaches. Importantly, the use of this modeling strategy not only predicts the outcome of the immune response, but also provides insights into the exact biological parameters and related cellular mechanisms that lead to differential immune response.
在过去十年中，靶向免疫检查点通路以引发针对肿瘤的免疫反应彻底改变了临床肿瘤学。阻断PD-1/PD-L1通路的抗体已在癌症患者中显示出有效的抗肿瘤活性，并被批准用于治疗几种不同类型的癌症。然而，许多患者并未经历持久的有益临床反应。预测免疫治疗反应的能力是一项临床需求，对肿瘤治疗的优化具有直接影响。在这项工作中，我们开发并测试了基于主体的模型（ABM）根据个性化免疫表型预测记忆T细胞对抗PD-L1阻断抗体的体外免疫反应的能力。我们对健康志愿者的血样进行了混合淋巴细胞反应（MLR）实验，以模拟对抗PD-L1抗体免疫反应的剂量反应动力学。此外，外周淋巴细胞和单核细胞群体的免疫表型用于建模和预测。使用基于ABM的细胞工作室平台进行了计算机模拟MLR实验，并比较了体外实验与计算机模拟实验的结果。我们的ABM准确地再现了源自MLR的免疫反应，预测准确率超过80%。值得注意的是，鉴于所测试的队列相对较小，这样的结果通常不可能仅用基于统计或数据驱动方法的模型来模拟。重要的是，使用这种建模策略不仅可以预测免疫反应的结果，还可以深入了解导致不同免疫反应的精确生物学参数和相关细胞机制。
