{"title":"基于多种免疫耗竭状态减轻检查点抑制的非遗传抗性。","authors":"Irina Kareva, Jana L Gevertz","doi":"10.1038/s41540-024-00336-6","DOIUrl":null,"url":null,"abstract":"<p><p>Despite the revolutionary impact of immune checkpoint inhibition on cancer therapy, the lack of response in a subset of patients, as well as the emergence of resistance, remain significant challenges. Here we explore the theoretical consequences of the existence of multiple states of immune cell exhaustion on response to checkpoint inhibition therapy. In particular, we consider the emerging understanding that T cells can exist in various states: fully functioning cytotoxic cells, reversibly exhausted cells with minimal cytotoxicity, and terminally exhausted cells. We hypothesize that inflammation augmented by drug activity triggers transitions between these phenotypes, which can lead to non-genetic resistance to checkpoint inhibitors. We introduce a conceptual mathematical model, coupled with a standard 2-compartment pharmacometric (PK) model, that incorporates these mechanisms. Simulations of the model reveal that, within this framework, the emergence of resistance to checkpoint inhibitors can be mitigated through altering the dose and the frequency of administration. Our analysis also reveals that standard PK metrics do not correlate with treatment outcome. However, we do find that levels of inflammation that we assume trigger the transition from the reversibly to terminally exhausted states play a critical role in therapeutic outcome. A simulation of a population that has different values of this transition threshold reveals that while the standard high-dose, low-frequency dosing strategy can be an effective therapeutic design for some, it is likely to fail a significant fraction of the population. Conversely, a metronomic-like strategy that distributes a fixed amount of drug over many doses given close together is predicted to be effective across the entire simulated population, even at a relatively low cumulative drug dose. We also demonstrate that these predictions hold if the transitions between different states of immune cell exhaustion are triggered by prolonged antigen exposure, an alternative mechanism that has been implicated in this process. 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However, we do find that levels of inflammation that we assume trigger the transition from the reversibly to terminally exhausted states play a critical role in therapeutic outcome. A simulation of a population that has different values of this transition threshold reveals that while the standard high-dose, low-frequency dosing strategy can be an effective therapeutic design for some, it is likely to fail a significant fraction of the population. Conversely, a metronomic-like strategy that distributes a fixed amount of drug over many doses given close together is predicted to be effective across the entire simulated population, even at a relatively low cumulative drug dose. We also demonstrate that these predictions hold if the transitions between different states of immune cell exhaustion are triggered by prolonged antigen exposure, an alternative mechanism that has been implicated in this process. 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引用次数: 0
摘要
尽管免疫检查点抑制疗法对癌症治疗产生了革命性的影响,但部分患者缺乏反应以及耐药性的出现仍是重大挑战。在此,我们探讨了免疫细胞存在多种衰竭状态对检查点抑制疗法反应的理论影响。特别是,我们考虑到新出现的认识,即 T 细胞可以存在于不同的状态:功能完全正常的细胞毒性细胞、细胞毒性极低的可逆衰竭细胞和终末衰竭细胞。我们假设,由药物活性增强的炎症会触发这些表型之间的转换,从而导致对检查点抑制剂的非遗传抗性。我们引入了一个概念数学模型,该模型与标准的二室药理学(PK)模型相结合,包含了这些机制。对模型的模拟显示,在此框架内,可以通过改变给药剂量和频率来缓解检查点抑制剂耐药性的出现。我们的分析还显示,标准的 PK 指标与治疗结果并不相关。不过,我们确实发现,我们假定触发从可逆衰竭状态向终末衰竭状态过渡的炎症水平在治疗结果中起着至关重要的作用。对具有不同过渡阈值的人群进行模拟后发现,虽然标准的高剂量、低频率给药策略对某些人来说是一种有效的治疗设计,但它很可能会使相当一部分人的治疗失败。与此相反,一种类似于节拍器的策略将固定剂量的药物分多次给药,即使累积药物剂量相对较低,也能对整个模拟人群有效。我们还证明,如果免疫细胞衰竭的不同状态之间的转换是由长时间的抗原暴露触发的,那么这些预测也是成立的。我们的理论分析证明了通过剂量调节减轻检查点抑制剂耐药性的潜力。
Mitigating non-genetic resistance to checkpoint inhibition based on multiple states of immune exhaustion.
Despite the revolutionary impact of immune checkpoint inhibition on cancer therapy, the lack of response in a subset of patients, as well as the emergence of resistance, remain significant challenges. Here we explore the theoretical consequences of the existence of multiple states of immune cell exhaustion on response to checkpoint inhibition therapy. In particular, we consider the emerging understanding that T cells can exist in various states: fully functioning cytotoxic cells, reversibly exhausted cells with minimal cytotoxicity, and terminally exhausted cells. We hypothesize that inflammation augmented by drug activity triggers transitions between these phenotypes, which can lead to non-genetic resistance to checkpoint inhibitors. We introduce a conceptual mathematical model, coupled with a standard 2-compartment pharmacometric (PK) model, that incorporates these mechanisms. Simulations of the model reveal that, within this framework, the emergence of resistance to checkpoint inhibitors can be mitigated through altering the dose and the frequency of administration. Our analysis also reveals that standard PK metrics do not correlate with treatment outcome. However, we do find that levels of inflammation that we assume trigger the transition from the reversibly to terminally exhausted states play a critical role in therapeutic outcome. A simulation of a population that has different values of this transition threshold reveals that while the standard high-dose, low-frequency dosing strategy can be an effective therapeutic design for some, it is likely to fail a significant fraction of the population. Conversely, a metronomic-like strategy that distributes a fixed amount of drug over many doses given close together is predicted to be effective across the entire simulated population, even at a relatively low cumulative drug dose. We also demonstrate that these predictions hold if the transitions between different states of immune cell exhaustion are triggered by prolonged antigen exposure, an alternative mechanism that has been implicated in this process. Our theoretical analyses demonstrate the potential of mitigating resistance to checkpoint inhibitors via dose modulation.
期刊介绍:
npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology.
We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.