Project 3: Mechanisms of immunotherapy action

项目3.免疫治疗的作用机制。这个项目的目标是收集数据 并构建计算模型，以提供对系统级的无数交互作用的了解， 有助于癌症的免疫监测，从而提高我们操纵这些相互作用的能力 癌症治疗。我们将研究扰动(遗传和药物诱导的)对新陈代谢和 小鼠模型中信号通路及抗肿瘤T细胞功能的研究。预计这将显著地 增加我们对T细胞抗肿瘤反应的了解，并产生所需的临床前数据 设计新的联合疗法，以便有可能应用于临床。我们的目标是计算模型， 根据治疗前状态的分析预测治疗干预的后果。 免疫-肿瘤的相互作用依赖于细胞内的状态，我们将在 基因表达、信号转导和细胞代谢水平。新陈代谢状态会影响 免疫细胞在肿瘤细胞杀伤中发挥作用，免疫检查点抑制剂改变T细胞代谢。 因此，代谢酶代表了一类新兴的治疗靶点，旨在通过阻断或减轻T细胞耗尽的影响来增强抗肿瘤免疫反应。由于单元-非自治 机制在ICI中起着重要作用，计算模型将重点关注细胞之间的相互作用，其中 关于细胞状态的数据被建模为影响这些交互的强度。我们假设这样的情况 模型将揭示通过结合ICIS、靶向治疗来提高免疫治疗效果的新方法 以及调节新陈代谢酶活性的药物。 AIM 6.1将通过暴露以下内容来定义免疫检查点受体之间的细胞和代谢相互作用 抗免疫检查点受体抗体同基因小鼠肿瘤模型的建立 结合，然后使用多个图谱测量对肿瘤和免疫细胞状态的影响 单细胞分辨率的技术。我们希望找出可能被下药的“耗尽目标” 提高免疫检查点封锁的效果。AIM 6.2将研究已知的免疫信号网络 在T细胞生物学和ICI功能中很重要，并将这些网络的活动与以下物质的代谢状态联系起来 包括肿瘤细胞和免疫细胞。大量证据表明，免疫细胞的代谢和信号状态 在肿瘤监测中很重要，但进行的平行研究相对较少 免疫和肿瘤向新陈代谢发出信号。AIM 6.3将研究细胞和分子事件 使用同基因和基因工程小鼠(GEM)进行成功的联合免疫治疗 ICIS、细胞因子和淋巴结靶向疫苗组合导致的模型 很好的肿瘤。我们还将评估是否可以在循环中检测到有效的反应 血液中的免疫细胞，这是开发一种用于人类的便捷反应生物测定的第一步。

SUMMARY – PROJECT 3. Mechanisms of immunotherapy action. The goal of this project is to collect data and construct computational models that provide a systems-level understanding of the myriad interactions that contribute to immune surveillance of cancer, thereby improving our ability to manipulate these interactions for cancer therapy. We will study the effects of perturbations (genetic and drug-induced) on metabolic and signaling pathways and on anti-tumor T cell function in mouse models. This is expected to significantly increase our understanding of anti-tumor responses by T cells and to generate pre-clinical data needed to design new combination therapies for possible translation into the clinic. We aim for computational models that predict the consequences of therapeutic intervention based on assays of pre-treatment state. Immune-tumor interactions are dependent on the intracellular states of cells, which we will measure at the levels of gene expression, signal transduction and cellular metabolism. Metabolic state affects the ability of immune cells to function in tumor cell killing and immune checkpoint inhibitors alter T cell metabolism. Metabolic enzymes thus represent an emerging class of targets for therapeutics that aim to augment anti-tumor immune responses by blocking or mitigating the effects of T-cell exhaustion. Since cell-non-autonomous mechanisms play a major role in ICI, computational models will focus on interactions among cells, in which data on cell state is modeled as influencing the strength of these interactions. We hypothesize that such models will reveal new ways to enhance the efficacy of immunotherapy by combining ICIs, targeted therapies and drugs that modulate the activity of metabolic enzymes. Aim 6.1 Will define cellular and metabolic interactions among immune checkpoint receptors by exposing syngeneic mouse tumor models to antibodies against immune checkpoint receptors individually and in combination, and then measuring the effects on tumor and immune cell states using multiple profiling technologies at single cell resolution. We hope to identify “exhaustion targets” that might be drugged to increase the efficacy of immune checkpoint blockade. Aim 6.2 Will study immune signaling networks known to be important in T-cell biology and ICI function and link the activities of these networks to the metabolic states of both tumor and immune cells. Extensive evidence shows that metabolic and signaling states of immune cells are important in tumor surveillance but relatively few parallel studies have been performed linking activity of immune and tumor signaling to metabolism. Aim 6.3 Will investigate the cellular and molecular events underlying successful combination immunotherapy using syngeneic and genetically engineered mouse (GEM) models in which a combination of ICIs, cytokines and a lymph node-targeted vaccine results in regression of well-established tumors. We will also assess whether efficacious responses can be detected in circulating immune cells in the blood, a first step towards developing a convenient response bioassay for use in humans.