Project 3: Mechanisms of immunotherapy action

项目3：免疫治疗作用机制

基本信息

批准号：
10343841
负责人：
MARCIA HAIGIS
金额：
$ 23.88万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-03-08 至 2023-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10343841
关键词：
Address Affect Albumins Antibodies Antitumor Response Biological Assay Blood CTLA4 gene Cell physiology Cells Cellular Metabolic Process Cellular biology Clinic Clinical Data Combination immunotherapy Combined Modality Therapy Complement Computer Models Critical Pathways Data Environment Enzymes Equilibrium Event Failure Functional disorder Gene Expression Genetic Genetically Engineered Mouse Goals Half-Life Human Immune Immune checkpoint inhibitor Immune response Immune signaling Immunity Immunologic Surveillance Immunotherapy In Vitro In complete remission Individual Inflammation Interleukin-2 Least-Squares Analysis Letters Link Malignant Neoplasms Measures Mediating Mediator of activation protein Metabolic Metabolic Pathway Metabolism Methods Modeling Molecular Mus Pathway interactions Patients Pharmaceutical Preparations Pharmacology Play Process Proteomics Receptor Signaling Regulation Resolution Role Signal Pathway Signal Transduction Signal Transduction Pathway Signaling Molecule Signaling Protein System T cell response T-Cell Activation T-Lymphocyte Technology Testing Therapeutic Therapeutic Intervention Translations Transplantation Tumor Antibodies Tumor-infiltrating immune cells Vaccines anti-CTLA4 anti-PD-1 anti-tumor immune response base cancer therapy cell killing cell type checkpoint receptors cytokine design exhaustion immune checkpoint immune checkpoint blockade improved in vivo inhibitor interest lymph nodes metabolomics mouse model neoplastic cell network models new combination therapies new therapeutic target pre-clinical predictive modeling programmed cell death protein 1 receptor refractory cancer response response biomarker small molecule synergism targeted treatment therapeutic target triple-negative invasive breast carcinoma tumor vaccination outcome

项目摘要

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.

摘要 – 项目 3。免疫治疗作用机制该项目的目标是收集数据。和计算构造模型，提供对无数相互作用的系统级理解有助于癌症的免疫监视，从而提高我们操纵这些相互作用的能力我们将研究扰动（遗传和药物引起的）对代谢和癌症治疗的影响。信号通路和小鼠模型中的抗肿瘤 T 细胞功能。增加我们对 T 细胞抗肿瘤反应的理解，并生成所需的临床前数据设计新的组合疗法以可能转化为临床。根据治疗前状态的分析来预测治疗干预的后果。免疫-肿瘤相互作用取决于细胞的细胞内状态，我们将在基因表达水平、信号转导和细胞代谢状态影响着细胞的能力。免疫细胞发挥杀伤肿瘤细胞的作用，而免疫检查点抑制剂则改变 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 细胞耗竭的影响来增强抗肿瘤免疫反应，因为细胞非自主性。机制在 ICI 中发挥着重要作用，计算模型将重点关注细胞之间的相互作用，其中细胞状态数据被建模为影响这些相互作用的强度。模型将揭示通过结合 ICI、靶向治疗来增强免疫治疗疗效的新方法以及调节代谢酶活性的药物。目标 6.1 将通过暴露来定义免疫检查点受体之间的细胞和代谢相互作用同基因小鼠肿瘤模型，针对免疫检查点受体抗体单独和在组合，然后使用多重分析来测量对肿瘤和免疫细胞状态的影响我们希望能够识别出可能被药物作用的“疲惫目标”。提高免疫检查点封锁的功效目标 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 T 细胞的代谢状态联系起来大量证据表明免疫细胞的代谢和信号传导状态。在肿瘤监测中很重要，但很少有相对平行的研究将其活性联系起来目标 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的基础模型中，ICIs、细胞因子和淋巴结靶向疫苗的组合导致了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，这是开发用于人类的便捷反应生物测定的第一步。