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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.

概要-项目3。免疫治疗作用机制。这个项目的目标是收集数据并构建计算模型，提供对无数交互的系统级理解，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，癌症治疗我们将研究干扰（遗传和药物诱导）对代谢和信号通路和小鼠模型中抗肿瘤T细胞功能。预计这将大大增加我们对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，以便可能转化为临床。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-肿瘤相互作用取决于细胞的细胞内状态，我们将在第一阶段进行测量。基因表达水平、信号转导和细胞代谢。代谢状态会影响免疫细胞在肿瘤细胞杀伤中起作用，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，其目的是通过阻断或减轻T细胞耗竭的影响来增强抗肿瘤免疫应答。由于细胞非自治机制在ICI中起着重要作用，计算模型将专注于细胞之间的相互作用，其中关于单元状态的数据被建模为影响这些相互作用的强度。我们假设，模型将揭示通过结合ICIs，靶向治疗，以及调节代谢酶活性的药物。目的6.1将通过暴露免疫检查点受体，同基因小鼠肿瘤模型对针对免疫检查点受体的抗体的单独和联合应用组合，然后使用多重分析测量对肿瘤和免疫细胞状态的影响单细胞分辨率的技术。我们希望能够确定可能被麻醉的“疲惫目标”，提高免疫检查点阻断的效力。目标6.2将研究免疫信号网络，在T细胞生物学和ICI功能中是重要的，并将这些网络的活动与细胞的代谢状态联系起来。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是重要的，但相对较少的平行研究已经进行了联系的活动，免疫和肿瘤信号传导到代谢。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）的潜在成功联合免疫疗法在这些模型中，ICI、细胞因子和淋巴结靶向疫苗的组合导致已确诊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。这是开发用于人类的方便的反应生物测定法的第一步。