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

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

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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。免疫疗法作用的机制。该项目的目的是收集数据并构建计算模型，该模型提供了对多种相互作用的系统级别的理解有助于癌症的免疫监视，从而提高了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）对代谢和代谢的影响信号通路和小鼠模型中的抗肿瘤T细胞功能。预计这将大大提高我们对T细胞对抗肿瘤反应的理解，并生成所需的临床前数据设计新的组合疗法，以便将其转化为诊所。我们的目标是计算模型根据治疗态的测定，预测热干预的后果。免疫肿瘤相互作用取决于细胞的细胞内态，我们将在该状态下测量基因表达，信号翻译和细胞代谢水平。代谢状态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了T细胞代谢。因此，代谢酶代表了一种新兴的治疗靶标，旨在通过阻止或减轻T细胞耗尽的影响来增强抗肿瘤免疫反应。自细胞非自主机制在ICI中起主要作用，计算模型将集中于细胞之间的相互作用，其中对细胞状态的数据建模为影响这些相互作用的强度。我们假设这样模型将通过结合ICIS，有针对性的疗法来揭示提高免疫疗法效率的新方法以及调节代谢酶活性的药物。 AIM 6.1将通过暴露在于免疫切解点受体之间的细胞和代谢相互作用与免疫碰撞受体抗体的合成小鼠肿瘤模型单独和IN 组合，然后使用多个分析测量对肿瘤和免疫细胞态的影响单细胞分辨率的技术。我们希望确定可能被吸毒的“疲惫目标” 提高免疫检查点阻滞的效率。 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，并将这些网络的活性与代谢状态联系起来肿瘤和免疫细胞。大量证据表明，免疫细胞的代谢和信号态在肿瘤监测中很重要，但是已经进行了相对较少的平行研究，将免疫和肿瘤信号传导对代谢。 AIM 6.3将研究细胞和分子事件基本的成功组合免疫疗法使用合元和基因工程小鼠（GEM） ICIS，细胞因子和以淋巴结靶向疫苗的结合的模型导致回归建立的肿瘤。我们还将评估是否可以在循环中检测到有效的响应血液中的免疫细胞，这是开发出一种方便的响应生物测定的第一步，用于人类使用。