Integrating targeted and immunotherapy to treat genetically heterogeneous cancers

整合靶向治疗和免疫治疗来治疗遗传异质性癌症

基本信息

批准号：
9363115
负责人：
ALLAN BALMAIN
金额：
$ 106.56万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-17 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9363115
关键词：
Address Advanced Malignant Neoplasm Antibodies Antigen Presentation Antigens Antineoplastic Agents CRISPR screen Cancer Model Candidate Disease Gene Categories Cells Clinical Clinical Trials Collection Competence Computer Retrieval of Information on Scientific Projects Database DNA Damage Databases Diagnostic Disease Drug Combinations Drug Targeting Effector Cell Gene Expression Genes Genetic Goals Grant Human Immune Immune response Immune system Immunologics Immunophenotyping Immunotherapy Infiltration Laboratories Lead Malignant Neoplasms Measures Metabolic stress Modeling Mus Mutation Myeloid Cells Network-based Neuroblastoma Pathway Analysis Pathway interactions Patients Pharmaceutical Preparations Pharmacology Phase Point Mutation Predisposition Proteomics Reagent Squamous cell carcinoma T cell response T-Cell Activation T-Lymphocyte Testing Tumor Cell Line cancer immunotherapy cancer therapy data mining high throughput screening immune checkpoint blockade immunoregulation improved inhibitor/antagonist innovation monocyte mouse model neoplastic cell novel oncology pre-clinical response screening small molecule transcriptome sequencing treatment response tumor tumor microenvironment

项目摘要

Identification of cancer drug targets using high throughput screens of tumor cell lines has led to a number of agents presently in clinical trials. In addition, recent advances in drugs that attack immune cells within tumors, such as αCTLA4 and αPD-1, have highlighted the importance of immune modulation as a strategy for cancer therapy. The next phase of cancer drug target discovery will seek to integrate these strategies to identify combinations of drugs that most efficiently target both tumor cells and the immune components in advanced cancers. The goal of this proposal is to identify and validate these combinations using large-scale data mining and mouse pre-clinical cancer models that mimic the major genetic features of human cancer. This proposal addresses both mechanisms of immune escape by a) finding genetic targets that may enhance tumor mutation load, and b) carrying out high throughout screens in T cells or myeloid cells for targets that promote immune cell infiltration. We will exploit unique mouse models that mirror major genetic categories of human cancer – high vs low mutation load, and strong vs weak immune infiltrate. Applying single-cell RNAseq and mass cytometric proteomic analyses, cutting edge immune composition databases and novel computational network approaches to cancer target discovery using existing large databases, we propose to identify vulnerabilities addressed by combining small molecule drugs with immunotherapy. We will make immunologically “cold” tumors, that do not engage the immune system, into “hot” tumors that present more or stronger antigens, or that encourage infiltration by immune effector cells. To achieve this goal, we propose three highly innovative aims centered on perturbation of specific targets: first by a CRISP/Cas9 screen in immune cells of the tumor microenvironment, second through increasing antigen load in tumors to optimize immune recognition and finally through a network-based identification of tumor-expressed targets that may confer susceptibility to existing immune-oncology therapies. This represents a true `network' of our collective expertise as well as a measured collection of candidate and screening approaches. AIM 1 –We will perform CRISPR screens in monocytes and T-cells to identify genes associated with tumor entry and function in two distinct tumor types. AIM 2– We will use genetic or pharmacological perturbation of newly generated candidate genes involved in metabolic stress and ROS-induced DNA damage to increase mutation load and antigen abundance in a tumor- specific manner, leading to improved responses to immunotherapy. AIM 3 – We will exploit gene expression networks to identify druggable targets and pathways that augment immune responses. This proposal identifies pathways and perturbants for accelerating immunotherapies.

使用肿瘤细胞系的高通量筛选来鉴定癌症药物靶标已导致许多此外，攻击肿瘤内免疫细胞的药物的最新进展，例如 αCTLA4 和 αPD-1，强调了免疫调节作为癌症策略的重要性癌症药物靶标发现的下一阶段将寻求整合这些策略来识别。最有效地靶向肿瘤细胞和免疫成分的药物组合该提案的目标是使用大规模数据挖掘来识别和验证这些组合。以及模仿人类癌症主要遗传特征的小鼠临床前癌症模型。通过 a) 寻找可能增强肿瘤突变的遗传靶点来解决这两种免疫逃逸机制 b) 在 T 细胞或骨髓细胞中进行高筛选，以寻找促进免疫的靶标我们将开发反映人类癌症主要遗传类别的独特小鼠模型 - 高与低突变负荷，以及强与弱免疫渗透应用单细胞 RNAseq 和质量。细胞计数蛋白质组分析、尖端免疫成分数据库和新颖的计算网络使用现有大型数据库发现癌症靶标的方法，我们建议识别漏洞通过将小分子药物与免疫疗法相结合来解决这个问题，我们将使免疫学变得“冷”。不参与免疫系统的肿瘤转变为呈现更多或更强抗原的“热”肿瘤，或者为了实现这一目标，我们提出了三种高度创新的方法。目标集中在扰动特定目标：首先通过肿瘤免疫细胞中的 CRISP/Cas9 筛选微环境，其次通过增加肿瘤中的抗原负载来优化免疫识别和最终通过基于网络的肿瘤表达靶点识别，这些靶点可能赋予对肿瘤的易感性这代表了我们集体专业知识的真正“网络”以及衡量候选人的收集和筛选方法。目标 1 – 我们将在单核细胞和 T 细胞中进行 CRISPR 筛选，以鉴定与肿瘤相关的基因两种不同肿瘤类型的进入和功能。目标 2 – 我们将利用新生成的候选基因的遗传或药理学扰动参与代谢应激和 ROS 诱导的 DNA 损伤会增加肿瘤中的突变负荷和抗原丰度特定的方式，从而改善对免疫疗法的反应。目标 3 – 我们将利用基因表达网络来识别可增强药物作用的靶点和途径免疫反应。该提案确定了加速免疫治疗的途径和干扰因素。