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

• 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
• 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

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吞吐量筛选鉴定癌症药物靶标已导致许多 临床试验中存在的代理。此外，攻击肿瘤中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的策略的重要性 治疗。下一阶段的癌症药物目标发现将寻求整合这些策略以识别 最有效地靶向肿瘤细胞和先进的免疫成分的药物组合 癌症。该建议的目的是使用大规模数据挖掘来识别和验证这些组合 和小鼠临床前癌症模型，它们模仿人类癌的主要遗传特征。这个建议 通过a）找到可能增强肿瘤突变的遗传靶标的免疫逃脱的两种机制 负载，b）在T细胞或髓样细胞中的整个屏幕上高高地进行促进免疫的靶标 细胞浸润。我们将利用独特的小鼠模型，反映人类癌的主要遗传类别 - 高与低突变负荷以及强与弱免疫浸润。应用单细胞RNASEQ和质量 细胞仪蛋白质组学分析，尖端免疫组成数据库和新型计算网络 使用现有大型数据库进行癌症目标发现的方法，我们建议确定漏洞 通过将小分子药物与免疫疗法相结合来解决。我们将在免疫学上“冷” 不接合免疫系统的肿瘤进入“热”肿瘤，这些肿瘤具有更多或更强壮的抗原，或者 免疫效应细胞的鼓励浸润。为了实现这一目标，我们提出了三个高度创新的 目的集中于特定目标的扰动：首先是由肿瘤免疫细胞中的酥脆/cas9筛选 微环境，其次是增加肿瘤中抗原负荷以优化免疫识别和 最后，通过基于网络的肿瘤表达靶标的识别，这可能会使对 现有的免疫肿瘤疗法。这代表了我们集体专业知识的真正“网络” 测量的候选和筛选方法的收集。 AIM 1 - 我们将在单核细胞和T细胞中执行CRISPR屏幕以识别与肿瘤相关的基因 进入和功能在两种不同的肿瘤类型中。 AIM 2 - 我们将使用涉及的新生成的候选基因的遗传或药物扰动 代谢应激和ROS诱导的DNA损伤，以增加肿瘤中的突变负荷和抗原丰度 特定方式，导致对免疫疗法的反应改善。 AIM 3 - 我们将探索基因表达网络，以识别可吸毒的靶标和途径 免疫反应。 该提案确定了加速免疫疗法的途径和扰动。