Regulators of Cancer Immunotherapy Response

癌症免疫治疗反应的调节者

基本信息

批准号：
10251015
负责人：
MYLES A BROWN
金额：
$ 60.44万
依托单位：
DANA-FARBER CANCER INST
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-05-15 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10251015
关键词：
4T1 ATAC-seq Antigen Presentation Big Data Biological Markers Biological Response Modifiers CD8-Positive T-Lymphocytes CRISPR screen CRISPR/Cas technology CT26 CTLA4 gene Cell-Mediated Cytolysis Cells Chromatin Remodeling Factor Clinical Clustered Regularly Interspaced Short Palindromic Repeats Collection Colon Carcinoma Computational Biology Computer Models Computing Methodologies Custom Data Development Exclusion Functional disorder Gene Expression Genes Hodgkin Disease Immune Immune System Diseases Immunologic Markers Immunology Immunotherapy Individual Interferon Type II Knock-out Libraries Lung Neoplasms Machine Learning Malignant Neoplasms Malignant neoplasm of lung Mediating Microsatellite Instability Minority Modeling Molecular Mus Mutation Names Patients Performance Regulator Genes Renal carcinoma Sampling Screening for cancer Statistical Models T-Lymphocyte Techniques Training Tumor Escape Tumor-infiltrating immune cells anti-CTLA4 anti-PD-1 base cancer cell cancer immunotherapy cancer therapy cancer type cohort cytokine data mining deep learning epigenomics functional genomics genetic signature genome-wide immune checkpoint blockade immune checkpoint blockers improved in vivo insight learning strategy machine learning algorithm malignant breast neoplasm melanoma mouse model neoplastic cell novel patient response patient subsets predictive marker programmed cell death ligand 1 response response biomarker single-cell RNA sequencing success transcriptome treatment response tumor tumor immunology tumor-immune system interactions web server

项目摘要

PROJECT SUMMARY Despite enormous success in treating several types of cancer, immune checkpoint blocker (ICB) therapy still only shows efficacies in a subset of patients. Identifying novel regulators of immunotherapy response as well as improving the response rate of cancer immunotherapies remain open questions. Recently, we used CRISPR screens in mouse models to investigate T-cell infiltration, proliferation, and killing efficacy, and identified PBAF of the SWI/SNF chromatin remodeling complex as one novel regulator of T-cell mediated cytotoxicity. We also developed a computational model, TIDE, to identify gene signatures of CD8 T-cell dysfunction in immune hot tumors and T-cell exclusion in immune cold tumors. The resulting signatures, computed from tumor profiles in non-immunotherapy setting, show promising results in predicting melanoma and lung cancer patient response to immune checkpoint blockade based on pre-treatment tumor expression profiles. This proposed project aims to improve the TIDE biomarkers, identify novel regulators, and elucidate their mechanisms underlying ICB response. In Aim 1, we will develop machine learning approaches on large collection of clinical tumor transcriptome profiles from non-ICB settings to refine the TIDE predictive biomarker of ICB response, and develop a web server to comprehensively evaluate different ICB response biomarkers in all the available ICB cohorts. In Aim 2, we will conduct in vivo CRISPR screens in mouse syngeneic tumor models to identify cancer-cell intrinsic regulators of ICB response, which can serve as novel targets to improve ICB response. In Aim 3, we will elucidate the mechanism underlying two novel regulators of ICB response and characterize their effects on the tumor immune microenvironment using single-cell RNA-seq, single-cell ATAC- seq, and computational modeling. Our investigative team has combined expertise in computational methodology immunotherapy. immunology and big data mining, functional genomics profiling Our proposed studies, if successfully executed, and translational benefits to cancer immunotherapy. and screening, cancer immunology and could provide new insights into cancer

项目概要尽管在治疗多种癌症方面取得了巨大成功，但免疫检查点阻断剂 (ICB) 疗法仍然仅在一小部分患者中显示出疗效。还识别免疫治疗反应的新调节因子提高癌症免疫疗法的反应率仍然是一个悬而未决的问题。最近，我们使用在小鼠模型中进行 CRISPR 筛选，以研究 T 细胞浸润、增殖和杀伤功效，以及确定 SWI/SNF 染色质重塑复合物的 PBAF 是 T 细胞介导的一种新型调节剂细胞毒性。我们还开发了一种计算模型 TIDE，用于识别 CD8 T 细胞的基因特征免疫热肿瘤中的功能障碍和免疫冷肿瘤中的 T 细胞排斥。由此产生的签名，根据非免疫治疗环境中的肿瘤概况计算，在预测黑色素瘤方面显示出有希望的结果基于治疗前肿瘤表达的肺癌患者对免疫检查点阻断的反应配置文件。该拟议项目旨在改进 TIDE 生物标志物、识别新型调节剂并阐明其作用 ICB 反应的机制。在目标 1 中，我们将开发大规模机器学习方法从非 ICB 设置中收集临床肿瘤转录组图谱，以完善 TIDE 预测生物标志物的ICB反应，并开发一个网络服务器来综合评估不同ICB反应生物标志物所有可用的 ICB 队列。在目标 2 中，我们将在小鼠同基因肿瘤中进行体内 CRISPR 筛选模型来识别癌细胞 ICB 反应的内在调节因子，这可以作为改善的新靶点 ICB 回应。在目标 3 中，我们将阐明 ICB 反应和 ICB 反应的两种新型调节剂的潜在机制。使用单细胞 RNA-seq、单细胞 ATAC- 表征它们对肿瘤免疫微环境的影响 seq 和计算建模。我们的调查团队结合了计算方面的专业知识方法论免疫疗法。免疫学和大数据挖掘、功能基因组学分析我们提出的研究如果成功执行，以及癌症免疫疗法的转化效益。和筛查、癌症免疫学和可以为癌症提供新的见解