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

项目摘要尽管在治疗几种类型的癌症方面取得了巨大成功，但免疫检查点阻滞剂（ICB）疗法仍然仅显示一部分患者的功效。识别新型免疫疗法反应调节剂随着提高癌症免疫疗法的反应率仍然是空旷的问题。最近，我们使用了小鼠模型中的CRISPR屏幕研究T细胞浸润，增殖和杀死功效，以及鉴定出SWI/SNF染色质重塑复合物的PBAF为T细胞介导的一个新型调节剂细胞毒性。我们还开发了一个计算模型潮汐，以识别CD8 T细胞的基因特征免疫热肿瘤的功能障碍和免疫冷肿瘤中的T细胞排除。由此产生的签名，根据非免疫疗法的肿瘤特征计算出来，在预测黑色素瘤方面显示出令人鼓舞的结果肺癌患者根据治疗前肿瘤表达对免疫检查点封锁的反应概况。该提出的项目旨在改善潮汐生物标志物，确定新颖的调节剂并阐明其 ICB响应的基础机制。在AIM 1中，我们将开发大型机器学习方法从非ICB设置收集了临床肿瘤转录组轮廓图，以完善潮汐预测生物标志物 ICB响应，并开发Web服务器以全面评估不同的ICB响应生物标志物所有可用的ICB队列。在AIM 2中，我们将在小鼠同性肿瘤中进行体内CRISPR屏幕识别ICB反应的癌细胞内在调节剂的模型，该模型可以作为改进的新目标 ICB响应。在AIM 3中，我们将阐明ICB响应和使用单细胞RNA-seq，单细胞ATAC-来表征它们对肿瘤免疫微环境的影响 SEQ和计算建模。我们的调查团队已将计算专业知识结合在一起方法论免疫疗法。免疫学以及大数据挖掘，功能基因组学分析我们提出的研究，如果成功执行，以及对癌症免疫疗法的转化益处。以及筛查，癌症免疫学和可以为癌症提供新的见解