Regulators of Cancer Immunotherapy Response

癌症免疫治疗反应的调节者

• 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
• 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

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的成功，但免疫检查点阻断剂（ICB）疗法仍然是一种有效的治疗方法。 仅在一部分患者中显示出有效性。识别免疫治疗反应的新型调节剂 因为提高癌症免疫疗法的应答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， 将SWI/SNF染色质重塑复合物的PBAF鉴定为T细胞介导的免疫调节的一种新的调节剂。 细胞毒我们还开发了一个计算模型，TIDE，以识别CD 8 T细胞的基因签名， 免疫热肿瘤中的功能障碍和免疫冷肿瘤中的T细胞排斥。产生的签名， 根据非免疫治疗环境中的肿瘤特征计算，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 数据区. 该项目旨在改进TIDE生物标志物，鉴定新的调节剂，并阐明其作用机制。 ICB反应的机制。在目标1中，我们将开发大规模的机器学习方法。 从非ICB环境中收集临床肿瘤转录组谱，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，以全面评估不同的ICB反应生物标志物， 所有可用的ICB队列。在目标2中，我们将在小鼠同源肿瘤中进行体内CRISPR筛选， 模型，以确定ICB反应的癌细胞内在调节因子，这可以作为新的目标，以改善 ICB响应。在目标3中，我们将阐明ICB反应的两种新型调节剂的机制， 使用单细胞RNA-seq、单细胞ATAC- seq和计算建模。我们的调查小组结合了计算机科学的专业知识 方法 免疫疗法 免疫学 大数据挖掘，功能基因组分析 如果我们的研究成功， 和癌症免疫治疗的转化益处。 癌症免疫学， 可以为癌症提供新的见解