A Statistical Physics Framework for Understanding the Role of Repeat RNA in Tumor Immunity

用于理解重复 RNA 在肿瘤免疫中的作用的统计物理框架

基本信息

批准号：
10683142
负责人：
Benjamin Greenbaum
金额：
$ 63.65万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10683142
关键词：
Antigen-Presenting Cells Antigens Archives Biological Assay Cancer Patient Cancer cell line Cancerous Cells Clinical Colon Carcinoma Colorectal Cancer Cytotoxic T-Lymphocytes Dendritic Cells Detection Double-Stranded RNA Epigenetic Process Evolution Family Genetic Transcription Goals Human Immune Immune response Immune signaling Immune system Immunohistochemistry Immunologic Markers Immunologic Receptors Immunologics Immunotherapy In Situ Hybridization In Vitro Individual Innate Immune Response Innate Immune System Length Link Machine Learning Macrophage Malignant Neoplasms Malignant neoplasm of pancreas Malignant neoplasm of prostate Measures Mediating Methods Modeling Molecular Biology Normal tissue morphology Oncornaviruses Outcome Pathway interactions Patients Pattern Phenotype Physics Plasma Procedures Production Prognosis Property RNA RNA analysis Recording of previous events Repetitive Sequence Role Sampling Solid Neoplasm Source Spatial Distribution Statistical Models Structure System Technology Transcript Treatment Efficacy Tumor Immunity Validation Virus cancer immunotherapy cohort cost cytokine design exosome experience fitness immune activation immune checkpoint blockade in silico in vitro Assay in vivo interdisciplinary approach liquid biopsy mathematical model mimicry neoantigens novel novel marker pathogen patient subsets receptor response role model single molecule stem therapeutic target tool transcriptome sequencing transcriptomics treatment response tumor tumor immunology tumor microenvironment tumor-immune system interactions uptake

项目摘要

PROJECT SUMMARY Transcriptional dysregulation in tumors can induce the abundant expression of repetitive elements in cancerous cells compared to normal tissues, where they are often transcriptionally silent. Such transcripts have been associated with better outcomes to cancer immunotherapies, as they can modulate the tumor immune microenvironment and generate an under-quantified source of tumor neoantigens. Therefore, it has been hypothesized that the aberrant transcription of repeat RNA is both a critical mechanism for initiating the immune response in the tumor microenvironment and an untapped source of potential therapeutic targets. Using a set of approaches from statistical physics, our team predicted repetitive element RNA directly stimulates receptors of the innate immune system, confirmed this hypothesis in a key subset of immune cells, and showed repeat expression can correlate with response to checkpoint blockade immunotherapies. Repeat RNA is therefore both a novel biomarker for the innate immune response in cancer and a potential therapeutic target to modulate tumor immunity. We will utilize a set of tools, developed by our team, from statistical physics to characterize repeat RNA recognition by innate immune receptors in silico and their role in tumor-immune co-evolution, both with and without the application of immunotherapy (Aim 1). Next, we will characterize the spatial context of repeat RNAs in the tumor immune microenvironment and the co-localization of predicted immunostimulatory RNA with activation of immune signaling, along with in depth immune-phenotyping of the state of the immune microenvironment in vivo (Aim 2). Finally, we will perform functional validation of our predictions on human immune cells to validate mechanisms of recognition and the specific immune subsets responsible for repeat recognition via a set of in vitro assays (Aim 3). Our goal is to use approaches from statistical physics to quantify the role of repetitive elements in tumor immunology, their rules of recognition by innate immune receptors and their part in facilitating cytolytic T cell activity. In doing so we will combine novel RNA detection technologies to study their spatial distribution and localization in cancers; state of the art immune-phenotyping; and mathematical models to characterize their direct role in tumor evolution. We hypothesize that our approach from statistical physics will identify the key structural and sequence features of repeat mediated immune activation in solid tumors and shed light on their specific consequences for tumor evolution and therapeutic efficacy.

项目概要肿瘤中的转录失调可诱导重复元件的大量表达与正常组织相比，癌细胞通常是转录沉默的。这样的成绩单与癌症免疫疗法的更好结果相关，因为它们可以调节肿瘤免疫微环境并产生定量不足的肿瘤新抗原。因此，它有假设重复 RNA 的异常转录是启动肿瘤微环境中的免疫反应和潜在治疗靶点的未开发来源。使用一组统计物理学方法，我们的团队直接预测了重复元素 RNA 刺激先天免疫系统的受体，在免疫细胞的一个关键子集中证实了这一假设，并显示重复表达可能与检查点阻断免疫疗法的反应相关。重复因此，RNA 既是癌症先天免疫反应的新型生物标志物，又是一种潜在的治疗方法调节肿瘤免疫的目标。我们将利用我们团队开发的一套统计物理学工具来表征重复 RNA 先天免疫受体在计算机中的识别及其在肿瘤免疫协同进化中的作用，以及不应用免疫疗法（目标 1）。接下来，我们将描述重复的空间背景肿瘤免疫微环境中的RNA以及预测的免疫刺激RNA与的共定位免疫信号的激活，以及免疫状态的深入免疫表型分析体内微环境（目标 2）。最后，我们将对人类的预测进行功能验证免疫细胞验证识别机制和负责重复的特定免疫子集通过一系列体外测定进行识别（目标 3）。我们的目标是使用统计物理学的方法量化重复元件在肿瘤免疫学中的作用及其被先天免疫识别的规则受体及其在促进溶细胞 T 细胞活性中的作用。为此，我们将结合新型 RNA 检测研究它们在癌症中的空间分布和定位的技术；最先进的免疫表型分析；和数学模型来表征它们在肿瘤进化中的直接作用。我们假设我们的方法来自统计物理学的研究将确定重复介导的免疫的关键结构和序列特征实体瘤中的激活并揭示其对肿瘤进化和治疗的具体影响功效。

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Spatial transcriptomics reveals distinct tissue niches linked with steroid responsiveness in acute gastrointestinal GVHD.

空间转录组学揭示了急性胃肠道 GVHD 中与类固醇反应相关的独特组织生态位。

DOI：
发表时间：
2023-11-23
期刊：
影响因子：
20.3
作者：
Patel, Bidish K;Raabe, Michael J;Lang, Evan R;Song, Yuhui;Lu, Chenyue;Deshpande, Vikram;Nieman, Linda T;Aryee, Martin J;Chen, Yi;Ting, David T;DeFilipp, Zachariah
通讯作者：
DeFilipp, Zachariah