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

• 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
• 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

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.

项目摘要 肿瘤中的转录失调可以诱导重复元素的丰富表达 与正常组织相比，癌细胞通常在转录上保持沉默。这样的成绩单 与癌症免疫疗法的更好结果有关，因为它们可以调节肿瘤 免疫微环境并产生不足的肿瘤新抗原来源。因此，它有 假设重复RNA的异常转录既是启动的关键机制 肿瘤微环境中的免疫反应和潜在治疗靶标的未开发的来源。 使用统计物理学的一组方法，我们的团队可以直接预测重复元素RNA 刺激先天免疫系统的受体，在免疫细胞的关键子集中证实了这一假设， 并显示出重复表达可以与对检查点阻断障碍免疫疗法的响应相关。重复 因此，RNA既是癌症先天免疫反应的新型生物标志物，又是潜在的治疗方法 调节肿瘤免疫的靶标。 我们将利用团队开发的一组工具，从统计物理学来表征重复RNA 与先天免疫受体在计算机中的识别及其在肿瘤免疫共同进化中的作用，包括和 不应用免疫疗法（AIM 1）。接下来，我们将描述重复的空间上下文 肿瘤免疫微环境中的RNA和预测的免疫刺激RNA的共定位 免疫信号传导的激活，以及免疫状态的深度免疫 - 表型 体内微环境（AIM 2）。最后，我们将对人类的预测进行功能验证 免疫细胞以验证识别机制和负责重复的特定免疫子集 通过一组体外测定识别（AIM 3）。我们的目标是使用从统计物理学到 量化重复元素在肿瘤免疫学中的作用，它们是先天免疫的认可规则 受体及其在促进细胞溶解T细胞活性方面的作用。这样我们将结合新的RNA检测 研究其在癌症中的空间分布和定位的技术；最先进的免疫 - 表型； 和数学模型以表征其在肿瘤进化中的直接作用。我们假设我们的方法 统计物理学将确定重复介导的免疫的关键结构和序列特征 在实体瘤中激活，并阐明其对肿瘤进化和治疗的特定后果 功效。