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

项目摘要 肿瘤中的转录失调可以诱导肿瘤细胞中重复元件的丰富表达， 与正常组织相比，癌细胞通常是转录沉默的。这样的成绩单 与癌症免疫治疗的更好结果相关，因为它们可以调节肿瘤 免疫微环境并产生定量不足肿瘤新抗原来源。因此具有 已经假设重复RNA的异常转录是启动细胞凋亡的关键机制， 肿瘤微环境中的免疫应答和潜在治疗靶点的未开发来源。 利用统计物理学的一系列方法，我们的团队直接预测了重复元件RNA 刺激先天免疫系统的受体，在免疫细胞的一个关键亚群中证实了这一假设， 并且显示重复表达可以与对检查点阻断免疫疗法的应答相关。重复 因此，RNA既是癌症先天免疫反应的新生物标志物，也是潜在的治疗药物。 靶向调节肿瘤免疫。 我们将利用我们团队开发的一套工具，从统计物理学角度来表征重复RNA 先天免疫受体的计算机识别及其在肿瘤免疫协同进化中的作用， 不应用免疫疗法（目的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与肿瘤免疫微环境中的RNA的共定位 免疫信号传导的激活，沿着免疫状态的深入免疫表型分析， 体内微环境（Aim 2）。最后，我们将对我们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亚群 通过一组体外测定（Aim 3）识别。我们的目标是利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，它们被先天免疫识别的规则， 受体及其促进溶细胞T细胞活性的部分。在这样做的过程中，我们将联合收割机结合新的RNA检测 研究其在癌症中的空间分布和定位的技术; 和数学模型来表征它们在肿瘤演变中的直接作用。我们假设我们的方法 将确定重复介导免疫的关键结构和序列特征， 激活，并阐明其对肿瘤演变和治疗的具体后果 功效

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.