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

项目总结 肿瘤中转录调控异常可诱导肿瘤组织中重复元件的大量表达 癌细胞与正常组织相比，在正常组织中通常转录沉默。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，并产生一个不能量化的肿瘤新抗原来源。因此，它有 已经提出假设，重复RNA的异常转录既是启动TRAN的关键机制 肿瘤微环境中的免疫反应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素RNA 刺激先天免疫系统的受体，在免疫细胞的关键亚群中证实了这一假设， 研究表明，重复表达与对检查点阻断免疫疗法的反应有关。重复 因此，RNA既是一种新的癌症天然免疫反应的生物标志物，也是一种潜在的治疗方法 靶向调节肿瘤免疫。 我们将利用我们团队开发的一套工具，从统计物理学出发，来表征重复RNA 硅胶中天然免疫受体的识别及其在肿瘤-免疫协同进化中的作用 没有应用免疫治疗(目标1)。接下来，我们将描述Repeat的空间语境 肿瘤免疫微环境中的RNA及其与预测免疫刺激RNA的共定位 免疫信号的激活，以及免疫状态的深入免疫表型 体内微环境(目标2)。最后，我们将在人类身上对我们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亚群 通过一套体外试验进行识别(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天然免疫识别规则 受体及其在促进细胞溶解T细胞活性中的作用。在此过程中，我们将结合新的RNA检测 研究它们在癌症中的空间分布和定位的技术；最新的免疫表型； 以及数学模型来描述它们在肿瘤进化中的直接作用。我们假设我们的方法 将从统计物理学中确定重复序列介导免疫的关键结构和序列特征 实体瘤中的激活并阐明其对肿瘤演变和治疗的特定后果 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.