Pan-Cancer characterization of 3’UTR somatic mutations controlling tumor immune evasion

摘要 肿瘤疾病的所有阶段，从发展到转移，都与癌症免疫交织在一起。 逃避。参与调节肿瘤免疫格局的表观遗传机制是强烈的 作为生物标志物或治疗靶点进行研究。3‘未翻译区域(3’UTRs)指定后- 转录信使核糖核酸的命运，经常作为调控分子的靶点，如microRNAs(MiRNAs)和 RNA结合蛋白(RBPs)。已有研究表明，肿瘤细胞通过突变或突变来逃避这种严格的调控。 截断这些区域。第一次确定的这样的事件揭示了躯体调节机制 可能会潜在地影响肿瘤免疫逃逸、对免疫治疗的反应和患者管理。然而， 转录组范围内3‘非编码区体细胞事件及其功能特征的检测、验证和功能表征 对肿瘤免疫格局的影响仍然是迫切的--但尚未得到满足--需求。 在这个项目中，我们将部署一个结合了大规模并行变体的计算机/实验框架 验证、空间转录和生物信息检测/功能化技术 泛癌和转录组范围的3‘UTR体细胞突变/截断事件，以及评估其 有可能作为免疫编辑机制、患者分层的标记和新的治疗靶点。 目标1：通过有效整合原始多基因组数据集，我们将识别体细胞3‘非编码区突变和截断 在33种癌症类型的10,000多名癌症患者中。我们将优先考虑所有影响基因的3‘非编码区变异 在顺式基因中的表达，描绘了3‘UTR介导的跨癌症类型的调控图景。目标2：我们将 确定影响癌症免疫表型和肿瘤微环境的突变和干扰电路。 我们将利用广泛的转录后数据/实验资源来揭示调控因子(miRNAs， 限制性商业惯例)和参与这种免疫编辑事件的机制。目标3a：我们将验证多达20,000个体细胞 3‘非编码区事件使用大规模并行测序技术。将在中调查按优先级排列的交互 以及使用仿生3D设备来表征它们对基因调控和T细胞杀伤的影响， 而患者样本将使用空间转录组学进行研究。目标3b)我们将评估 基于深度学习的3‘非编码区领先事件和基因作为免疫治疗应答预测因子的潜力 模型，针对接受免疫检查点抑制治疗的300名癌症患者的广泛队列。 我们非常适合进行这种深入的描述，因为我们的研究团队包括领先的 转录后调控和免疫肿瘤学研究人员，而相关资源(在Silico， 试验性的，样本)已经到位。我们将进行第一个泛癌症和全基因组转录组 3‘端非编码区体细胞突变和截短对肿瘤免疫逃逸的研究我们将使用一种创新的 在电子实验框架中，识别可用作标记的3‘UTR事件、miRNAs和RBP 有效的患者分层以及新的免疫治疗靶点。

SUMMARY All stages of neoplastic disease, from its development to metastasis, are intertwined with cancer immune evasion. The epigenetic mechanisms involved in the regulation of the tumor immune landscape are intensely investigated as biomarkers or therapeutic targets. 3' untranslated regions (3'UTRs) dictate the post- transcriptional mRNA fate and are often targeted by regulatory molecules such as microRNAs (miRNAs) and RNA binding proteins (RBPs). Tumor cells have been shown to evade this tight regulation by mutating or truncating these regions. The first such identified events have shed light on somatic regulatory mechanisms that could potentially affect tumor immune evasion, response to immunotherapy, and patient management. However, the transcriptome-wide detection, validation, and functional characterization of 3'UTR somatic events and their effects on the tumor immune landscape are still pressing -yet unmet- needs. In this project we will deploy an in silico/experimental framework that combines massively parallel variant validation, spatial transcriptomics, and bioinformatic detection/functionalization technologies to characterize Pan-Cancer and transcriptome-wide 3'UTR somatic mutation/truncation events, as well as to assess their potential as immunoediting mechanisms, markers for patient stratification, and novel therapeutic targets. Aim 1: By efficiently integrating raw multi-omic datasets, we will identify somatic 3'UTR mutations and truncations in more than 10,000 cancer patients across 33 cancer types. We will prioritize all 3'UTR variants affecting gene expression in cis, delineating the 3'UTR-mediated regulatory landscape across cancer types. Aim 2: We will identify mutations and disrupted circuitry affecting cancer immunophenotypes and the tumor microenvironment. We will utilize extensive post-transcriptional data/experimental resources to uncover the regulators (miRNAs, RBPs) and mechanisms involved in such immunoediting events. Aim 3a: We will validate up to 20,000 somatic 3'UTR events using a massively parallel sequencing technology. Prioritized interactions will be investigated in vitro as well as using a biomimetic 3D device to characterize their effects on gene regulation and T-Cell killing, while patient samples will be investigated using spatial transcriptomics. Aim 3b) We will assess the translational potential of the leading 3'UTR events and genes as predictors of immunotherapy response using Deep Learning models, against an extensive cohort of >300 cancer patients treated with immune checkpoint inhibition. We are uniquely suited to perform this in-depth characterization, since our research team comprises leading post transcriptional regulation and immune-oncology researchers, while the relevant resources (in silico, experimental, samples) are already in place. We will perform the first Pan-Cancer and transcriptome-wide investigation of tumor immune evasion by 3'UTR somatic mutations and truncations. We will use an innovative in silico-experimental framework to identify 3'UTR events, miRNAs, and RBPs that can be used as markers for efficient patient stratification as well as novel immunotherapeutic targets.