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

控制肿瘤免疫逃避的 3-UTR 体细胞突变的泛癌症特征

• 批准号: 10364784
• 负责人: Ioannis Vlachos
• 金额: $ 61.85万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10364784
• 关键词: 3' Untranslated Regions; 3-Dimensional; Affect; Alleles; Bioinformatics; Biological Assay; Biological Markers; Biomimetics; Bypass; Cancer Patient; Cell Fraction; Cells; Clinical; Coculture Techniques; Code; Data; Data Set; Detection; Development; Devices; Disease; ERBB2 gene; Epigenetic Process; Evaluation; Event; Exhibits; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Hematoxylin and Eosin Staining Method; Image; Immune; Immune checkpoint inhibitor; Immune system; Immunologic Surveillance; Immunooncology; Immunophenotyping; Immunotherapeutic agent; Immunotherapy; In Situ; In Vitro; Investigation; Light; Malignant Neoplasms; Massive Parallel Sequencing; Mediating; Messenger RNA; Methods; MicroRNAs; Microfluidic Microchips; Minority; Molecular; Mutate; Mutation; Natural Immunity; Neoplasm Metastasis; Oncogenes; Oncogenic; Outcome; Pathology; Pathway interactions; Patients; Phenotype; Polyadenylation; Post-Transcriptional Regulation; Process; Quantitative Trait Loci; RNA Editing; RNA-Binding Proteins; Regulation; Research; Research Personnel; Resistance; Resources; Role; Sampling; Somatic Mutation; T-Lymphocyte; Technology; The Cancer Genome Atlas; Time; Translations; Tumor Escape; Tumor-Infiltrating Lymphocytes; Untranslated RNA; Validation; Variant; adaptive immunity; brca gene; cancer immunotherapy; cancer type; cell killing; checkpoint inhibition; cohort; deep learning model; exome; genome sequencing; genomic data; immune checkpoint; immunoregulation; in silico; innovation; malignant breast neoplasm; multimodal data; multiple omics; neoplastic; neoplastic cell; new therapeutic target; novel; novel therapeutics; patient stratification; predicting response; response; statistical learning; therapeutic target; transcriptome; transcriptome sequencing; transcriptomics; translational potential; treatment response; tumor; tumor immunology; tumor microenvironment

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.

概括 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。参与肿瘤免疫景观调节的表观遗传机制是非常重要的 作为生物标志物或治疗靶点进行研究。 3'非翻译区（3'UTR）决定了后翻译 转录 mRNA 的命运，通常是调控分子的靶标，例如 microRNA (miRNA) 和 RNA 结合蛋白 (RBP)。肿瘤细胞已被证明可以通过突变或 截断这些区域。第一个此类确定的事件揭示了体细胞调节机制 可能会影响肿瘤免疫逃避、免疫治疗反应和患者管理。然而， 3'UTR 体细胞事件及其转录组范围的检测、验证和功能表征 对肿瘤免疫环境的影响仍然是紧迫但尚未得到满足的需求。 在这个项目中，我们将部署一个结合大规模并行变体的计算机/实验框架 验证、空间转录组学和生物信息检测/功能化技术来表征 泛癌症和转录组范围的 3'UTR 体细胞突变/截断事件，以及评估其 作为免疫编辑机制、患者分层标记和新治疗靶点的潜力。 目标 1：通过有效整合原始多组学数据集，我们将识别体细胞 3'UTR 突变和截短 涉及 33 种癌症类型的 10,000 多名癌症患者。我们将优先考虑影响基因的所有 3'UTR 变异 顺式表达，描绘了跨癌症类型的 3'UTR 介导的调控景观。目标2：我们将 识别影响癌症免疫表型和肿瘤微环境的突变和破坏的电路。 我们将利用广泛的转录后数据/实验资源来揭示调控因子（miRNA、 RBP）和参与此类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 使用大规模并行测序技术的 3'UTR 事件。将调查优先交互 体外以及使用仿生 3D 设备来表征它们对基因调控和 T 细胞杀伤的影响， 同时将使用空间转录组学研究患者样本。目标 3b) 我们将评估转化 使用深度学习研究主要 3'UTR 事件和基因作为免疫治疗反应预测因子的潜力 模型，针对超过 300 名接受免疫检查点抑制治疗的癌症患者的广泛队列。 我们非常适合进行这种深入的表征，因为我们的研究团队由领先的 转录后调控和免疫肿瘤学研究人员，而相关资源（在计算机上， 实验、样品）已经就位。我们将进行第一次泛癌症和转录组范围的研究 通过 3'UTR 体细胞突变和截断研究肿瘤免疫逃避。我们将使用创新的 在计算机实验框架中识别可用作标记的 3'UTR 事件、miRNA 和 RBP 有效的患者分层以及新的免疫治疗靶点。