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: 10556376
• 负责人: Ioannis Vlachos
• 金额: $ 61.04万
• 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/10556376
• 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; 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; Multiomic Data; Mutate; Mutation; Natural Immunity; Neoplasm Metastasis; Nucleic Acid Regulatory Sequences; Oncogenes; Oncogenic; Outcome; Pathology; Pathway interactions; Patients; Phenotype; Poly A; 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; neoplastic; neoplastic cell; new therapeutic target; novel; patient stratification; posttranscriptional; 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 从发育到转移的肿瘤疾病的所有阶段都与癌症免疫交织在一起 逃避。肿瘤免疫景观调节涉及的表观遗传机制是强烈的 被调查为生物标志物或治疗靶标。 3'未翻译区域（3'UTRS）决定了后 转录mRNA命运，通常是由microRNA（miRNA）和 RNA结合蛋白（RBP）。肿瘤细胞已显示通过突变或 截断这些区域。第一个此类确定的事件揭示了躯体调节机制 可能会影响肿瘤免疫逃避，对免疫疗法的反应和患者治疗。然而， 3'UTR体细胞事件及其的全转录组检测，验证和功能表征 对肿瘤免疫景观的影响仍在按下 - 基因未满足的需求。 在这个项目中，我们将在硅/实验框架中部署一个合并大量并行变体的框架 验证，空间转录组学和生物信息学检测/功能化技术以表征 Pan-Canter和全转录组3'UTR体细胞突变/截断事件，并评估其 作为免疫编辑机制，患者分层的标志物和新型治疗靶标的潜力。 AIM 1：通过有效整合原始多摩变数据集，我们将识别体细胞3'UTR突变和截断 在33种癌症类型的10,000多名癌症患者中。我们将优先考虑影响基因的所有3'UTR变体 在顺式中表达，描述了跨癌症类型的3'UTR介导的调节景观。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破坏电路。 我们将利用广泛的转录后数据/实验资源来揭示监管机构（mirnas， RBP）和此类免疫编辑事件涉及的机制。 AIM 3A：我们将验证多达20,000个躯体 3'UTR事件使用大量并行测序技术。优先的互动将在 体外以及使用仿生3D设备来表征它们对基因调节和T细胞杀伤的影响， 虽然将使用空间转录组学研究患者样品。目标3b）我们将评估翻译 领先的3'UTR事件和基因作为免疫疗法反应的预测指标的潜力 模型，以> 300名受到免疫检查点抑制治疗的癌症患者进行广泛的队列。 我们非常适合执行此深入的特征，因为我们的研究团队包括领先 转录后调节和免疫肿瘤研究人员，而相关资源（在Silico， 实验，样品）已经到位。我们将执行第一个Pan-Cancer和全转录组的范围 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 在有机实验框架中，可以识别可用作标记的3'UTR事件，miRNA和RBP 有效的患者分层以及新型的免疫治疗靶标。