Characterization unit

表征单元

• 批准号: 10016228
• 负责人: Kun Zhang
• 金额: $ 163.32万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10016228
• 关键词: Acute Lymphocytic Leukemia; Atlases; Biological Assay; Blood specimen; Cell Communication; Cell Nucleus; Cells; Chromatin; Clinical; Clonal Evolution; Computer Analysis; Cytometry; DNA Methylation; Data; Data Analyses; Development; Diagnosis; Ecosystem; Ensure; Epigenetic Process; Evolution; Fluorescent in Situ Hybridization; Gene Expression Profile; Genes; Genomics; Germ-Line Mutation; Glioma; Goals; Heterogeneity; Hypersensitivity; Image; Immune; In Situ; Intrinsic factor; Malignant Childhood Neoplasm; Malignant Neoplasms; Mediating; Medical; Metadata; Mind; Molecular; Mutation; Mutation Detection; Neuroblastoma; Pathway interactions; Patients; Pattern; Pediatric Neoplasm; Phylogenetic Analysis; Proteins; RNA; Research Personnel; Resolution; Sampling; Site; Small Nuclear RNA; Somatic Mutation; Spatial Distribution; Stromal Cells; T-Lymphocyte; Technology; Tissues; Transcript; Trees; base; biomarker panel; bisulfite sequencing; cancer cell; cancer heterogeneity; cancer type; cell type; chimeric antigen receptor T cells; effective therapy; epigenome; epigenomics; genome sequencing; high risk; longitudinal dataset; multiple omics; neoplastic cell; outcome forecast; patient subsets; personalized medicine; single molecule; transcriptome; transcriptome sequencing; transcriptomics; tumor; tumor heterogeneity; tumor microenvironment; whole genome

Abstract The overarching goal of the Characterization Unit is to capture the molecular and cellular heterogeneity and clonal evolution of three major pediatric cancers, high-grade glioma (pHGG), neuroblastoma (NB), and very high-risk acute lymphoblastic leukemia (VHR- ALL). Although many studies have characterized the interplay between DNA methylation, chromatin accessibility and transcriptome using bulk tumor samples, how the epigenome and transcriptome relate at the single-cell level and how such relationships contribute to cancer heterogeneity is not well understood. In addition to factors intrinsic to malignant cells, extrinsic factors in the tumor microenvironment also need to be better characterized at single-cell resolution. With these considerations in mind, we will apply a two-pronged, spatial multi-omics approach in this project. Our approach is enabled by the diverse array of cutting-edge single-cell and in situ technologies developed by our team of investigators, including: massive parallel single nucleus RNA-Seq, single-cell transposome hypersensitivity site sequencing, multiplexed single molecule RNA fluorescence in situ hybridization and imaging mass cytometry. The multidimensional and longitudinal data set will be analyzed and integrated by the Data Analysis Unit to construct atlases of the tumor ecosystems before and after therapy. We propose the following four specific aims: 1) To identify somatic and germline mutations using whole genome sequencing of bulk tumor samples and blood samples. For each cancer type, we will collect tumor samples from 20 patients before and after medical therapy. The mutation data will be integrated with single-cell omics data to construct phylogenetic tree of cancer evolution during therapy; 2) To conduct transcriptome profiling of both bulk and single-cell tumor samples. For VHR-ALL samples, we will profile malignant cells only. For NB and pHGG samples, we will profile malignant cells, immune cells, and stromal cells in the tumor microenvironment; 3) To conduct epigenomic profiling of both bulk and single-cell tumor samples. We will profile 5-methylcytosine and chromatin accessibility using whole-genome bisulfite sequencing and transposome hypersensitive site sequencing, respectively; 4) To conduct targeted in situ RNA and protein assays. A panel of marker genes for each tumor will be nominated based on computational analysis of the omics data collected in Aims 1-3.

摘要 表征单元的首要目标是捕获分子和细胞 三种主要儿科癌症、高级别胶质瘤异质性和克隆进化 （pHGG）、神经母细胞瘤（NB）和极高危急性淋巴细胞白血病（VHR-1）。 所有）。尽管许多研究已经描述了DNA之间 甲基化，染色质可及性和转录组使用散装肿瘤样本，如何 表观基因组和转录组在单细胞水平上相关， 导致癌症异质性的关系还没有得到很好的理解。除了 恶性细胞的内在因素，肿瘤微环境中的外在因素也 需要在单细胞分辨率下更好地表征。出于这些考虑 请记住，我们将在这个项目中采用双管齐下的空间多组学方法。我们 这种方法是由各种各样的尖端单细胞和原位 我们的研究团队开发的技术，包括： 核RNA-Seq，单细胞转座体超敏位点测序，多重 单分子RNA荧光原位杂交和成像质谱细胞术。 多维和纵向数据集将由 数据分析股构建治疗前后肿瘤生态系统的地图集 疗法我们提出以下四个具体目标：1）识别体细胞和种系 使用大量肿瘤样品和血液样品的全基因组测序来分析突变。 对于每种癌症类型，我们将在治疗前后从20名患者中收集肿瘤样本。 药物治疗突变数据将与单细胞组学数据整合， 构建治疗过程中肿瘤演变的系统发育树; 2）进行 这可以用于批量和单细胞肿瘤样品的转录组谱分析。对于VHR-ALL 样本，我们将只分析恶性细胞。对于NB和pHGG样品，我们将分析 肿瘤微环境中的恶性细胞、免疫细胞和基质细胞; 3） 对散装和单细胞肿瘤样品进行表观基因组分析。我们会侧写 5-使用全基因组亚硫酸氢盐测序的甲基胞嘧啶和染色质可及性 和转座体超敏位点测序; 4）进行靶向基因测序， 原位RNA和蛋白质测定。每个肿瘤的一组标记基因将被提名 基于目标1-3中收集的组学数据的计算分析。