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Inference of variable chromatin loops in glioblastoma tumors and single-cells

胶质母细胞瘤和单细胞中可变染色质环的推断

基本信息

批准号：
9751627
负责人：
Caleb Andrew Lareau
金额：
$ 2.65万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2020-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9751627
关键词：
Acute Myelocytic Leukemia Address Binomial Model Bioinformatics Biological Biological Assay Biology Cancer Biology Carcinogenesis Mechanism Cell Line Cell Nucleus Cells Cellular Assay Chromatin Chromatin Interaction Analysis by Paired-End Tag Sequencing Chromatin Loop Chromatin Structure Clinical Computational Technique Computer software Computing Methodologies DNA DNA Folding DNA mapping Data Dimensions Doctor of Philosophy Epigenetic Process Future Genes Genetic Genome Genomics Glioblastoma Goals Hair Heterogeneity Human In Situ Individual Inter-tumoral heterogeneity Isocitrate Dehydrogenase Link Malignant Neoplasms Malignant neoplasm of brain Maps Measures Methodology Methods Modeling Mutation Nuclear Oncogene Activation Oncogenic Outcome PDGFRA gene Patients Pattern Phenotype Population Research Research Personnel Role Self Care Shapes Structure Techniques Technology Training Transposase Variant Width Work bioinformatics tool cancer type chromosome conformation capture clinical phenotype computer framework design differential expression experience flexibility foot genome-wide genome-wide analysis human disease implicit bias improved insight interest mutant personalized care single-cell RNA sequencing therapeutic development therapy resistant three dimensional structure tool transcriptome transcriptomics treatment strategy tumor tumor heterogeneity

项目摘要

Project Summary A common feature in most cancers is both inter- (between patients) and intra- (within a patient) tumor heterogeneity. An important step toward improving treatment strategies and enabling personal care is mapping how these types of heterogeneity impact clinical phenotypes, especially among deadly tumors such as glioblastoma multiform (GBM). Recent studies have identified instances where the three-dimensional folding of chromatin into DNA loops is associated with inter-tumor heterogeneity. Presently, intra-tumor DNA looping variability has not been measured though this is likely responsible for single-cell transcriptional differences observed within patient tumors. To identify DNA loops genome wide, many chromatin conformation capture (3C)-derived assays have been developed. However, reliably using DNA loops to uncover tumor heterogeneity is hindered by two key deficiencies. First, a direct comparison of 3C-derived techniques has not been conducted to assess assay- specific biases in identifying inter-tumor variable DNA loops. Second, each of these approaches requires millions of cells to infer chromatin structure, obscuring differences at the single-cell level. Here, I propose methodological advances to address these two deficiencies through computational approaches that will elucidate the role of DNA looping in inter- and intra- tumor heterogeneity in GBM. In Aim 1, I will use data generated in my sponsor's lab for three different 3C-derived methods mapping DNA loops in isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma cell lines. I will identify biases specific to each assay and determine differential loops associated with the IDH mutation. This work will be critical for developing future computational techniques for identifying important DNA loops. Moreover, this analysis will reveal the epigenetic effects of the IDH mutation, which is prevalent in GBM and other cancers (e.g. acute myeloid leukemia). Results from this aim will be broadly applicable to bioinformatics researchers developing tools for DNA looping data as well as cancer biologists seeking to understand the IDH mutation. In Aim 2, I propose to resolve single-cell differences in the same glioblastoma cell lines to infer patterns of chromatin loop variability within individual tumors. Specifically, I will build a computational framework integrating DNA loops nominated by bulk populations with single-cell chromatin accessibility (scATAC-seq) data. I will work with the inventor of the scATAC-seq technology to develop a sensitive, zero-inflated model to identify chromatin loops that are variable within individual GBM tumor models. The research results from this proposal will yield critical insights into chromatin biology associated with tumor heterogeneity of GBM and other cancers, which will motivate future therapeutic development strategies.

项目摘要大多数癌症的一个共同特征是患者之间和患者内部的肿瘤。异质性。改善治疗策略和实现个人护理的一个重要步骤是绘制地图这些类型的异质性如何影响临床表型，特别是在致命的肿瘤中，如多形性胶质母细胞瘤(GBM)。最近的研究已经确定了一些例子，即三维折叠染色质进入DNA环与肿瘤间的异质性有关。目前，肿瘤内DNA环路变异性还没有被测量，尽管这可能是单细胞转录差异的原因在患者肿瘤内观察到的。为了识别基因组范围的DNA环，许多染色质构象捕获(3C)衍生的分析方法已经已经被开发出来了。然而，可靠地使用DNA环来揭示肿瘤的异质性受到两个关键因素的阻碍不足之处。首先，还没有对3C衍生技术进行直接比较来评估化验- 识别肿瘤间可变DNA环的特异性偏倚。其次，这些方法中的每一个都需要数百万个细胞来推断染色质结构，掩盖了单细胞水平上的差异。在这里，我建议通过计算方法解决这两个缺陷的方法学进步阐明DNA环在基底膜瘤内和瘤内异质性中的作用。在目标1中，我将使用我的赞助商的实验室中生成的数据进行三种不同的3C派生方法映射异柠檬酸脱氢酶(IDH)突变型和野生型胶质母细胞瘤细胞系中的dna环。我会找出偏见并确定与IDH突变相关的差异环。这项工作将是对于开发未来识别重要DNA环的计算技术至关重要。此外，这一点分析将揭示IDH突变的表观遗传效应，这种突变普遍存在于GBM和其他癌症中 (例如，急性髓系白血病)。这一目标的结果将广泛适用于生物信息学研究人员开发用于DNA循环数据的工具，以及癌症生物学家试图了解IDH突变。在目标2中，我建议解决相同胶质母细胞瘤细胞系中的单细胞差异以推断模式个体肿瘤内染色质环的可变性。具体来说，我将构建一个计算框架将群体提名的DNA环与单细胞染色质可及性整合(scatac-seq) 数据。我将与scatac-seq技术的发明者合作，开发一种灵敏的零充气模型，以确定在单个GBM肿瘤模型中可变的染色质环。这项提案的研究结果将对染色质生物学产生关键的见解基底膜和其他癌症的肿瘤异质性，这将激励未来的治疗发展战略。