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.

项目概要大多数癌症的一个共同特征是肿瘤间（患者之间）和肿瘤内（患者内部）异质性。改善治疗策略和实现个人护理的重要一步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，例如多形性胶质母细胞瘤（GBM）。最近的研究已经确定了三维折叠的实例染色质进入 DNA 环与肿瘤间异质性相关。目前，肿瘤内DNA循环尚未测量变异性，但这可能是单细胞转录差异的原因在患者肿瘤内观察到。为了在全基因组范围内识别 DNA 环，许多染色质构象捕获 (3C) 衍生的检测已已开发。然而，可靠地使用 DNA 环来揭示肿瘤异质性受到两个关键因素的阻碍的不足。首先，尚未对 3C 衍生技术进行直接比较来评估测定- 识别肿瘤间可变 DNA 环的特定偏差。其次，每种方法都需要数百万个细胞来推断染色质结构，模糊了单细胞水平的差异。在此，我建议通过计算方法解决这两个缺陷的方法论进步阐明 DNA 环在 GBM 肿瘤间和肿瘤内异质性中的作用。在目标 1 中，我将使用赞助商实验室生成的数据进行三种不同的 3C 衍生方法映射异柠檬酸脱氢酶 (IDH) 突变体和野生型胶质母细胞瘤细胞系中的 DNA 环。我会识别偏见针对每个检测并确定与 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。而且，这分析将揭示 IDH 突变的表观遗传效应，该突变在 GBM 和其他癌症中普遍存在（例如急性髓性白血病）。该目标的结果将广泛适用于生物信息学研究人员开发 DNA 循环数据工具以及寻求了解 IDH 突变的癌症生物学家。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单个肿瘤内染色质环的变异性。具体来说，我将构建一个计算框架将大量群体指定的 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个灵敏的零膨胀模型识别个体 GBM 肿瘤模型中可变的染色质环。该提案的研究结果将对与染色质相关的生物学产生重要的见解 GBM 和其他癌症的肿瘤异质性，这将激发未来的治疗开发策略。