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

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

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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中的一个共同特征既是患者之间（患者）和（在患者内）肿瘤异质性。改善治疗策略和实现个人护理的重要一步是映射这些类型的异质性如何影响临床表型，尤其是在致命肿瘤（例如胶质母细胞瘤多形（GBM）。最近的研究确定了三维折叠的实例染色质进入DNA环与肿瘤间异质性有关。目前，肿瘤内DNA循环尽管这可能导致单细胞转录差异，但尚未测量可变性在患者肿瘤中观察到。为了鉴定DNA环基因组宽，许多染色质构象捕获（3C）衍生的测定已开发。然而，可靠地使用DNA环以发现肿瘤异质性的两个键阻碍了肿瘤异质性缺陷。首先，尚未进行3C衍生技术的直接比较来评估测定法 - 识别肿瘤间可变DNA环的特定偏见。其次，这些方法都需要数百万个细胞推断染色质结构，从而掩盖了单细胞水平的差异。在这里，我建议通过计算方法解决这两种缺陷的方法论进步阐明了DNA循环在GBM中肿瘤间和肿瘤内异质性中的作用。在AIM 1中，我将使用赞助商实验室中生成的数据进行三种不同的3C衍生方法映射异位酸脱氢酶（IDH）突变体和野生型胶质母细胞瘤细胞系中的DNA环。我将确定偏见特定于每个测定法，并确定与IDH突变相关的差分环。这项工作将是对于开发未来的计算技术至关重要，以识别重要的DNA循环。而且，这分析将揭示IDH突变的表观遗传作用，该突变在GBM和其他癌症中很普遍（例如，急性髓样白血病）。该目标的结果将广泛适用于生物信息学研究人员开发用于DNA循环数据的工具以及试图了解IDH突变的癌症生物学家。在AIM 2中，我建议解决同一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，我将建立一个计算框架集成由散装人群提名的DNA环与单细胞染色质可及性（SCATAC-SEQ）数据。我将与Scatac-Seq技术的发明者合作，以开发一个敏感的，充气的模型识别单个GBM肿瘤模型中可变的染色质环。该提案的研究结果将产生对与染色质生物学相关的染色质生物学的关键见解。 GBM和其他癌症的肿瘤异质性，这将激发未来的治疗发展策略。