High-Content Image Analysis of Global DNA Methylation Patterns for Drug Screening

用于药物筛选的全局 DNA 甲基化模式的高内涵图像分析

基本信息

批准号：
7896876
负责人：
Arkadiusz Gertych
金额：
$ 17.48万
依托单位：
CEDARS-SINAI MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-03-02 至 2012-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7896876
关键词：
3-Dimensional Acetylation Address Adjuvant Antibodies Antineoplastic Agents Architecture Archives Area Azacitidine Binding Proteins Biological Biological Assay Biological Markers Cancer Diagnostics Cataloging Catalogs Cell Culture Techniques Cell Cycle Cell Death Cell Line Cell Nucleus Cell Proliferation Cell membrane Cell model Cells Classification Clinical Clinical Trials Color Complex Computer Systems Development Computer software Confocal Microscopy Cultured Cells Cytotoxic agent DNA DNA Methylation DNA Methyltransferase Inhibitor DU145 Data Data Analyses Databases Dependency Detection Development Diagnostic Disease Dose Doxorubicin Drug Evaluation Drug resistance Dyes Environment Environmental Risk Factor Epigenetic Process Epithelial Cells Evaluation Evolution Flow Cytometry Fluorescence Microscopy Future Gene Expression Gene Mutation Generations Genome Genomics Goals Histone Deacetylase Histone Deacetylase Inhibitor Histone H3 Histones Housing Image Image Analysis In Situ Individual Informatics Knowledge Label Lead Link Lysine Malignant Neoplasms Malignant neoplasm of prostate Mammalian Cell Maps Measurement Measures Methods Methyl-CpG-Binding Protein 2 Methylation Microscope Microscopic Microscopy Modeling Molecular Molecular Target Monitor Morphology Mutation Nuclear Nuclear Extract Nutritional Operating System Optics Outcome Outcomes Research Output PC3 cell line Patient Monitoring Patients Pattern Pattern Recognition Performance Pharmaceutical Preparations Phase Population Population Sizes Preclinical Drug Evaluation Process Propidium Diiodide Prostate Proteins Qualitative Methods Radiation therapy Reading Reporting Research Resistance Resolution Screening procedure Shapes Signal Transduction Site Solutions Specificity Staging Staining method Stains Support System System Systems Analysis Systems Integration Techniques Testing Therapeutic Three-Dimensional Image Three-Dimensional Imaging Time Treatment Protocols Validation Valproic Acid Visible Radiation Visual Work annexin A5 base biosignature cancer cell cancer therapy cell fixing chemotherapy comparative cost efficient design drug development drug discovery epigenomics fluorophore heterochromatin-specific nonhistone chromosomal protein HP-1 image processing imaging modality improved interest light microscopy mammalian genome model design multidisciplinary novel operation pre-clinical prognostic public health relevance response statistics tool treatment effect tumor vector

项目摘要

DESCRIPTION (provided by applicant): Cancer is the result of accumulations of genetic changes (gene mutations, chromosomal imbalances) and epigenetic changes (DNA methylation and histone methylation/acetylation) in mammalian cells. In contrast to genetic changes, the epigenetic changes are reversible, thus presenting a promising target in drug discovery for novel epigenetic cancer therapies. Demethylating drugs cause changes in global DNA methylation that can be visualized as large reorganizations of the genome within a cell's nucleus by light microscopy. These changes can be measured as significant differential distribution patterns in the methylated DNA of individual cells using high-resolution (HR) three-dimensional (3-D) fluorescence microscopy. Thus, quantitative DNA methylation imaging (QDMI) will be a vital tool in the characterization of cancer cells in epigenetic therapy, for both drug discovery and personalized cancer diagnostics and prognostics. To the best of our knowledge there is no high-content (HC) informatics system supporting HR 3-D imaging-based evaluation of cancer cells, with automatic analysis of differential DNA methylation. For advancing this field beyond existing HC solutions, we propose our new statistics-based tools to be integrated into the methylation pattern recognition workflow. The system envisaged will perform the following tasks: (i) automatic extraction of nuclear DNA methylation patterns and features, (ii) evaluation of cell culture dissimilarity by means of Kullback-Leibler's divergence, and (iii) feature based classification to distinguish nuclei with hypermethylated DNA and hypomethylated DNA. The drug response in the cell culture will be assessed as a combined product from the two last steps, visualized as pseudo-colored maps superimposed on the original images for experts' visual and quantitative assessment. System performance will be tested and validated on the prostate cancer cell line DU145, treated with three anticancer drugs: doxorubicin, 5-azacytidine, and valproic acid, with different doses for seven days. A dedicated database will be used to archive, link and integrate image, drug, treatment and quantitative data. The HC Informatics System will support the evolution of image-based DNA methylation analysis from a proven technique into a diagnostic system. We aim to bring a fundamental contribution to a quickly growing research field that combines multidisciplinary efforts required for producing fast, automated image-based techniques. The goal is high-throughput epigenetic screening of thousands of mammalian cells in their native environment, with long-term potential benefits including: (i) finding targets for epigenetic treatment and predicting responsiveness to cancer therapy, (ii) assessing nutritional and environmental factors that impact the epigenomic makeup of cells, (iii) characterization of complex epigenomics-related diseases on a cellular basis, and (iv) enabling the use of cellular models in epigenetic drug development. PUBLIC HEALTH RELEVANCE: The aim of this research is to develop a high-content analysis method for imaging-based cancer cell screening and evaluation of drug effects in epigenetic cancer treatment. Automatic analysis and quantification of global DNA methylation patterns in individual cells allows the evaluation of drug response and treatment effects.

描述（申请人提供）：癌症是哺乳动物细胞中遗传变化（基因突变、染色体失衡）和表观遗传变化（DNA 甲基化和组蛋白甲基化/乙酰化）积累的结果。与遗传变化相比，表观遗传变化是可逆的，因此为新型表观遗传癌症疗法的药物发现提供了一个有前途的目标。去甲基化药物会引起整体 DNA 甲基化的变化，通过光学显微镜可以将其视为细胞核内基因组的大规模重组。使用高分辨率 (HR) 三维 (3-D) 荧光显微镜，可以将这些变化测量为单个细胞甲基化 DNA 中的显着差异分布模式。因此，定量 DNA 甲基化成像 (QDMI) 将成为表观遗传治疗中癌细胞表征的重要工具，用于药物发现和个性化癌症诊断和预后。据我们所知，目前还没有高内涵 (HC) 信息学系统支持基于 HR 3-D 成像的癌细胞评估，并自动分析差异 DNA 甲基化。为了推动这一领域超越现有的 HC 解决方案，我们建议将新的基于统计的工具集成到甲基化模式识别工作流程中。设想的系统将执行以下任务：（i）自动提取核DNA甲基化模式和特征，（ii）通过Kullback-Leibler散度评估细胞培养物的差异性，以及（iii）基于特征的分类来区分超甲基化的细胞核DNA 和低甲基化 DNA。细胞培养物中的药物反应将作为最后两个步骤的组合产品进行评估，可视化为叠加在原始图像上的伪彩色图，以供专家进行视觉和定量评估。系统性能将在前列腺癌细胞系 DU145 上进行测试和验证，该细胞系用三种抗癌药物：阿霉素、5-氮杂胞苷和丙戊酸，以不同剂量处理 7 天。专用数据库将用于存档、链接和集成图像、药物、治疗和定量数据。 HC 信息学系统将支持基于图像的 DNA 甲基化分析从成熟技术发展为诊断系统。我们的目标是为快速发展的研究领域做出根本性贡献，该领域结合了产生快速、自动化的基于图像的技术所需的多学科努力。目标是对原生环境中的数千个哺乳动物细胞进行高通量表观遗传筛查，其长期潜在益处包括：(i) 寻找表观遗传治疗的靶点并预测对癌症治疗的反应，(ii) 评估营养和环境因素影响细胞的表观基因组构成，(iii) 在细胞基础上表征复杂的表观基因组相关疾病，以及 (iv) 使细胞模型能够在表观遗传药物开发中使用。公共健康相关性：本研究的目的是开发一种高内涵分析方法，用于基于成像的癌细胞筛查和表观遗传癌症治疗中药物效果的评估。对单个细胞中整体 DNA 甲基化模式的自动分析和量化可以评估药物反应和治疗效果。