Integrative Cancer Epigenomic Data Analysis Center (ICE-DAC)

综合癌症表观基因组数据分析中心（ICE-DAC）

基本信息

批准号：
10474482
负责人：
PETER W LAIRD
金额：
$ 44.69万
依托单位：
VAN ANDEL RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10474482
关键词：
ATAC-seq Address Age Attention Base Sequence Binding Sites Biological Assay Cancer Biology Cancer Center Cancer Detection Cancer Model Cells ChIP-seq Chemoresistance Clinical Clinical Data Clinical Trials Cluster Analysis Code Competence Complement Custom Cytosine DNA DNA Integration DNA Methylation DNA Modification Process DNA Sequence Alteration Data Data Analyses Defect Development Enhancers Epigenetic Process Event Evolution Formalin Functional disorder Gastrointestinal Neoplasms Genes Genome Genome Data Analysis Network Genomics Growth Higher Order Chromatin Structure Human Hypermethylation MLH1 gene Malignant Neoplasms Malignant neoplasm of gastrointestinal tract Methylation Microsatellite Instability Mismatch Repair Mitosis Molecular Mutation Nature Oncogene Activation Outcome Paraffin Embedding Pathway Analysis Play Prevalence Proteins Proteomics Quality Control Race Reader Recurrent Malignant Neoplasm Role Sampling Somatic Mutation Specimen Time Tumor Suppressor Genes Validation Variant base bioinformatics tool bisulfite bisulfite sequencing cancer cell cancer classification cancer genome cancer genomics cancer subtypes chromatin remodeling clinical trial analysis clinically relevant data harmonization driver mutation epigenetic silencing epigenomics exceptional responders gene repair genetic information genome analysis histone methylation histone modification innovation insight interest molecular pathology mouse model multiple data types novel programs promoter sample fixation sex tool transcription factor transcriptome sequencing transcriptomics tumor

项目摘要

PROJECT SUMMARY / ABSTRACT The widespread nature of epigenetic abnormalities in human cancers has become increasingly appreciated in the past decade, with clinical impacts in cancer detection, cancer classification, chemoresistance prediction, and therapy. Large-scale cancer genomic screens have revealed a previously unrecognized prevalence of somatic mutations among epigenetic regulators in human cancers, including chromatin remodelers, as well as histone or DNA methylation readers, writers, and erasers. Epigenetic alterations can serve as driver events in cancer by inactivating tumor-suppressor genes. The finding that these silencing events are mutually exclusive with structural or mutational inactivation of the same gene reinforces the functional significance of epigenetic silencing. The majority of cases of microsatellite instability in sporadic human tumors can be attributed to epigenetic silencing of the MLH1 mismatch repair gene. Clearly, epigenetic mechanisms play a key role in human cancer, and a comprehensive molecular characterization of cancer should include epigenomic profiling. In 2015 we established an Integrative Cancer Epigenomic Data Analysis Center (ICE-DAC) to provide specialized expertise in epigenomic data analysis as part of the Genome Data Analysis Network (GDAN). We have made major contributions to the GDAN in the past five years, developing cutting-edge DNA methylation bioinformatics tools, leading analysis teams in PanCanAtlas, ATAC-Seq, Pan-Gastrointestinal (Pan-GI) Cancers and Tumor Molecular Pathology (TMP), and making major contributions to the Exceptional Responders (ER) study, testicular germcell tumors (TGCT), data harmonization (QC), CCG Ancestry Informative Markers (AIM), and many other projects. Here we propose to sustain this productive activity in a continuation of the ICE-DAC, lending our deep expertise in epigenomic data analysis to collaborative, integrative genomic and epigenomic analyses of clinical specimens within the NCI GDAN. In Specific Aim 1, we will provide advanced specialized analysis of bulk and single-cell cancer epigenomic data generated by programs within the NCI Center for Cancer Genomics. We have developed various tools for epigenetic analysis and implemented an automated workflow to provide timely primary data analysis for AWGs. In Specific Aim 2 we will implement innovative tools to extract additional information from specialized data types. This Aim maximizes the utility of the data generated, and adds to the rigor of analysis by providing orthogonal validation. These analyses will include prediction of common covariates (such as sex, age and race) for the samples, analysis of tumor purity and composition, inferences of genetic information (including genetic mutation, copy number and large structural variants), all from the DNA methylation assays. In Specific Aim 3 we will integrate epigenomic data with other genomic, transcriptomic, proteomic, and clinical data to derive biologically and clinically relevant novel insights. Our deep expertise in specialized epigenomics will address a core competency required in GDAN, and complement other genomic analyses of clinical trial samples.

项目概要/摘要人类癌症中表观遗传异常的广泛性已得到越来越多的认识在过去的十年中，在癌症检测、癌症分类、化疗耐药预测等方面产生了临床影响，和治疗。大规模癌症基因组筛查揭示了以前未被认识到的患病率人类癌症表观遗传调节因子之间的体细胞突变，包括染色质重塑因子以及组蛋白或 DNA 甲基化读取器、写入器和擦除器。表观遗传改变可以作为驱动事件通过灭活肿瘤抑制基因来预防癌症。研究发现这些沉默事件是相互排斥的同一基因的结构或突变失活增强了表观遗传的功能意义沉默。散发性人类肿瘤中微卫星不稳定性的大多数病例可归因于 MLH1 错配修复基因的表观遗传沉默。显然，表观遗传机制在其中发挥着关键作用。人类癌症，癌症的全面分子特征应包括表观基因组分析。 2015年，我们建立了综合癌症表观基因组数据分析中心（ICE-DAC），提供作为基因组数据分析网络 (GDAN) 的一部分，表观基因组数据分析方面的专业知识。我们在过去五年中为 GDAN 做出了重大贡献，开发了尖端的 DNA 甲基化生物信息学工具，PanCanAtlas、ATAC-Seq、泛胃肠道 (Pan-GI) 领域领先的分析团队癌症和肿瘤分子病理学（TMP），并为杰出贡献做出了重大贡献反应者 (ER) 研究、睾丸生殖细胞肿瘤 (TGCT)、数据协调 (QC)、CCG 祖先信息标记（AIM）和许多其他项目。在这里，我们建议以一种可持续的方式维持这种生产活动 ICE-DAC 的延续，将我们在表观基因组数据分析方面的深厚专业知识用于协作、 NCI GDAN 内临床标本的综合基因组和表观基因组分析。在具体目标 1 中，我们将为大量和单细胞癌症表观基因组数据提供先进的专业分析 NCI 癌症基因组学中心的项目。我们开发了多种表观遗传学工具分析并实施自动化工作流程，为 AWG 提供及时的主要数据分析。在具体目标 2 我们将实施创新工具，从专门的数据类型中提取附加信息。该目标最大限度地提高所生成数据的效用，并通过提供正交数据来增加分析的严谨性验证。这些分析将包括对常见协变量（例如性别、年龄和种族）的预测样本、肿瘤纯度和成分分析、遗传信息（包括遗传信息）的推断突变、拷贝数和大的结构变异），全部来自 DNA 甲基化测定。具体目标 3 我们将表观基因组数据与其他基因组、转录组、蛋白质组和临床数据整合，以得出生物学和临床相关的新颖见解。我们在专业表观基因组学方面深厚的专业知识将解决 GDAN 所需的核心能力，并补充临床试验样本的其他基因组分析。