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）

• 批准号: 10005291
• 负责人: PETER W LAIRD
• 金额: $ 50.45万
• 依托单位: VAN ANDEL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10005291
• 关键词: Attention; Base Sequence; Binding Sites; Biological; Cancer Biology; Cancer Model; Cells; Cellularity; Clinical; Clinical Data; Clinical Trials; Cluster Analysis; Code; Computers; Custom; Cytosine; DNA Integration; DNA Methylation; DNA Modification Process; DNA Sequence Alteration; Data; Data Analyses; Data Set; Defect; Development; Enhancers; Epigenetic Process; Event; Formalin; Frequencies; Genes; Genetic Fingerprintings; Genome; Genome Data Analysis Network; Genomics; Growth; Human; MLH1 gene; Malignant Neoplasms; Methods; Methylation; Microsatellite Instability; Mismatch Repair; Molecular; Mutation; Non-Malignant; Outcome; Paraffin Embedding; Pathway Analysis; Play; Principal Investigator; Production; Proteins; Proteomics; Publishing; Quality Control; Reader; Role; Scientist; Somatic Mutation; Specimen; Structure; Techniques; The Cancer Genome Atlas; Time; Tumor Suppressor Genes; Tumor-Derived; Untranslated RNA; Variant; Visualization; analysis pipeline; base; bioinformatics tool; bisulfite; bisulfite sequencing; cancer genome; cancer subtypes; clinical trial analysis; clinically relevant; computerized data processing; epigenomics; experience; flexibility; gene repair; genome analysis; genome annotation; improved; innovation; insight; mouse model; novel; sample fixation; skills; subclonal heterogeneity; tool; tool development; transcription factor; transcriptome sequencing; transcriptomics; tumor; tumor heterogeneity; tumorigenesis; Attention; Base Sequence; Binding Sites; Biological; Cancer Biology; Cancer Model; Cells; Cellularity; Clinical; Clinical Data; Clinical Trials; Cluster Analysis; Code; Computers; Custom; Cytosine; DNA Integration; DNA Methylation; DNA Modification Process; DNA Sequence Alteration; Data; Data Analyses; Data Set; Defect; Development; Enhancers; Epigenetic Process; Event; Formalin; Frequencies; Genes; Genetic Fingerprintings; Genome; Genome Data Analysis Network; Genomics; Growth; Human; MLH1 gene; Malignant Neoplasms; Methods; Methylation; Microsatellite Instability; Mismatch Repair; Molecular; Mutation; Non-Malignant; Outcome; Paraffin Embedding; Pathway Analysis; Play; Principal Investigator; Production; Proteins; Proteomics; Publishing; Quality Control; Reader; Role; Scientist; Somatic Mutation; Specimen; Structure; Techniques; The Cancer Genome Atlas; Time; Tumor Suppressor Genes; Tumor-Derived; Untranslated RNA; Variant; Visualization; analysis pipeline; base; bioinformatics tool; bisulfite; bisulfite sequencing; cancer genome; cancer subtypes; clinical trial analysis; clinically relevant; computerized data processing; epigenomics; experience; flexibility; gene repair; genome analysis; genome annotation; improved; innovation; insight; mouse model; novel; sample fixation; skills; subclonal heterogeneity; tool; tool development; transcription factor; transcriptome sequencing; transcriptomics; tumor; tumor heterogeneity; tumorigenesis

PROJECT SUMMARY / ABSTRACT The past three decades have witnessed an accumulating body of evidence that epigenetic mechanisms play an instrumental role in human cancer. 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. One of the most striking discoveries to emerge from cancer genome projects has been the previously unappreciated preponderance of somatic mutations in epigenetic regulators in most types of human cancer. Clearly, epigenetic mechanisms play a key role in human cancer, and a comprehensive molecular characterization of cancer should include epigenomic profiling. We propose to create an Integrative Cancer Epigenomic Data Analysis Center (ICE-DAC) to provide specialized analysis pipelines and expertise as part of the Genome Data Analysis Network (GDAN). We anticipate that epigenomic data will be provided as bisulfite-based sequence data or as DNA methylation BeadArray data, and we provide an analysis workflow that can accommodate either. We propose to apply specialized epigenetic analyses we have developed for both data types in our extensive experience in cancer genome consortia. In Specific Aim 1, we will develop, improve and implement analytic bioinformatic tools for epigenomic data analysis, including improvements to analysis tools for processing bisulfite sequence data. We will continue the development of analysis tools that use DNA methylation data to analyze tumor heterogeneity and subclonal structure, including the deconstruction of non-malignant cellular composition of the tumor. In Specific Aim 2, we will provide advanced specialized analysis of cancer epigenomic data generated by the Genome Characterization Center and/or provided through the Data Processing GDAC. Our automated workflow will provide timely primary data analysis for AWGs, and can accommodate both sequence-based or array-based DNA methylation data. This workflow will call differentially methylated regions (DMRs), identify cancer subtypes through stratified cluster analysis, analyze CpH methylation, and analyze tumor purity and subclonal heterogeneity. Performing variant analysis from bisulfite sequence data allows us to determine the impact of non-coding mutations on epigenetic state. 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. Integration of DNA methylation and RNA-Seq data will be used for epigenetic silencing calls and for our custom enhancer identification ELMER pipeline, both of which will feed into pathway and network analyses.

项目摘要 /摘要 在过去的三十年中 在人类癌症中发挥重要作用。表观遗传改变可以通过 失活的肿瘤抑制基因。这些沉默事件是相互排斥的发现 同一基因的结构或突变失活增强了表观遗传学的功能意义 沉默。偶发人类肿瘤中的大多数微卫星不稳定性病例可以归因于 MLH1不匹配修复基因的表观遗传沉默。从中出现的最引人注目的发现之一 癌症基因组项目一直是先前未欣赏的体细胞突变的优势 大多数类型的人类癌症的表观遗传调节剂。显然，表观遗传机制在人类中起着关键作用 癌症和癌症的全面分子表征应包括表观基因组分析。我们 提议创建一个综合性癌症基依性数据分析中心（ICE-DAC）以提供专业 分析管道和专业知识作为基因组数据分析网络（GDAN）的一部分。我们预料到这一点 表观基因组数据将作为基于甲基硫酸盐的序列数据或DNA甲基化珠图数据提供， 我们提供一个可以容纳的分析工作流程。我们建议应用专业 我们已经为两种数据类型开发的表观遗传分析在我们在癌症基因组方面的丰富经验中 财团。在特定目标1中，我们将开发，改进和实施分析生物信息学工具 表观基因组数据分析，包括改进用于处理Bisulfite序列数据的分析工具。我们 将继续开发使用DNA甲基化数据来分析肿瘤异质性的分析工具 和亚克隆结构，包括肿瘤非恶性细胞组成的解构。在 具体目标2，我们将提供对由 通过数据处理GDAC提供的基因组表征中心和/或。我们的自动化 工作流将为AWG提供及时的主要数据分析，并且可以容纳基于序列的或 基于阵列的DNA甲基化数据。该工作流将称为差异甲基化区域（DMR），识别 通过分层簇分析，分析CPH甲基化并分析肿瘤纯度和 亚克隆异质性。从Bisulfite序列数据进行变体分析使我们能够确定 非编码突变对表观遗传状态的影响。在特定的目标3中，我们将将表观基因组数据与 其他基因组，转录组，蛋白质组学和临床数据，可在生物学和临床上获得新颖的新型 见解。 DNA甲基化和RNA-seq数据的整合将用于表观遗传沉默调用，用于 我们的自定义增强器识别Elmer管道，这两者都将进入途径和网络分析。