Discovery of clinically distinct CLL subgroups by integrative mapping of large-scale CLL genetic, expression and clinical data

通过大规模 CLL 遗传、表达和临床数据的综合绘图发现临床上不同的 CLL 亚组

• 批准号: 10005157
• 负责人: GAD A GETZ
• 金额: $ 33.16万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-09-16
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10005157
• 关键词: Address; Alleles; B-Lymphocytes; Biological; Cancerous; Catalogs; Cell Line; Cell physiology; Cells; Characteristics; Chemicals; Chromosomal Instability; Chronic Lymphocytic Leukemia; Clinical; Clinical Data; Collaborations; Communities; Computer Analysis; DNA Methylation; Data; Data Set; Databases; Development; Diagnosis; Disease; Dissection; Epigenetic Process; Event; Evolution; Exhibits; Frequencies; Genes; Genetic; Genome; Genomics; Goals; Heterogeneity; Indolent; International; Kinetics; Malignant Neoplasms; Maps; Massive Parallel Sequencing; Methods; Methylation; Modality; Modeling; Molecular; Molecular Profiling; Mutation; Names; Nature; Neighborhoods; Oncogenes; Outcome; Pathway interactions; Patient-Focused Outcomes; Patients; Pattern; Phenotype; Point Mutation; Prediction of Response to Therapy; Process; Protein Isoforms; Research; Research Personnel; Resources; Risk Factors; Seminal; Statistical Models; Structure of molecular layer of cerebellar cortex; Subgroup; Testing; Work; base; cancer cell; cancer genome; chemotherapy; cohort; computerized tools; data portal; design; driver mutation; exome; falls; fludarabine; genome editing; genomic data; high dimensionality; improved; methylome; molecular subtypes; multidimensional data; multimodal data; multimodality; novel; outcome forecast; outcome prediction; phenome; precision medicine; predictive modeling; programs; sample collection; targeted agent; tool; transcriptome; treatment planning; treatment response; tumor

Project Summary By creating seminal tools for the computational analysis of massively parallel sequencing data, the Getz group has generated vital pipelines and the analysis framework for large-scale processing and systematic analysis of cancer genome datasets. Through collaborations with an international network of investigators, we have gathered whole-exome, matched transcriptome, and methylome data from >1000 CLL patients. Through saturation analysis and statistical modeling, we have calculated this collection of samples to provide sufficient statistical power to detect all intermediate and high frequency genetic drivers of this disease (94% power to detect events in >2% of patients), based on the background mutation frequency of CLLs. The goals of our analyses are to: (1) Build a comprehensive catalog of all genetic and epigenetic drivers of CLL and their interdependencies, both clonal and subclonal, integrating information on somatic point mutations, copy- number changes, and DNA methylation; (2) Integrate all genomic data modalities to identify molecular subtypes of CLL and associate with drivers, cellular processes and cancer hallmarks; and (3) Develop new models to predict outcome based on the genomic map of CLL subtypes. Designing a framework and tools to maximize our understanding of the relevant genetic and epigenetic determinants of CLL development and response to treatment is the focus of this project. These results and framework will generate a valuable resource for the CLL and the broader cancer community.

项目概要 通过创建用于大规模并行测序数据的计算分析的开创性工具，Getz 小组 生成了用于大规模处理和系统分析的重要管道和分析框架 癌症基因组数据集。通过与国际研究人员网络的合作，我们已经 收集了超过 1000 名 CLL 患者的全外显子组、匹配的转录组和甲基化组数据。通过 饱和度分析和统计建模，我们计算了这组样本，以提供足够的 检测该疾病所有中频和高频遗传驱动因素的统计功效（94% 的功效 根据 CLL 的背景突变频率，检测 >2% 的患者中的事件。我们的目标 分析的目的是： (1) 建立 CLL 的所有遗传和表观遗传驱动因素的综合目录， 它们的相互依赖性，克隆和亚克隆，整合体细胞点突变的信息，复制 数量变化和 DNA 甲基化； (2) 整合所有基因组数据模式来识别分子 CLL 的亚型以及与驱动因素、细胞过程和癌症特征的相关性； (3) 开发新的 基于 CLL 亚型基因组图谱预测结果的模型。设计框架和工具 最大限度地了解 CLL 发展的相关遗传和表观遗传决定因素， 对治疗的反应是该项目的重点。这些结果和框架将产生有价值的 为 CLL 和更广泛的癌症社区提供资源。