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

项目摘要 通过为大规模并行测序数据的计算分析创建开创性的工具，Getz小组 已经生成了重要的管道和分析框架，用于大规模处理和系统分析 癌症基因组数据集。通过与国际调查人员网络的合作，我们已经 收集了1000名慢性淋巴细胞性白血病患者的完整外显子组、匹配的转录组和甲基组数据。穿过 饱和度分析和统计建模，我们已经计算了这组样本，提供了足够的 统计能力，以检测该疾病的所有中高频遗传驱动因素(94%的能力 根据CLL的背景突变频率检测2%的患者中的事件)。我们的目标是 分析的目的是：(1)建立CLL和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，包括克隆和亚克隆，整合了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以识别分子 CLL的亚型，与驱动因素、细胞过程和癌症标志相关；以及(3)开发新的 基于CLL亚型基因组图谱预测预后的模型。设计框架和工具以 最大限度地了解CLL发育的相关遗传和表观遗传决定因素 对治疗的反应是这个项目的重点。这些成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。

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.