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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘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，我们有从> 1000名CLL患者中收集的全外检查，匹配的转录组和甲基化合物数据。通过饱和分析和统计建模，我们已经计算了此样品集以提供足够的样品检测该疾病的所有中间和高频遗传驱动因素的统计能力（94％的功率根据CLL的背景突变频率，检测到> 2％的患者中的事件。我们的目标分析是：（1）构建CLL和所有遗传驱动因素和表观遗传驱动因素的综合目录它们的相互依存关系，包括克隆和亚克隆，整合了有关体点突变的信息，拷贝数量变化和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，并与驱动因素，细胞过程和癌症标志相关；（3）开发新的基于CLL亚型的基因组图预测结果的模型。设计一个框架和工具最大化我们对CLL发育的相关遗传和表观遗传决定因素的理解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 CLL和更广泛的癌症社区的资源。