Harvard Genome Characterization Center

哈佛基因组表征中心

• 批准号: 8322121
• 负责人: Raju S. Kucherlapati
• 金额: $ 200.91万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-29 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8322121
• 关键词: Award; Base Sequence; Boston; DNA Resequencing; Data; Data Analyses; Data Coordinating Center; Data Storage and Retrieval; Databases; Deposition; Genes; Genetic; Genome; Genomics; Goals; Grant; Informatics; Loss of Heterozygosity; Malignant Neoplasms; Methods; Oligonucleotide Microarrays; Patients; Phase; Process; Research Infrastructure; Sampling; Stratification; The Cancer Genome Atlas; Time; base; cancer Biomedical Informatics Grid; cancer genome; cancer therapy; comparative genomic hybridization; cost effective; density; experience; improved; interest; member; novel strategies; novel therapeutics; tool; tumor; tumor initiation; tumor progression

DESCRIPTION (provided by applicant): We are seeking support to establish a Genome Characterization Center, as a part of the Cancer Genome Atlas Project in Boston. The goal of the proposed effort is to analyze 2,000-2,500 tumor samples each year over a five-year period of time and identify a set of genes that can be resequenced by the members of The Cancer Genome Atlas (TCGA) project. It is well established that regions of the cancer genome that are amplified or show loss of heterozygosity or deletion harbor genes that are important for tumor initiation and progression. We will initially identify such regions in the cancer genome by conducting array comparative genomic hybridization (aCGH). Based upon detailed comparisons of many different platforms we have chosen to use the high-density Agilent oligonucleotide arrays for our studies. We have used the Agilent platform to characterize several hundreds of tumors and their corresponding controls, much of it a part of the current TCGA project. We already have the ability to have a throughput of processing four to five thousand samples during the first year of the proposed grant. During the first year we propose to use a sequencing based approach to determine copy number changes in tumors. In the initial phase we will examine 200 samples by a sequence tag counting approach and compare the results with the Agilent and other platforms. Based upon our experience we anticipate that this is eminently feasible and we will gradually switch the copy number analysis to the sequence based platform. In the second year 40% of the samples will be processed by the sequencing platform and the remaining 60% by the array platform. In year three 60% of the samples will be processed by sequencing and in years 4 and 5 we plan to completely switch to the sequencing approach. We have a well established pipeline to collect, store, retrieve and analyze the data from these two platforms. We will develop Level 1-4 data as defined by the consortium and deposit these data in a timely fashion in the Data Coordinating Center. We also propose to use powerful informatics tools that we have developed and propose to improve to analyze the aCGH and the sequence based data and extract a list of most interesting genes for resequencing. We have established an award winning IT infrastructure that will be deployed for LIMS, data storage, data retrieval, data analysis and interface with caBIG. Our proposed approach also has the ability to generate additional useful data for tumor and patient stratification.

描述（由申请人提供）：我们正在寻求支持建立基因组表征中心，作为波士顿癌症基因组图谱项目的一部分。拟议工作的目标是在五年内每年分析 2,000-2,500 个肿瘤样本，并确定一组可由癌症基因组图谱 (TCGA) 项目成员重新测序的基因。众所周知，癌症基因组中扩增或显示杂合性丧失或缺失的区域含有对肿瘤发生和进展很重要的基因。我们将首先通过进行阵列比较基因组杂交（aCGH）来识别癌症基因组中的此类区域。基于对许多不同平台的详细比较，我们选择使用高密度安捷伦寡核苷酸阵列进行我们的研究。我们使用安捷伦平台来表征数百种肿瘤及其相应的对照，其中大部分是当前 TCGA 项目的一部分。在拟议拨款的第一年，我们已经有能力处理四到五千个样本。在第一年，我们建议使用基于测序的方法来确定肿瘤中的拷贝数变化。在初始阶段，我们将通过序列标签计数方法检查 200 个样品，并将结果与​​安捷伦和其他平台进行比较。根据我们的经验，我们预计这是非常可行的，我们将逐步将拷贝数分析切换到基于序列的平台。第二年，40%的样本将由测序平台处理，其余60%由芯片平台处理。在第三年，60% 的样本将通过测序进行处理，在第四年和第五年，我们计划完全转向测序方法。我们拥有完善的管道来收集、存储、检索和分析这两个平台的数据。我们将开发联盟定义的1-4级数据，并将这些数据及时存入数据协调中心。我们还建议使用我们开发的强大信息学工具，并建议改进以分析 aCGH 和基于序列的数据，并提取最有趣的基因列表以进行重新测序。我们已经建立了屡获殊荣的 IT 基础设施，将用于 LIMS、数据存储、数据检索、数据分析以及与 caBIG 的接口。我们提出的方法还能够为肿瘤和患者分层生成额外的有用数据。