Cancer Genomics Center

癌症基因组中心

• 批准号: 7908618
• 负责人: Raju S. Kucherlapati
• 金额: $ 64.93万
• 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: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7908618
• 关键词: Atlases; Award; Boston; DNA Resequencing; Data; Data Analyses; Data Storage and Retrieval; Electrophoresis; Gene Expression Profiling; Genes; Goals; Grant; Informatics; Libraries; Loss of Heterozygosity; Methodology; Methods; Molecular Profiling; Oligonucleotide Microarrays; Patients; Process; RNA; Research Infrastructure; Sampling; Stratification; Time; base; cancer Biomedical Informatics Grid; cancer genome; cancer genomics; comparative genomic hybridization; cost; density; improved; interest; member; serial analysis of gene expression; tool; tumor; tumor initiation

DESCRIPTION (provided by applicant): We are seeking support to establish a Cancer Genome Characterization Center in Boston. The goal of the proposed effort is to analyze 3000 tumor samples over a three-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 propose to 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 already have the ability to have a throughput of processing a thousand samples during the first year of the proposed grant. We plan to continually improve the processes and reduce the cost of obtaining these data during the remaining portion of the grant period. The regions of amplification or deletion harbor many genes. An optimal way to identify the critical gene within these intervals is through gene expression profiling of RNA from tumors. There are several platforms for expression profiling and one of the most informative methods is through the use of a platform called Serial Analysis of Gene Expression (SAGE). Although SAGE is powerful it involves construction of "Tag" libraries and sequencing these libraries. The cost of sequencing each of the libraries with conventional methodologies is prohibitive. We have developed and validated a method called "Polony" sequencing that is capable of providing highly accurate sequencing information at a small fraction of cost required for conventional electrophoresis or pyrosequencing based methods. We have already successfully used this method to obtain expression profiles from small amount of polyA RNA and we propose to utilize this method to generate data from 3000 tumor samples in the proposed grant period. During the first six months we will implement this method and will be able to achieve the goal of generating data at the rate of 1,000 tumors/year. We also propose to use powerful informatics tools that we have developed or implemented to integrate the aCGH and expression profiling 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）：我们正在寻求支持在波士顿建立一个癌症基因组表征中心。这项工作的目标是在三年的时间里分析3000个肿瘤样本，并确定一组基因，这些基因可以由癌症基因组图谱（TCGA）项目的成员进行重新测序。已经确定的是，癌症基因组中被扩增或显示杂合性缺失或缺失的区域包含对肿瘤的发生和发展至关重要的基因。我们建议通过进行阵列比较基因组杂交（aCGH）来鉴定癌症基因组中的这些区域。基于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。在申请拨款的第一年，我们已经有能力处理1000个样品。我们计划在剩余的资助期内不断改进流程，降低获取这些数据的成本。扩增或删除区域包含许多基因。在这些间隔内识别关键基因的最佳方法是通过肿瘤RNA的基因表达谱。有几种表达谱分析平台，其中最具信息量的方法之一是通过使用称为基因表达序列分析（SAGE）的平台。尽管SAGE功能强大，但它涉及“标签”库的构建和这些库的测序。使用传统方法对每个库进行排序的成本令人望而却步。我们已经开发并验证了一种称为“Polony”测序的方法，该方法能够以传统电泳或焦磷酸测序方法所需的一小部分成本提供高度准确的测序信息。我们已经成功地使用这种方法从少量的polyA RNA中获得了表达谱，我们建议在拟议的资助期内利用这种方法从3000个肿瘤样本中生成数据。在前六个月，我们将实施这种方法，并将能够实现以每年1000个肿瘤的速度生成数据的目标。我们还建议使用我们开发或实现的强大的信息学工具来整合aCGH和表达谱数据，并提取最感兴趣的基因列表进行重测序。我们已经建立了一个屡获殊荣的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产生额外的有用数据。

项目成果

A developmental taxonomy of glioblastoma defined and maintained by MicroRNAs.

• DOI: 10.1158/0008-5472.can-10-4117
• 发表时间: 2011-05-01
• 期刊: Cancer research
• 影响因子: 11.2
• 作者: Kim TM;Huang W;Park R;Park PJ;Johnson MD
• 通讯作者: Johnson MD

ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.

• DOI: 10.1093/bioinformatics/btq170
• 发表时间: 2010-06-15
• 期刊: Bioinformatics (Oxford, England)
• 作者: Wilkerson MD;Hayes DN
• 通讯作者: Hayes DN