Integrated genomic approaches to identify and validate cancer targets

识别和验证癌症靶标的综合基因组方法

• 批准号: 7433034
• 负责人: William C. Hahn
• 金额: $ 51.78万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-15 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7433034
• 关键词: Atlas of Cancer Mortality in the United States; Biological Assay; Breast Cancer Cell; Breast Cancer Model; Cancer Model; Cancer cell line; Carcinoma; Cell Proliferation; Chromosomal Duplication; Classification; Collection; Complement; Data Set; Development; Epithelial; Epithelial Cells; Essential Genes; Event; Exhibits; Experimental Models; Foundations; Gene Mutation; Gene Targeting; Genes; Genome; Genomics; Goals; Human; Knowledge; Laboratories; Libraries; Malignant - descriptor; Malignant Neoplasms; Mammary Neoplasms; Mammary gland; Maps; Methodology; Methods; Molecular; Mutation; Normal tissue morphology; Oncogenes; Open Reading Frames; Pathway Analysis; Phenotype; Protein Overexpression; Protein-Protein Interaction Map; Proteins; RNA Interference; Reagent; Recurrence; Relative (related person); Signal Pathway; Signal Transduction; Single Nucleotide Polymorphism; Technology; Therapeutic; Validation; Work; base; cancer genome; cell transformation; comparative genomic hybridization; density; gain of function; gene discovery; loss of function; malignant breast neoplasm; neoplastic; novel; programs; research study; size; therapeutic target; tool; tumor

DESCRIPTION (provided by applicant): Most human tumors, particularly those derived from epithelial cancers, exhibit global genomic alterations that make it difficult to identify mutations critical for cell transformation and to define the consequences of specific cancer-associated mutations. Recent advances in technologies to identify structural changes in human cancers now make it possible to consider enumerating all of the genetic alterations harbored by a particular tumor. Despite these advances in annotating structural alterations in cancer genomes, identifying the genes targeted by specific amplification or deletion events and deciphering the function of targeted gene mutations remains a major challenge. Indeed, the parallel development of efficient methods to annotate the function of cancer-associated genes is necessary to distill validated cancer targets from this structural description of cancer genomes. This proposal focuses on the integration of newly developed, high throughput methods to functionally annotate the cancer genome. Specifically, methods to perform large scale loss-of function, gain-of-function, and protein-protein network analyses will be combined in a novel integrated program to identify and validate functionally important cancer genes. Specifically, these studies build upon prior work by our laboratories to develop and implement genome scale RNA interference libraries, complete collections of human open reading frames (ORFs) and comprehensive protein-protein interaction maps. Although the basic tools required to perform large-scale studies are now available, the integration of such whole genome approaches represents an entirely new endeavor that requires the further development of these nascent technologies, the fabrication of comprehensive reagents and the creation of new ways to connect these datasets to achieve a scale beyond what has been previously performed. As such, the overarching goals of this R33 application is to apply these technologies in an integrated manner while simultaneously identifying and validating genes of particular promise for therapeutic targeting. The long-term goal of these studies is to provide a foundation for the expansion of these efforts at genome scale.

描述（由申请人提供）：大多数人类肿瘤，特别是源自上皮癌的肿瘤，表现出全局基因组改变，使得难以识别对细胞转化至关重要的突变，并难以定义特定癌症相关突变的后果。在识别人类癌症结构变化的技术方面的最新进展现在使得有可能考虑枚举特定肿瘤所包含的所有遗传改变。尽管在注释癌症基因组中的结构改变方面取得了这些进展，但鉴定特异性扩增或缺失事件靶向的基因以及破译靶向基因突变的功能仍然是一个重大挑战。事实上，同时开发有效的方法来注释癌症相关基因的功能是必要的，以从癌症基因组的结构描述中提取有效的癌症靶点。 该提案的重点是整合新开发的高通量方法，以功能性地注释癌症基因组。具体而言，进行大规模功能丧失、功能获得和蛋白质-蛋白质网络分析的方法将结合在一个新的综合计划中，以识别和验证功能重要的癌症基因。具体而言，这些研究建立在我们实验室先前的工作基础上，以开发和实施基因组规模的RNA干扰文库，完整的人类开放阅读框架（ORF）和全面的蛋白质-蛋白质相互作用图谱。虽然现在已经有了进行大规模研究所需的基本工具，但这种全基因组方法的整合代表了一种全新的努力，需要进一步开发这些新生技术，制造综合试剂，并创造新的方法来连接这些数据集，以实现超越以前的规模。因此，这项R33应用的首要目标是以综合的方式应用这些技术，同时识别和验证具有治疗靶向特别前景的基因。这些研究的长期目标是为在基因组规模上扩大这些努力提供基础。