Oncogenic Gene Regulatory Networks

致癌基因调控网络

• 批准号: 8204440
• 负责人: JOSEPH R NEVINS
• 金额: $ 28.75万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8204440
• 关键词: Automobile Driving; Biological; Biological Assay; Biology; Cell Culture Techniques; Cells; Collection; Data; Development; Disease; Event; Gene Expression; Gene Expression Profile; Gene Mutation; Genome; Goals; Heterogeneity; Human; Individual; Libraries; Link; Malignant Neoplasms; Measures; Methodology; Molecular Profiling; Mutation; Oncogenic; Pathway interactions; Patients; Pattern; Phenotype; Process; Proteins; Regulation; Regulator Genes; Sampling; Series; Signal Pathway; Signal Transduction; Small Interfering RNA; Subgroup; System; Therapeutic; Translating; Variant; Work; base; cell growth; clinical practice; computerized tools; design; genome-wide; loss of function; malignant breast neoplasm; novel; public health relevance; therapeutic target; tool; treatment strategy; tumor

DESCRIPTION (provided by applicant): The phenotypic heterogeneity of human cancers presents major challenges to advancing our understanding of disease mechanisms as well as to developing effective strategies for therapeutic design. This heterogeneity is also reflected in the variation in activity of cell signaling pathways that control cell growth and determine cell fate, processes critical for driving the cancer phenotype. The primary goal of the work described in this proposal is to take advantage of recent developments in the use of genome-scale measures of gene expression, together with advanced computational tools, to develop a more detailed understanding of the gene regulatory networks associated with the action of various oncogenic activities. Our focus is two-fold: develop a better understanding of the function and inter-connection of cell signaling pathways and second, utilize this information to translate to opportunities in clinical practice. A central focus of our work has been the development of expression signatures as a representation of a biological state, in this instance the activation of a pathway. These signatures will be expanded to a large collection of pathways relevant for cancer phenotypes; we will also develop and utilize novel statistical methodologies to dissect the complexity of cell signaling pathways, developing a library of verified pathway sub-signatures. In addition, the gene expression signatures reflecting cell signaling pathway activation, including pathway sub-signatures, will be used to define subgroups of cancer as the basis for defining distinct mechanisms of disease. And finally, genome-wide siRNA targeting will be used to identify cellular that influence the activity of cell signaling pathways. PUBLIC HEALTH RELEVANCE: Recent studies describing in-depth analyses of gene mutations in a number of human cancers have emphasized the complexity and heterogeneity of cancer and the importance of placing such analyses in pathway-specific contexts. A major focus of our work has been the use of gene expression signatures to define and predict the activity of a variety of cell signaling pathways that contribute to the oncogenic phenotype. Importantly, not only do these signatures provide a mechanism to dissect the heterogeneity of human cancers but they also provide an understanding of the events associated with this heterogeneity. Furthermore, since these signatures also predict sensitivity to various targeted therapeutics, these tools provide a framework for developing a strategy for treatment of individual patients.

描述（由申请人提供）：人类癌症的表型异质性对增进我们对疾病机制的理解以及开发有效的治疗设计策略提出了重大挑战。这种异质性也反映在控制细胞生长和决定细胞命运的细胞信号通路活性的变化上，这些过程对于驱动癌症表型至关重要。本提案中描述的工作的主要目标是利用基因组规模基因表达测量的最新进展以及先进的计算工具，以更详细地了解与该作用相关的基因调控网络各种致癌活性。我们的重点有两个：更好地了解细胞信号传导途径的功能和相互联系，其次，利用这些信息转化为临床实践的机会。我们工作的中心重点是开发表达特征作为生物状态的代表，在本例中是通路的激活。这些特征将扩展到与癌症表型相关的大量通路；我们还将开发和利用新的统计方法来剖析细胞信号传导途径的复杂性，开发经过验证的途径子特征库。此外，反映细胞信号通路激活的基因表达特征（包括通路子特征）将用于定义癌症亚组，作为定义不同疾病机制的基础。最后，全基因组 siRNA 靶向将用于识别影响细胞信号通路活性的细胞。 公共健康相关性：最近的研究描述了对多种人类癌症基因突变的深入分析，强调了癌症的复杂性和异质性以及将此类分析置于特定途径环境中的重要性。我们工作的一个主要重点是使用基因表达特征来定义和预测有助于致癌表型的各种细胞信号传导途径的活性。重要的是，这些特征不仅提供了一种剖析人类癌症异质性的机制，而且还提供了对与这种异质性相关的事件的理解。此外，由于这些特征还可以预测对各种靶向治疗的敏感性，因此这些工具为制定个体患者的治疗策略提供了框架。