Structural, Computational and Epidemiologic Analyses of*

* 的结构、计算和流行病学分析

• 批准号: 7126388
• 负责人: NOAH D KAUFF
• 金额: $ 9.03万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-28 至 2008-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7126388
• 关键词: brca gene; breast neoplasms; chemical models; clinical research; computational biology; computer assisted sequence analysis; computer program /software; computer system design /evaluation; functional /structural genomics; human genetic material tag; molecular biology information system; neoplasm /cancer epidemiology; point mutation; structural biology

DESCRIPTION (provided by applicant): A major limitation of genetic testing for BRCA mutations using sequence-based approaches is that missense mutations of uncertain clinical significance are frequently identified. Despite the identification of such missense mutations in 13-17% of individuals who undergone genetic testing, there is no information regarding the biologic significance of these changes in the majority of these cases to assist in guiding clinical management. In order to address this issue, we propose to conduct a combined structural, computational and epidemiologic analysis of BRCA2 missense mutations. Briefly, we plan to develop and refine a computational protocol to incorporate structural modeling and protein superfamily analysis to predict the biologic significance of specific BRCA2 missense mutations. We will then use this computational protocol to gain insight into the functional importance of BRCA2 missense mutations that have been frequently reported to the Breast Cancer Information Core database. Missense mutations predicted by the computational analysis to likely be functionally significant will then be analyzed in association and cosegregation studies in an attempt to further elucidate the functional significance of these mutations. The combined approach used in this proposal will build upon identified strengths at our institution in Structural and Computational Biology, Genetic Epidemiology, and Clinical Genetics. This combined approach we believe will allow us to develop what we hope to be a more powerful and biologically relevant method of analyzing BRCA2 variants of uncertain significance, which if confirmed, will be directly and immediately translatable to individuals and families with these mutations.

描述（由申请人提供）：使用基于序列的方法对 BR​​CA 突变进行基因检测的一个主要限制是经常识别出临床意义不确定的错义突变。尽管在接受基因检测的个体中发现了 13-17% 的错义突变，但没有关于大多数病例中这些变化的生物学意义的信息来帮助指导临床管理。为了解决这个问题，我们建议对 BRCA2 错义突变进行结构、计算和流行病学的综合分析。简而言之，我们计划开发和完善计算方案，将结构建模和蛋白质超家族分析结合起来，以预测特定 BRCA2 错义突变的生物学意义。然后，我们将使用此计算协议来深入了解经常报告给乳腺癌信息核心数据库的 BRCA2 错义突变的功能重要性。通过计算分析预测可能具有功能意义的错义突变将在关联和共分离研究中进行分析，以试图进一步阐明这些突变的功能意义。该提案中使用的组合方法将建立在我们机构在结构和计算生物学、遗传流行病学和临床遗传学方面已确定的优势的基础上。我们相信，这种组合方法将使我们能够开发出一种更强大且生物学相关的方法来分析具有不确定意义的 BRCA2 变异，如果得到证实，将直接立即应用于具有这些突变的个人和家庭。