Functional consequences of cancer mutations

描述(由申请人提供)：了解癌症中的基因变化将导致更好的诊断、预后和治疗。纪念斯隆-凯特琳癌症中心的一个计算生物学研究团队计划与大规模癌症基因组学项目(如癌症基因组Altas(TCGA))协同工作，以阐明在许多人类肿瘤样本中观察到的数千种蛋白质突变的作用。在线突变评估器(OMA)是一个可在网络上访问的计算资源，它将把关于蛋白质的进化和结构及其在生物分子途径中的相互作用的详细知识与先进的算法和软件技术结合起来。其目标是为研究人员提供对在肿瘤样本中观察到的突变的功能影响的日益准确和信息丰富的估计。特别是，我们的目标是开发下一代算法和软件，以评估非同义癌症相关突变的功能影响。我们将把这些应用于所有已知的突变，并将由此产生的预测嵌入到高质量生物信息系统的网络中，如基因组数据库。在与临床研究人员的密切合作下，我们将分析在数十种癌症亚型的数千个肿瘤样本中观察到的蛋白质突变。由此产生的详细预测将允许根据相对重要性对突变进行排名，并极大地提高靶向实验的效率。了解突变可能产生的功能影响将有助于更好地利用现有疗法，并为药物开发发现新的药物靶点。当转化为临床实践时，关于个人突变特征的详细知识将使个性化的治疗方法成为可能。

DESCRIPTION (provided by applicant): Understanding genetic changes in cancer will lead to a better diagnosis, prognosis and therapy. In synergy with large scale cancer genomics projects, such as The Cancer Genome Altas (TCGA), a computational biology research team at Memorial Sloan Kettering Cancer Center plans to elucidate the role of thousands of protein mutations observed in many human tumor samples. The Online Mutation Assessor (OMA), a web-accessible computational resource, will combine detailed knowledge about the evolution and structure of proteins and their interactions in biomolecular pathways with advanced algorithms and software technology. The goal is to provide researchers with an increasingly accurate and informative estimate of the functional impact of mutations observed in humantumor samples. In particular, we aim to develop a next generation of algorithms and software for the purpose of assessing the functional impact of non-synonymous cancer- associated mutations. We will apply these to all known mutations and embed the resulting predictions in the web of high-quality biological information systems such as genome databases. In close collaboration with clinical researchers, we will then analyze protein mutations observed in thousands of tumor samples in tens of cancer subtypes. The resulting detailed predictions will allow ranking of mutations in terms of relative importance and greatly increase the efficiency of targeted experiments. Knowing the likely functional impact of mutations will point to the better use of existing therapies and lead to the discovery of new drug targets for pharmaceutical development. When translated to clinical practice, detailed knowledge about an individual's mutation profile will enable a personalized approach to therapy.