Functional consequences of cancer mutations

癌症突变的功能后果

基本信息

批准号：
8210896
负责人：
CHRIS SANDER
金额：
$ 68.74万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-04-01 至 2014-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8210896
关键词：
Access to Information Accounting Algorithms Amino Acid Substitution Amino Acids Animal Experiments Automobile Driving Biochemical Biological Cells Characteristics Clinical Collaborations Colon Carcinoma Combined Modality Therapy Communication Communities Computational Biology Computational algorithm Computer Analysis Computer software Computing Methodologies DNA Resequencing DNA copy number Data Data Set Databases Development Diagnosis Diagnostic Disease Drug Delivery Systems Epidermal Growth Factor Receptor Evolution Family Fostering Funding Future Genes Genetic Genetic Variation Genomics Genotype Goals HRAS gene Hand Hereditary Disease Human Individual Information Resources Information Systems Institutes Institution Internet Knowledge Laboratories Lead Link Loss of Heterozygosity Malignant Neoplasms Maps Memorial Sloan-Kettering Cancer Center Methods Molecular Molecular Genetics Molecular Structure Motivation Mutate Mutation Non-Small-Cell Lung Carcinoma Oncogenes Oncogenic Participant Pathway interactions Patients Pattern Pharmaceutical Preparations Pharmacologic Substance Pilot Projects Point Mutation Predisposition Preparation Process Protein Family Proteins Relative (related person)Reporting Research Research Personnel Resources Role Sampling Structure Technology Testing The Cancer Genome Atlas Therapeutic Time Translating Tumor Subtype Ursidae Family Validation Work anticancer research base cancer cell cancer classification cancer genetics cancer genome cancer genomics cancer therapy cancer type clinical practice computerized tools computing resources cost disease-causing mutation gain of function gene function genome database improved innovation loss of function malignant breast neoplasm meetings next generation novel outcome forecast prognostic protein complex protein function protein structure research study software development therapeutic target three dimensional structure tool tumor tumorigenesis web site web-accessible

项目摘要

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.

描述（由申请人提供）：了解癌症的遗传变化将有助于更好的诊断、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小组计划与癌症基因组 Altas (TCGA) 等大规模癌症基因组学项目相结合，计划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) 是一种可通过网络访问的计算资源，它将有关蛋白质进化和结构及其在生物分子途径中相互作用的详细知识与先进的算法和软件技术相结合。目标是为研究人员提供对人类肿瘤样本中观察到的突变的功能影响越来越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，并将结果预测嵌入到高质量生物信息系统（例如基因组数据库）网络中。然后，我们将与临床研究人员密切合作，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序，并大大提高目标实验的效率。了解突变可能对功能产生的影响将有助于更好地利用现有疗法，并导致药物开发新药物靶点的发现。当转化为临床实践时，有关个体突变谱的详细知识将能够实现个性化的治疗方法。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Using MEMo to discover mutual exclusivity modules in cancer.

使用 MEMo 发现癌症中的相互排斥模块。

DOI：
发表时间：
2013-03
期刊：
影响因子：
0
作者：
Ciriello, Giovanni;Cerami, Ethan;Aksoy, Bulent Arman;Sander, Chris;Schultz, Nikolaus
通讯作者：
Schultz, Nikolaus

Predicting interactome networks perturbations in human cancer : application to gene fusions in acute lymphoblastic leukemia

预测人类癌症中的相互作用组网络扰动：在急性淋巴细胞白血病基因融合中的应用

DOI：
发表时间：
2024-09-14
期刊：
影响因子：
0
作者：
L. Hajingabo;Sarah Daakour;Maud Martin;R. Grausenburger;R. Panzer;F. Dequiedt;Nicolas Simonis;J. Twizere
通讯作者：
J. Twizere

Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers.

利用癌症突变的预测功能影响揭示癌症选择。