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An integrated computational and functional genomics discovery engine for preclini

用于临床前的集成计算和功能基因组学发现引擎

基本信息

批准号：
8495704
负责人：
CHRISTOPHER J KEMP
金额：
$ 108.88万
依托单位：
FRED HUTCHINSON CANCER RESEARCH CENTER
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-06-18 至 2017-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8495704
关键词：
Bioinformatics Biological Models Cancer Biology Candidate Disease Gene Cell Culture Techniques Cell Line Cells Cisplatin Clinic Clinical Clinical Oncology Clinical Treatment Clinical Trials Clinical Trials Design Computational Biology Data Set Databases Development Disease Resistance Doxorubicin Drug Targeting Epithelial Exhibits Funding Future Gene Mutation Genes Genetic Genome Genotype Gold Hand Head and Neck Squamous Cell Carcinoma Heterogeneity Human Investments Knowledge Laboratories Lead Lesion Lethal Genes Libraries Malignant Neoplasms Mammalian Cell Methods Molecular Mutagens Mutation Oncogenes Oncogenic Outcome Pancreatic Ductal Adenocarcinoma Pathway interactions Patients Pharmaceutical Preparations Play Pre-Clinical Model Primary Neoplasm RNA Interference Resistance Resources Robotics Role Sampling Scientist Small Interfering RNA Synapses Synthetic Genes System TP53 gene Testing Therapeutic Time Tumor Suppressor Genes Tumor Suppressor Proteins Xenograft procedure base cancer cell cancer genomics cancer therapy cancer type candidate validation data sharing experience functional genomics gemcitabine genome-wide high throughput screening inhibitor/antagonist innovation malignant breast neoplasm miniaturize mutant novel oncology open source public health relevance screening small molecule stem success triple-negative invasive breast carcinoma tumor

项目摘要

DESCRIPTION (provided by applicant): Targeted therapies represent the future of oncology treatment. Toward this end, we have recently identified and subsequently validated novel drug targets in four independent settings that are effective and specific to tumors bearing mutations in common oncogenes and tumor suppressor genes. Our functional genetic approach, based on the genetic principle of synthetic lethality, utilizes a state-of-the-art high-throughput RNA interference and small molecule screening platform, genetically defined cell systems as well as patient-derived cultures for target discovery, advanced computational biology methods using publically available datasets to prioritize targets, and patient-derived xenografts to validate thee novel targets. Through the development and expansion of this integrative discovery engine, we will generate a gold standard synthetic lethal database for several major oncogenes/tumor suppressor genes and identify novel drug targets for three major cancer types. In the very near future this pipeline could be utilized for iterative clinical trial design and personalized cancer treatment.

描述（由申请人提供）：靶向治疗代表了肿瘤治疗的未来。为此，我们最近在四个独立的环境中鉴定并随后验证了新的药物靶点，这些靶点对常见癌基因和肿瘤抑制基因突变的肿瘤有效且特异。我们的功能遗传学方法基于合成致死的遗传学原理，利用最先进的高通量RNA干扰和小分子筛选平台，遗传定义的细胞系统以及用于靶点发现的患者来源的培养物，使用生物学上可用的数据集来优先考虑靶点的先进计算生物学方法，以及患者来源的异种移植物来验证新靶点。通过这个综合发现引擎的开发和扩展，我们将为几个主要的癌基因/肿瘤抑制基因生成一个金标准的合成致死数据库，并为三种主要的癌症类型确定新的药物靶点。在不久的将来，这条管道可以用于迭代临床试验设计和个性化癌症治疗。