CGDnet: Cancer Gene Drug Network: Using patient-specific drug-gene networks for recommending targeted cancer therapies.

CGDnet：癌症基因药物网络：使用患者特定的药物基因网络来推荐靶向癌症治疗。

• 批准号: 9923991
• 负责人: Simina Maria Boca
• 金额: $ 3.67万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-06 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9923991
• 关键词: Affect; Biological Markers; Biometry; Cancer Patient; Cancer cell line; Clinical; Clinical Research; Communities; Computer software; Data; Data Analyses; Data Set; FAIR principles; Failure; Gene Mutation; Gene Proteins; Genes; Goals; Hospitals; Imagery; Individual; Informatics; Information Networks; KRAS2 gene; Knowledge; Lead; Letters; MEKs; Malignant Neoplasms; Methods; Molecular; Molecular Biology; Molecular Profiling; Movement; Mutation; Network-based; Oncogenes; Pathway interactions; Patients; Pharmaceutical Preparations; Physicians; Population; Prevalence; Process; Proteins; Publishing; Recommendation; Reporting; Reproducibility; Research Design; Research Personnel; Research Support; Resistance; Resources; Signal Pathway; System; Testing; Variant; Work; actionable mutation; analytical tool; anticancer research; base; cancer type; clinical decision support; clinical decision-making; computer framework; computer science; data management; data visualization; design; evidence base; experience; genetic variant; genomic profiles; improved; improved outcome; individual patient; inhibitor/antagonist; interactive tool; knowledge base; member; method development; molecular diagnostics; molecular marker; molecular oncology; multiple omics; oncology; open source; personalized cancer therapy; precision medicine; precision oncology; response; software development; success; targeted cancer therapy; targeted treatment; tool; treatment risk; tumor

PROJECT SUMMARY/ABSTRACT: Molecular profiling – the practice of molecularly testing tumors in order to find specific gene or protein alterations which can be used to recommend targeted therapies – is increasingly used in oncology and is fast becoming a major part of precision medicine. In practice, each patient or physician generally receives a list of molecular anomalies and a list of therapies which are predicted to be beneficial or not beneficial based on the tumor molecular profile. This may lead to a difficult process of prioritizing therapies for individual patients. In the current proposal, we will develop computational network-based approaches to therapy recommendation by using existing resources to inform the connections between drugs and gene or protein variants. We will consider approaches both for creating “average” networks based on population-level data and for creating “patient-specific” networks based on an individual’s specific tumor profile. We will also design and implement an interactive data visualization approach for these networks which will be usable by both clinical researchers and clinicians. The methods and tools developed as part of this project will be entirely reproducible and shared with the community via open-source software packages and interactive tools. We believe our project could eventually lead the way to improving the way therapies are targeted to cancer patients.

项目摘要/摘要： 分子分析 - 分子测试肿瘤的实践，以找到特定的基因或蛋白质 可用于推荐靶向疗法的改动 - 越来越多地用于肿瘤学，并且很快 成为精密医学的主要组成部分。实际上，每个患者或身体都会收到一个清单 分子异常和一系列疗法清单，这些疗法被认为是有益或无益的疗法 肿瘤分子谱。这可能会导致对个别患者的优先疗法的艰难过程。在 当前的建议，我们将开发基于计算网络的治疗方法，以通过 使用现有资源来告知药物与基因或蛋白质变体之间的联系。我们将 考虑基于人口级数据创建“平均”网络的方法，并创建 基于个人的特定肿瘤特征的“患者特异性”网络。我们还将设计和实施 这些网络的交互式数据可视化方法，这两个临床研究人员都可以使用 和临床医生。作为该项目的一部分开发的方法和工具将完全复制和共享 与社区通过开源软件包和交互式工具。我们相信我们的项目可以 最终导致改善疗法针对癌症患者的方式。