Tools for Normalizing and Interpreting the Clinical Actionability of Genomic Variants

用于标准化和解释基因组变异临床可操作性的工具

• 批准号: 10443901
• 负责人: Alex Handler Wagner
• 金额: $ 24.25万
• 依托单位: RESEARCH INST NATIONWIDE CHILDREN'S HOSP
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-02 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10443901
• 关键词: Biological Markers; Biomedical Research; Client; Clinic; Clinical; Clinical Trials; Clinical assessments; Cohort Studies; Collection; Combined Modality Therapy; Communities; Consensus; Data; Databases; Development; Disease; Engineering; Explosion; Genes; Genome; Genomic medicine; Genomics; Goals; Health; Human; Human Genome; Internet; Knowledge; Level of Evidence; Link; Literature; Malignant Neoplasms; Manuals; Methods; Patients; Pharmaceutical Preparations; Phase; Procedures; Recording of previous events; Reporting; Research; Research Personnel; Resource Development; Resources; Role; Services; Shapes; Side; Source; Structure; Suggestion; System; Techniques; The Vanderbilt-Ingram Cancer Center at the Vanderbilt University; Variant; Visualization; Work; application programming interface; base; cancer type; clinical practice; clinically actionable; cohort; cost; data modeling; design; drug discovery; drug resource; gene interaction; genetic variant; genomic variation; human disease; improved; individual patient; knowledgebase; learning strategy; machine learning framework; molecular marker; novel; novel therapeutics; open source tool; precision medicine; profiles in patients; programs; response; therapeutic biomarker; tool; tumor; user-friendly; web app; web interface; web-based tool

PROJECT SUMMARY/ABSTRACT The availability of high-throughput, low-cost sequencing has transformed the landscape of biomedical research by dramatically expanding our capacity to interrogate the sequence of the human genome. Consequently, there has been an explosion of biomedical literature describing the role of specific genomic variants and their impact on human diseases. These advances are bringing sequencing into the clinic to shape clinical practice from the patient’s genomic content, a paradigm colloquially referred to as genomic or precision medicine. There remain many obstacles to fully realizing our potential in the era of precision medicine. Among them is a recognized need for robust, well-engineered systems that provide knowledge about genomic variants and their role in disease. Ideally, such systems would provide a comprehensive summary of all knowledge that is relevant to the patient’s unique genomic content. An early bottleneck to realizing precision medicine was that, despite the substantial literature and several established knowledgebases that define interactions between drugs and genes, querying across them was extremely challenging. In response to this need, the Drug-Gene Interaction database (DGIdb, dgidb.org) was developed. Through a combination of automated processing and manual curation, drug-gene interaction information was collected, structured, and connected (normalized) from these diverse sources of data and entered into a database with a user-friendly search interface and an application programming interface (API). However, linking drug and drug-gene interaction concepts across resources remains an extremely challenging task, and aggregated drug-gene interactions are also challenging to represent in a way that highlights the utility of the collected knowledge for precision medicine efforts. This proposal seeks to improve our ability to normalize and interpret drug-gene interactions corresponding to patient genomic variants. We will achieve this goal through two specific aims. First, the DGIdb normalization routines will be improved through incorporation of new content and features. Among these, the DGIdb will support collections of drugs, including combination therapies and drug classes. Also, the DGIdb will have new community submission and curation features, allowing users to incorporate new knowledge into the database. Second, the Variant Interpretation Aggregator database (VIAdb) will be created to normalize knowledge across several disparate sources focused on the clinical interpretations of genomic variants. The VIAdb will operate as a stand-alone web tool and API and will behave as a source of relevant interpretations to DGIdb. Finally, we will develop techniques for automated identification of drug-gene interactions and variant interpretation consensus to assist community curation efforts. If successful, this research will improve breadth and consistency of variant interpretations and drug-gene interactions for precision medicine efforts.

项目摘要/摘要 高通量，低成本测序的可用性改变了生物医学研究的景观 通过急剧扩大我们询问人类基因组序列的能力。因此，那里 一直是生物医学文献的爆炸，描述了特定基因组变异的作用及其影响 关于人类疾病。这些进步正在将测序带入诊所，以从 患者的基因组含量，一种典型的范式称为基因组或精度药物。剩下 在精确医学时代，充分意识到我们的潜力的许多障碍。其中有一个公认的 需要强大，设计良好的系统，这些系统提供有关基因组变体及其在其中作用的知识的需求 疾病。理想情况下，这样的系统将提供与所有知识有关的全面摘要 患者的独特基因组含量。 意识到精确药物的早期瓶颈是，多斯蒂特（Dospite）大量文献和几个 确定药物与基因之间相互作用的知识库，遍布它们的互动是 极其挑战。根据这种需求，吸毒的交互数据库（DGIDB，dgidb.org）为 发达。通过自动处理和手动策展的组合，药物 - 基因相互作用 收集，结构化和连接（归一化）来自这些潜水员的数据来源的信息， 输入具有用户友好的搜索接口和应用程序编程接口（API）的数据库。 但是，将药物和药物 - 基因互动概念之间的跨资源联系起来仍然是一个极其挑战 任务和汇总的药物互动也受到挑战，要以突出公用事业的方式代表 收集的知识以进行精确医学工作。该建议旨在提高我们正常化的能力 并解释与患者基因组变异相对应的药物 - 基因相互作用。 我们将通过两个具体目标来实现这一目标。首先，将改进DGIDB归一化例程 通过合并的内容和功能。其中，DGIDB将支持药物的收集， 包括组合疗法和药物类别。此外，DGIDB将有新的社区提交和 策展功能，使用户可以将新知识纳入数据库。第二，变体 将创建解释聚合数据库（VICDB），以使几个不同的知识归一化 来源着眼于基因组变异的临床解释。 VIADB将作为独立网络运行 工具和API，将作为对DGIDB的相关解释的来源。最后，我们将开发技术 为了自动识别药物 - 基因互动和变体解释共识，以帮助社区 策划工作。如果成功，这项研究将提高变体解释的广度和一致性和 用于精确医学工作的药物互动。