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.

项目总结/摘要 高通量、低成本测序技术的出现改变了生物医学研究的格局 通过极大地扩展我们对人类基因组序列的研究能力。因此，在 描述特定基因组变异的作用及其影响的生物医学文献激增 关于人类疾病。这些进步将测序带入临床，从临床实践中塑造临床实践。 患者的基因组内容，一种通俗地称为基因组或精确医学的范例。仍有 在精准医疗时代充分发挥我们的潜力的许多障碍。其中一个是公认的 需要强大的，精心设计的系统，提供有关基因组变异及其作用的知识， 疾病理想的情况是，这种系统将提供一个全面的总结，所有的知识是有关的， 患者独特的基因组内容。 实现精准医疗的早期瓶颈是，尽管有大量的文献和一些 建立了定义药物和基因之间相互作用的知识库， 极具挑战性。为了满足这一需求，开发了药物-基因相互作用数据库（DGIdb，dgidb.org）。 开发通过自动化处理和人工管理相结合， 从这些不同的数据来源收集、结构化和连接（规范化）信息， 利用用户友好的搜索界面和应用程序编程界面（API）输入数据库。 然而，将药物和药物-基因相互作用概念跨资源联系起来仍然是一个极具挑战性的问题。 任务，以及聚合的药物-基因相互作用也具有挑战性，以突出效用的方式表示 为精准医疗所收集的知识。这项建议旨在提高我们的能力， 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实现这一目标。首先，将改进DGIdb规范化例程 加入新的内容和功能。其中，DGIdb将支持收集药物， 包括组合疗法和药物类别。此外，DGIdb将有新的社区提交， 策展功能，允许用户将新知识纳入数据库。第二，变量 将创建解释聚合器数据库（VIAdb），以规范跨几个不同的 来源集中在基因组变异的临床解释。VIAdb将作为一个独立的网站运行 工具和API，并将作为DGIdb相关解释的来源。最后，我们将开发技术 用于自动识别药物-基因相互作用和变异解释共识，以帮助社区 策展工作。如果成功，这项研究将提高变异解释的广度和一致性， 药物-基因相互作用的精准医学研究。