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/文摘