My Cancer Genome: Coevolution of Precision Oncology and a Molecular Oncology Knowledgebase

作者：Marilyn Elaine Holt, Kathleen F. Mittendorf, Michele L. Lenoue-Newton, Neha M Jain, Ingrid A. Anderson, Christine Marie Lovly, Travis John Osterman, Christine Micheel, Mia Alyce Levy · 发表于：JCO Clinical Cancer Informatics · 年份：2021 · DOI：10.1200/cci.21.00084 · 被引用次数：51 · 研究领域：Biomedical Text Mining and Ontologies、Cancer Genomics and Diagnostics、Topic Modeling

PURPOSE: The My Cancer Genome (MCG) knowledgebase and resulting website were launched in 2011 with the purpose of guiding clinicians in the application of genomic testing results for treatment of patients with cancer. Both knowledgebase and website were originally developed using a wiki-style approach that relied on manual evidence curation and synthesis of that evidence into cancer-related biomarker, disease, and pathway pages on the website that summarized the literature for a clinical audience. This approach required significant time investment for each page, which limited website scalability as the field advanced. To address this challenge, we designed and used an assertion-based data model that allows the knowledgebase and website to expand with the field of precision oncology. METHODS: Assertions, or computationally accessible cause and effect statements, are both manually curated from primary sources and imported from external databases and stored in a knowledge management system. To generate pages for the MCG website, reusable templates transform assertions into reconfigurable text and visualizations that form the building blocks for automatically updating disease, biomarker, drug, and clinical trial pages. RESULTS: Combining text and graph templates with assertions in our knowledgebase allows generation of web pages that automatically update with our knowledgebase. Automated page generation empowers rapid scaling of the website as assertions with new biomarkers and d...