CUSTOM-SEQ: a prototype for oncology rapid learning in a comprehensive EHR environment. (23rd March 2016)

• Record Type: Journal Article
• Authors: Warner, Jeremy L; Wang, Lucy; Pao, William; Sosman, Jeffrey A; Atreya, Ravi V; Carney, Pam; Levy, Mia A
• Is Part Of: Journal of the American Medical Informatics Association. Volume 23:Number 4(2016:Jul.)
• Page Start: 692
• Page End: 700
• Publication Date: 2016-03-23
• Subjects: health information management -- electronic health records -- genomics -- information science -- precision medicine -- neoplasms; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocw008
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Background: As targeted cancer therapies and molecular profiling become widespread, the era of "precision oncology" is at hand. However, cancer genomes are complex, making mutation-specific outcomes difficult to track. We created a proof-of-principle, CUSTOM-SEQ: Continuously Updating System for Tracking Outcome by Mutation, to Support Evidence-based Querying, to automatically calculate and display mutation-specific survival statistics from electronic health record data. Methods: Patients with cancer genotyping were included, and clinical data was extracted through a variety of algorithms. Results were refreshed regularly and injected into a standard reporting platform. Significant results were highlighted for visual cueing. A subset was additionally stratified by stage, smoking status, and treatment exposure. Results: By August 2015, 4310 patients with a median follow-up of 17 months had sufficient data for survival calculation. As expected, epidermal growth factor receptor (EGFR) mutations in lung cancer were associated with superior overall survival, hazard ratio (HR) = 0.53 ( P < .001), validating the approach. Guanine nucleotide binding protein (G protein), q polypeptide (GNAQ) mutations in melanoma were associated with inferior overall survival, a novel finding (HR = 3.42, P < .001). Smoking status was not prognostic for epidermal growth factor receptor–mutated lung cancer patients, who also lived significantly longer than their counterparts, even with advanced disease (HR = 0.54, P = .001). Interpretation: CUSTOM-SEQ represents a novel rapid learning system for a precision oncology environment. Retrospective studies are often limited by study of specific time periods and can lead to incomplete conclusions. Because data is continuously updated in CUSTOM-SEQ, the evidence base is constantly growing. Future work will allow users to interactively explore populations by demographics and treatment exposure, in order to further investigate significant mutation-specific signals.