CUSTOM-SEQ: A Prototype for Oncology Rapid Learning in a Comprehensive EHR Environment

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...
Source: Journal of the American Medical Informatics Association - Category: Information Technology Authors: Tags: Research and Applications Source Type: research