title: CUSTOM-SEQ: a prototype for oncology rapid learning in a comprehensive EHR environment.
abstract: 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.
  
  
  
  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.
  
  
  
  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).
  
  
  
  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.
authors.affiliation: Department of Medicine, Division of Hematology/Oncology, Vanderbilt University, Nashville, TN, USA. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. jeremy.warner@vanderbilt.edu.
authors.forename: Jeremy L
authors.initials: JL
authors.lastname: Warner
authors.affiliation: Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA.
authors.forename: Lucy
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Medicine, Division of Hematology/Oncology, Vanderbilt University, Nashville, TN, USA.
authors.forename: William
authors.initials: W
authors.lastname: Pao
authors.affiliation: Department of Medicine, Division of Hematology/Oncology, Vanderbilt University, Nashville, TN, USA.
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Sosman
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
authors.forename: Ravi V
authors.initials: RV
authors.lastname: Atreya
authors.affiliation: Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA.
authors.forename: Pam
authors.initials: P
authors.lastname: Carney
authors.affiliation: Department of Medicine, Division of Hematology/Oncology, Vanderbilt University, Nashville, TN, USA. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA.
authors.forename: Mia A
authors.initials: MA
authors.lastname: Levy
mesh_terms: D000465:Algorithms; D015331:Cohort Studies; D019295:Computational Biology; D004273:DNA, Neoplasm; D057286:Electronic Health Records; D004815:Epidermal Growth Factor / Q000235:genetics; D005500:Follow-Up Studies; D005838:Genotype; D006801:Humans; D016247:Information Storage and Retrieval; D053208:Kaplan-Meier Estimate; D008175:Lung Neoplasms / Q000235:genetics* / Q000401:mortality; D009154:Mutation; D009369:Neoplasms / Q000235:genetics* / Q000401:mortality; D057285:Precision Medicine; D016016:Proportional Hazards Models; D000073869:Tobacco Smoking
chemical_list: D004273:DNA, Neoplasm; D004815:Epidermal Growth Factor
keywords: electronic health records; genomics; health information management; information science; neoplasms; precision medicine