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J Am Med Inform Assoc. 2016 Jul;23(4):692-700. doi: 10.1093/jamia/ocw008. Epub 2016 Mar 23.

CUSTOM-SEQ: a prototype for oncology rapid learning in a comprehensive EHR environment.

Author information

1
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.
2
Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA.
3
Department of Medicine, Division of Hematology/Oncology, Vanderbilt University, Nashville, TN, USA.
4
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
5
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.

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.

KEYWORDS:

electronic health records; genomics; health information management; information science; neoplasms; precision medicine

PMID:
27008846
PMCID:
PMC4926743
DOI:
10.1093/jamia/ocw008
[Indexed for MEDLINE]
Free PMC Article

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