Name
Title | ||
|---|---|---|
CUSTOM-SEQ: a prototype for oncology rapid learning in a comprehensive EHR environment. |
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1093/jamia/ocw008 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | Field | DocType |
health information management,electronic health records,genomics,information science,precision medicine,neoplasms | Lung cancer,Data mining,Precision medicine,GNAQ,Medicine,Hazard ratio,Oncology,Disease,Proportional hazards model,Internal medicine,Bioinformatics,Retrospective cohort study,Cancer | Journal |
Volume | Issue | ISSN |
23 | 4 | 1067-5027 |
Citations | PageRank | References |
1 | 0.63 | 1 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremy Warner | 1 | 51 | 8.71 |
| Lucy Wang | 2 | 1 | 0.63 |
| William Pao | 3 | 7 | 1.16 |
| Jeffrey A Sosman | 4 | 1 | 0.63 |
| Ravi V Atreya | 5 | 1 | 0.63 |
| Pam Carney | 6 | 1 | 0.63 |
| Mia A. Levy | 7 | 19 | 5.15 |