Jeremy L Warner1, Lucy Wang2, William Pao3, Jeffrey A Sosman3, Ravi V Atreya4, Pam Carney2, Mia A Levy5. 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.
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.
Authors: Griffin M Weber; Shawn N Murphy; Andrew J McMurry; Douglas Macfadden; Daniel J Nigrin; Susanne Churchill; Isaac S Kohane Journal: J Am Med Inform Assoc Date: 2009-06-30 Impact factor: 4.497
Authors: Hubing Shi; Willy Hugo; Xiangju Kong; Aayoung Hong; Richard C Koya; Gatien Moriceau; Thinle Chodon; Rongqing Guo; Douglas B Johnson; Kimberly B Dahlman; Mark C Kelley; Richard F Kefford; Bartosz Chmielowski; John A Glaspy; Jeffrey A Sosman; Nicolas van Baren; Georgina V Long; Antoni Ribas; Roger S Lo Journal: Cancer Discov Date: 2013-11-21 Impact factor: 39.397
Authors: Honghan Wu; Giulia Toti; Katherine I Morley; Zina M Ibrahim; Amos Folarin; Richard Jackson; Ismail Kartoglu; Asha Agrawal; Clive Stringer; Darren Gale; Genevieve Gorrell; Angus Roberts; Matthew Broadbent; Robert Stewart; Richard J B Dobson Journal: J Am Med Inform Assoc Date: 2018-05-01 Impact factor: 4.497
Authors: D Révész; E G Engelhardt; J J Tamminga; F M N H Schramel; B D Onwuteaka-Philipsen; E M W van de Garde; E W Steyerberg; E P Jansma; H C W De Vet; V M H Coupé Journal: BMC Med Inform Decis Mak Date: 2017-10-02 Impact factor: 2.796