A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations.
Schissler, A Grant
Halonen, Marilyn J
Jackson, Daniel J
Martinez, Fernando D
Lussier, Yves A
AffiliationDepartment of Medicine, University of Arizona, Tucson, AZ, USA
BIO5 Institute, University of Arizona, Tucson, AZ, USA
Center for Biomedical Informatics and Biostatistics, University of Arizona, Tucson, AZ, USA
Interdisciplinary Program in Statistics, University of Arizona, Tucson, AZ, USA
Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
Telethon Institute for Child Health Research, Perth, Australia
Banner Health, Phoenix, AZ, USA
Department of Pharmacology, University of Arizona, Tucson, AZ, USA
Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, WI, USA
Department of Pediatrics, University of Arizona, Tucson, AZ, USA
UA Cancer Center, University of Arizona, Tucson, AZ, USA
MetadataShow full item record
PublisherOXFORD UNIV PRESS
CitationGardeux, V., Berghout, J., Achour, I., Schissler, A. G., Li, Q., Kenost, C., ... Lussier, Y. A. (2017). A genome-by-environment interaction classifier for precision medicine: Personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations. Journal of the American Medical Informatics Association, 24(6), 1116-1126. DOI: 10.1093/jamia/ocx069 Access to Document 10.1093/jamia/ocx069 Link to publication in Scopus
JournalJ Am Med Inform Assoc.
RightsVC The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/bync-nd/4.0/).
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractTo introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation. Within an illustrative case study to predict asthma exacerbation, we designed a stimulation assay that reveals individualized transcriptomic response to human rhinovirus. Gene expression from peripheral blood mononuclear cells was quantified from 23 pediatric asthmatic patients and stimulated in vitro with human rhinovirus. Responses were obtained via the single-subject gene set testing methodology "N-of-1-pathways." The classifier was trained on a related independent training dataset (n = 19). Novel visualizations of personal transcriptomic responses are provided. Of the 23 pediatric asthmatic patients, 12 experienced recurrent exacerbations. Our classifier, using individualized responses and trained on an independent dataset, obtained 74% accuracy (area under the receiver operating curve of 71%; 2-sided P = .039). Conventional classifiers using messenger RNA (mRNA) expression within the viral-exposed samples were unsuccessful (all patients predicted to have recurrent exacerbations; accuracy of 52%). Prognosis based on single time point, static mRNA expression alone neglects the importance of dynamic genome-by-environment interplay in phenotypic presentation. Individualized transcriptomic response quantified at the pathway (gene sets) level reveals interpretable signals related to clinical outcomes.
NoteOpen access article.
VersionFinal published version
SponsorsFDM and YAL are supported in part by the Arizona Health Sciences Center and the BIO5 Institute. This study was funded in part by the following grants: K22 LM008308‐04, 5U10HL064307, the University of Arizona Cancer Center (P30CA023074), and the Center for Biomedical Informatics and Biostatistics of the University of Arizona Health Sciences.
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