Dataset Information

A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations.

ABSTRACT: Objective:To introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation. Materials and Methods: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. Results: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%). Discussion: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. Conclusion:The proposed framework provides an innovative approach to precision medicine. We show that quantifying personal pathway-level transcriptomic response to a disease-relevant environmental challenge predicts disease progression. This genome-by-environment interaction assay offers a noninvasive opportunity to translate omics data to clinical practice by improving the ability to predict disease exacerbation and increasing the potential to produce more effective treatment decisions.

SUBMITTER: Gardeux V

PROVIDER: S-EPMC6080688 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations.

Gardeux Vincent V Berghout Joanne J Achour Ikbel I Schissler A Grant AG Li Qike Q Kenost Colleen C Li Jianrong J Shang Yuan Y Bosco Anthony A Saner Donald D Halonen Marilyn J MJ Jackson Daniel J DJ Li Haiquan H Martinez Fernando D FD Lussier Yves A YA

Journal of the American Medical Informatics Association : JAMIA 20171101 6

<h4>Objective</h4>To introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation.<h4>Materials and methods</h4>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 v ...[more]

PMID: 29016970

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Project description:RationaleAllergic inflammation has been linked to increased susceptibility to viral illnesses, but it is unclear whether this association is causal.ObjectivesTo test whether omalizumab treatment to reduce IgE would shorten the frequency and duration of rhinovirus (RV) illnesses in children with allergic asthma.MethodsIn the PROSE (Preventative Omalizumab or Step-up Therapy for Severe Fall Exacerbations) study, we examined children with allergic asthma (aged 6-17 yr; n = 478) from low-income census tracts in eight U.S. cities, and we analyzed virology for the groups randomized to treatment with guidelines-based asthma care (n = 89) or add-on omalizumab (n = 259). Weekly nasal mucus samples were analyzed for RVs, and respiratory symptoms and asthma exacerbations were recorded over a 90-day period during the fall seasons of 2012 or 2013. Adjusted illness rates (illnesses per sample) by treatment arm were calculated using Poisson regression.Measurements and main resultsRVs were detected in 97 (57%) of 171 exacerbation samples and 2,150 (36%) of 5,959 nonexacerbation samples (OR, 2.32; P < 0.001). Exacerbations were significantly associated with detection of rhinovirus C (OR, 2.85; P < 0.001) and rhinovirus A (OR, 2.92; P < 0.001), as well as, to a lesser extent, rhinovirus B (OR, 1.98; P = 0.019). Omalizumab decreased the duration of RV infection (11.2 d vs. 12.4 d; P = 0.03) and reduced peak RV shedding by 0.4 log units (95% confidence interval, -0.77 to -0.02; P = 0.04). Finally, omalizumab decreased the frequency of RV illnesses (risk ratio, 0.64; 95% confidence interval, 0.49-0.84).ConclusionsIn children with allergic asthma, treatment with omalizumab decreased the duration of RV infections, viral shedding, and the risk of RV illnesses. These findings provide direct evidence that blocking IgE decreases susceptibility to RV infections and illness. Clinical trial registered with www.clinicaltrials.gov (NCT01430403).

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Dataset Information

A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations.

Publications

A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations.

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