Accuracy of digital mRNA profiling of oesophageal biopsies as a novel diagnostic approach to eosinophilic oesophagitis. Issue 8 (August 2015)

• Record Type: Journal Article
• Authors: Lexmond, W. S.; Hu, L.; Pardo, M.; Heinz, N.; Rooney, K.; LaRosa, J.; Dehlink, E.; Fiebiger, E.; Nurko, S.
• Is Part Of: Clinical & experimental allergy. Volume 45:Issue 8(2015:Aug.)
• Page Start: 1317
• Page End: 1327
• Publication Date: 2015-08
• Subjects: Allergy -- Periodicals; Immunology -- Periodicals; 616.97
• DOI: 10.1111/cea.12523
• Languages: English
• ISSNs: 0954-7894

Abstract

<abstract abstract-type="main" id="cea12523-abs-0001"> <title>Summary</title> <sec id="cea12523-sec-0001" sec-type="section"> <title>Background</title> <p>Quantification of tissue eosinophils remains the golden standard in diagnosing eosinophilic oesophagitis (EoE), but this approach suffers from poor specificity. It has been recognized that histopathological changes that occur in patients with EoE are associated with a disease‐specific tissue transcriptome.</p> </sec> <sec id="cea12523-sec-0002" sec-type="section"> <title>Objective</title> <p>We hypothesized that digital mRNA profiling targeted at a set of EoE‐specific and Th2 inflammatory genes in oesophageal biopsies could help differentiate patients with EoE from those with reflux oesophagitis (RE) or normal tissue histology (NH).</p> </sec> <sec id="cea12523-sec-0003" sec-type="section"> <title>Methods</title> <p>The mRNA expression levels of 79 target genes were defined in both proximal and distal biopsies of 196 patients with nCounter® (Nanostring) technology. According to clinicopathological diagnosis, these patients were grouped in a training set (35 EoE, 30 RE, 30 NH) for building of a three‐class prediction model using the random forest method, and a blinded predictive set (<italic>n</italic> = 47) for model validation.</p> </sec> <sec id="cea12523-sec-0004" sec-type="section"> <title>Results</title> <p>A diagnostic model built on ten differentially expressed genes was able to differentiate with 100% sensitivity and specificity between conditions in the training set. In a blinded predictive set, this model was able to correctly predict EoE in 14 of 18 patients in distal (sensitivity 78%, 95% CI 52–93%) and 16 of 18 patients in proximal biopsies (sensitivity 89%, 95% CI 64–98%), without false‐positive diagnosis of EoE in RE or NH patients (specificity 100%, 95% CI 85–100%). Sensitivity was increased to 94% (95% CI 71–100%) when either the best predictive distal or proximal biopsy was used.</p> </sec> <sec id="cea12523-sec-0005" sec-type="section"> <title>Conclusion and Clinical Relevance</title> <p>We conclude that mRNA profiling of oesophageal tissue is an accurate diagnostic strategy in detecting EoE.</p> </sec> </abstract>