An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning

Abstract

Background: Diagnostic evaluation of eosinophilic esophagitis (EoE) remains difficult, particularly the assessment of the patient's allergic status.

Objective: This study sought to establish an automated medical algorithm to assist in the evaluation of EoE.

Methods: Machine learning techniques were used to establish a diagnostic probability score for EoE, p(EoE), based on esophageal mRNA transcript patterns from biopsies of patients with EoE, gastroesophageal reflux disease and controls. Dimensionality reduction in the training set established weighted factors, which were confirmed by immunohistochemistry. Following weighted factor analysis, p(EoE) was determined by random forest classification. Accuracy was tested in an external test set, and predictive power was assessed with equivocal patients. Esophageal IgE production was quantified with epsilon germ line (IGHE) transcripts and correlated with serum IgE and the T_h2-type mRNA profile to establish an IGHE score for tissue allergy.

Results: In the primary analysis, a 3-class statistical model generated a p(EoE) score based on common characteristics of the inflammatory EoE profile. A p(EoE) ≥ 25 successfully identified EoE with high accuracy (sensitivity: 90.9%, specificity: 93.2%, area under the curve: 0.985) and improved diagnosis of equivocal cases by 84.6%. The p(EoE) changed in response to therapy. A secondary analysis loop in EoE patients defined an IGHE score of ≥37.5 for a patient subpopulation with increased esophageal allergic inflammation.

Conclusions: The development of intelligent data analysis from a machine learning perspective provides exciting opportunities to improve diagnostic precision and improve patient care in EoE. The p(EoE) and the IGHE score are steps toward the development of decision trees to define EoE subpopulations and, consequently, will facilitate individualized therapy.

Keywords: Allergy diagnosis; IgE; chronic allergic inflammation; eosinophilic esophagitis; eosinophils; machine learning; medical algorithm.

FIG 1.

Recruitment and dimensionality reduction of normalized mRNA transcripts. A, Patient selection. B, Determination of gene weights. Volcano plots of normalized mRNA transcripts displayed as fold difference (x-axis) and significance (y-axis) were used for the calculation of the factors differentiating EoE and GERD in the proximal and distal esophagus of the training set (n = 113). C, Transcript weights of the factors differentiating EoE and GERD in the distal biopsy. Red indicates weight > 10.

FIG 2.

HIF1A protein expression in patient biopsies. A, Immunohistochemistry of esophageal tissue sections from EoE, control, and GERD patients stained for HIF1A (n = 5 per group; representative distal biopsy). Tissue sections are shown at 200× and 400×. B, Isotype control (400×). C, ImageJ quantification; P values as calculated by Dunn multiple comparison test after Kruskal-Wallis test.

FIG 3.

Establishing diagnostic probability scores. A, Training set analysis strategy. B, Probability scores displayed as p(EoE), p(GERD), and p(Control). C, Cluster analysis based on probability scores.

FIG 4.

Testing diagnostic and predictive accuracy. A, External test set and equivocal test set analysis strategy. B, ROC analysis of p(EoE) as a diagnostic parameter for EoE, p(Control) as a diagnostic marker for controls, and p(GERD) as a diagnostic marker for GERD. C, Diagnosis of the external test set based on probability scores. D, Probability score-based diagnosis of the equivocal test set.

FIG 5.

p(EoE) as a composite score to monitor therapy response. A, p(EoE) before and after steroid treatment. B, Examples of alterations in highly weighted EoE transcripts to steroid treatment. C, Heat map of most significantly altered transcripts. *P < .05 and **P < .01, using ratio paired t test.

FIG 6.

Serum IgE, esophageal IGHE transcript levels and local T_H2-type inflammation. A, Patient key. B, Correlation of serum IgE titers and esophageal IGHE transcript levels. Separation of EoE patients based on serum IgE and IGHE. C, Eosinophil counts in IGHE-low (white circles: normal serum IgE; black circles: elevated IgE) and IGHE-high patients (orange circles: normal IgE; red circles: elevated IgE). D, Representative heat map of patients from the IGHE-high/serum-IgE-normal and IGHE-low/serum-IgE-elevated. Only the most enriched genes are shown. E, Representative transcripts: FcεRIβ, CCL5, and IL-13. **P < .01, ***P < .001, as calculated by Dunn multiple comparison test after Kruskal-Wallis test.

FIG 7.

IGHE score as a readout of esophageal allergic inflammation. A, Primary and secondary analysis loop. B, EoE patient groups stratified base on a composite score to distinguish IGHE-high and IGHE-low groups. C, ROC analysis. D, Correlation of IGHE-score with FcεRIβ mRNA. For patient key, see Fig 6, A.