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

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 Th2-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.

Emerging roles of basophils in allergic inflammation.

Basophils have long been neglected in immunological studies because they were regarded as only minor relatives of mast cells. However, recent advances in analytical tools for basophils have clarified the non-redundant roles of basophils in allergic inflammation. Basophils play crucial roles in both IgE-dependent and -independent allergic inflammation, through their migration to the site of inflammation and secretion of various mediators, including cytokines, chemokines, and proteases. Basophils are known to produce large amounts of IL-4 in response to various stimuli. Basophil-derived IL-4 has recently been shown to play versatile roles in allergic inflammation by acting on various cell types, including macrophages, innate lymphoid cells, fibroblasts, and endothelial cells. Basophil-derived serine proteases are also crucial for the aggravation of allergic inflammation. Moreover, recent reports suggest the roles of basophils in modulating adaptive immune responses, particularly in the induction of Th2 differentiation and enhancement of humoral memory responses. In this review, we will discuss recent advances in understanding the roles of basophils in allergic inflammation.