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An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning  Benjamin F. Sallis, BS, Lena Erkert,

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Presentation on theme: "An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning  Benjamin F. Sallis, BS, Lena Erkert,"— Presentation transcript:

1 An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning  Benjamin F. Sallis, BS, Lena Erkert, BSc, Sherezade Moñino-Romero, MS, Utkucan Acar, MD, Rina Wu, BS, Liza Konnikova, MD, Willem S. Lexmond, MD, Matthew J. Hamilton, MD, W. Augustine Dunn, PhD, Zsolt Szepfalusi, MD, Jon A. Vanderhoof, MD, Scott B. Snapper, PhD, Jerrold R. Turner, PhD, Jeffrey D. Goldsmith, MD, Lisa A. Spencer, PhD, Samuel Nurko, MD, Edda Fiebiger, PhD  Journal of Allergy and Clinical Immunology  Volume 141, Issue 4, Pages e9 (April 2018) DOI: /j.jaci Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

2 Journal of Allergy and Clinical Immunology 2018 141, 1354-1364
Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

3 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. Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

4 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. Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

5 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. Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

6 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. Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

7 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. Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

8 Fig 6 Serum IgE, esophageal IGHE transcript levels and local Th2-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. Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

9 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. Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

10 Fig E1 Diagnostic classification by unsupervised machine learning. Principal component (PC) analysis plot of normalized transcript counts of the training set (n = 113). Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

11 Fig E2 Factors for the weighted component analysis. Volcano plots of normalized mRNA transcripts display fold difference (x-axis) and significance (y-axis) used for the calculation of the factors differentiating EoE and controls as well as GERD and controls in the proximal and distal esophagus in the training set (n = 113). Red indicates transcripts with a weight >10. Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

12 Fig E3 PCA of weighted factors. Plot of the training set after dimensionality reduction (n = 113). Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

13 Fig E4 Comparative ROC analysis of single transcripts for differentiating EoE from GERD and controls. AUC, Area under the curve. Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

14 Fig E5 Correlation of p(EoE) with disease severity markers. Correlation with eosinophil counts. Correlation with CCL26 mRNA levels in the training set (n = 113). Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

15 Fig E6 Eosinophil count and p(EoE) in atopic versus nonatopic patients. Comparison of eosinophil counts per hpf (A) and p(EoE) (B) in patients with and without atopic comorbidities. Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

16 Fig E7 p(EoE), Serum IgE, esophageal IGHE transcript levels, and local Th2-type inflammation. A, Patient key. Quantification of p(EoE) (B), IGHE transcript counts (C), and serum IgE levels (D). E, Representative transcripts: IL5, CPA3, and CCL2. **P < .01; ***P < .001; ****P < .0001; as calculated by Dunn multiple comparison test after Kruskal-Wallis test. Journal of Allergy and Clinical Immunology  , e9DOI: ( /j.jaci ) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

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