Blood DNA methylation biomarkers predict clinical reactivity in food-sensitized infants David Martino, PhD, Thanh Dang, PhD, Alexandra Sexton-Oates, BSc,

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Presentation transcript:

Blood DNA methylation biomarkers predict clinical reactivity in food-sensitized infants David Martino, PhD, Thanh Dang, PhD, Alexandra Sexton-Oates, BSc, Susan Prescott, MBBS, FRACP, PhD, Mimi L.K. Tang, MBBS, PhD, FRACP, FRCPA, FAAAAI, Shyamali Dharmage, MBBS, MSc, MD, PhD, Lyle Gurrin, PhD, Jennifer Koplin, PhD, Anne-Louise Ponsonby, MD, PhD, Katrina J. Allen, MBBS, FRACP, FAAAAI, PhD, Richard Saffery, PhD Journal of Allergy and Clinical Immunology Volume 135, Issue 5, Pages 1319-1328.e12 (May 2015) DOI: 10.1016/j.jaci.2014.12.1933 Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 1 Development of a predictive model based on blood-derived DNAm levels. A, Multidimensional scaling analysis of sample relationships based on 4485 probes significantly associated (P < .01, ANOVA) with phenotype. EA, Egg allergic; ES, egg sensitized; PA, peanut allergic; PS, peanut sensitized. B, Error curves from 10-fold cross-validation experiments. The model was trained to predict food challenge outcome. Misclassification errors are shown (y-axis) for each threshold at which noisy CpGs were removed. C, Class centroids are represented for each food challenge outcome group. Approximately 50 centroids are shown for ease of visualization. D, Scatter plots of β DNAm values from the top 6 ranked CpG sites based on average cross-validation scores. The x-axis shows samples (red, FA; blue, FS), and the y-axis shows the β methylation value. E, Multidimensional scaling analysis of validation study. The top panel shows sample relationships based on all somatic probes, and the bottom panel shows sample relationships based on the 96 predictive CpG signatures. Samples are numbered by age (0, birth; 1, 12 months) and colored by phenotype (FA, green; NA, orange). PC, Principal component of variation. Journal of Allergy and Clinical Immunology 2015 135, 1319-1328.e12DOI: (10.1016/j.jaci.2014.12.1933) Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 2 Sensitivity and specificity analysis of DNAm patient scores. A, Distribution of methylation ratios for the 96 CpG signatures stratified by phenotype. B, Box plots of total DNAm scores showing medians and ranges (statistical analysis using the Mann-Whitney test). C, ROC curve analysis of DNAm scores for predicting clinical allergy between groups. D, Performance comparisons of DNAm scores against serum IgE measures. E, Performance comparisons of DNAm scores against egg wheal size among egg-sensitized patients. F, Performance comparisons of DNAm scores against peanut SPT wheal sizes among peanut-sensitized patients. Journal of Allergy and Clinical Immunology 2015 135, 1319-1328.e12DOI: (10.1016/j.jaci.2014.12.1933) Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 3 Starburst plot of patient methylation scores for the discovery (left) and replication (right) cohorts. Concentric numerals denote sample numbers, and patients' scores are shown on the vertical axis in boldface. Methylation scores derived from CD4+ T cells (red) were consistently lower than scores derived from total PBMCS (blue); however, differences between phenotype classes were conserved in each cohort. Solid circles are visual guides for diagnostic cutoffs determined by means of sensitivity analysis. Note that y-axis scales differ in the 2 plots. Journal of Allergy and Clinical Immunology 2015 135, 1319-1328.e12DOI: (10.1016/j.jaci.2014.12.1933) Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 4 Diagnosing FA with a combination of IgE and methylation testing. The number of challenges required was determined by using 95% positive predictive values (PPV) for skin tests (>2 mm and <4 mm for egg and >2 mm and <8 mm for peanut) and sIgE tests (>0.35 kUA/L and <1.17 kUA/L for egg and >0.35 kUA/L and <15 kUA/L for peanut). Methylation predictions for challenge outcome were based on a cutoff of greater than 47.5 (95% negative predictive value). Error rates are shown. Journal of Allergy and Clinical Immunology 2015 135, 1319-1328.e12DOI: (10.1016/j.jaci.2014.12.1933) Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig E1 Quality control results for methylation data. A, Box plots and density distribution of raw unprocessed β values. B, Processed data after correcting for probe chemistry, probe level quality control, and normalization. Journal of Allergy and Clinical Immunology 2015 135, 1319-1328.e12DOI: (10.1016/j.jaci.2014.12.1933) Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig E2 Assessment of confounding because of cellular heterogeneity of PBMC samples. A, Correlation between flow cytometry–measured total CD4+ counts and in silico–estimated CD4+ counts using cell type–predictive CpGs determined by using the method of Houseman. Correlations between gold standard and data-driven methods were highly comparable. B, Between-group comparisons of cell composition in PBMC fractions. By using in silico estimates for blood cell subsets, there was no evidence of differential methylation between primary outcome groups because of cell type proportion. C, Cell counts for each subtype are shown. Statistical analysis was done by using the Mann-Whitney U test. Journal of Allergy and Clinical Immunology 2015 135, 1319-1328.e12DOI: (10.1016/j.jaci.2014.12.1933) Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions