A computational model to predict severity of atopic eczema from 30 serum proteins

Krause, Linda; Mourantchanian, Vagkan; Brockow, Knut; Theis, Fabian J.; Schmidt‐Weber, Carsten B.; Knapp, Bettina L.; Mueller, Nikola S.; Eyerich, Stefanie

doi:10.1016/j.jaci.2016.04.018

Cited by 14 publications

(15 citation statements)

References 5 publications

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“…Although variable results have been obtained using skin, and particularly blood, biomarkers (Guttman-Yassky et al, 2008Krause et al, 2016;Nomura et al, 2003;Quaranta et al, 2014;Suarez-Farinas et al, 2011;Wenzel et al, 2008), the independent correlations between the serum cytokines and disease activity as measured by SCORAD, and of skin biomarkers with disease activity, argue that both may be important in generating clinically relevant disease activity. Nevertheless, the lack of good correlation between serum biomarkers and lesional skin biomarkers also argues that the circulating cytokine profile reflects activation of T cells and other cells reported to produce cytokines, including eosinophils, basophils, macrophages, epithelial cells (Lacy and Stow, 2011), and cellular responses beyond the skin.…”

Section: Discussionmentioning

confidence: 99%

An Integrated Model of Atopic Dermatitis Biomarkers Highlights the Systemic Nature of the Disease

Ungar

Garcet

Gonzalez

et al. 2017

Journal of Investigative Dermatology

171

168

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Current atopic dermatitis (AD) models link epidermal abnormalities in lesional skin to cytokine activation. However, there is evolving evidence of systemic immune activation and detectable abnormalities in nonlesional skin. Because some of the best single correlations with severity (Scoring of AD, or SCORAD) are detected not only in lesional but also nonlesional skin and blood, more complex biomarker models of AD are needed. We thus performed extensive biomarker measures in these compartments using univariate and multivariate approaches to correlate disease biomarkers with SCORAD and with a combined hyperplasia score [thickness and keratin 16 (K16) mRNA] at baseline and after cyclosporine A treatment in 25 moderate to severe AD patients. Increases in serum cytokines and chemokines (IL-13, IL-22, CCL17) were found in AD versus healthy individuals and were reduced with treatment. SCORAD correlated with immune (IL-13, IL-22) and epidermal (thickness, K16) measures in lesional and, even more strongly, in nonlesional AD. Serum cytokines also had higher correlations with nonlesional markers at baseline and with treatment. Multivariate U statistics improved baseline and treatment-response SCORAD correlations. Nonlesional models showed the strongest correlations, with further improvement upon integration of serum markers. Even better correlations were obtained between biomarkers and the hyperplasia score. Larger cohorts are needed to confirm these preliminary data.

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Section: Discussionmentioning

confidence: 99%

An Integrated Model of Atopic Dermatitis Biomarkers Highlights the Systemic Nature of the Disease

Ungar

Garcet

Gonzalez

et al. 2017

Journal of Investigative Dermatology

171

168

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“…We thank Thijs et al 1 for the helpful comments on our article. 2 Indeed, this correspondence led to the identification of misassignments of healthy controls and atopic eczema.…”

Section: Replymentioning

confidence: 97%

Multiplex platform technology and bioinformatics are essential for development of biomarkers in atopic dermatitis

Thijs

Herath

Bruin‐Weller

et al. 2017

Journal of Allergy and Clinical Immunology

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Multiplex platform technology and bioinformatics are essential for development of biomarkers in atopic dermatitis To the Editor: In the recently published letter to the Editor by Krause et al, 1 the authors tried to establish a combination of serum proteins that could predict disease severity in patients with atopic dermatitis (AD). Serum levels of 32 protein markers were determined in 52 patients with AD and 20 healthy controls. To our surprise, the authors found no significant differences between the levels of 31 serum proteins in patients and controls, with the exception of total IgE levels. This is highly remarkable because many previous publications have reported increased levels of these serum biomarkers, including IL-4, macrophage-derived chemokine, and thymus and activation-regulated chemokine (TARC/CCL-17) in patients with AD compared with healthy controls. 2-4 Krause et al found serum TARC levels in healthy controls ranging from around 90 pg/mL to more than 20,000 pg/mL. This is remarkable because from literature and our own experience we know that serum TARC levels in healthy controls do not exceed 600 pg/mL. 2,5-7 Krause et al also investigated correlations between serum biomarker levels and disease severity measured by SCORing Atopic Dermatitis. Surprisingly, they found no significant correlations between biomarker levels and SCORing Atopic Dermatitis. This also is in contrast to several previous publications, which are summarized in a recently published meta-analysis. 8 In this meta-analysis, we show that serum TARC levels have been determined in a relatively large number of patients from different studies, showing pooled correlation coefficients of 0.60 (95% CI, 0.48-0.70) and 0.64 (95% CI, 0.57-0.70) in longitudinal and cross-sectional studies, respectively. 9 The authors conclude that their findings are in line with our publication. We indeed reported that TARC levels vary between patients within cross-sectional cohorts of patients who have similar disease severity scores; however, the overall correlation that we found between serum TARC levels and disease severity is still strong. 5,10 Considering the interindividual differences in serum TARC levels and the heterogeneous character of AD, we do agree with the authors on the necessity of a combination of biomarkers for measuring disease severity in AD. Therefore, we recently conducted a pilot study in which we demonstrated that a multivariate signature, consisting of serum TARC, PARC/CCL18, IL-22, and sIL-2R levels, showed a correlation coefficient of 0.86 to disease severity. 11 We strongly believe that state-of-the-art multiplex platform technology and bioinformatics can help in developing reliable and objective prediction tools to measure therapeutic effects in AD from serum.

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“…38 Biomarkers A trial of bioinformatics approach to predict SCORAD scores has been done. 41 Epidermal TSLP predicts the development of AD during infancy. 45 The correlative nature of nonlesional AD skin with serum biomarkers has been examined.…”

Section: New Therapiesmentioning

confidence: 98%

“…Several parameters, such as thymus and activation-regulated chemokine (CCL17), CCL22, and lactate dehydrogenase, have been proposed as biomarkers for AD, but their increase is not necessarily specific to AD and might not significantly correlate with the SCORAD score because there are high interindividual differences in serum concentrations of these proteins. 40 Because no single protein has been identified as a suitable indicator of AD severity to date, Krause et al 41 proposed a unique approach to investigate the potency of a biomarker signature consisting of a combination of serum proteins rather than focusing on a single biomarker. Using the Bio-Plex Pro Human Cytokine 27plex assay (Bio-Rad, Munich, Germany) and ELISA, they analyzed 32 proteins in sera from 52 patients with AD and 20 healthy control subjects, selected a protein combination, and created a computational model to predict the SCORAD score.…”

Section: Biomarkers For Admentioning

confidence: 99%

“…Unfortunately, even the model based on the bestfit combination of proteins could not achieve successful prediction of the real SCORAD score. 41 Nevertheless, this type of state-of-the-art bioinformatics approach remains necessary for the objective measurement of AD severity and will help in developing reliable and objective predictive tools to evaluate the therapeutic effects of various AD treatments from serum. 42,43 Ungar et al 44 performed a unique approach to evaluate the biomarkers of AD, in which they found correlative nature of nonlesional AD skin with serum biomarkers.…”

Section: Biomarkers For Admentioning

confidence: 99%

See 1 more Smart Citation

Advances in atopic dermatitis and urticarial in 2016

Honda

Nomura

Kabashima

2017

Journal of Allergy and Clinical Immunology

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This review highlights recent key advances in the pathology and therapies of inflammatory skin diseases, focusing on atopic dermatitis (AD) and chronic spontaneous urticaria (CSU). Regarding AD, transcriptomic analysis with human samples revealed different immune profiles between childhood and adult AD. Phase III clinical trials of dupilumab, an anti-IL-4 receptor α antibody, in the treatment of AD have successfully finished, and dupilumab will appear in clinical practice as the first biologic for AD in 2017. In addition, a novel biologic that targets IL-31 shows promising results in a phase II trial. As for the skin microbiome study, novel insights into the mechanisms of microbial dysbiosis, such as colonization of Staphylococcus aureus, a common feature of AD, were proposed. Regarding CSU, autoreactive CD4 T cells that react to FcεRI were discovered, which might contribute to the development of CSU. These findings provide new insights into the pathogenesis of AD and CSU and will lead to more specific and personalized treatments.

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A computational model to predict severity of atopic eczema from 30 serum proteins

Cited by 14 publications

References 5 publications

An Integrated Model of Atopic Dermatitis Biomarkers Highlights the Systemic Nature of the Disease

An Integrated Model of Atopic Dermatitis Biomarkers Highlights the Systemic Nature of the Disease

Multiplex platform technology and bioinformatics are essential for development of biomarkers in atopic dermatitis

Advances in atopic dermatitis and urticarial in 2016

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