Early identification of atopic dermatitis patients in need of systemic immunosuppressive treatment

Atopic dermatitis (AD) is one of the most common chronic inflammatory skin diseases that is known to have profoundly negative effects on patient's quality of life.1 The majority of AD patients can be controlled with topical corticosteroids, but those with insufficient responses or who cannot reduce the potency/frequency of topical steroids to acceptable levels, will require treatment with systemic immunosuppressive/immunomodulating drugs.


Funding information Regeneron Pharmaceuticals
To the editor, Atopic dermatitis (AD) is one of the most common chronic inflammatory skin diseases that are known to have profoundly negative effects on patient's quality of life. 1 The majority of AD patients can be controlled with topical corticosteroids, but those with insufficient responses or who cannot reduce the potency/frequency of topical steroids to acceptable levels, will require treatment with systemic immunosuppressive/immunomodulating drugs. This group of patients can be defined as "difficult-to-treat" AD. In daily practice, the decision whether or not to start systemic therapy should be based on several factors, like disease severity, quality of life and comorbidities. 2 A single severity measurement can, however, easily over-or underestimate the long-term disease severity of a patient, since AD is characterized by exacerbations and remissions. Difficult-to-treat AD patients often experience a significant delay before optimal treatment is started. Early identification of this group might prevent unnecessary treatment delay. Therefore, the aim of this study was to construct a predictive serum biomarker signature, measured on a single time-point, contributing to the separation between difficultto-treat AD patients requiring systemic treatment and those who can be controlled with only topical therapy. During this treatment period, 74 severe AD patients (EASI > 21 before start of treatment) could be controlled with topical steroids ("controlled disease" group), and 78 severe AD patients (EASI > 21 before start of treatment) eventually required treatment with systemic immunosuppressive drugs ("difficult-to-treat" group; Table 1).
Serum was collected before start of intensive topical treatment, and 129 serum biomarkers (Table S1), measured using Luminex-based multiplex immunoassays, were included for analysis. To construct the prognostic biomarker signature, we used a statistical algorithm previously developed by Mamtani et al 3 (detailed methods related to patient and sample selection, serum biomarker measurements and statistical analysis are available in the article's Online Appendix S1).
Of the eight identified biomarkers, four have previously been shown to contribute to chronic skin inflammation or AD pathogenesis. PF4/CXCL4 and CTACK/CCL27 are higher expressed in serum of AD patients compared to healthy controls and correlate with AD severity. 4,5 Levels of PF4 were, correspondingly, significantly higher, whereas levels of CTACK were significantly lower in our difficultto-treat group, in which median EASI score was significantly higher (29.8, IQR 25.3-39.0 versus 27.8, IQR 24.7-31.5 in the controlled disease group). However, this small absolute difference in disease severity is not considered to be clinically relevant. 6 Despite PF4 and CTACK have been found to correlate with AD disease severity, both markers are considered not to be the optimal markers to pick up a small difference in disease severity. Serum levels of thymus and activation-regulated chemokine (TARC/CCL17), currently the best performing biomarker for assessing disease severity in AD, 5 did not significantly differ between the two groups and was not included in the final model, indicating that the current model is not solely based on differences in disease severity based on a single EASI score, but may reflect the more long-term disease severity and treatment response. IL-1b is a pro-inflammatory cytokine, which can induce IL-20 production and thereby keratinocyte differentiation. 7,8 Gamma-tubulin complex protein 2 (GCP-2) is a chemoattractant for  The role of the four remaining biomarkers in the pathogenesis of AD has not been explored yet. This is the first study investigating these markers in a large cohort of AD patients. Trappin-2, SOST, sPD-1 and LAIR-1 have all been associated with immune regulation, and might thus play a role in AD pathogenesis. Our results imply that pathophysiological heterogeneity in immunological pathways might underlie differences in treatment responses, and may be used to distinguish a specific subpopulation of difficult-to-treat AD patients in need of systemic treatment from patients who can be controlled with topical therapy.
In the current study, patients were stratified based on treatment history necessary to control the AD. The decision whether or not to start systemic therapy in AD patients is not always easy; several factors need to be considered. 2 The lack of response to adequately applied topical treatment or long-term need of large amounts of topical steroids is a very important indicator for systemic treatment, taken into consideration that much effort should be made to optimize topical treatment.
In all included patients, much attention was paid to adherence to topical treatment and evaluation of self-management. However, treatment compliance to topical therapy cannot be fully guaranteed.
With the correlation coefficient, NPV and PPV of the final model appearing to be sub-optimal, a potential danger of using this predictive signature in clinical practice might be unnecessary treatment with systemic immunosuppressive drugs due to incorrectly assigning a patient as "difficult-to-treat". Hence, we do not aim to replace clinical decision making by our biomarker signature. Instead, this signature might serve as a valuable addition to the decision whether or not to start systemic therapy in individual AD patients and might accelerate the initiation of optimal therapy. Validation of our biomarker signature in a prospective patient population is necessary to evaluate its applicability and predictive capacity.
In conclusion, this study shows that a constructed predictive signature of eight serum biomarkers is able to identify a subgroup of severe, difficult-to-treat AD patients with a sensitivity of 78% and a specificity of 86%, which might contribute to earlier identification.
This signature might serve as a valuable addition to the decision whether to start systemic therapy or not in individual AD patients, and the statistical algorithm used in this study may also be applied to construct biomarker signatures predicting treatment response to systemic immunosuppressive drugs, dupilumab or other therapies in the future. Since more targeted therapies will play an increasingly important role in AD treatment, prediction of treatment response can significantly contribute to selecting the right treatment for the right patient.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are available from the corresponding author upon reasonable request.