Type 2 Immune-Dominant Endotype Is Not Associated With Increased Responsiveness to Dupilumab Treatment in Adult Atopic Dermatitis Patients

Abstract

Treatment of atopic dermatitis (AD) is advancing towards more personalised medicine with novel therapies targeting specific immune pathways. Endotyping is important to identify patients who will benefit most from certain therapies [1]. Previously, we stratified AD patients into four clusters, of which two were defined as Type 2(T2)-dominant and the other two as T2 non-dominant [2, 3]. As dupilumab specifically targets the T2 pathway by inhibiting IL-4/IL-13 signalling, it is often assumed that patients assigned to a T2-dominant cluster would respond better to dupilumab. This study investigated serum protein profiles in AD patients with different responses to dupilumab and assessed the role of serum proteins in predicting treatment response.

Adult dupilumab-treated AD patients participating in the BioDay- or TREATgermany registries, and who provided written informed consent for data extraction, were screened for inclusion. Both registries are prospective, containing daily practice data regarding novel therapies for AD (ClinicalTrials.gov identifiers BioDay/TREAT:NCT03549416/NCT03057860) and have ethics committee approval (BioDay:METC 18-239/TREAT:No. EK TUD 118032016). Included patients, all with a baseline Eczema Area and Severity Index (EASI) ≥ 12, were categorised based on their treatment response at the first follow-up visit (12–16 weeks) compared to baseline as follows: improvement of the EASI of at least 90% (EASI ≥ 90), improvement of at least 75% but less than 90% (EASI ≥ 75) and improvement less than 50% (EASI < 50). Additionally, the EASI was collected after 24–28 and 52 weeks of treatment. For each patient, concentrations of 60 candidate AD biomarker proteins were measured in baseline serum samples and for a subgroup of patients at 12–16 weeks by using Luminex technology. Baseline protein concentrations were compared between response groups and clusters were defined using principal component analysis followed by K-means clustering. Random forest (RF) models trained on baseline data were used to assess cluster stability during treatment and to determine if treatment response can be predicted.

A total of 127 patients were selected, with a median age of 46.7 years (IQR: 31.4–60.3) and median baseline EASI of 19.4 (IQR: 15.6–27.3). Forty-seven patients were grouped in EASI ≥ 90, 49 in EASI ≥ 75 and 31 in EASI < 50. Only HGF (Hepatocyte Growth Factor) at baseline was significantly different between the response groups (p = 0.03), with higher concentrations in the EASI < 50 group compared to the EASI ≥ 90 group. A RF model trained to predict treatment response achieved an accuracy of only 35%, suggesting that the measured baseline proteins are not predictive of response in our cohort. Based on the expression pattern of the measured baseline proteins, a T2-dominant and non-dominant clusters were identified (Figure 1), which constituted of 22.8% and 77.2% of patients, respectively. Thirty-five proteins were significantly different between these two clusters. There was no significant difference in clinical characteristics between the two clusters and they were not associated with treatment response. Additionally, protein concentrations were measured in follow-up samples of 27 patients. A RF model (98% accuracy) showed that all patients, except one, were assigned to the same cluster at follow-up as that they were at baseline based on serum protein concentrations. Additional clinical data at Weeks 24–28 and Week 52 indicated potential further improvement of the efficacy of dupilumab beyond the initial follow-up period. However, RF models trained to predict treatment response at these timepoints achieved accuracies of only 25% and 31%. A detailed description of the methods and results is available in the following repository: https://zenodo.org/records/13833930.

Although T2-dominant and non-dominant clusters were identified, our results show that T2-dominant AD patients do not respond better to dupilumab treatment than T2 non-dominant patients. Similarly, Wu et al. [4] found no association between patient clusters and dupilumab efficacy and Nakahara et al. [5] stated that the EASI improves during dupilumab regardless of baseline biomarker concentrations. Notably, these studies involved Asian AD patients, who may have increased Th17 polarisation compared to European patients [6]. Despite this, the T2 pathway remains highly activated across all AD populations, which could explain the challenge in predicting dupilumab response in a T2-high disease such as AD. Currently, no convincing data exist for a predictive marker to guide AD treatment. Although the HGF concentration at baseline was significantly different between the response groups, our results showed that the concentration was unaffected by dupilumab and was not predictive of treatment response. No predictive proteins for dupilumab response were identified. Furthermore, supervised machine learning methods could not predict response at any timepoint using all measured baseline protein concentrations (accuracies ranging from 25% to 35%). These results reaffirm that many AD patients benefit from dupilumab, and that the measured baseline protein concentrations are not associated with response. Although cluster analysis in AD patients using various omics data (e.g., proteomics and transcriptomics) has been done, no previous studies have investigated whether patients might switch between clusters during treatment [2, 3, 7, 8]. Our results suggest cluster stability during dupilumab treatment, though changes may possibly occur after longer treatment periods. Longitudinal studies with extended follow-up are needed to confirm our results.

While we investigated serum, which is suitable for routine use in clinical settings, skin tissue might offer predictive power as effects of dupilumab also occur locally in the skin. Future research could use tape-stripping, which—although not suitable for routine use—is a minimally invasive approach for skin sampling and has already been used to evaluate changes in immune markers in dupilumab-treated AD patients [9]. Based on this study, we propose that every AD patient should be considered for dupilumab treatment irrespective of the T2-dominant or non-dominant endotype.

All authors have made substantial contributions to the conception and design, acquisition of data or analysis and interpretation of data. All authors have been involved in drafting the manuscript or revising it critically and have given final approval of the version to be published.

F.v.W. is a speaker and/or consultant for Janssen, Johnson & Johnson and Takeda. She has received research funding from Leo Pharma, Takeda, Galapagos, Sanofi and Bristol-Myers Squibb, all unrelated to this research. Unrelated to this study, T.W. has received institutional research grants from Beiersdorf, LEO Pharma and Novartis, has performed consultancies for Abbvie, Almirall, Janssen, Galderma, LEO, Lilly, Novartis, Pfizer, Sanofi-Regeneron and, he has also lectured at educational events sponsored by Abbvie, Janssen, Celgene, Galderma, LEO Pharma, Lilly, Sanofi and Novartis and is involved in performing clinical trials for various pharmaceutical industries that manufacture drugs used for the treatment of atopic dermatitis. Unrelated to this study, J.S. reports institutional grants for investigator-initiated research from the German GBA, the BMG, BMBF, EU, Federal State of Saxony, Novartis, Sanofi, ALK and Pfizer. He also participated in advisory board meetings as a paid consultant for Sanofi, Lilly and ALK. J.S. serves the German Ministry of Health as a member of the German Council for Health and Care. Unrelated to this study, S.A. has served as a consultant, lecturer, researcher, and/or has received research grants from AbbVie, Amgen, BMS, Beiersdorf, Janssen, LEO Pharma, Lilly, Novartis, Pfizer, Sanofi, Takeda and UCB. J.T. is a speaker for Sanofi, Janssen, Almirall, LEO Pharma and participated in advisory board meetings as a paid consultant for LEO Pharma. Unrelated to this study, S.W. reports institutional grants for investigator-initiated research from Leo Pharma, Pfizer Inc. and Sanofi. He also received honoraria as consultant and/or speaker from Abbvie, Boehringer, Galderma, Leo Pharma, Lilly, Pfizer Inc., Regeneron and Sanofi. M.d.B.-W. is a consultant, advisory board member and/or speaker for AbbVie, Almirall, Aslan, Arena, Eli Lilly, Galderma, Janssen, Leo Pharma, Pfizer, Regeneron and Sanofi. Unrelated to this study, D.B. is a speaker for Sanofi and LEO Pharma. All other authors have nothing to disclose. The included patients participated in the BioDay registry or TREATgermany registry. The BioDay registry is sponsored by Eli Lilly, Sanofi Genzyme, LEO Pharma, Abbvie and Pfizer. The TREATgermany registry is supported by AbbVie Deutschland GmbH & Co KG, Almirall, LEO Pharma, Eli Lilly, Pfizer, Galderma and Sanofi Deutschland GmbH. The sponsors of both registries were not involved in the analyses, interpretation of the data and preparation of the manuscript.