Transcriptomic Profiling of Tape-Strips From Moderate to Severe Atopic Dermatitis Patients Treated With Dupilumab
Bibliographic record
Abstract
BACKGROUND: Tape-strips are a minimally invasive approach to characterize skin biomarkers in atopic dermatitis (AD). However, they have not yet been used for tracking gene expression changes with systemic treatment. OBJECTIVE: The aim of the study was to evaluate gene expression changes and therapeutic response biomarkers in AD patients before and after dupilumab (interleukin 4Rα antibody) treatment using tape-strips to obtain epidermal tissue for analysis. METHODS: Lesional and nonlesional tape-stripped skin was sampled from 18 AD patients before and after dupilumab treatment and from 17 healthy subjects and analyzed by RNA-seq. RESULTS: At baseline, we detected 6745 and 4859 differentially expressed genes between lesional and nonlesional skin versus normal, respectively, whereas 841 and 977 genes were differentially expressed after treatment, respectively (fold change >1.5 and false discovery rate <0.05). Tape-strips captured significant modulation with dupilumab in key AD immune (eg, C-C motif chemokine ligand 13 [CCL13], CCL17, CCL18) and barrier (eg, periplakin, FA2H) biomarkers. Changes in biomarkers (CCL20, interleukin 34, FABP7) were also significantly correlated with clinical disease improvements (Eczema Area and Severity Index; R > 0.5 or R < -0.4, P < 0.05). CONCLUSIONS: This real-life study represents the first comprehensive RNA-seq molecular profiling of tape-strips from moderate to severe AD patients after dupilumab therapy. Analysis of tape strip specimens detected significant gene expression changes in key AD biomarkers with dupilumab treatment, suggesting that this approach may be useful to monitor therapeutic responses in inflammatory skin diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".