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Cytokine responses in nonlesional psoriatic skin as clinical predictor to anti-TNF agents

Published:August 02, 2021DOI:https://doi.org/10.1016/j.jaci.2021.07.024

      Background

      A major issue with the current management of psoriasis is our inability to predict treatment response.

      Objective

      Our aim was to evaluate the ability to use baseline molecular expression profiling to assess treatment outcome for patients with psoriasis.

      Methods

      We conducted a longitudinal study of 46 patients with chronic plaque psoriasis treated with anti-TNF agent etanercept, and molecular profiles were assessed in more than 200 RNA-seq samples.

      Results

      We demonstrated correlation between clinical response and molecular changes during the course of the treatment, particularly for genes responding to IL-17A/TNF in keratinocytes. Intriguingly, baseline gene expressions in nonlesional, but not lesional, skin were the best marker of treatment response at week 12. We identified USP18, a known regulator of IFN responses, as positively correlated with Psoriasis Area and Severity Index (PASI) improvement (P = 9.8 × 10−4) and demonstrate its role in regulating IFN/TNF responses in keratinocytes. Consistently, cytokine gene signatures enriched in baseline nonlesional skin expression profiles had strong correlations with PASI improvement. Using this information, we developed a statistical model for predicting PASI75 (ie, 75% of PASI improvement) at week 12, achieving area under the receiver-operating characteristic curve value of 0.75 and up to 80% accurate PASI75 prediction among the top predicted responders.

      Conclusions

      Our results illustrate feasibility of assessing drug response in psoriasis using nonlesional skin and implicate involvement of IFN regulators in anti-TNF responses.

      Key words

      Abbreviations used:

      BSA (Body surface area), FDR (False-discovery rate), PASI (Psoriasis Area and Severity Index), PASI75 (≥75% improvement in Psoriasis Area and Severity Index), sPGA (Static Physician’s Global Assessment)
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