Lam C Tsoi1, Matthew T Patrick2, Shao Shuai3, Mrinal K Sarkar2, Sunyi Chi4, Bethany Ruffino2, Allison C Billi2, Xianying Xing2, Ranjitha Uppala2, Cheng Zang2, Joseph Fullmer2, Zhi He5, Emanual Maverakis6, Nehal N Mehta7, Bethany E Perez White8, Spiro Getsios8, Yolanda Helfrich2, John J Voorhees2, J Michelle Kahlenberg9, Stephan Weidinger10, Johann E Gudjonsson11. 1. Department of Dermatology, University of Michigan Medical School, Ann Arbor, Mich; Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Mich; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Mich. Electronic address: alextso@umich.edu. 2. Department of Dermatology, University of Michigan Medical School, Ann Arbor, Mich. 3. Department of Dermatology, University of Michigan Medical School, Ann Arbor, Mich; Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shannxi, China. 4. Department of Dermatology, University of Michigan Medical School, Ann Arbor, Mich; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Mich. 5. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Mich. 6. Department of Dermatology, School of Medicine, UC-Davis Medical Center, Sacramento, Calif. 7. Section of Inflammation and Cardiometabolic Diseases, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Md. 8. Department of Dermatology, Northwestern University, Chicago, Ill. 9. Division of Rheumatology, Department of Internal Medicine, University of Michigan, Ann Arbor, Mich. 10. Department of Dermatology and Allergy, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany. 11. Department of Dermatology, University of Michigan Medical School, Ann Arbor, Mich. Electronic address: johanng@med.umich.edu.
Abstract
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.
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.
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