A framework for multi-omic prediction of treatment response to biologic therapy for psoriasis

• Verfasst von: Foulkes, Amy [VerfasserIn]
• Verfasst von: Anders, Simon [VerfasserIn]
• Titel: A framework for multi-omic prediction of treatment response to biologic therapy for psoriasis
• Verf.angabe: Amy C. Foulkes, David S. Watson, Daniel F. Carr, John G. Kenny, Timothy Slidel, Richard Parslew, Munir Pirmohamed, The PSORT Consortium, Simon Anders, Nick J. Reynolds, Christopher E.M. Griffiths, Richard B. Warren and Michael R. Barnes
• Jahr: 2019
• Jahr des Originals: 2018
• Umfang: 8 S.
• Fussnoten: corrected proof published online 12 October 2018 ; Montagna Symposium on the Biology of Skin ; Gesehen am 22.07.2019
• Titel Quelle: Enthalten in: The journal of investigative dermatology
• Ort Quelle: Amsterdam : Elsevier, 1938
• Jahr Quelle: 2019
• Band/Heft Quelle: 139(2019), 1, Seite 100-107
• ISSN Quelle: 1523-1747
• DOI: doi:10.1016/j.jid.2018.04.041
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1669532542

Abstract

Biologic therapies have shown high efficacy in psoriasis, but individual response varies and is poorly understood. To inform biomarker discovery in the Psoriasis Stratification to Optimise Relevant Therapy (i.e., PSORT) study, we evaluated a comprehensive array of omics platforms across three time points and multiple tissues in a pilot investigation of 10 patients with severe psoriasis, treated with the tumor necrosis factor (TNF) inhibitor, etanercept. We used RNA sequencing to analyze mRNA and small RNA transcriptome in blood, lesional and nonlesional skin, and the SOMAscan platform to investigate the serum proteome. Using an integrative systems biology approach, we identified signals of treatment response in genes and pathways associated with TNF signaling, psoriasis pathology, and the major histocompatibility complex region. We found association between clinical response and TNF-regulated genes in blood and skin. Using a combination of differential expression testing, upstream regulator analysis, clustering techniques, and predictive modeling, we show that baseline samples are indicative of patient response to biologic therapies, including signals in blood, which have traditionally been considered unreliable for inference in dermatology. In conclusion, our pilot study provides both an analytical framework and empirical basis to estimate power for larger studies, specifically the ongoing PSORT study, which we show as powered for biomarker discovery and patient stratification.