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J Invest Dermatol. 2018 Jul 17. pii: S0022-202X(18)32355-8. doi: 10.1016/j.jid.2018.04.041. [Epub ahead of print]

A Framework for Multi-Omic Prediction of Treatment Response to Biologic Therapy for Psoriasis.

Author information

1
The Dermatology Centre, Salford Royal NHS Foundation Trust, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK. Electronic address: Amy.foulkes@manchester.ac.uk.
2
Centre for Translational Bioinformatics, William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, UK.
3
Wolfson Centre for Personalised Medicine, The University of Liverpool, Liverpool, UK.
4
Centre for Genomic Research, The University of Liverpool, Liverpool, UK.
5
MedImmune Ltd, Sir Aaron Klug Building, Granta Park Cambridge, UK.
6
Dermatology Department, Kent Lodge, Broadgreen Hospital, Liverpool, UK.
7
Centre for Molecular Biology of the University of Heidelberg (ZMBH), Heidelberg, Germany.
8
Institute of Cellular Medicine, Newcastle University and Department of Dermatology, Royal Victoria Infirmary, Newcastle upon Tyne, UK.
9
The Dermatology Centre, Salford Royal NHS Foundation Trust, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.

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.

PMID:
30030151
DOI:
10.1016/j.jid.2018.04.041

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