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

Lookup NU author(s): Professor Nick Reynolds

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

© 2018 The Authors 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.


Publication metadata

Author(s): Foulkes AC, Watson DS, Carr DF, Kenny JG, Slidel T, Parslew R, Pirmohamed M, Anders S, Reynolds NJ, Griffiths CEM, Warren RB, Barnes MR

Publication type: Article

Publication status: Published

Journal: Journal of Investigative Dermatology

Year: 2018

Volume: 139

Issue: 1

Pages: 100-107

Print publication date: 01/01/2019

Online publication date: 17/07/2018

Acceptance date: 02/04/2018

Date deposited: 20/02/2019

ISSN (print): 0022-202X

ISSN (electronic): 1523-1747

Publisher: Elsevier

URL: https://doi.org/10.1016/j.jid.2018.04.041

DOI: 10.1016/j.jid.2018.04.041


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