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

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 (PSORT) study, we evaluated a comprehensive array of omics platforms across three time-points and multiple tissues in a pilot investigation of ten severe psoriasis patient, treated with the tumor necrosis factor (TNF) inhibitor, etanercept. We used RNA-sequencing to analyse mRNA and small-RNA transcriptome in blood, lesional and non-lesional 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 signalling, psoriasis pathology and the MHC region. Notably, 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 modelling, we demonstrate 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 demonstrate as powered for biomarker discovery and patient stratification.