Importance: The ability to predict the efficacy of systemic psoriasis therapy based on immune profiles in skin biopsies could reduce the use of inappropriate treatment and its associated costs and adverse events. It could considerably decrease drug development trial costs as well.
Objective: To develop a bioinformatic gene signature score derived from skin mRNA to predict psoriasis treatment outcomes for a variety of therapies.
Design, setting, and participants: In this decision analytical model using 1145 skin samples from different cohorts of 12 retrospective psoriasis studies, samples were analyzed using the CIBERSORT algorithm to define the immune landscape of psoriasis lesions and controls. Random forest classification and principal component analysis algorithms were used to estimate psoriatic microenvironment (PME) signature genes and construct a PME score. Overall, 85 and 421 psoriasis lesions from 1 and 4 independent cohorts were used as discovery and validation studies, respectively. Among them, 157, 71, 89, and 90 psoriasis lesions were treated with etanercept, tofacitinib, adalimumab, and methotrexate, respectively.
Main outcomes and measures: Number of weeks after treatment initiation when responders and nonresponders could be predicted.
Results: Overall, 22 immune cell subtypes formed infiltration patterns that differentiated psoriasis lesions from healthy skin. In psoriasis lesions, the expression of 33 PME signature genes defined 2 immune phenotypes and in aggregate could be simplified to a numerical PME score. A high PME score, characterized by keratinocyte differentiation, correlated with a better treatment response (Psoriasis Area and Severity Index [PASI] reduction, 75.8%; 95% CI, 69.4% to 82.2%; P = .03), whereas a low PME score exhibited an immune activation signature and was associated with a worse response (PASI reduction, 53.5%; 95% CI, 45.3% to 61.7%; P = .03). The PME score at week 4 after treatment initiation correlated with future responder vs nonresponder to treatment status 8 to 12 weeks earlier than PASI reduction for etanercept, methotrexate plus adalimumab, and tofacitinib.
Conclusions and relevance: The PME score is a biometric score that may predict clinical efficacy of systemic psoriasis therapy in advance of clinical responses. As an application of personalized medicine, it may reduce the exposure of patients with psoriasis to ineffective and expensive therapies.