The psoriatic microenvironment (PME) score presents a novel bioinformatic analysis of the psoriatic microenvironment, which can be used to predict the response of psoriasis to a new environment. The objective of this study was to develop a bioinformatic gene signature score to predict psoriasis treatment outcomes for different types of therapies. 

This decision analysis model included 1,145 skin samples from cohorts of 12 retrospective psoriasis studies. The samples were analyzed to define the immune landscape of psoriasis lesions and controls. The primary outcome of the study was the number of weeks after treatment initiation when responders and nonresponders could be predicted.

The findings suggested 22 immune cell subtypes that formed infiltration patterns that differentiated psoriasis lesions from healthy skin. In psoriasis lesions, a total of 33 PME signature genes defined 2 immune phenotypes, which in aggregate, could be simplified into a PME score. A high PME score was associated with better treatment response, and a low PME score indicated immune activation signature with worse treatment response.

The research concluded that the PME score could be used as a biometric score to predict the clinical efficacy of psoriasis therapies prior to clinical response. It may also reduce the exposure of patients to ineffective psoriatic therapies.