Association of the Psoriatic Microenvironment With Treatment Response

Abstract

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 PARTICIPANTSIn 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. RESULTSOverall, 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 RELEVANCEThe 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.

Immune Cell Infiltration Analysis Demonstrates Excessive Mast Cell Activation in Psoriasis

“…Distinct expression profiles of different cell type markers determine the infiltration of the immune cells of a particular environment ( 29 , 30 ). Thus, we intended to investigate the dynamic expression of 547 marker genes specific in different immune cell types ( Supplementary Table S4 ).…”

“…Gary R. et al recently showed that Th17 cells (expressing IL17A, IL17F, IFNG, IL22, and IL26) and type 2 macrophage (M2) are significantly enriched in lesional skin of psoriasis (7); Gaofeng W. et al showed that Psoriatic Microenvironment (PME) score could predict the treatment response of psoriasis patients (29), these studies provide insights into the pathogenic research of psoriasis. However, a systematical investigation of immune cell niche alternations in psoriatic skin requires further investigations.…”

Single-cell immune ecosystem and metabolism reprogramming imprinted by psoriasis niche

“…The immune microenvironment of psoriasis is unique compared to the normal skins. For example, a retrospective study composed of 1,145 skin samples showed that psoriatic microenvironment can be clustered into two distinct immunophenotypes ( 4 ). Such immunophenotype can effectively predict the drug responsive patterns and tightly associate with clinical outcomes.…”

“…Recent high-throughput single-cell RNA-sequencing (scRNA-seq) data showed that T helper 17 (Th17) cells displayed distinct transcriptome profiles in the psoriasis niche ( 3 ). Further computational evidence showed that the immune microenvironment of psoriasis was strongly associated with the skin phenotypes and clinical outcomes ( 4 ). All those results highlight the fundamental role of the immune microenvironment in controlling the fate of psoriasis initiation and progression.…”

Commensal microbiome promotes hair follicle regeneration by inducing keratinocyte HIF-1α signaling and glutamine metabolism

“…To calculate the metabolism score, we identified genes of interest in each metabolism pathway from the Kyoto Encyclopedia of Genes and Genomes (KEGG), GSEA-Hallmark, or GO databases and then used the RMA normalization method to convert gene expression of each identified gene to a z -score to obtain the relative expression of the two groups of samples as previously described ( 54 ). The principal components analysis (PCA) algorithm assigns different weighted coefficients according to the contribution of different genes in the metabolism pathway and finally yields the metabolism score by adding the z -score of each single gene in the pathway with its paired coefficient.…”