Machine learning applied to atopic dermatitis transcriptome reveals distinct therapy-dependent modification of the keratinocyte immunophenotype.

ABSTRACT

BACKGROUND: Atopic dermatitis (AD) arises from a complex interaction between an impaired epidermal barrier, environmental exposures, and the infiltration of T helper (Th)1/Th2/Th17/Th22 T cells. Transcriptomic analysis has advanced our understanding of gene expression in cells and tissues. However, molecular quantitation of cytokine transcripts does not predict the importance of a specific pathway in AD or cellular responses to different inflammatory stimuli. OBJECTIVES: To understand changes in keratinocyte transcriptomic programmes in human cutaneous disease during development of inflammation and in response to treatment. METHODS: We performed in silico deconvolution of the whole-skin transcriptome. Using co-expression clustering and machine-learning tools, we resolved the gene expression of bulk skin (seven datasets, n = 406 samples), firstly, into keratinocyte phenotypes identified by unsupervised clustering and, secondly, into 19 cutaneous cell signatures of purified populations from publicly available datasets. RESULTS: We identify three unique transcriptomic programmes in keratinocytes - KC1, KC2 and KC17 - characteristic of immune signalling from disease-associated Th cells. We cross-validate those signatures across different skin inflammatory conditions and disease stages and demonstrate that the keratinocyte response during treatment is therapy dependent. Broad-spectrum treatment with ciclosporin ameliorated the KC17 response in AD lesions to a nonlesional immunophenotype, without altering KC2. Conversely, the specific anti-Th2 therapy, dupilumab, reversed the KC2 immunophenotype. CONCLUSIONS: Our analysis of transcriptomic signatures in cutaneous disease biopsies reveals the effect of keratinocyte programming in skin inflammation and suggests that the perturbation of a single axis of immune signal alone may be insufficient to resolve keratinocyte immunophenotype abnormalities.