C-T.52: Multi-modal Transcriptomics Reveals Receiver–Effector Mismatch and Downstream Inflammatory Positioning of CSF2 in Psoriasis
CSF2 is consistently elevated in psoriatic lesions and supported by preclinical studies, yet anti-CSF2 therapy failed to produce meaningful clinical benefit in a phase II trial. To investigate this translational discrepancy, we developed an integrative multi-modal transcriptomic framework combining bulk discovery and validation cohorts (n=110 and n=90), single-cell, spatial, and longitudinal treatment data from psoriatic lesions.
CSF2 axis activity, defined by coordinated ligand–receptor expression scoring, marked a myeloid-enriched inflammatory lesion state characterised by antigen presentation and cytokine–chemokine signalling. Bayesian network structure learning positioned the CSF2 axis downstream of NF-κB-associated signalling with strong support (bnlearn strength=0.99), and this relationship was confirmed by structural equation modelling across independent cohorts (β=0.52 and 0.46, both p<0.001). At single-cell resolution, functional CSF2 receptor co-expression was concentrated in dendritic cell and monocyte populations and was essentially absent from keratinocytes, contrasting with IL-17 and TNF receptor accessibility in keratinocyte effectors and IL-23 receptor accessibility in pathogenic T-cell populations.
Spatial transcriptomics showed CSF2 co-receptor-high regions were spatially segregated from barrier-dominant epidermal zones and enriched within immune-associated tissue niches (Wilcoxon p=1.9×10â»âµ, 16/18 sections). Longitudinal anti-TNF data showed CSF2 axis scores declined with inflammatory resolution but did not clearly predict baseline treatment response.
Together, these findings support a model in which the limited therapeutic relevance of CSF2 in psoriasis may reflect its downstream inflammatory positioning and receiver–effector mismatch. More broadly, this integrative transcriptomic framework provides a generalisable strategy for evaluating cytokine target tractability beyond preclinical disease associations.
Co-authors: Christina Hillig, Stefanie Eyerich, Kilian Eyerich, Natalie Garzorz-Stark, Michael Menden, Martin Meinel
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