Classifying chronic inflammatory skin diseases by T1-, T2-, T3-, and T4 cytokine signatures for selecting anti-cytokine therapies and restoring healthy skin

Prinicipal Investigator

Scientific interest within the context of the graduate college:

Chronic inflammatory skin diseases with high prevalence such as psoriasis, atopic dermatitis, or lichen planus exemplify how failure of tissue-resident immune regulation can lead to distinct immune response patterns. These skin diseases are manifestations of specific cytokine-driven inflammatory endotypes, commonly referred to as Type 1 (T1; e.g. lichen planus), Type 2 (T2; e.g. atopic dermatitis), Type 3 (T3; e.g. psoriasis), and Type 4 (T4) immune diseases. Type 4 inflammation includes fibrosing (T4a) and granulomatous (T4b) patterns that are relevant for diseases such as morphea and cutaneous sarcoidosis. Understanding the cytokine signaling signatures underlying these diseases offers not only diagnostic precision but also enables rational therapeutic selection. This project integrates cytokine profiling into the framework of skin health and resilience, supporting the overarching goal of the graduate college to define molecular pathways of health, early immune deviation, and preventive intervention.

Project description:

Inflammatory skin diseases are increasingly being classified according to immunological patterns. The classification into T1 (IFN-γ, IL-2, IL-12, TNF), T2 (IL-4, IL-5, IL-13, IL-31), T3 (IL-17A/F, IL-23, IL-1, IL-36), and T4 inflammation (including fibrosing T4a and granulomatous T4b patterns, driven by TGF-β, IL-10, IL-18, and IFN-γ) enables a structured understanding of disease heterogeneity and guides targeted therapy. Currently, more than 25 different anti-cytokine therapeutics are approved for the treatment of immune diseases. Nevertheless, the selection of the bet targeted therapy is indication-based without taking the actual immune signature into account. In this project, we aim to develop and validate a method for characterizing cytokine signatures in inflammatory skin diseases compared to healthy skin. Our goal is not only to improve disease classification but also to enable individualized therapeutic decision-making based on targetable cytokine pathways, thereby supporting the restoration of immune homeostasis. This includes assessing whether treatment-induced cytokine normalization corresponds with clinical remission and molecular markers of tissue recovery. To this end, we will establish a diagnostic framework based on cytokine profiles from retrospective FFPE samples and prospective patient cohorts. The ultimate goal is to identify actionable immune patterns for patient stratification and to better define molecular transitions from health to disease. This approach may help to prevent flares of cytokine-driven skin diseases.

Aim 1: Retrospective cytokine profiling in FFPE skin tissue samples. Patients with newly diagnosed or actively treated chronic inflammatory skin diseases will be recruited for the study. To monitor dynamic changes in tissue inflammation, skin biopsy samples will be obtained at baseline and at defined time points during the course of therapy. In parallel, serum samples will be subjected to multiplex cytokine analysis using high-throughput platforms such as Olink or Luminex to capture systemic immune signatures. The resulting cytokine profiles will be correlated with histological findings and clinical parameters to identify meaningful associations between immune activation patterns, disease characteristics, and treatment response. Based on these data, a cytokine-based scoring index will be developed to classify inflammation types and to support the prediction of therapeutic outcomes.

Aim 2: Prospective validation in serum and fresh skin biopsies. Patients with newly diagnosed or actively treated chronic inflammatory skin diseases will be recruited for the study. Skin biopsy samples will be collected prior to therapy initiation and at defined time points during treatment to capture dynamic changes in tissue inflammation. In parallel, serum samples will be analyzed using multiplex cytokine profiling platforms such as Olink or Luminex to assess systemic immune signatures. The resulting cytokine profiles will be correlated with histological findings and clinical parameters to identify markers associated with disease activity and therapeutic response. Based on these data, a cytokine-based scoring index will be derived to classify inflammation types and support the prediction of treatment outcomes.

Application details

References

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