J. Piñon Hofbauer

Background While most cutaneous squamous cell carcinomas (cSCCs) are treatable, certain high‐risk cSCCs, such as those in recessive dystrophic epidermolysis bullosa (RDEB) patients, are particularly aggressive. Owing to repeated wounding, inflammation and unproductive healing, RDEB patients have a 68% cumulative risk of developing life‐threatening cSCCs by the age of 35, and a 70% risk of death by the age of 45. Despite aggressive treatment, cSCC represents the leading cause of premature mortality in these patients, highlighting an unmet clinical need. Increasing evidence points to a role of altered metabolism in the initiation and maintenance of cSCC, making metabolism a potential therapeutic target. Objectives We sought to determine the feasibility of targeting tumour cell energetics as a strategy to selectively hinder the growth advantage of aggressive cSCC. Methods We evaluated the cell energetics profiles of RDEB‐SCC cells by analysing available gene expression data against multiple gene signatures and single‐gene targets linked to metabolic reprogramming. Additionally, we employed real‐time metabolic profiling to measure glycolysis and respiration in these cells. Furthermore, we investigated the anti‐neoplastic properties of the metformin against human and murine high‐risk cSCCs in vitro and in vivo. Results Gene expression analyses highlighted a divergence in cell energetics profiles between RDEB‐SCC and non‐malignant RDEB keratinocytes, with tumour cells demonstrating enhanced respiration and glycolysis scores. Real‐time metabolic profiling supported these data and additionally highlighted a metabolic plasticity of RDEB‐SCC cells. Against this background, metformin exerted an anti‐neoplastic potential by hampering both respiration and glycolysis, and by inhibiting proliferation in vitro. Metformin treatment in an analogous model of fast‐growing murine cSCC resulted in delayed tumour onset and slower tumour growth, translating to a 29% increase in median overall survival. Conclusions Our data indicate that metformin exerts anti‐neoplastic properties in aggressive cSCCs that exhibit high‐risk features by interfering with respiration and glycolytic processes.

Machine learning has been proven to be a powerful tool in the identification of diagnostic tumor biomarkers but is often impeded in rare cancers due to small patient numbers. In patients suffering from recessive dystrophic epidermolysis bullosa (RDEB), early-in-life development of particularly aggressive cutaneous squamous-cell carcinomas (cSCCs) represents a major threat and timely detection is crucial to facilitate prompt tumor excision. As miRNAs have been shown to hold great potential as liquid biopsy markers, we characterized miRNA signatures derived from cultured primary cells specific for the potential detection of tumors in RDEB patients. To address the limitation in RDEB-sample accessibility, we analyzed the similarity of RDEB miRNA profiles with other tumor entities derived from the Cancer Genome Atlas (TCGA) repository. Due to the similarity in miRNA expression with RDEB-SCC, we used HN-SCC data to train a tumor prediction model. Three models with varying complexity using 33, 10 and 3 miRNAs were derived from the elastic net logistic regression model. The predictive performance of all three models was determined on an independent HN-SCC test dataset (AUC-ROC: 100%, 83% and 96%), as well as on cell-based RDEB miRNA-Seq data (AUC-ROC: 100%, 100% and 91%). In addition, the ability of the models to predict tumor samples based on RDEB exosomes (AUC-ROC: 100%, 93% and 100%) demonstrated the potential feasibility in a clinical setting. Our results support the feasibility of this approach to identify a diagnostic miRNA signature, by exploiting publicly available data and will lay the base for an improvement of early RDEB-SCC detection.