Probabilistic network modeling identifies RBPJ as a driver of stemness in Merkel cell carcinoma

Merkel cell carcinoma (MCC) is a poorly differentiated neuroendocrine carcinoma with limited treatment options, primarily immunotherapy, to which only ∼50% of patients respond. Lineage plasticity drives its poorly differentiated phenotype, in turn promoting tumor aggressiveness and treatment resistance. Targeting the mechanisms underlying lineage plasticity could help induce differentiation, reduce proliferation, and potentially sensitize the tumor to existing therapies, yet such strategies are underdeveloped in MCC. Here, we integrate scRNA-seq and bulk ATAC-seq data to generate Boolean networks, simulate their dynamics, and predict a key regulator of differentiation, which we validated in vitro . Using CytoTRACE2 across two independent datasets, we revealed the existence of tumor subpopulations with distinct developmental potency states. We then constructed and refined transcription factor regulatory networks using BooleaBayes and expanded them with ATAC-seq inferred regulatory interactions. Across multiple network constructions, simulations of single-gene perturbations consistently identified the Notch effector RBPJ as the key regulator predicted to shift MCC cells toward a more differentiated state. Experimental knockdown of RBPJ in an MCC cell line altered expression of differentiation-associated genes, reduced the expression of MCC markers, and drastically reduced cell growth. These findings identify RBPJ as a regulator of MCC lineage plasticity and candidate for targeted treatment, while highlighting the utility of probabilistic network modeling for prioritizing therapeutic targets in translational cancer research.