BOOLEAN NETWORK MODELING OF LINEAGE PLASTICITY IN MERKEL CELL CARCINOMA

Abstract

Merkel Cell Carcinoma (MCC) is an aggressive neuroendocrine (NE) skin cancer frequently associated with Merkel Cell Polyomavirus (MCPyV) integration. Current treatments remain limited and fail to specifically target MCC's underlying regulatory mechanisms. Reversing the neuroendocrine differentiation characteristic of MCC could induce terminal differentiation or enhance sensitivity to existing therapies. Here, we employed computational modeling and single-cell RNA sequencing data from two independent datasets of MCPyV-positive MCC tumors. Analysis with CytoTRACE2 revealed distinct subpopulations with varying neuroendocrine differentiation states, characterized by differential expression of key transcription factors. We constructed a regulatory network of these transcription factors and using BooleaBayes, we identified five transcription factors (FOS, KLF4, ATOH1, RBPJ, and EGR1 ) as key regulators whose inhibition significantly shifts MCC cells toward a more differentiated and potentially more therapeutically responsive state. Future experimental work will validate these in silico predictions by targeting these transcription factors in MCC cell lines, aiming to uncover novel therapeutic targets in MCC.