Modeling Heterogeneity of Triple-Negative Breast Cancer Uncovers a Novel Combinatorial Treatment Overcoming Primary Drug Resistance

Abstract

AbstractTriple-negative breast cancer (TNBC) is a highly aggressive breast cancer subtype characterized by a remarkable molecular heterogeneity. Currently, there are no effective druggable targets and advanced preclinical models of the human disease. Here, we generated a unique mouse model (MMTV-R26Metmice) of mammary tumors driven by a subtle increase in the expression of the wild-type MET receptor.MMTV-R26Metmice develop spontaneous, exclusive TNBC tumors, recapitulating primary resistance to treatment of patients. Proteomic profiling ofMMTV-R26Mettumors and machine learning approach showed that the model faithfully recapitulates inter-tumoral heterogeneity of human TNBC. Further signaling network analysis highlighted potential druggable targets, of which co-targeting of WEE1 and BCL-XL synergistically killed TNBC cells and efficiently induced tumor regression. Mechanistically, BCL-XL inhibition exacerbates the dependency of TNBC cells on WEE1 function, leading to Histone H3 and phosphoS33RPA32 upregulation, RRM2 downregulation, cell cycle perturbation, mitotic catastrophe and apoptosis. Our study introduces a unique, powerful mouse model for studying TNBC formation and evolution, its heterogeneity, and for identifying efficient therapeutic targets.