Modeling Heterogeneity of Triple‐Negative Breast Cancer Uncovers a Novel Combinatorial Treatment Overcoming Primary Drug Resistance
Fabienne Lamballe, Fahmida Ahmad, Yaron Vinik, Olivier Castellanet, Fabrice Daian, Anna‐Katharina Müller, Ulrike A. Köhler, A.L. Bailly, Emmanuelle Josselin, Rémy Castellano, Christelle Cayrou, Emmanuelle Charafe‐Jauffret, Gordon B. Mills, Vincent Géli, Jean‐Paul Borg, Sima Lev, Flavio Maina
Abstract
Abstract Triple‐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, a unique mouse model ( MMTV‐R26 Met mice) of mammary tumors driven by a subtle increase in the expression of the wild‐type MET receptor is generated. MMTV‐R26 Met mice develop spontaneous, exclusive TNBC tumors, recapitulating primary resistance to treatment of patients. Proteomic profiling of MMTV‐R26 Met tumors and machine learning approach show that the model faithfully recapitulates intertumoral heterogeneity of human TNBC. Further signaling network analysis highlights potential druggable targets, of which cotargeting of WEE1 and BCL‐XL synergistically kills TNBC cells and efficiently induces tumor regression. Mechanistically, BCL‐XL inhibition exacerbates the dependency of TNBC cells on WEE1 function, leading to Histone H3 and phosphoS 33 RPA32 upregulation, RRM2 downregulation, cell cycle perturbation, mitotic catastrophe, and apoptosis. This study introduces a unique, powerful mouse model for studying TNBC formation and evolution, its heterogeneity, and for identifying efficient therapeutic targets.