Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer

Abstract

Most primary Triple Negative Breast Cancers (TNBCs) show amplification of the Epidermal Growth Factor Receptor (EGFR) gene, leading to increased protein expression. However, unlike other EGFR-driven cancers, targeting this receptor in TNBC yields inconsistent therapeutic responses. To elucidate the underlying mechanisms of this variability, we employ cellular barcoding and single-cell transcriptomics to reconstruct the subclonal dynamics of EGFR-amplified TNBC cells in response to afatinib, a tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR. Integrated lineage tracing analysis revealed a rare pre-existing subpopulation of cells with distinct biological signature, including elevated expression levels of Insulin-Like Growth Factor Binding Protein 2 (IGFBP2). We show that IGFBP2 overexpression is sufficient to render TNBC cells tolerant to afatinib treatment by activating the compensatory insulin-like growth factor I receptor (IGF1-R) signalling pathway. Finally, based on reconstructed mechanisms of resistance, we employ deep learning techniques to predict the afatinib sensitivity of TNBC cells. Our strategy proved effective in reconstructing the complex signalling network driving EGFR-targeted therapy resistance, offering new insights for the development of individualized treatment strategies in TNBC.