Blockade of AXL activation overcomes acquired resistance to EGFR tyrosine kinase inhibition in non-small cell lung cancer
Background: Although epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have significantly improved outcomes in patients with advanced non-small cell lung cancer (NSCLC) harboring EGFR-activating mutations, resistance to these therapies almost inevitably develops. Recent studies, including our own, have identified AXL activation as a key mechanism driving both intrinsic and acquired resistance to EGFR TKIs, functioning as a bypass pathway similar to MET amplification. This study investigates how targeted AXL inhibition may help overcome EGFR TKI resistance in NSCLC.
Methods: EGFR TKI–resistant NSCLC cell lines were treated with AXL inhibitors—MGCD265 (glesatinib), MGCD516 (sitravatinib), and R428 (BGB-324)—either alone or in combination with erlotinib. Effects on cell proliferation, cell cycle progression, and apoptosis were evaluated. RNA sequencing was performed to assess changes in gene expression, and follow-up analyses explored alterations in cellular pathways, particularly those related to migration and epithelial-mesenchymal transition (EMT).
Results: Co-treatment with AXL inhibitors and erlotinib markedly suppressed the growth of erlotinib-resistant NSCLC cells, primarily by inducing G2/M phase cell cycle arrest and promoting apoptosis, outperforming monotherapies. Transcriptomic analysis revealed upregulation of genes associated with apoptosis and survival inhibition, while genes involved in DNA replication, cell cycle regulation, and repair were downregulated. These molecular changes corresponded with impaired cell migration and a reversal of EMT characteristics following combination therapy.
Conclusion: Targeting the AXL signaling axis in combination with EGFR inhibition offers a promising approach to overcome resistance in EGFR-mutant NSCLC. These findings support further investigation of AXL inhibitors as part of a precision medicine strategy for biomarker-defined patient subsets.