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| http://dx.doi.org/10.21037/tlcr-24-144 | DOI Listing |
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Anti-MET Antibody Therapies in Non-Small-Cell Lung Cancer: Current Progress and Future Directions.
Antibodies (Basel)
October 2024
Department of Medicine, Division of Medical Oncology, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA.
Non-small-cell lung cancer (NSCLC) remains a leading cause of cancer mortality globally, though advances in targeted therapies have improved treatment outcomes. The mesenchymal-epithelial transition () gene plays a significant role in NSCLC, often through protein overexpression, exon 14 skipping mutations, and gene amplification, many of which arise as resistance mechanisms to other oncogenic drivers like epidermal growth factor receptor () mutations. This review examines the development and clinical efficacy of anti-MET antibody therapies.
View Article and Find Full Text PDFREGN5093-M114: can an antibody-drug conjugate overcome the challenge of resistance to epidermal growth factor receptor and mesenchymal epithelial transition tyrosine kinase inhibitors in non-small cell lung cancer?
Transl Lung Cancer Res
August 2024
Service de Biopathologie, Institut de Cancérologie de Lorraine, Vandoeuvre-les-Nancy, France.
Preclinical Study of a Biparatopic METxMET Antibody-Drug Conjugate, REGN5093-M114, Overcomes MET-driven Acquired Resistance to EGFR TKIs in EGFR-mutant NSCLC.
Clin Cancer Res
January 2023
Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of South Korea.
Purpose: MET amplification is a frequent mechanism of resistance to EGFR tyrosine kinase inhibitors (TKI) in patients with EGFR-mutated non-small cell lung cancer (NSCLC), and combined treatment with EGFR TKIs and MET TKIs has been explored as a strategy to overcome resistance. However, durable response is invariably limited by the emergence of acquired resistance. Here, we investigated the preclinical activity of REGN5093-M114, a novel antibody-drug conjugate targeting MET in MET-driven patient-derived models.
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