Preclinical Study of a Biparatopic METxMET Antibody–Drug Conjugate, REGN5093-M114, Overcomes MET-driven Acquired Resistance to EGFR TKIs in EGFR-mutant NSCLC

Seung Yeon Oh, You Won Lee, Eun Ji Lee, Jae Hwan Kim, Young Joon Park, Seong Gu Heo, Mi Ra Yu, Min Hee Hong, John DaSilva, Christopher Daly, Byoung Chul Cho, Sun Min Lim, Mi Ran Yun

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

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. Experimental Design: Patient-derived organoids, patient-derived cells, or ATCC cell lines were used to investigate the in vitro/in vivo activity of REGN5093-M114. Results: REGN5093-M114 exhibited significant antitumor efficacy compared with MET TKI or unconjugated METxMET biparatopic antibody (REGN5093). Regardless of MET gene copy number, MET-overexpressed TKI-naïve EGFR-mutant NSCLC cells responded to REGN5093-M114 treatment. Cell surface MET expression had the most predictive power in determining the efficacy of REGN5093-M114. REGN5093-M114 potently reduced tumor growth of EGFR-mutant NSCLC with PTEN loss or MET Y1230C mutation after progression on prior osimertinib and savolitinib treatment. Conclusions: Altogether, REGN5093-M114 is a promising candidate to overcome the challenges facing functional MET pathway blockade.

Original languageEnglish
Pages (from-to)221-232
Number of pages12
JournalClinical Cancer Research
Volume29
Issue number1
DOIs
Publication statusPublished - 2023 Jan 1

Bibliographical note

Publisher Copyright:
©2022 American Association for Cancer Research.

All Science Journal Classification (ASJC) codes

  • General Medicine

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