Abstract 3015: EGFR x MET tsAb: Differentiated MET biparatopic design with optimal MET inhibitory activity to pursue best-in-class opportunity
Abstract Acquired resistant mutation after small molecule inhibitor treatment in non-small cell lung cancer (NSCLC) develops invariably through mutations in EGFR or through activation of compensatory pathways such as MET. BG-T187 is a tri-specific antibody (TsAb) targeting EGFR and MET with a differentiated MET biparatopic designation designed to treat tumors driven by activated EGFR and/or MET signaling. Stronger antiproliferative effects and MET signaling inhibition by BG-T187 compared to traditional EGFR/MET bi-specific antibody were observed in vitro. Additionally, potent in vivo anti-tumor activity was observed upon BG-T187 treatment of human tumor xenograft models driven by EGFR activation mutation and/or MET amplification. Interestingly, stronger anti-tumor activity of BG-T187 than that of traditional EGFR/MET bi-specific antibody was observed in xenograft models with MET amplification. Through a comprehensive assessment of EGFR on-target toxicity risk, we demonstrated that BG-T187 show better HEKn selectivity than EGFR/MET bi-specific antibody, indicating potential lower on-target toxicity risk of BG-T187. Collectively, our findings represent a novel EGFR/MET tri-specific antibody with differentiated MET biparatopic and pursue best-in-class potential. Citation Format: Yanzhou Zhang, Ming Jiang, Hongyu Chen, Mengran Qian, Renke Li, Huang Yan, Huan Li, Xiaona Sun, Sujuan Huang, Xuechun Zhang, Ming Fang, Yue Wu, Fan Wang, Mingchao Kang, Xiaomin Song, Xi Wu. EGFR x MET tsAb: Differentiated MET biparatopic design with optimal MET inhibitory activity to pursue best-in-class opportunity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3015.
Authors
- Ming Fang (ORCID: https://orcid.org/0000-0002-7239-7604)
- Xiaomin Song
- Mingchao Kang (ORCID: https://orcid.org/0000-0002-1838-5960)
- Ren‐Ke Li (ORCID: https://orcid.org/0000-0002-2584-060X)
- X. N. Sun (ORCID: https://orcid.org/0000-0002-6179-3556)
- Xuechun Zhang
- Sujuan Huang (ORCID: https://orcid.org/0000-0002-3791-3052)
- Min Qian (ORCID: https://orcid.org/0000-0003-1214-8830)
- Fan Wang (ORCID: https://orcid.org/0000-0003-0677-6515)
- Huan Li (ORCID: https://orcid.org/0009-0002-3838-0267)
- Yanzhou Zhang (ORCID: https://orcid.org/0000-0003-3952-1762)
- Yan Huang (ORCID: https://orcid.org/0000-0002-1045-4044)
- Ming Jiang (ORCID: https://orcid.org/0000-0003-0047-9077)
- Yue Wu (ORCID: https://orcid.org/0000-0002-5832-0037)
- Xi Wu (ORCID: https://orcid.org/0009-0004-5302-1371)
- Hongyu Chen (ORCID: https://orcid.org/0000-0003-4888-0441)
Institutions
- Shanghai CASB Biotechnology (China) (CN)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-3015
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36