Targeting the epidermal growth factor receptor using IgM antibodies: toward next generation cancer immunotherapy

Targeting the epidermal growth factor receptor using IgM antibodies: toward next generation cancer immunotherapy

Frontiers in Immunology

10.3389/fimmu.2025.1733907

https://doi.org/10.3389/fimmu.2025.1733907

Kamolrat Somboon, Peter J. Bond, Firdaus Samsudin

Antibody structure, monoclonal antibodies

09 January 2026

Somboon, K., Bond, P. J., & Samsudin, F. (2026). Targeting the epidermal growth factor receptor using IgM antibodies: toward next generation cancer immunotherapy. Frontiers in Immunology, 16. https://doi.org/10.3389/fimmu.2025.1733907

Immunoglobulin G (IgG) monoclonal antibodies dominate current cancer immunotherapy but face challenges including resistance development, limited tumor penetration, and suboptimal avidity. In contrast, the pentameric or hexameric architecture of immunoglobulin M (IgM) offers up to twelve antigen-binding sites and potent complement activation, positioning IgM as a promising next-generation therapeutic scaffold. Here, we present integrative structural modeling and multiscale molecular dynamics simulations of IgM versions of Cetuximab and Matuzumab targeting the epidermal growth factor receptor (EGFR), a clinically validated oncogenic driver. Our analyses reveal that IgM antibodies maintain a rigid, glycan-stabilized Fc core while their Fab domains exhibit high mobility, enabling multivalent EGFR binding. Compared with IgG, IgM antibodies demonstrated enhanced binding avidity, prolonged receptor engagement, and slower dissociation kinetics. These properties suggest superior therapeutic durability and potential to overcome current limitations of IgG-based therapies. By providing mechanistic insight into how IgM isotypes can improve therapeutic engagement with tumor-associated antigens, our study supports the development of IgM antibodies as a new class of cancer immunotherapies.

Attribution 4.0 International (CC BY 4.0)

This research / project is supported by the A*STAR - Advanced Manufacturing and Engineering (AME) Young Individual Research Grants
Grant Reference no. : A2084c0159

This research is supported by core funding from: A*STAR Bioinformatics Institute (BII)
Grant Reference no. :

©2026 Somboon, Bond and Samsudin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. This paper was first published by Frontiers Media at https://doi.org/10.3389/fimmu.2025.1733907

https://oar.a-star.edu.sg/communities-collections/articles/23307

1664-3224

Bioinformatics Institute