The prediction of treatment outcome in NSCLC patients harboring an EGFR exon 20 mutation using molecular modeling

F Zwierenga; L Zhang; J Melcr; E Schuuring; B A M H van Veggel; A J de Langen; H J M Groen; M R Groves; A J van der Wekken

doi:10.1016/j.lungcan.2024.107973

The prediction of treatment outcome in NSCLC patients harboring an EGFR exon 20 mutation using molecular modeling

Lung Cancer. 2024 Nov:197:107973. doi: 10.1016/j.lungcan.2024.107973. Epub 2024 Sep 30.

Authors

F Zwierenga¹, L Zhang², J Melcr³, E Schuuring⁴, B A M H van Veggel⁵, A J de Langen⁵, H J M Groen⁶, M R Groves², A J van der Wekken⁶

Affiliations

¹ Department of Pulmonary Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. Electronic address: f.zwierenga@umcg.nl.
² Structural Biology in Drug Design, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, the Netherlands.
³ Protyon B.V., Groningen, the Netherlands.
⁴ Department of Pathology and Molecular Biology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
⁵ Department of Thoracic Oncology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands.
⁶ Department of Pulmonary Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

PMID: 39374568
DOI: 10.1016/j.lungcan.2024.107973

Abstract

Introduction: The structural effect of uncommon heterogenous in-frame deletion and/or insertion mutations within exon 20 (EGFRex20+) in relation to therapy response is poorly understood. This study aims to elucidate the structural alterations caused by EGFRex20+ mutations and correlate these changes with patient responses.

Material and method: We selected EGFRex20+ mutations from advanced NSCLC patients in the Position20 and AFACET studies for computational analysis. Homology models representing both inactive and active conformations of these mutations were generated using the Swiss-Model server. Molecular docking studies with EGFR-TKIs was conducted using smina, followed by Molecular Dynamic (MD) simulations performed with GROMACS. These computational findings were compared with clinical outcomes to evaluate their potential in predicting patient response.

Results: Our docking studies of 29 EGFRex20+ mutations revealed that the binding energies of afatinib, osimertinib, zipalertinib, and sunvozertinib, compared to the wild type, do not significantly impact either TKI's efficacy. MD simulations for eight EGFRex20+ mutations (A763_Y764insFQEA, A767_V769dup, S768_D770dup, D770_N771insG, D770_P772dup, N771_H773dup, H773_V774insY and H773_V774delinsLM) revealed varying degrees of instability. For six variants, predicted activation based on the αC-helix stability and orientation, as well as TKI sensitivity, aligned well with clinical observations from the Position20 and AFACET studies. Two mutations (D770_N771insG and N771_H773dup) predicted as poor to moderate responders, showed minimal activation of the αC-helix region, warranting further investigation.

Conclusion: In conclusion, MD simulations can effectively predict patient outcomes by connecting computational results with clinical data and advancing our understanding of EGFR mutations and their therapeutic responses.

Keywords: EGFR; Exon 20 mutation; Molecular modeling; NSCLC; TKIs.

MeSH terms

Carcinoma, Non-Small-Cell Lung* / drug therapy
Carcinoma, Non-Small-Cell Lung* / genetics
Carcinoma, Non-Small-Cell Lung* / pathology
ErbB Receptors* / genetics
Exons* / genetics
Humans
Lung Neoplasms* / drug therapy
Lung Neoplasms* / genetics
Lung Neoplasms* / pathology
Models, Molecular
Molecular Docking Simulation
Molecular Dynamics Simulation*
Mutation*
Prognosis
Protein Kinase Inhibitors* / therapeutic use
Treatment Outcome

Substances

ErbB Receptors
EGFR protein, human
Protein Kinase Inhibitors