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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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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.

Original languageEnglish
Article number107973
Number of pages8
JournalLung Cancer
Volume197
Early online date30-Sept-2024
DOIs
Publication statusPublished - Nov-2024

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