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
{"title":"利用分子建模预测表皮生长因子受体外显子 20 基因突变的非小细胞肺癌患者的治疗效果。","authors":"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","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><div>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.</div></div><div><h3>Material and method</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":18129,"journal":{"name":"Lung Cancer","volume":"197 ","pages":"Article 107973"},"PeriodicalIF":4.5000,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The prediction of treatment outcome in NSCLC patients harboring an EGFR exon 20 mutation using molecular modeling\",\"authors\":\"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\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><div>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.</div></div><div><h3>Material and method</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>\",\"PeriodicalId\":18129,\"journal\":{\"name\":\"Lung Cancer\",\"volume\":\"197 \",\"pages\":\"Article 107973\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Lung Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169500224005075\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lung Cancer","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169500224005075","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
The prediction of treatment outcome in NSCLC patients harboring an EGFR exon 20 mutation using molecular modeling
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.
期刊介绍:
Lung Cancer is an international publication covering the clinical, translational and basic science of malignancies of the lung and chest region.Original research articles, early reports, review articles, editorials and correspondence covering the prevention, epidemiology and etiology, basic biology, pathology, clinical assessment, surgery, chemotherapy, radiotherapy, combined treatment modalities, other treatment modalities and outcomes of lung cancer are welcome.