The prediction of treatment outcome in NSCLC patients harboring an EGFR exon 20 mutation using molecular modeling
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.
Authors
- Bianca van Veggel (ORCID: https://orcid.org/0000-0002-3223-1255)
- Anthonie J. van der Wekken (ORCID: https://orcid.org/0000-0001-7175-0026)
- Josef Melcr (ORCID: https://orcid.org/0000-0003-4729-3990)
- L. Zhang
- Matthew R. Groves (ORCID: https://orcid.org/0000-0001-9859-5177)
- Ed M. D. Schuuring (ORCID: https://orcid.org/0000-0003-3655-143X)
- Fenneke Zwierenga (ORCID: https://orcid.org/0000-0001-8993-0105)
- Harry J.M. Groen (ORCID: https://orcid.org/0000-0002-2978-5265)
- Adrianus J. de Langen
Institutions
- University Medical Center Groningen (NL)
- University of Groningen (NL)
- The Netherlands Cancer Institute (NL)
Publication Details
- Journal
- Lung Cancer
- Published
- 2024-09-30
- DOI
- https://doi.org/10.1016/j.lungcan.2024.107973
- Citations
- 3
- Primary Topic
- Lung Cancer Treatments and Mutations
- Type
- article
- Field-Weighted Citation Impact
- 1.26