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Establishment and Characterization of Three Afatinib-resistant Lung Adenocarcinoma PC-9 Cell Lines Developed with Increasing Doses of Afatinib
Published on: June 26, 2019
Selectivity profile of afatinib for EGFR-mutated non-small-cell lung cancer
Debby D Wang1, Victor H F Lee2, Guangyu Zhu3
1Caritas Institute of Higher Education, 18 Chui Ling Road, New Territories, Hong Kong, China. danwang6-c@my.cityu.edu.hk and Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China.
Abstract:
EGFR-mutated non-small-cell lung cancer (NSCLC) has long been a research focus in lung cancer studies. Besides reversible tyrosine kinase inhibitors (TKIs), new-generation irreversible inhibitors, such as afatinib, embark on playing an important role in NSCLC treatment. To achieve an optimal application of these inhibitors, the correlation between the EGFR mutation status and the potency of such an inhibitor should be decoded. In this study, the correlation was profiled for afatinib, based on a cohort of patients with the EGFR-mutated NSCLC. Relying on extracted DNAs from the paraffin-embedded tumor samples, EGFR mutations were detected by direct sequencing. Progression-free survival (PFS) and the response level were recorded as study endpoints. These PFS and response values were analyzed and correlated to different mutation types, implying a higher potency of afatinib to classic activation mutations (L858R and deletion 19) and a lower one to T790M-related mutations. To further bridge the mutation status with afatinib-related response or PFS, we conducted a computational study to estimate the binding affinity in a mutant-afatinib system, based on molecular structural modeling and dynamics simulations. The derived binding affinities were well in accordance with the clinical response or PFS values. At last, these computational binding affinities were successfully mapped to the patient response or PFS according to linear models. Consequently, a detailed mutation-response or mutation-PFS profile was drafted for afatinib, implying the selective nature of afatinib to various EGFR mutants and further encouraging the design of specialized therapies or innovative drugs.
Insights
Afatinib shows higher potency against common EGFR mutations (L858R, deletion 19) in non-small-cell lung cancer (NSCLC) than T790M mutations. Computational modeling confirmed these clinical findings, aiding personalized NSCLC therapy development.
Area of Science:
- Oncology
- Pharmacology
- Computational Biology
Background:
- Epidermal Growth Factor Receptor (EGFR) mutations are key drivers in non-small-cell lung cancer (NSCLC).
- New-generation irreversible tyrosine kinase inhibitors (TKIs), like afatinib, are crucial for NSCLC treatment.
- Understanding the correlation between EGFR mutation status and TKI potency is vital for optimal therapeutic application.
Purpose of the Study:
- To investigate the correlation between EGFR mutation types and the clinical efficacy of afatinib in NSCLC patients.
- To computationally model and validate the binding affinity of afatinib to different EGFR mutants.
- To develop a predictive profile for afatinib response based on EGFR mutation status.
Main Methods:
- EGFR mutation detection in tumor samples using direct sequencing.
- Clinical endpoint recording: progression-free survival (PFS) and response level.
- Computational analysis: molecular modeling and dynamics simulations to estimate binding affinities.
- Statistical analysis: linear modeling to correlate computational binding affinities with clinical outcomes.
Main Results:
- Afatinib demonstrated higher potency against classic EGFR activation mutations (L858R, deletion 19) compared to T790M-related mutations.
- Computational binding affinities accurately reflected observed clinical responses and PFS.
- A detailed mutation-response and mutation-PFS profile for afatinib was established.
Conclusions:
- Afatinib exhibits selectivity towards different EGFR mutations in NSCLC.
- Computational modeling provides a reliable method to predict TKI efficacy based on mutation status.
- Findings support the development of targeted therapies and personalized treatment strategies for NSCLC.
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