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Updated: Mar 1, 2026
Establishing Dual Resistance to EGFR-TKI and MET-TKI in Lung Adenocarcinoma Cells In Vitro with a 2-step Dose-escalation Procedure
Published on: August 11, 2017
Establishment of a Novel Method for Screening Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor Resistance
Hong-Xia Tian1, Xu-Chao Zhang1, Zhen Wang2
1Medical Research Center, Guangdong Lung Cancer Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China.
Background:
Drug resistance to targeted therapies occurs in lung cancer, and resistance mechanisms related to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are continuously being discovered. We aimed to establish a novel method for highly parallel multiplexed detection of genetic mutations related to EGFR TKI-resistant lung cancer using Agena iPLEX chemistry and matrix-assisted laser desorption ionization time-of-flight analysis on the MassARRAY mass spectrometry platform.
Methods:
A review of the literature revealed 60 mutation hotspots in seven target genes (EGFR, KRAS, PIK3CA, BRAF, ERBB2, NRAS, and BIM) that are closely related to EGFR TKI resistance to lung cancer. A total of 183 primers comprised 61 paired forward and reverse amplification primers, and 61 matched extension primers were designed using Assay Design Software. The detection method was established by analyzing nine cell lines, and by comparison with LungCarta™ kit in ten lung cancer specimens. EGFR, KRAS, and BIM genes in all cell lines and clinical samples were subjected to Sanger sequencing for confirming reproducibility.
Results:
Our data showed that designed panel was a high-throughput and robust tool, allowing genotyping for sixty hotspots in the same run. Moreover, it made efficient use of patient diagnostic samples for a more accurate EGFR TKIs resistance analysis. The proposed method could accurately detect mutations in lung cancer cell lines and clinical specimens, consistent with those obtained by the LungCarta™ kit and Sanger sequencing. We also established a method for detection of large-fragment deletions based on single-base extension technology of MassARRAY platform.
Conclusions:
We established an effective method for high-throughput detection of genetic mutations related to EGFR TKI resistance based on the MassARRAY platform, which could provide more accurate information for overcoming cancers with de novo or acquired resistance to EGFR-targeted therapies.
Insights
This study developed a high-throughput MassARRAY method for detecting 60 genetic mutations linked to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) resistance in lung cancer, improving diagnostic accuracy.
Area of Science:
- Oncology
- Genetics
- Biotechnology
Background:
- Drug resistance to targeted therapies is a significant challenge in lung cancer treatment.
- Mechanisms of resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are continually being elucidated.
- Accurate detection of resistance mutations is crucial for effective treatment strategies.
Purpose of the Study:
- To establish a novel, highly parallel multiplexed method for detecting genetic mutations associated with EGFR TKI resistance in lung cancer.
- To utilize Agena iPLEX chemistry and MassARRAY mass spectrometry for robust mutation profiling.
Main Methods:
- Literature review identified 60 mutation hotspots in seven key genes (EGFR, KRAS, PIK3CA, BRAF, ERBB2, NRAS, BIM) related to EGFR TKI resistance.
- Primer sets for amplification and extension were designed using specialized software.
- The method was validated using lung cancer cell lines and clinical specimens, with comparisons to the LungCarta™ kit and Sanger sequencing.
Main Results:
- The developed panel demonstrated high-throughput and robust genotyping of 60 hotspots in a single run.
- The method efficiently utilized diagnostic samples for accurate EGFR TKI resistance analysis.
- Results were consistent with established methods, confirming the accuracy of mutation detection and the capability to identify large-fragment deletions.
Conclusions:
- An effective high-throughput method for detecting EGFR TKI resistance mutations was established using the MassARRAY platform.
- This method provides more accurate genetic information to guide treatment decisions for lung cancers with de novo or acquired resistance.
- The findings support improved therapeutic strategies for overcoming resistance to EGFR-targeted therapies.
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