The detection of somatic epidermal growth factor receptor (EGFR) mutations is valuable when an appropriate therapy, either EGFR-tyrosine kinase inhibitor (TKI) therapy or chemotherapy, for patients with advanced non-small cell lung cancer (NSCLC) needs to be selected. Although it is well-understood that EGFR mutation detection is significant for the decision-making regarding treatment, no consensus on the methodology that should be the most preferebale for detecting mutations in clinical practice has been reached. The presence of false positives due to the technique carried out for mutation analysis affects the accurate estimation of response EGFR-TKI therapy. Furthermore, false negatives directly exclude the potential application of an EGFR-TKI. In the present study, we present the results of detecting EGFR mutations in individual sample types using three different low- or high-sensitivity techniques. We suggest that the choice of the method used should be made based on the type of the sample. Our results revealed that EGFR mutations were less frequently detected in bronchoscopic biopsies, regardless of the method used. However, the amplification refractory mutation system (ARMS) was optimal owing to the small amount of DNA prepared for biopsy. The cytology sample was a valuable alternative to traditional samples, given that a sensitive method for detecting mutations was used. For surgical resections, the testing method may be selected based on the expertise of each laboratory, but direct sequencing is highly recommended. We also suggest that two methods should be used sequentially (the screening and targeted methods) in clinical practice due to the presence of non-neglected discordance between any method from its own benefits and drawbacks.
CITATION STYLE
Yi, S., Zhuang, Y., Zhou, J., Ma, H., Huang, J., Wang, L., … Guo, F. (2014). A comparison of epidermal growth factor receptor mutation testing methods in different tissue types in non-small cell lung cancer. International Journal of Molecular Medicine, 34(2), 464–474. https://doi.org/10.3892/ijmm.2014.1789
Mendeley helps you to discover research relevant for your work.