Application of amplicon-based targeted sequencing with the molecular barcoding system to detect uncommon minor EGFR mutations in patients with treatment-naïve lung adenocarcinoma

Abstract

Abstract Background In lung cancer, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor sensitizing mutations co-existing with rare minor EGFR mutations are known as compound mutations. These minor EGFR mutations can lead to acquired resistance after EGFR tyrosine kinase inhibitor treatment, so determining the mutation status of patients is important. However, using amplicon-based targeted deep sequencing based on next-generation sequencing to characterize mutations is prone to sequencing error. We therefore assessed the benefit of incorporating molecular barcoding with high-throughput sequencing to investigate genomic heterogeneity in treatment-naïve patients who have undergone resection of their non-small cell lung cancer (NSCLC) EGFR mutations. Methods We performed amplicon-based targeted sequencing with the molecular barcoding system (MBS) to detect major common EGFR mutations and uncommon minor mutations at a 0.5% allele frequency in fresh–frozen lung cancer samples. Results Profiles of the common mutations of EGFR identified by MBS corresponded with the results of clinical testing in 63 (98.4%) out of 64 cases. Uncommon mutations of EGFR were detected in seven cases (10.9%). Among the three types of major EGFR mutations, patients with the G719X mutation had a significantly higher incidence of compound mutations than those with the L858R mutation or exon 19 deletion (p = 0.0052). This was validated in an independent cohort from the Cancer Genome Atlas dataset (p = 0.018). Conclusions Our findings demonstrate the feasibility of using the MBS to establish an accurate NSCLC patient genotype. This work will help understand the molecular basis of EGFR compound mutations in NSCLC, and could aid the development of new treatment modalities.

Keywords

• Non-small cell lung cancer
• Epidermal growth factor receptor
• Compound mutations
• Molecular barcoding
• Genomic heterogeneity
• Patient genotype