Real‐world utility of next‐generation sequencing for targeted gene analysis and its application to treatment in lung adenocarcinoma

PURPOSE: This study investigated the clinical utility of next‐generation sequencing (NGS) for detection of genetic alterations and its implications on treatment of lung adenocarcinoma in real‐world practice. PATIENTS AND METHODS: Data were reviewed for 391 patients with lung adenocarcinoma who underwent NGS between March 2017 and October 2018. Formalin‐fixed, paraffin‐embedded archival samples were used for performing NGS targeting 382 genes, including all exons of 199 genes, 184 hotspots, and the partial introns of 8 genes often rearranged in cancer. Survival analysis was performed for stage IV disease. RESULTS: Among the 391 patients, at least one actionable mutation was identified in 294 patients (75.2%). The most commonly mutated gene was EGFR (n = 130, 33.2%), involving EGFR exon 19 deletion (n = 48, 12.3%), L858R (n = 47, 12%), and others (n = 35, 9%), followed by KRAS (n = 48, 12.3%), ALK (n = 40, 10.2%), RET (6%), MET (3%), ROS‐1 (3%), and BRAF (2%) mutations. TP53 (46.9%) and CDKN2A (12.6%) mutations were common co‐mutations in patients with AMs. With a median follow‐up duration of 16.8 months, median overall survival was 36.8 months in patients with stage IV disease. Patients treated with the corresponding targeted therapy for AMs based on NGS reports lived significantly longer than those not treated with such therapy (p < 0.001). After multivariate analysis, targeted therapy for AM was a significantly favorable factor for survival (AM without targeted therapy vs. AM with targeted therapy, hazard ratio 2.58, 95% confidence interval 1.57–4.25; p < 0.001). CONCLUSION: This study revealed that AMs could be comparably detected using NGS. Based on these NGS results, a suitable targeted therapy can be selected, which may improve survival in patients with lung adenocarcinoma. This NGS‐based approach is useful in real‐world practice to provide guidance when selecting targeted therapy.