An Optimized Next-Generation Sequencing Method for Detecting De Novo MET Amplification in Non-Small Cell Lung Cancer:...

MET amplification (MET^amp) is a noteworthy genomic alteration that can occur in patients with non-small cell lung cancer (NSCLC). It has been demonstrated to occur as a primary oncogenic driver that may exist prior to any treatment and is referred to as de novo MET^amp. Despite the recognized significance of this genetic alteration, routine large-scale screening for the early detection of de novo MET^amp is currently lacking in clinical practice and the clinical impact of de novo MET^amp on NSCLC remains poorly investigated. In this study, we developed a next-generation sequencing--based screening method for detecting and stratifying MET^amp optimized in silico, validated in a patient cohort (n = 72) and applied to 1932 patients with NSCLC. Clinical outcomes (overall survival [OS] and progression-free survival) were assessed in de novo MET^amp cases (n = 46). The optimized next-generation sequencing--based method achieved high confidence (F-score > 0.99) during in silico optimization. In vivo validation demonstrated high sensitivity (0.93) and specificity (0.97) compared with fluorescence in situ hybridization. De novo MET^amp was found in 2.4% of cases stratified into the following distinct amplification groups based on the amplification copy number ratio (CNR): low (1.5 < CNR ≤ 2.2), medium (2.2 < CNR ≤ 4), and high amplification (CNR > 4). Significant differences in patient outcome (P < .001) were observed between the low- (median OS: 35.9 months), medium- (median OS: 14.3 months) and high-amplification (median OS: 3.3 months) groups. Progression-free survival under chemotherapy was notably reduced in the medium-/high-amplification groups compared with the low-amplification group (P = .001). Screening for MET^amp detection followed by stratification based on MET^amp levels may be considered in all patients with NSCLC at diagnosis. This approach could potentially enhance treatment management effectiveness by facilitating inclusion in clinical trials.