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

MET amplification (METamp) 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 METamp. Despite the recognized significance of this genetic alteration, routine large-scale screening for the early detection of de novo METamp is currently lacking in clinical practice and the clinical impact of de novo METamp on NSCLC remains poorly investigated. In this study, we developed a next-generation sequencing-based screening method for detecting and stratifying METamp 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 METamp 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 METamp 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 METamp detection followed by stratification based on METamp 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. (c) 2025 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.