PubMed HealthSearch

PubMed · 42174600

Comprehensive characterization of MET exon 14 skipping mutations in non-small cell lung cancer.

Abstract

BACKGROUND: MET exon 14 skipping mutation (METΔex14) is a key driver event in non-small cell lung cancer (NSCLC) and can emerge as an acquired drug resistance mechanism to MET, EGFR or ALK inhibitors. The clinical and genomic features of METΔex14 in NSCLC require further characterization. METHODS: Our study included a total of 585 patients with METΔex14 + NSCLC, comprising 556 baseline samples, 53 samples from patients exhibiting resistance to MET inhibitors, and 16 samples from patients resistant to EGFR/ALK inhibitors. Genomic data from targeted next-generation sequencing (NGS) of tissue and/or plasma samples using GeneseeqPrime™ (a 425 pan-cancer gene panel) were analyzed. RESULTS: Overall, METΔex14 exhibited a prevalence of 1.02% (n = 585) in the screened NSCLC population, with a higher incidence in patients with a sarcomatoid histology. METΔex14 was predominantly detected at the splice donor site, though the non-coding region adjacent to the splice acceptor site contributed considerably to the complexity of METΔex14. Common concurrent alterations identified at baseline included those in TP53 (40.8%), CDK4 (16%) and EGFR (12.4%). Concurrent MET amplification and cell cycle pathway mutations were both associated with worse outcomes in patients treated with crizotinib, with significant co-occurrences observed also among these concurrent genomic variations. In addition, increased chromosomal instability and intra-tumoral heterogeneity correlated with a poorer response to crizotinib. Mechanisms of acquired resistance to MET inhibitors were primarily attributed to on-target MET D1228X/Y1230X mutations or off-target alterations within genes in the RTK/RAS/MAPK and PI3K/AKT/mTOR pathways. Intriguingly, our exploratory analysis also identified the FGFR3::TACC3 fusion as a potential resistance mechanism to savolitinib. Moreover, METΔex14 was identified in 16 patients following progression on EGFR and ALK inhibitors, highlighting the need for developing tailored therapeutic strategies to overcome resistance. CONCLUSIONS: This study provides a comprehensive characterization of METΔex14 in NSCLC, revealing its dual role as a primary driver of oncogenesis and a potential resistance mechanism to EGFR/ALK inhibitors. The identification of concurrent genetic alterations and potential resistance mechanisms enhances our molecular understanding of treatment responses. These findings highlight the need for further investigation into targeted therapies that consider the genomic complexity of METΔex14 to improve treatment efficacy and patient outcomes.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jie Lin, Guojie Xia, Yijuan Wu, Lingfeng Chen, Yaru Zhang, Ya Ma, Jiani C Yin, Zhenyu Zhang, Xiaojie Pan. 2026-05-23. Comprehensive characterization of MET exon 14 skipping mutations in non-small cell lung cancer.. https://doi.org/10.1186/s12931-026-03626-1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans