Genomic landscape of stage 0-IA lung adenocarcinoma identified by on-site reflex targeted NGS.

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PubMedPublished 2026Medicine & Clinical ResearchSurgery & Treatment
Journal
Lung cancer (Amsterdam, Netherlands)
Record source
PubMed Research Library

Abstract / Description

INTRODUCTION: Early molecular profiling in non-squamous non-small cell lung carcinoma (NSCLC), particularly lung adenocarcinoma (LUAD), is critical for guiding individualized treatment strategies. Limited data exist on the genomic landscape of Stage 0-IA LUAD. This study assessed the feasibility and clinical relevance of reflex targeted next-generation sequencing (NGS) performed on-site at diagnosis in resected early-stage LUAD. METHODS: We retrospectively analyzed 239 consecutive Stage 0-IA LUAD cases diagnosed between 2022 and 2024 at a single institution. Ultra-fast reflex DNA- and RNA-based NGS was performed on resected specimens using a 50-gene targeted panel. Alterations were classified according to the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT). Associations between genomic alterations, histologic subtypes, and tumor grades were evaluated. RESULTS: Stage IA1 was the most frequent diagnosis (46%). High-quality sequencing data were obtained in all cases, with a median turnaround time of 102 h. At least one genomic alteration was detected in 80% of tumors. KRAS mutations were most frequent (35.8%), including KRAS G12C in 16%. EGFR mutations were present in 27.2%, primarily classical sensitizing alterations. Other actionable findings included ALK fusions (3.3%), RET rearrangements (1.2%), MET exon 14 skipping (2.4%), HER2 mutations (3.7%), and BRAF V600E (0.8%). ESCAT Level I alterations were found in 34% of tumors; 20% of these co-occurred with TP53 mutations. Significant associations were observed between genomic alterations, histologic subtypes, and tumor grades. CONCLUSIONS: Reflex NGS at diagnosis in resected Stage 0-IA LUAD is feasible, rapid, and reveals a high rate of actionable alterations, which may support its integration in the future into early-stage diagnostic workflows.
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