Metabolic transcriptomic subtyping defines distinct immunogenomic and clinical landscapes in non-small cell lung...

This study aimed to evaluate the clinical significance of metabolic dysregulation and heterogeneity in patients with non-small cell lung cancer (NSCLC). We performed NanoString-based transcriptomic profiling of tumors from patients with advanced NSCLC receiving anti-PD-(L)1 therapy. Five distinct metabolic clusters were identified. Gene set analysis suggested differential enrichment of metabolic pathways across clusters, including immune signaling-dominant with minimal metabolic dysregulation (Cluster 1), amino acid metabolism-related (Cluster 2), hypoxia/autophagy-associated (Cluster 3), glycolytic/proliferative (Cluster 4), and mixed dysregulated metabolic features (Cluster 5). OXPHOS-related signatures were enriched in Clusters 3-5, whereas immune cell infiltration in the tumor was more prominent in Clusters 1-2. Survival outcomes differed significantly across clusters (progression-free survival, P = 0.035; overall survival, P = 0.008), with Cluster 1 showing the most favorable outcomes and Cluster 3 the worst following anti-PD-(L)1 therapy. Metabolic clusters were associated with clinicopathological and genomic features, including histology, smoking status, PD-L1 expression, and oncogenic driver alterations. In particular, RAS-RAF-MEK pathway gene alterations were more frequent in Clusters 3-5 than in Clusters 1-2. Together, metabolic transcriptomic profiling identifies clinically meaningful subtypes of NSCLC with distinct immunogenomic features and differential efficacy from anti-PD-(L)1 therapy.