Abstract
PURPOSE: Lung Squamous Cell Carcinoma (SCC) is a Non-Small Cell Lung Cancer (NSCLC) subtype with a strong clinical association with smoking habits and a very low incidence in never-smokers. Molecular profiling of SCC in never-smokers could unveil tumor vulnerabilities and new treatment strategies. PATIENTS AND METHODS: We considered a patient cohort of 17 former or current smokers (51.5%) and 16 never-smoker SCC patients (48.5%). TruSight Oncology(®) 500, investigating hotspots in 523 cancer-related genes, Tumor mutation burden (TMB) and microsatellite instability (MSI), and RNA sequencing was performed on tumor tissue. Genomic and transcriptomic profiles were compared between smokers and never-smoker patients. RESULTS: The most frequently altered genes were TP53 (67%), CDKN2A (20%) and PIK3CA (17%), with no substantial differences between groups, except for TP53 which was more frequently mutated in smokers (86.7% vs 46.7%, p = 0.05), who also showed a higher TMB with respect to non-smokers (median 11 mut/Mb vs 5.5 mut/Mb, p = 0.028); all patients were stable for MSI score (median 1.87 vs 1.82, p = 0.87). Activating mutations in EGFR and MET were found in one and two never-smokers, respectively. Three smoker patients had simultaneous amplifications in FGF3, FGF19 and FGF4. Enrichment analyses showed that cyclin-dependent protein Ser/Thr kinase activity and PI3K signaling pathways were affected in both groups, while cellular damage response was exclusively altered in never-smokers. Unsupervised hierarchical clustering on transcriptomes effectively identified different specific transcriptional subtypes between smokers and never-smokers. Gene set enrichment analysis highlighted that tumors from never-smokers are characterized by dysregulation in cell membrane potential and ion homeostasis across cell membrane pathways. CONCLUSION: Genomic and transcriptomic profiles deeply differentiate SCC occurring in never-smokers with respect to SCC in smoker patients. Moreover, SCC could carry canonical NSCLC) activating mutations. Our data suggest that deep molecular analyses resolve tumor heterogeneity and may help with new algorithm-based treatment strategies for SCC.