Genomic and Transcriptomic Profiles in Smokers and Never-Smokers Lung Squamous Cell Carcinoma Patients

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.

Keywords: lung squamous cell carcinoma; next generation sequencing; smoking habits; transcriptomics.

Figure 1

Oncoprint plot for gene alterations in the patients’ cohort. Altered genes at a higher frequency than 6% are reported, ordered by number of mutations per patient. Columns represent individual samples and are divided by smoking habits. The different colors represent the different alterations, as indicated in the figure legends. The bar plots at the top and right of the oncoprint indicate the count of events found respectively in each sample and in each gene. In the lower part of the oncoprint, the smoking habit is reported for each sample.

Figure 2

Box plots and Pathway enrichment bubble plot. The box plots comparing the rate of pathogenic alterations (A), tumor mutation burden (B) and microsatellite stability (C) between never-smokers and smokers or former smokers. Box Plots were obtained using GraphPad Prism 8.4.3 (GraphPad Software, Inc., San Diego, CA). *p-value: 0.028 **p-value: 0.004. (D) The plot showed only the differentially enriched pathways between the 2 groups of smoker and never-smoker patients. In the scatter plot are presented the top 5 enriched GO pathways. Pathway enrichment bubble was generated using SRplot.

Figure 3

Heatmap performed by unsupervised clustering in the whole patient cohort and Gene Set Enrichment Analysis. (A) Considering a fold change > 2 and p ≤ 0.05, 2188 genes were found differentially expressed between the clinical subgroups of smokers, former smokers and never-smoker patients. Genes (rows) and patients (columns) are ordered by hierarchical clustering based on gene expression, using the Euclidean distance method and average linkage clustering for both genes and samples. (B) The analysis showed the transcriptome for pathways differentiating smokers patients versus never-smoker patients. A negative or positive Normalized Enrichment Score (NES) indicates an enriched pathway in the never-smokers group or in the smokers group, respectively. FDR: False Discovery Rate.