BackgroundLung cancer is the worldwide leading cause of death from cancer. Tobacco usage is the major pathogenic factor, but all lung cancers are not attributable to smoking. Specifically, lung cancer in never-smokers has been suggested to represent a distinct disease entity compared to lung cancer arising in smokers due to differences in etiology, natural history and response to specific treatment regimes. However, the genetic aberrations that differ between smokers and never-smokers’ lung carcinomas remain to a large extent unclear.MethodsUnsupervised gene expression analysis of 39 primary lung adenocarcinomas was performed using Illumina HT-12 microarrays. Results from unsupervised analysis were validated in six external adenocarcinoma data sets (n=687), and six data sets comprising normal airway epithelial or normal lung tissue specimens (n=467). Supervised gene expression analysis between smokers and never-smokers were performed in seven adenocarcinoma data sets, and results validated in the six normal data sets.ResultsInitial unsupervised analysis of 39 adenocarcinomas identified two subgroups of which one harbored all never-smokers. A generated gene expression signature could subsequently identify never-smokers with 79-100% sensitivity in external adenocarcinoma data sets and with 76-88% sensitivity in the normal materials. A notable fraction of current/former smokers were grouped with never-smokers. Intriguingly, supervised analysis of never-smokers versus smokers in seven adenocarcinoma data sets generated similar results. Overlap in classification between the two approaches was high, indicating that both approaches identify a common set of samples from current/former smokers as potential never-smokers. The gene signature from unsupervised analysis included several genes implicated in lung tumorigenesis, immune-response associated pathways, genes previously associated with smoking, as well as marker genes for alveolar type II pneumocytes, while the best classifier from supervised analysis comprised genes strongly associated with proliferation, but also genes previously associated with smoking.ConclusionsBased on gene expression profiling, we demonstrate that never-smokers can be identified with high sensitivity in both tumor material and normal airway epithelial specimens. Our results indicate that tumors arising in never-smokers, together with a subset of tumors from smokers, represent a distinct entity of lung adenocarcinomas. Taken together, these analyses provide further insight into the transcriptional patterns occurring in lung adenocarcinoma stratified by smoking history.

Characterization of molecules within important oncogenetic pathways may have future implications for development of therapies and biomarkers in lung cancer. One such target is the tyrosine kinase receptor KIT (c-KIT). We evaluated alterations and expression of KIT and its ligand, KITLG (also known as SCF), in 72 clinical lung tumor specimens of different histologies. Gene copy number, mRNA expression levels, and protein expression were assayed using array-based comparative genomic hybridization, real-time quantitative reverse transcription PCR and immunohistochemistry, respectively. For validation, we investigated copy number alterations and mRNA expression in external microarray data sets of 1,600 and 555 primary lung tumors, respectively. Positivity for KIT staining was most common in large cell neuroendocrine carcinoma (LCNEC) which also showed the highest KIT mRNA expression levels whereas expression was lowest in squamous cell carcinoma (SqCC). KIT mRNA expression levels were higher in KIT immunopositive samples, but expression was not affected by KIT copy numbers. Copy number gains of KIT were significantly more frequent in SqCC compared with adenocarcinoma in our own series and in the 1,600-sample data set. Immunopositivity for both KIT and KITLG in the same tumor was rare except in LCNEC. Our results highlight an increased KIT mRNA expression and frequent KIT immunopositivity in LCNEC but point out a poor correlation between KIT copy numbers and expression in SqCC, perhaps reflecting the existence of a protective mechanism against KIT alterations in this subgroup.

Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expressionbased AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminalrespiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample