Abstract
Although several prognostic signatures have been developed in lung cancer, their application in clinical practice has been limited because they have not been validated in multiple independent data sets. Moreover, the lack of common genes between the signatures makes it difficult to know what biological process may be reflected or measured by the signature. By using classical data exploration approach with gene expression data from patients with lung adenocarcinoma (n = 186), we uncovered two distinct subgroups of lung adenocarcinoma and identified prognostic 193-gene gene expression signature associated with two subgroups. The signature was validated in 4 independent lung adenocarcinoma cohorts, including 556 patients. In multivariate analysis, the signature was an independent predictor of overall survival (hazard ratio, 2.4; 95% confidence interval, 1.2 to 4.8; p = 0.01). An integrated analysis of the signature revealed that E2F1 plays key roles in regulating genes in the signature. Subset analysis demonstrated that the gene signature could identify high-risk patients in early stage (stage I disease), and patients who would have benefit of adjuvant chemotherapy. Thus, our study provided evidence for molecular basis of clinically relevant two distinct two subtypes of lung adenocarcinoma.
Highlights
Lung cancer is one of the most common cancers worldwide, accounting for an estimated 226,160 new cases and 160,340 deaths in 2012 in the United States alone [1]
By analyzing gene-expression data from lung adenocarcinoma tissues, we identified a limited number of genes (193 genes) whose expression is significantly associated with prognosis
Since current staging systems and biomarkers are limited in their ability to assess risk of recurrence and benefit from adjuvant chemotherapy in lung adenocarcinoma, our new gene-expression signature may represent a tool that could help further refine treatment decisions based on the tumors’ molecular profiles
Summary
Lung cancer is one of the most common cancers worldwide, accounting for an estimated 226,160 new cases and 160,340 deaths in 2012 in the United States alone [1]. The vast majority of lung cancers are non-small cell lung cancers (NSCLCs), of which adenocarcinoma is the most common histology (approximately 50% of all NSCLCs) [2]. Surgical resection is potentially curative and the most effective treatment for patients with early-stage NSCLC, 35% to 50% of patients with AJCC-defined stage I disease will experience a recurrence within 5 years [3,4,5]. This indicates that NSCLC is a very heterogeneous cancer even in the earliest stage, and this underlying heterogeneity is not well-reflected in the current staging system. To improve patient care and management, it is important to further characterize molecular subgroups significantly associated with this differential response to standard treatment and to develop models to predict those who would receive greatest benefit from these treatments
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