Gene Expression Clustering of Lung Adenocarcinoma Tumors as a Prognosis Tool to Infer Progression-Free Survival.

Introduction: Lung cancer is the leading cause of cancer-related death. Studies have reported distinct tumor type based on gene expression signatures and attempted to use this information to predict survival, with limited replicability due to tumor heterogeneity, technical artefacts, and cohort size. Methods: RNA expression profiling was performed on 515 early stage resected lung adenocarcinomas from a single institution. We then used this data set to explore the potential of gene expression to stratify patients into distinct classes predictive of survival using unsupervised clustering and Cox proportional hazards model. Results: Several clinicopathologic variables, including smoking history, tumor grade, and stage, were predictive of survival. Gene expression clustering analysis supports two distinct groups of tumors as the optimal solution. Despite weak clustering of tumors based on gene expression, clusters were predictive of survival, independent of clinicopathologic variables. We also tested gene expression signatures from the literature, which were again predictive of survival, but not more than pathologic staging. Nevertheless, when restricted to early stage tumors, gene expression clustering had better predictive power than pathologic staging. Conclusion: We revealed that gene expression is naturally highly variable in tumors and that the optimal number of clusters is closer to two, instead of three as often reported. We argue that although pathologic stage remains an important predictive variable, gene expression-based biomarkers can serve as a useful prognostic tool for early stage lung adenocarcinomas.