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Gene expression alterations in response to cigarette smoke have been characterized in normal-appearing bronchial epithelium of healthy smokers, and it has been suggested that adjacent histologically normal tissue displays tumor-associated molecular abnormalities. We sought to delineate the spatial and temporal molecular lung field of injury in smoker patients with early-stage non-small cell lung cancer (NSCLC; n = 19) who were accrued into a surveillance clinical trial for annual follow-up and bronchoscopies within 1 year after definitive surgery. Bronchial brushings and biopsies were obtained from six different sites in the lung at the time of inclusion in the study and at 12, 24, and 36 months after the first time point. Affymetrix Human Gene 1.0 ST arrays were used for whole-transcript expression profiling of airways (n = 391). Microarray analysis identified gene features (n = 1,165) that were nonuniform by site and differentially expressed between airways adjacent to tumors relative to more distant samples as well as those (n = 1,395) that were significantly altered with time up to 3 years. In addition, gene interaction networks mediated by phosphoinositide 3-kinase (PI3K) and extracellular signal-regulated kinase (ERK)1/2 were modulated in adjacent compared with contralateral airways and the latter network with time. Furthermore, phosphorylated AKT and ERK1/2 immunohistochemical expression were significantly increased with time (nuclear pAKT, P = 0.03; cytoplasmic pAKT, P &lt; 0.0001; pERK1/2, P = 0.02) and elevated in adjacent compared with more distant airways (nuclear pAKT, P = 0.04; pERK1/2, P = 0.03). This study highlights spatial and temporal cancer-associated expression alterations in the molecular field of injury of patients with early-stage NSCLCs after definitive surgery that warrant further validation in independent studies.

cancer prevention research

Characterizing the Molecular Spatial and Temporal Field of Injury in Early-Stage Smoker Non–Small Cell Lung Cancer Patients after Definitive Surgery by Expression Profiling