Abstract
PurposeTo identify stage I lung adenocarcinoma patients with a poor prognosis who will benefit from adjuvant therapy.Patients and MethodsWhole gene expression profiles were obtained at 19 time points over a 48-hour time course from human primary lung epithelial cells that were stimulated with epidermal growth factor (EGF) in the presence or absence of a clinically used EGF receptor tyrosine kinase (RTK)-specific inhibitor, gefitinib. The data were subjected to a mathematical simulation using the State Space Model (SSM). “Gefitinib-sensitive” genes, the expressional dynamics of which were altered by addition of gefitinib, were identified. A risk scoring model was constructed to classify high- or low-risk patients based on expression signatures of 139 gefitinib-sensitive genes in lung cancer using a training data set of 253 lung adenocarcinomas of North American cohort. The predictive ability of the risk scoring model was examined in independent cohorts of surgical specimens of lung cancer.ResultsThe risk scoring model enabled the identification of high-risk stage IA and IB cases in another North American cohort for overall survival (OS) with a hazard ratio (HR) of 7.16 (P = 0.029) and 3.26 (P = 0.0072), respectively. It also enabled the identification of high-risk stage I cases without bronchioalveolar carcinoma (BAC) histology in a Japanese cohort for OS and recurrence-free survival (RFS) with HRs of 8.79 (P = 0.001) and 3.72 (P = 0.0049), respectively.ConclusionThe set of 139 gefitinib-sensitive genes includes many genes known to be involved in biological aspects of cancer phenotypes, but not known to be involved in EGF signaling. The present result strongly re-emphasizes that EGF signaling status in cancer cells underlies an aggressive phenotype of cancer cells, which is useful for the selection of early-stage lung adenocarcinoma patients with a poor prognosis.Trial RegistrationThe Gene Expression Omnibus (GEO) GSE31210
Highlights
Lung cancer is the leading cause of cancer-related death in the world
The risk scoring model enabled the identification of high-risk stage IA and IB cases in another North American cohort for overall survival (OS) with a hazard ratio (HR) of 7.16 (P = 0.029) and 3.26 (P = 0.0072), respectively
It enabled the identification of high-risk stage I cases without bronchioalveolar carcinoma (BAC) histology in a Japanese cohort for OS and recurrence-free survival (RFS) with HRs of 8.79 (P = 0.001) and 3.72 (P = 0.0049), respectively
Summary
Lung cancer is the leading cause of cancer-related death in the world. With the recent advances in diagnostic imaging technology such as computed tomography, the number of patients diagnosed with stage I non-small cell lung cancer (NSCLC), adenocarcinoma, the commonest histological type, has been increasing [1,2]. Even among patients with the earliest form, stage IA (tumors #3 cm in diameter with no evidence of regional lymph node and/or regional metastasis, according to the American Joint Cancer Committee/Union Internationale Contre Le Cancer [AJCC/UICC] 6th Edition), treated by surgery with curative intent, 10–30% will relapse and die of recurrence [3]. Several whole gene expression profiling studies have been conducted to obtain gene signatures applicable as biomarkers for clinical use [4,5,6,7,8,9,10,11]. There is still little evidence to support the use of gene signatures in preference to clinical factors, including stage, age, and sex [5]. To the best of our knowledge, gene signatures that enable prediction of the outcomes of stage IA patients have not been reported
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