Abstract 4028: Identification of universal survival predictors in lung adenocarcinoma.
Authors: Tzu-Pin Lu, Eric Y. Chuang, James J. Chen
Journal: Cancer Research (2013)

Abstract Lung cancer is the leading cause of cancer-related death in the whole world, and adenocarcinoma and squamous cell carcinoma are the two major subtypes. For the past two decades, tremendous research efforts have been devoted in elucidating the etiology of lung cancer and improving treatment procedures. However, the average five-year overall survival rates are still less than 20%. Although several studies have successfully identified many biomarkers in predicting survival by using high-throughput technologies, the inconsistency and irreproducibility across different studies pose a major challenge in translating the findings into clinical practice. In this study, we proposed an approach by examining the associations between survival and gene expression in functional relevant gene groups rather than the traditional gene-by-gene analysis. We used a microarray dataset from Shedden et al. as the training set. Log-rank test was performed to exclude genes showing no association with survival, and only those genes with p-values &amp;lt; 0.1 remained for further analysis. Next, four biological databases, including KEGG, BioCarta, Pathway Interaction Database, and Reactome, were utilized to provide functional annotations for those selected genes. Since apoptosis, cell cycle and cell death regulations play dominant roles in cancer, we focused on 24 pathways related to these three functions in further analyses. Cox hazard regression model was used to explore the associations between gene expression and survival. Initially, each patient was assigned a score of 0. For each gene, a patient was given a score 1 if the fitted Cox coefficient is greater than 0 and the patient's expression level is higher than the median, or if the coefficient is less than 0 and the expression level is smaller than the median. Patients were divided into two groups based on the median of their given scores after summarizing all genes within the same functional category. The log-rank test was then utilized to evaluate the performances of the survival predictors identified; 11 out of 24 pathways were significant (P &amp;lt; 10-4). We further performed randomization test, by randomly permuting genes and samples simultaneously, the results suggested 6 significant pathways (P &amp;lt; 0.05), based on 1,000 repetitions. To further evaluate their performances across different studies, 5 additional microarray datasets were examined for validation. The analysis showed that a pathway involved in apoptosis consisting of 16 genes which can effectively classify lung adenocarcinoma patients into high risk and low risk groups (P &amp;lt; 0.05) for all 5 datasets. However, these 16 genes were unable to predict lung squamous cell carcinoma patients. In summary, we presented a new approach to identify consistent and reproducible survival predictors for lung adenocarcinoma, and those identified genes/pathways may be potentially applied for clinical use in the future. Citation Format: Tzu-Pin Lu, Eric Y. Chuang, James J. Chen. Identification of universal survival predictors in lung adenocarcinoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4028. doi:10.1158/1538-7445.AM2013-4028