Abstract Lung adenocarcinoma is the most common type of primary lung cancer. Previous studies have shown that gene expression signatures may predict clinical outcome. In this study, we used a systems biology approach, weighted gene co-expression network analysis (WGCNA), to identify survival related networks and gene signatures. We first analyzed whole genome gene expression profiles in 82 lung adenocarcinoma tumor tissues from never smokers (smoked <100 cigarettes lifetime) and found a significant correlation of an expression module with tumor grade of differentiation (r=0.38, p=4×10−4) and overall survival (OS, p =0.027, HR, 1.71; 95% CI, 1.06-2.74) after adjusting for potential confounders (age and tumor grade). When this module signature was incorporated and compared to the conventional model consisting of clinical variables only, the survival prediction accuracy was increased from 0.70 to 0.75. Gene Ontology (GO) enrichment analysis indicated this module was significantly enriched in biological process GO term “cell cycle” (p = 2.1×10−53). To explore the effect of this network module at germline level, we performed the WGCNA on normal lung tissues derived from 78 of the 82 patients and observed significant association of an expression module with OS (adjusted p=3×10−4, HR=1.98; 95% CI, 1.36-2.87). Interestingly, this module was also significantly enriched in cell cycle-related genes (p=4.1×10−41). To further validate this result, we analyzed an independent microarray gene expression dataset including 442 lung adenocarcinoma patients from NCI Director's Challenge Consortium. We found the same association: the cell cycle-enriched module was correlated with tumor grade of cell differentiation (r=0.55, p-value=5×10−37) and OS (adjusted p=7.4×10−3; HR, 1.20; 95% CI, 1.05-1.37). We further identified 30 genes in the OS-related module that overlapped between the discovery and validation data sets. Furthermore, all these 30 genes were found to be up-regulated in adenocarcinoma compared to adjacent normal lung tissues in the discovery data set. Of the 30 genes, three key genes, UBE2C, TPX2 and MELK, were identified with highest connectivity in cell cycle-enriched module; their expression levels were significantly associated with OS in both discovery and validation sets. UBE2C and TPX2 have been reported as gene signatures for human lung cancer prognosis in vitro lung carcinogenesis system; MELK was identified as a key regulator of the proliferation of malignant brain tumors and malignant grade in human astrocytomas, and was also associated with breast cancer prognosis. Our results suggest that genes involved in cell-cycle are likely related with tumor aggressiveness and therefore can predict survival in lung adenocarcinoma. Further studies are needed to validate the key genes that could improve survival prediction and provide potential new targets to intervene progression of lung adenocarcinoma. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5060. doi:10.1158/1538-7445.AM2011-5060
Genetic Variations in Multiple Drug Action Pathways and Survival in Advanced Stage Non–Small Cell Lung Cancer Treated with Chemotherapy
Abstract Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr LB-186.
A Rigorous and Comprehensive Validation: Common Genetic Variations and Lung Cancer
Abstract Background: Multiple recent genome-wide studies of single nucleotide polymorphisms (SNP) reported associations between candidate chromosome loci and lung cancer susceptibility. We evaluated five of the top candidate SNPs (rs402710, rs2736100, rs4324798, rs16969968, and rs8034191) for their effects on lung cancer risk and overall survival. Methods: Over 1,700 cases and 2,200 controls were included in this study. Seven independent, complementary case-control data sets were tested for risk assessment encompassing cigarette smokers and never smokers, using unrelated controls and unaffected full-sibling controls. Five patient groups were tested for survival prediction stratified by smoking status, histology subtype, and treatment. Results: After considering a history of chronic obstructive pulmonary disease as a risk factor altering lung cancer risk and comparing to sibling controls, none of the five SNPs remained significant. However, the variant rs4324798 was significant in predicting overall survival (hazard ratio, 0.46; 95% confidence interval, 0.30-0.73; P = 0.001) in small cell lung cancer. Conclusions: None of the five candidate SNPs in lung cancer risk can be confirmed in our study. The previously reported association could be explained by disparity in tobacco smoke exposure and chronic obstructive pulmonary disease history between cases and controls. Instead, we found rs4324798 to be an independent predictor in small cell lung cancer survival, warranting further elucidation of the underlying mechanisms.Cancer Epidemiol Biomarkers Prev; 19(1); 240–4