Citations

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

Abstract Background: Patients with lung cancer with mutations in EGF receptor (EGFR) tyrosine kinase have improved prognosis when treated with EGFR inhibitors. We hypothesized that EGFR mutations may be related to residential radon or passive tobacco smoke. Methods: This hypothesis was investigated by analyzing EGFR mutations in 70 lung tumors from a population of never and long-term former female smokers from Missouri with detailed exposure assessments. The relationship with passive smoking was also examined in never-smoking female lung cancer cases from the Mayo clinic. Results: Overall, the frequency of EGFR mutation was 41% [95% confidence interval (CI), 32%–49%]. Neither radon nor passive-smoking exposure was consistently associated with EGFR mutations in lung tumors. Conclusions: The results suggest that EGFR mutations are common in female, never-smoking lung cancer cases from the United States, and EGFR mutations are unlikely due to exposure to radon or passive smoking. Cancer Epidemiol Biomarkers Prev; 21(6); 988–92. ©2012 AACR.

Abstract Purpose: miRNA plays an important role in human disease and cancer. We seek to investigate the expression status, clinical relevance, and functional role of miRNA in non–small cell lung cancer. Experimental Design: We conducted miRNA expression profiling in matched lung adenocarcinoma and uninvolved lung using 56 pairs of fresh-frozen (FF) and 47 pairs of formalin-fixed, paraffin-embedded (FFPE) samples from never smokers. The most differentially expressed miRNA genes were evaluated by Cox analysis and log-rank test. Among the best candidate, miR-708 was further examined for differential expression in two independent cohorts. Functional significance of miR-708 expression in lung cancer was examined by identifying its candidate mRNA target and through manipulating its expression levels in cultured cells. Results: Among the 20 miRNAs most differentially expressed between tested tumor and normal samples, high expression level of miR-708 in the tumors was most strongly associated with an increased risk of death after adjustments for all clinically significant factors including age, sex, and tumor stage (FF cohort: HR, 1.90; 95% CI, 1.08–3.35; P = 0.025 and FFPE cohort: HR, 1.93; 95% CI, 1.02–3.63; P = 0.042). The transcript for TMEM88 gene has a miR-708 binding site in its 3′ UTR and was significantly reduced in tumors high of miR-708. Forced miR-708 expression reduced TMEM88 transcript levels and increased the rate of cell proliferation, invasion, and migration in culture. Conclusions: miRNA-708 acts as an oncogene contributing to tumor growth and disease progression by directly downregulating TMEM88, a negative regulator of the Wnt signaling pathway in lung cancer. Clin Cancer Res; 18(13); 3658–67. ©2012 AACR.

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.

Abstract Background: Gene expression profiling is an important tool in prediction, prognosis, and cancer modeling. The DASL® assay (cDNA –mediated Annealing, extension, Selection, and Ligation; Illumina; San Diego, CA) is a gene expression profiling system designed for degraded RNAs such as those derived from formalin-fixed paraffin-embedded (FFPE) tumor samples. FFPE tumor samples are invaluable resources for cancer research, being the most widely available materials for which patient outcomes are known. Purpose: The objective of this study was to evaluate the performance of Illumina's Whole Genome DASL platform (24,526; 24K) at distinguishing HER2-positive (HER2+) from HER2-negative (HER2-) FFPE breast tumors. Methods: We measured the relative gene expression of biological and technical replicates using nine HER2+ and 11 HER2- primary breast tumors from patients that were all node-positive and estrogen receptor-positive. The HER2 status of the tumors was determined using the Dako Hercept Test following the 2007 ASCO/CAP guidelines for HER2 positivity. The non-background corrected probe-expression data were exported from Illumina BeadStudio software and normalized using the software R and the function fastlo. Log2 transformed expression values were used to obtain the relative gene expression per probe. Spearman correlations were calculated between both types of replicates. Unequal-variance t-tests were conducted to test for differences in expression levels between HER2+ and HER2- tumors. An overall ERBB2 gene expression level was calculated by taking the average of the three ERBB2 probes for each tumor across all replicates. To simulate the possible outcome if only one replicate of each tumor was measured for gene expression, differential expression was tested for all possible combinations of single replicate probe expression values. Results: Spearman correlations for technical and biological replicates within tumor samples ranged from 0.9804 – 0.9946, and 0.9822 – 0.9945, respectively. The overall ERBB2 gene expression was significantly lower in HER2- tumors than in HER2+ tumors (p = 0.0000018). The top five most differentially expressed genes (p < 0.0005) included ERBB2, GRB7, C17ORF37, PERLD1, and ASF1B. For each of the three ERBB2 probes, there were 65,536 possible ways to combine the individual probe expression values. The p-values for testing differential expression in each of these 65,536 combinations of HER2+ tumors versus HER2- tumors ranged from 0.00029 – 0.0091 for ILMN_1717902, 0.0000078 – 0.00026 for ILMN_1728761, and 0.000000002 – 0.0000032 for ILMN-2352131. Conclusions: Illumina's Whole Genome Platform provided highly reproducible gene expression values among biological and technical replicates of breast tumors. This platform also demonstrated significant differential ERBB2 gene expression between HER2+ and HER- breast tumors, which correctly classified the breast tumors as HER2+ and HER2-. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 6127.