Project description:Tobacco smoking is responsible for over 90% of lung cancer cases, and yet the precise molecular alterations induced by smoking in lung that develop into cancer and impact survival have remained obscure. We performed gene expression analysis using HG-U133A Affymetrix chips on 135 fresh frozen tissue samples of adenocarcinoma and paired noninvolved lung tissue from current, former and never smokers, with biochemically validated smoking information. ANOVA analysis adjusted for potential confounders, multiple testing procedure, Gene Set Enrichment Analysis, and GO-functional classification were conducted for gene selection. Results were confirmed in independent adenocarcinoma and non-tumor tissues from two studies. We identified a gene expression signature characteristic of smoking that includes cell cycle genes, particularly those involved in the mitotic spindle formation (e.g., NEK2, TTK, PRC1). Expression of these genes strongly differentiated both smokers from non-smokers in lung tumors and early stage tumor tissue from non-tumor tissue (p<0.001 and fold-change>1.5, for each comparison), consistent with an important role for this pathway in lung carcinogenesis induced by smoking. These changes persisted many years after smoking cessation. NEK2 (p<0.001) and TTK (p=0.002) expression in the noninvolved lung tissue was also associated with a 3-fold increased risk of mortality from lung adenocarcinoma in smokers. Our work provides insight into the smoking-related mechanisms of lung neoplasia, and shows that the very mitotic genes known to be involved in cancer development are induced by smoking and affect survival. These genes are candidate targets for chemoprevention and treatment of lung cancer in smokers. Experiment Overall Design: Overall, 180 adenocarcinoma and non-tumor tissue samples were selected for the analyses, including duplicate or triplicate samples from 14 subjects for quality control. From the original 180 samples, 148 provided sufficient quantity of high-quality RNA for microarray analyses; 13 additional samples were excluded because of problematic assays. Normalization was conducted on the remaining 135 microarrays. After normalization, 13 samples were excluded because of low percentage of tumor cells in the tumor tissues. This report is based on 122 samples, of which 15 duplicates were averaged, resulting in 107 final expression values from 58 tumor and 49 non-tumor tissues from 20 never smokers, 26 former smokers, and 28 current smokers.

Project description:Lung cancer has been the leading cause of cancer death in the United States and worldwide for many decades. Low dose spiral computerized tomography (LDCT) is likely to become the first approved screening and early detection test in the upcoming year, but it is plagued by a high false-positive rate. There is a need to develop complementary screening and early detection tools. A blood-based lung cancer signature is an attractive solution. Given that our knowledge of the molecular biology of smoking-induced lung cancer has dramatically increased over the past few years, this approach is plausible. To date, this effort has been focused on the identification of genomic and proteomic signatures with limited success. A broader strategy that incorporates additional cancer traits is needed. It is well recognized that wide coverage of cellular metabolism in cancer could help provide valuable diagnostic biomarkers and potentially identify molecular drivers of tumorigenesis. Recent advances in mass spectrometry have enabled comprehensive metabolomic analyses of lipids, carbohydrates, amino acids, and nucleotides within a variety of biologic matrices. Early evidence from metabolomic investigation of cancer has identified many altered biochemical profiles. However, to date, there have been few investigations of lung cancer, and most studies have looked at blood plasma or were limited by small sample sizes with mixed histologies. In the current investigation, gas chromatography time-offlight mass spectrometry (GC-TOF) was used to measure 462 lipid, carbohydrate, amino acid, organic acid, and nucleotide metabolites in 39 malignant and nonmalignant lung tissue pairs from current or former smokers with early stage adenocarcinoma. This study cohort represents patient characteristics and tumor histology most likely to be detected with LDCT screening. We hypothesize that identification of cancer-induced cellular and tissue level biochemical changes can offer a robust method for identification of candidate circulating biomarkers and improve our understanding of biochemical changes involved in adenocarcinoma tumorigenesis.

Dataset Information

LC7 Proteomic Analysis of Matched Control and NSCLC Lung Adenocarcinoma Tissue.

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Proteomic profiling of lung adenocarcinoma indicates heightened DNA repair, antioxidant mechanisms and identifies LASP1 as a potential negative predictor of survival.

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