Project description:In addition to the generation and analysis of metabolomics data on cell lines, samples of normal lung tissue, adenocarcinoma lung tissue and small cell lung carcinoma tissue (seven samples/group) were processed and evaluated metabolite profile differences under the scope of the pilot and feasibility study. These data can be correlated to the metabolite profiles defined in the SCLC and NSCLC cell lines and integrated with the ABPP-determined metabolic kinases to identify distinct metabolic signatures or biomarkers (?oncometabolites?) that distinguish small cell lung cancer from non-small cell lung cancer.
Project description:Non-small cell lung cancer (NSCLC) cell lines are widely used model systems to study molecular aspects of lung cancer. Comparative and in-depth proteome expression data across many NSCLC cell lines has not been generated yet, but would be of utility for the investigation of candidate targets and markers in oncogenesis. We employed a SILAC reference approach to perform replicate proteome quantifications across 23 distinct NSCLC cell lines. On average, close to 4000 distinct proteins were identified and quantified per cell line. These included many known targets and diagnostic markers, indicating that our proteome expression data represents a useful resource for NSCLC pre-clinical research. To assess proteome diversity within the NSCLC cell line panel, we performed hierarchical clustering and principal component analysis of proteome expression data. Our results indicate that general proteome diversity among NSCLC cell lines supersedes potential effects common to K-Ras or epidermal growth factor receptor (EGFR) oncoprotein expression. However, we observed partial segregation of EGFR or KRAS mutant cell lines for certain principal components, which reflected biological differences according to gene ontology enrichment analyses. Moreover, statistical analysis revealed several proteins that were significantly overexpressed in KRAS or EGFR mutant cell lines. Biological significance Despite enormous progress in molecular characterization and targeted therapy NSCLC represents a major cause for cancer-related deaths. While pre-clinical models such as NSCLC cell lines have been studied on the genomic and transcriptional level, proteome composition is poorly characterized. We conducted quantitative profiling across 23 NSCLC cell lines and studied global proteome diversity in relation to the presence of oncogenic KRAS or EGFR mutations. Notably, in-depth bioinformatics analysis pointed to prominent biological processes as well as up-regulated proteins in KRAS and EGFR mutant cells, highlighting the utility of cancer cell proteomics to identify target or biomarker candidates in the context of specific oncogenic mechanisms.
Project description:Global gene expression data were generated from cultured non small cell lung cancer cell lines (NSCLC), normalized using MAS 5.0, filtered and used to predict response of cells to EGFR inhibition Gene expression data from additional cell lines and tumors was used to validate the predictive algorithm Total RNA was prepared from NSCLC cell lines and applied to Affymetric U133 2.0 microarrays
Project description:The tumor microenvironment strongly influences cancer development, progression and metastasis. The role of carcinoma-associated fibroblasts (CAFs) in these processes and their clinical impact has not been studied systematically in non-small cell lung carcinoma (NSCLC). We established primary cultures of CAFs and matched normal fibroblasts (NFs) from 15 resected NSCLC. We demonstrate that CAFs have greater ability than NFs to enhance the tumorigenicity of lung cancer cell lines. Microarray gene expression analysis of the 15 matched CAF and NF cell lines identified 46 differentially expressed genes, encoding for proteins that are significantly enriched for extracellular proteins regulated by the TGF-beta signaling pathway. We have identified a subset of 11 genes that formed a prognostic gene expression signature, which was validated in multiple independent NSCLC microarray datasets. Functional annotation using protein-protein interaction analyses of these and published cancer stroma-associated gene expression changes revealed prominent involvement of the focal adhesion and MAPK signalling pathways. Fourteen (30%) of the 46 genes also were differentially expressed in laser-capture micro-dissected corresponding primary tumor stroma compared to the matched normal lung. Six of these 14 genes could be induced by TGF-beta1 in NF. The results establish the prognostic impact of CAF-associated gene expression changes in NSCLC patients. This SuperSeries is composed of the following subset Series: GSE22862: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [expression profiling_CAFs] GSE22863: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [expression profiling_NSCLC stroma] GSE27284: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [methylation profiling] GSE27289: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [genome variation profiling]
Project description:Global gene expression data were generated from cultured non small cell lung cancer cell lines (NSCLC), normalized using MAS 5.0, filtered and used to predict response of cells to EGFR inhibition
Project description:The expression profiles of miRNAs in drug-resistant non-small cell lung cancer (NSCLC) cell lines were identified via next generation sequencing and the common dysregulated miRNAs in drug-resistant NSCLC cell lines were picked up for further analysis.