Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Gene Expression Patterns that Predict Sensitivity to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Lung Cancer Cell Lines and Human Lung Tumors

ABSTRACT: 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

ORGANISM(S): Homo sapiens

SUBMITTER: Esther Black

PROVIDER: E-GEOD-31625 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Gene expression patterns that predict sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer cell lines and human lung tumors.

Balko Justin M JM Potti Anil A Saunders Christopher C Stromberg Arnold A Haura Eric B EB Black Esther P EP

BMC genomics 20061110

<h4>Background</h4>Increased focus surrounds identifying patients with advanced non-small cell lung cancer (NSCLC) who will benefit from treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI). EGFR mutation, gene copy number, coexpression of ErbB proteins and ligands, and epithelial to mesenchymal transition markers all correlate with EGFR TKI sensitivity, and while prediction of sensitivity using any one of the markers does identify responders, individual marker ...[more]

PMID: 17096850

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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.

Dataset Information

Gene Expression Patterns that Predict Sensitivity to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Lung Cancer Cell Lines and Human Lung Tumors

Publications

Gene expression patterns that predict sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer cell lines and human lung tumors.

Similar Datasets

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