Dataset Information

Transcription profiling by array of human non-small cell lung cancer tissue

ABSTRACT: PURPOSE The development of reliable gene expression profiling technology is having an increasing impact on our understanding of lung cancer biology. The present study aims to determine whether the phenotypic heterogeneity and genetic diversity of lung cancer are correlated. PATIENTS AND METHODS In this study, microarray analysis was performed in a set of 91 non-small cell lung cancer (NSCLC) samples in order to: establish gene signatures in primary adenocarcinomas and squamous-cell carcinomas; determine differentially expressed gene sequences at different stages of the disease; and identify sequences with biological significance for tumor progression. After microarray analysis, the expression level of 92 selected genes was validated by qPCR in an independent set of 70 samples. RESULTS Gene sequences were differentially expressed as a function of tumor type, stage, and differentiation grade. High upregulation was observed for KRT15 and PKP1, which may be good markers to distinguish squamous cell carcinoma samples. High downregulation was observed for DSG3 in stage IA adenocarcinomas. CONCLUSION Expression signatures in NSCLC distinguish tumor type, stage, and differentiation grade. Keywords: Tumor vs control comparative genomics study 91 samples studied, 46 tumors and 45 controls. All samples are paired except three.

ORGANISM(S): Homo sapiens

SUBMITTER: Sanchez-Palencia A

PROVIDER: S-ECPF-GEOD-18842 | biostudies-other | 2011 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide. Within the molecular scope of NCSLC, a complex landscape of dysregulated cellular signaling has emerged, defined largely by mutations in select mediators of signal transduction, including the epidermal growth factor receptor (EGFR) and anaplastic lymphoma (ALK) kinases. Consequently, these mutant kinases become constitutively activated and targets for chemotherapeutic intervention. Encouragingly, small molecule inhibitors of these pathways have shown promise in clinical trials or are approved for clinical use. However, many protein kinases are dysregulated in NSCLC without genetic mutations. To quantify differences in tumor cell signaling that are transparent to genomic methods, we established a super-SILAC internal standard derived from NSCLC cell lines grown in vitro and labeled with heavy lysine and arginine, and deployed them in a phosphoproteomic workflow. We identified 9019 and 8753 phosphorylation sites in two separate tumors. Relative quantification of phosphopeptide abundance between tumor samples allowed for the determination of specific hubs and pathways differing between each tumor. Sites downstream of Ras showed decreased inhibitory phosphorylation (Raf/Mek) and increased activating phosphorylation (Erk1/2) in one tumor versus another. In this way, we were able to quantitatively access oncogenic kinase signaling in primary human tumors.Through the use of quantitative proteomics, we demonstrated the feasibility and coverage that large scale mass spectrometry can leverage for understanding kinase networks in cancer. By incorporating Super-SILAC based quantitation into a typical pathology workflow, we were able to access and compare tumors from multiple patients in this analysis with high accuracy and dynamic range. We analyzed tumors from patients diagnosed with non-small cell lung cancer and were able to detect comprehensive phosphorylation networks relaying through known hubs of oncogenesis in lung cancer. We hereby show that it is possible to track changes to phosphorylation networks across multiple tumors, opening up the possibility that drug susceptibility and patient-specific stratification can be implemented downstream of classical pathology.

Dataset Information

Transcription profiling by array of human non-small cell lung cancer tissue

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets