Project description:Small-cell lung carcinoma (SCLC) and large-cell neuroendocrine lung carcinoma (LCNEC) are high-grade lung neuroendocrine tumors (NET). However, comparative protein expression within SCLC and LCNEC remains unclear. Here, protein expression profiles were obtained via mass spectrometry-based proteomic analysis.
Project description:ChIP-Seq analysis performed on 5 ASCL1(+) cell lines and 2 ASCL1 (-) cell lines in order to understand the transcriptome of ASCL1 as it pertains to high-grade neuroendocrine lung cancers 5 ASCL1(+) lung cancer cell lines and 2 ASCL1(-) lung cancer cell lines were compared using ChIP-Seq analysis
Project description:The DNA isolated from 44 either frozen or FFPE Neuroendocrine Neoplasm (NEN) was analysed by NGS, to identify genes more likely to be subject to sequence variations among 523 cancer-related ones.
Project description:41 lung adenocarcinoma from never-smokers hybridized on Illumina SNP arrays on 13 HumanCNV370-Quadv3 chips. High-resolution array comparative genomic hybridization analysis of lung adenocarcinoma in 41 never smokers for identification of new minimal common regions (MCR) of gain or loss. The SNP array analysis validated copy-number aberrations and revealed that RB1 and WRN were altered by recurrent copy-neutral loss of heterozygosity.The present study has uncovered new aberrations containing cancer genes. The oncogene FUS is a candidate gene in the 16p region that is frequently gained in never smokers. Multiple genetic pathways defined by gains of MYC, deletions of RB1 and WRN or gains on 7p and 7q are involved in lung adenocarcinoma in never smokers. A 'Cartes d'Identite des Tumeurs' (CIT) project from the French National League Against Cancer (http://cit.ligue-cancer.net) 41 samples hybridized on Illumina SNP arrays. Submitter : Fabien PETEL petelf@ligue-cancer.net . Project leader : Pr Pierre FOURET pierre.fouret@psl.aphp.fr
Project description:In this study, we characterize the fusion protein produced by the EPC1-PHF1 translocation in Low Grade Endometrial Stromal Sarcoma (LG-ESS) and Ossifying FibroMyxoid Tumors (OFMT). We express the fusion protein and necessary controls in K562 Cells. The fusion protein assembles a mega-complex harboring both NuA4/TIP60 and PRC2 subunits and enzymatic activities and leads to mislocalization of chromatin marks in the genome, linked to aberrant gene expression.
Project description:In this study, we characterize the fusion protein produced by the EPC1-PHF1 translocation in Low Grade Endometrial Stromal Sarcoma (LG-ESS) and Ossifying FibroMyxoid Tumors (OFMT). We express the fusion protein and necessary controls in K562 Cells. The fusion protein assembles a mega-complex harboring both NuA4/TIP60 and PRC2 subunits and enzymatic activities and leads to mislocalization of chromatin marks in the genome, linked to aberrant gene expression.
Project description:In this study, we characterize the fusion protein produced by the EPC1-PHF1 translocation in Low Grade Endometrial Stromal Sarcoma (LG-ESS) and Ossifying FibroMyxoid Tumors (OFMT). We express the fusion protein and necessary controls in K562 Cells. The fusion protein assembles a mega-complex harboring both NuA4/TIP60 and PRC2 subunits and enzymatic activities and leads to mislocalization of chromatin marks in the genome, linked to aberrant gene expression.
Project description:Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology. Keywords: other