SCLC LCNEC FFPE LMD
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens (human)
SUBMITTER: Toshihide Nishimura
PROVIDER: PXD013583 | JPOST Repository | Tue May 14 00:00:00 BST 2019
REPOSITORIES: jPOST
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2015-03-05 | E-GEOD-57177 | biostudies-arrayexpress
Project description: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.
2010-10-01 | E-GEOD-18842 | biostudies-arrayexpress
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
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Project description:Lung cancer remains the leading cause of cancer death. Genome sequencing of lung tumors from patients with Squamous Cell Carcinoma has identified SMAD4 to be frequently mutated. Here we used a novel mouse model to determine the molecular mechanisms regulated by loss of Smad4 which lead to lung cancer progression. Mice with ablation of Pten and Smad4 in airway epithelium developed metastatic adenosquamous tumors. Comparative transcriptomic and in vivo cistromic analyses determined that loss of PTEN and SMAD4 resulted in activation of the ELF3 and the ErbB2 pathway due to decreased ERRFI1’s expression, a negative regulator of ERBB2 in mice and human cells. The combinatorial inhibition of ErbB2 and Akt signaling attenuated tumor progression and cell invasion, respectively. Expression profiles analysis of human lung tumors substantiated the importance of the ErbB2/Akt/ELF3 signaling pathway as both prognostic biomarkers and therapeutic drug targets for treating lung cancer.
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2015-03-05 | GSE57133 | GEO
Project description:Lung cancer remains the leading cause of cancer death. Genome sequencing of lung tumors from patients with Squamous Cell Carcinoma has identified SMAD4 to be frequently mutated. Here we used a novel mouse model to determine the molecular mechanisms regulated by loss of Smad4 which lead to lung cancer progression. Mice with ablation of Pten and Smad4 in airway epithelium developed metastatic adenosquamous tumors. Comparative transcriptomic and in vivo cistromic analyses determined that loss of PTEN and SMAD4 resulted in activation of the ELF3 and the ErbB2 pathway due to decreased ERRFI1 expression, a negative regulator of ERBB2 in mice and human cells. The combinatorial inhibition of ErbB2 and Akt signaling attenuated tumor progression and cell invasion, respectively. Expression profiles analysis of human lung tumors substantiated the importance of the ErbB2/Akt/ELF3 signaling pathway as both prognostic biomarkers and therapeutic drug targets for treating lung cancer. The microarray data includes two different ages of mouse lung samples. The microarray for the study on late stage was did on 12-month-old wild type mouse lungs and Ptend/dSmad4d/d mouse lung tumors. This study was used to identify the significantly changed genes between lung tumors and wild type lungs. The microarray for the study on early stage was did on 7-month-old wild type, Ptend/d and Ptend/dSmad4d/d mouse lungs. This study was used to identify the significantly changed driven genes before the lung tumor initiation and metastasis.
2015-03-05 | E-GEOD-57133 | biostudies-arrayexpress
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2011-03-04 | E-GEOD-27651 | biostudies-arrayexpress
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
2004-02-11 | GSE1037 | GEO
Project description:BACKGROUND: Despite the moderate incidence of papillary renal cell carcinoma; (PRCC), there is a disproportionately limited understanding of its underlying genetic programs. There is no effective therapy for metastatic PRCC, and patients are often excluded from kidney cancer trials. A morphological classification of PRCC into Type 1 and Type 2 tumors has been recently proposed, but its biological relevance remains uncertain. PATIENTS AND METHODS. We studied the gene expression profiles of 34 cases of PRCC using Affymetrix HGU133 Plus 2.0 arrays (54,675 probe sets) using both unsupervised and supervised analysis. Comparative genomic microarray analysis (CGMA) was used to infer cytogenetic aberrations, and pathways were ranked with a curated database. Expression of selected genes was validated by immunohistochemistry in 34 samples, with 15 independent tumors. RESULTS. We identified 2 highly distinct molecular PRCC subclasses with morphologic correlation. The first class, with excellent survival, corresponded to 3 histological subtypes: Type 1, low-grade Type 2 and mixed Type 1/low-grade Type 2 tumors. The second class, with poor survival, corresponded to high-grade Type 2 tumors (n=11). Dysregulation of G1/S and G2/M checkpoint genes were found in Class 1 and Class 2 tumors respectively, alongside characteristic chromosomal aberrations. We identified a 7-transcript predictor that classified samples on cross-validation with 97% accuracy. Immunohistochemistry confirmed high expression of cytokeratin 7 in Class 1 tumors, and of topoisomerase IIa in Class 2 tumors. CONCLUSIONS. We report 2 molecular subclasses of PRCC, which are biologically and clinically distinct, which may be readily distinguished in a clinical setting. Experiment Overall Design: 34 individual samples analyzed, no technical replicates. Based on histologic features and Fuhrman grading system, we designated four categories of tumors: type 1 (n = 14) characterized by small tumor cells of low Fuhrman grade (2); type 2A (n = 4) characterized by large eosinophilic tumor cells of low Fuhrman grade (2); combined type 1 and 2A (n = 5); and type 2B (n = 11) characterized by large eosinophilic tumor cells with Fuhrman grade.
2007-10-20 | E-GEOD-2748 | biostudies-arrayexpress
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2011-03-04 | GSE27651 | GEO