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Expression data from mouse neural cells and tumors

ABSTRACT: Neural stem cells (NSCs) are considered to be the cell-of-origin of brain tumor stem cells. To identify the genetic pathways responsible for the transformation of normal NSCs to brain-tumor-initiating cells, we used Sleeping Beauty (SB) transposons, to mutagenize NSCs. Mobilized SB transposons induced the immortalization of NSCs. Immortalized NSCs induced tumors upon subcutaneous transplantation in immunocompromized mice. To further classify the immortalized cells and mouse tumors, we performed Gene Set Enrichment Analysis (GSEA) using DNA microarray data. Ten immortalized NSC lines (four WT and six p53 mutant lines) and two normal NSCs were used for RNA extraction and hybridization on Affymetrix microarrays. CEL files were processed using the RMA algorithm to normalize the data. Unsupervised hierarchical clustering was applied to the normalized gene expression data across samples using the R module pvclust (Suzuki and Shimodaira, Bioinformatics 22, 1540-1542, 2006). Hierarchical clustering showed a close similarity in gene expression between all ten immortalized lines, compared with two normal NSCs. We then compared their gene expression profiles to those present in the brain transcriptome database (Cahoy et al., J Neurosci 28, 264-278, 2008). Using five gene sets that are associated with neurons, oligodendrocytes, OPCs, astrocytes and cultured astroglial cells, GSEA enrichment scores were calculated for the ten immortalized NSCs lines against two control normal NSCs. GSEA enrichment analysis was performed using GSEA software v. 2.07 (http://www.broadinstitute.org/gsea). We found a strong enrichment for genes that are differentially expressed in the cultured astroglial cells. Twelve tumors (six WT and six p53 mutant tumors) and three control subcutaneous tissues were used for RNA extraction and hybridization on Affymetrix microarrays. CEL files were processed using the RMA algorithm to normalize the data. Unsupervised hierarchical clustering was applied to the normalized gene expression data across samples using the R module pvclust (Suzuki and Shimodaira, Bioinformatics 22, 1540-1542, 2006). Hierarchical clustering of 12 tumors showed a close similarity between the tumors, compared with three control subcutaneous tissues. We next performed GSEA analysis on the tumors. Using gene sets specific to the four defined GBM subtypes (Verhaak et al., Cancer Cell 17, 98-110, 2010), a GSEA enrichment score was then calculated for the twelve tumor samples against three control subcutaneous tissue samples. GSEA enrichment analysis was performed using GSEA software v. 2.07 (http://www.broadinstitute.org/gsea). This analysis showed that the mouse tumors were significantly enriched for genes specific for the mesenchymal GBM.

ORGANISM(S): Mus musculus

PROVIDER: GSE36897 | GEO | 2012/03/31

SECONDARY ACCESSION(S): PRJNA157345

REPOSITORIES: GEO

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Project description:Neural stem cells (NSCs) are considered to be the cell-of-origin of brain tumor stem cells. To identify the genetic pathways responsible for the transformation of normal NSCs to brain-tumor-initiating cells, we used Sleeping Beauty (SB) transposons, to mutagenize NSCs. Mobilized SB transposons induced the immortalization of NSCs. Immortalized NSCs induced tumors upon subcutaneous transplantation in immunocompromized mice. To further classify the immortalized cells and mouse tumors, we performed Gene Set Enrichment Analysis (GSEA) using DNA microarray data. Ten immortalized NSC lines (four WT and six p53 mutant lines) and two normal NSCs were used for RNA extraction and hybridization on Affymetrix microarrays. CEL files were processed using the RMA algorithm to normalize the data. Unsupervised hierarchical clustering was applied to the normalized gene expression data across samples using the R module pvclust (Suzuki and Shimodaira, Bioinformatics 22, 1540-1542, 2006). Hierarchical clustering showed a close similarity in gene expression between all ten immortalized lines, compared with two normal NSCs. We then compared their gene expression profiles to those present in the brain transcriptome database (Cahoy et al., J Neurosci 28, 264-278, 2008). Using five gene sets that are associated with neurons, oligodendrocytes, OPCs, astrocytes and cultured astroglial cells, GSEA enrichment scores were calculated for the ten immortalized NSCs lines against two control normal NSCs. GSEA enrichment analysis was performed using GSEA software v. 2.07 (http://www.broadinstitute.org/gsea). We found a strong enrichment for genes that are differentially expressed in the cultured astroglial cells. Twelve tumors (six WT and six p53 mutant tumors) and three control subcutaneous tissues were used for RNA extraction and hybridization on Affymetrix microarrays. CEL files were processed using the RMA algorithm to normalize the data. Unsupervised hierarchical clustering was applied to the normalized gene expression data across samples using the R module pvclust (Suzuki and Shimodaira, Bioinformatics 22, 1540-1542, 2006). Hierarchical clustering of 12 tumors showed a close similarity between the tumors, compared with three control subcutaneous tissues. We next performed GSEA analysis on the tumors. Using gene sets specific to the four defined GBM subtypes (Verhaak et al., Cancer Cell 17, 98-110, 2010), a GSEA enrichment score was then calculated for the twelve tumor samples against three control subcutaneous tissue samples. GSEA enrichment analysis was performed using GSEA software v. 2.07 (http://www.broadinstitute.org/gsea). This analysis showed that the mouse tumors were significantly enriched for genes specific for the mesenchymal GBM. Immortalized cell lines and tumors were used for RNA extraction and hybridization on Affymetrix microarrays. Ten immortalized lines (Line 3, 28, 31, 59, 95, 116, 119, 120, 123, 331) were compared with two normal NSCs. Twelve tumors were generated from four lines (1, 31, 59, 123) and compared with three subcutaneous tissues (SubQ).

Project description:Molecular profiling of tumors has proven a valuable tool for identification of prognostic and diagnostic subgroups in medulloblastomas, glioblastomas and other cancers. However, the molecular landscape of atypical teratoid / rhabdoid tumors (AT/RTs) remains largely unexplored. To address this issue, we used microarrays to measure the gene expression profiles of 18 AT/RTs, and performed unsupervised hierarchical clustering to determine molecularly similar subgroups. Four major subgroups (clusters) were identified. These did not conform to gender, tumor location, or presence of monosomy 22. Clusters showed distinct gene signatures and differences in enriched biological processes, including elevated expression of choroid plexus genes in Cluster 4. In addition, survival differed significantly by cluster, with shortest survival (mean 4.7 months) in both Clusters 3 and 4 compared to Clusters 1 and 2 (mean 28.1 months). Analysis showed that multiple bone morphogenetic protein (BMP) pathway genes were up-regulated in the short survival clusters, with BMP4 showing the most significant up-regulation (270-fold). Thus, high expression of BMP pathway genes was negatively associated with survival in this dataset. Our study indicates that molecular subgroups exist within AT/RTs, and that molecular profiling of these comparatively rare tumors may be of diagnostic, prognostic and therapeutic value. Key Words: atypical teratoid / rhabdoid tumor; bone morphogenetic protein pathway; BMP4; survival; microarray Molecular profiling of 18 AT/RT patient tumor samples was performed using Affymetrix U133 Plus2 GeneChips. Data were background corrected and normalized using gcRMA (as implemented in Bioconductor). Unsupervised agglomerative hierarchical clustering was performed to identify subsets of AT/RTs with similar gene expression. Limma (moderated t-tests; Bioconductor) was used to identify signature genes for each cluster. Bioinformatics web tool DAVID was used to identify enriched biological processes for each cluster. Survival was analyzed using Kaplan-Meier curves and Cox Hazard Ratio. Bioinformatics tools Gene Set Enrichment (GSEA) and Ingenuity Pathways Analysis were also used to gain further insight into cluster differences.

Project description:Summary: Breast cancer cell lines have been used widely to investigate breast cancer pathobiology and new therapies. Breast cancer is a molecularly heterogeneous disease, and it is important to understand how well and which cell lines best model that diversity. In particular, microarray studies have identified molecular subtypes (luminal A, luminal B, ERBB2-associated, basal-like and normal-like) with characteristic gene-expression patterns and underlying DNA copy number alterations (CNAs). Here, we studied a collection of breast cancer cell lines to catalog molecular profiles and to assess their relation to breast cancer subtypes. Whole-genome DNA microarrays were used to profile gene expression and CNAs in a collection of 52 widely-used breast cancer cell lines, and comparisons were made to existing profiles of primary breast tumors. Hierarchical clustering was used to identify gene-expression subtypes, and Gene Set Enrichment Analysis (GSEA) to discover biological features of those subtypes. Genomic and transcriptional profiles were integrated to discover within high-amplitude CNAs candidate cancer genes with coordinately altered gene copy number and expression. Transcriptional profiling of breast cancer cell lines identified one luminal and two basal-like (A and B) subtypes. Luminal lines displayed an estrogen receptor (ER) signature and resembled luminal-A/B tumors, basal-A lines were associated with ETS-pathway and BRCA1 signatures and resembled basal-like tumors, and basal-B lines displayed mesenchymal and stem-cell characteristics. Compared to tumors, cell lines exhibited similar patterns of CNA, but an overall higher complexity of CNA (genetically simple luminal-A tumors were not represented), and only partial conservation of subtype-specific CNAs. We identified 80 high-level DNA amplifications and 13 presumptive homozygous deletions, and the resident genes with concomitantly altered gene-expression, highlighting known and novel candidate breast cancer genes. Overall, breast cancer cell lines were genetically more complex than tumors, but retained expression patterns with relevance to the luminal-basal subtype distinction. The compendium of molecular profiles defines cell lines suitable for investigations of subtype-specific pathobiology, biomarkers and therapies, and provides a resource for discovery of new breast cancer genes. HEEBO oligonucleotide microarrays from the Stanford Functional Genomics Facility were used to perform gene expression profiling of 50 human breast epithelial cell lines, in comparison to a universal RNA reference. Expression data were analyzed by hierarchical clustering to identify subgroups, and gene set enrichment analysis to identify subgroup-specific gene pathways.

Project description:Congenital glioblastoma multiforme (cGBM) historically has been considered an aggressive tumor of infancy requiring extensive chemotherapy to achieve cure. We report on 4 patients at our institution with cGBMs who were treated with surgery and chemotherapy (carboplatin and etoposide every 21 days for 2-6 cycles). Four of four patients are progression free at a median time of 27.5 months (22-103 months). To characterize the molecular biology of cGBM, we compared the gene expression profiles of 3 cGBMs to 12 pediatric and 6 primary adult glioblastomas collected at our institution. Unsupervised hierarchical clustering showed cGBMs grouped together with other high-grade gliomas. cGBMs demonstrated marked similarity to both pediatric and adult GBMs, with only a total of 31 differentially expressed genes identified (FDR < 0.05). Unique molecular features of congenital GBMs identified included over-expression of multiple genes involved in glucose metabolism and tissue hypoxia pathways. Four tyrosine kinases were also mong the up-regulated genes (RET, RASGRF2, EFNA5, ALK). Thus, at our institution congenital GBMs, while similar both histologically and molecularly to other GBMs, appear to have a good prognosis with surgery in combination with relatively moderate chemotherapy. Further study is needed to determine if the few gene expression differences that were identified may contribute to the better survival seen in these tumors compared to pediatric or adult GBMs. Key Words: glioblastoma; congenital; pediatric; gene expression; microarray Molecular profiling of 18 AT/RT patient tumor samples was performed using Affymetrix U133 Plus2 GeneChips. Data were background corrected and normalized using gcRMA (as implemented in Bioconductor). Unsupervised agglomerative hierarchical clustering was performed to identify subsets of AT/RTs with similar gene expression. Limma (moderated t-tests; Bioconductor) was used to identify signature genes for each cluster. Bioinformatics web tool DAVID was used to identify enriched biological processes for each cluster. Survival was analyzed using Kaplan-Meier curves and Cox Hazard Ratio. Bioinformatics tools Gene Set Enrichment (GSEA) and Ingenuity Pathways Analysis were also used to gain further insight into cluster differences.

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Expression data from mouse neural cells and tumors

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