Comparative analyses of gene copy number and mRNA expression in GBM tumors and GBM xenografts
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ABSTRACT:
OTHER RELATED OMICS DATASETS IN: PRJNA112037PRJNA114361PRJNA114381
ORGANISM(S): Homo sapiens
PROVIDER: GSE14806 | GEO | 2009/03/07
SECONDARY ACCESSION(S): PRJNA112037
REPOSITORIES: GEO
Similar Datasets
Project description:Development of model systems that recapitulate the molecular heterogeneity observed among glioblastoma multiforme (GBM) tumors will expedite the testing of targeted molecular therapeutic strategies for GBM treatment. In this study, we profiled DNA copy number and mRNA expression in 21 independent GBM tumor lines maintained as subcutaneous xenografts (GBMX), and compared GBMX molecular signatures to those observed in GBM clinical specimens derived from the Cancer Genome Atlas (TCGA). The predominant copy number signature in both tumor groups was defined by chromosome-7 gain/chromosome-10 loss, a poor-prognosis genetic signature. We also observed, at frequencies similar to that detected in TCGA GBM tumors, genomic amplification and overexpression of known GBM oncogenes, such as EGFR, MDM2, CDK6, and MYCN, and novel genes, including NUP107, SLC35E3, MMP1, MMP13, and DDX1. The transcriptional signature of GBMX tumors, which was stable over multiple subcutaneous passages, was defined by overexpression of genes involved in M phase, DNA replication, and chromosome organization (MRC) and was highly similar to the poor-prognosis mitosis and cell-cycle module (MCM) in GBM. Assessment of gene expression in TCGA-derived GBMs revealed overexpression of MRC cancer genes AURKB, BIRC5, CCNB1, CCNB2, CDC2, CDK2, and FOXM1, which form a transcriptional network important for G2/M progression and/or checkpoint activation. Our study supports propagation of GBM tumors as subcutaneous xenografts as a useful approach for sustaining key molecular characteristics of patient tumors, and highlights therapeutic opportunities conferred by this GBMX tumor panel for testing targeted therapeutic strategies for GBM treatment.
| S-EPMC2765338 | biostudies-literature
Project description:Comparative analyses of gene copy number and mRNA expression in GBM tumors and GBM xenografts
| PRJNA112037 | ENA
Project description:This SuperSeries is composed of the SubSeries listed below.
2010-11-21 | GSE24558 | GEO
Project description:BackgroundBos taurus and Bos indicus are two main sub-species of cattle. However, the differential copy number variations (CNVs) between them are not yet well studied.ResultsBased on the new high-quality cattle reference genome ARS-UCD1.2, we identified 13,234 non-redundant CNV regions (CNVRs) from 73 animals of 10 cattle breeds (4 Bos taurus and 6 Bos indicus), by integrating three detection strategies. While 6990 CNVRs (52.82%) were shared by Bos taurus and Bos indicus, large CNV differences were discovered between them and these differences could be used to successfully separate animals into two subspecies. We found that 2212 and 538 genes uniquely overlapped with either indicine-specific CNVRs and or taurine-specific CNVRs, respectively. Based on FST, we detected 16 candidate lineage-differential CNV segments (top 0.1%) under selection, which overlapped with eight genes (CTNNA1, ENSBTAG00000004415, PKN2, BMPER, PDE1C, DNAJC18, MUSK, and PLCXD3). Moreover, we obtained 1.74 Mbp indicine-specific sequences, which could only be mapped on the Bos indicus reference genome UOA_Brahman_1. We found these sequences and their associated genes were related to heat resistance, lipid and ATP metabolic process, and muscle development under selection. We further analyzed and validated the top significant lineage-differential CNV. This CNV overlapped genes related to muscle cell differentiation, which might be generated from a retropseudogene of CTH but was deleted along Bos indicus lineage.ConclusionsThis study presents a genome wide CNV comparison between Bos taurus and Bos indicus. It supplied essential genome diversity information for understanding of adaptation and phenotype differences between the Bos taurus and Bos indicus populations.
| S-EPMC7528262 | biostudies-literature
Project description:We present an allele-specific copy number analysis of the in vivo breast cancer genome. We describe a unique bioinformatics approach, ASCAT (allele-specific copy number analysis of tumors), to accurately dissect the allele-specific copy number of solid tumors, simultaneously estimating and adjusting for both tumor ploidy and nonaberrant cell admixture. This allows calculation of "ASCAT profiles" (genome-wide allele-specific copy-number profiles) from which gains, losses, copy number-neutral events, and loss of heterozygosity (LOH) can accurately be determined. In an early-stage breast carcinoma series, we observe aneuploidy (>2.7n) in 45% of the cases and an average nonaberrant cell admixture of 49%. By aggregation of ASCAT profiles across our series, we obtain genomic frequency distributions of gains and losses, as well as genome-wide views of LOH and copy number-neutral events in breast cancer. In addition, the ASCAT profiles reveal differences in aberrant tumor cell fraction, ploidy, gains, losses, LOH, and copy number-neutral events between the five previously identified molecular breast cancer subtypes. Basal-like breast carcinomas have a significantly higher frequency of LOH compared with other subtypes, and their ASCAT profiles show large-scale loss of genomic material during tumor development, followed by a whole-genome duplication, resulting in near-triploid genomes. Finally, from the ASCAT profiles, we construct a genome-wide map of allelic skewness in breast cancer, indicating loci where one allele is preferentially lost, whereas the other allele is preferentially gained. We hypothesize that these alternative alleles have a different influence on breast carcinoma development.
| S-EPMC2947907 | biostudies-other
Project description:BackgroundSmall intestinal neuroendocrine tumors (SI-NETs) are typically slow-growing tumors that have metastasized already at the time of diagnosis. The purpose of the present study was to further refine and define regions of recurrent copy number (CN) alterations (CNA) in SI-NETs.MethodsGenome-wide CNAs was determined by applying array CGH (a-CGH) on SI-NETs including 18 primary tumors and 12 metastases. Quantitative PCR analysis (qPCR) was used to confirm CNAs detected by a-CGH as well as to detect CNAs in an extended panel of SI-NETs. Unsupervised hierarchical clustering was used to detect tumor groups with similar patterns of chromosomal alterations based on recurrent regions of CN loss or gain. The log rank test was used to calculate overall survival. Mann-Whitney U test or Fisher's exact test were used to evaluate associations between tumor groups and recurrent CNAs or clinical parameters.ResultsThe most frequent abnormality was loss of chromosome 18 observed in 70% of the cases. CN losses were also frequently found of chromosomes 11 (23%), 16 (20%), and 9 (20%), with regions of recurrent CN loss identified in 11q23.1-qter, 16q12.2-qter, 9pter-p13.2 and 9p13.1-11.2. Gains were most frequently detected in chromosomes 14 (43%), 20 (37%), 4 (27%), and 5 (23%) with recurrent regions of CN gain located to 14q11.2, 14q32.2-32.31, 20pter-p11.21, 20q11.1-11.21, 20q12-qter, 4 and 5. qPCR analysis confirmed most CNAs detected by a-CGH as well as revealed CNAs in an extended panel of SI-NETs. Unsupervised hierarchical clustering of recurrent regions of CNAs revealed two separate tumor groups and 5 chromosomal clusters. Loss of chromosomes 18, 16 and 11 and gain of chromosome 20 were found in both tumor groups. Tumor group II was enriched for alterations in chromosome cluster-d, including gain of chromosomes 4, 5, 7, 14 and gain of 20 in chromosome cluster-b. Gain in 20pter-p11.21 was associated with short survival. Statistically significant differences were observed between primary tumors and metastases for loss of 16q and gain of 7.ConclusionOur results revealed recurrent CNAs in several candidate regions with a potential role in SI-NET development. Distinct genetic alterations and pathways are involved in tumorigenesis of SI-NETs.
| S-EPMC3819709 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2016-05-07 | GSE81184 | GEO
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2014-04-07 | E-GEOD-49280 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below.
2010-08-27 | GSE20710 | GEO
Project description: Not available
| S-BSST517 | biostudies-other