The REMBRANDT study – a large collection of genomic data from brain cancer patients [SNP]
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ABSTRACT:
PROVIDER: PRJNA427348 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The Rembrandt brain cancer dataset and includes clinical and biospecimen data from this study for 671 patients collected from 14 contributing institutions in 2005. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes alongside clinical outcomes this large brain cancer study. In 2015, the NCI retired the REMBRANDT data portal, and all molecular data including microarray gene expression, copy number, and clinical data, was migrated to the Georgetown Database of Cancer (G-DOC). The G-DOC platform can be accessed at: https://gdoc.georgetown.edu. G-DOC is a data integration platform that offers advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information. After migration of the REMBRANDT dataset into G-DOC, we applied our novel algorithm for copy number data analysis called Chromosomal Instability Index (CINdex), published as an open source Bioconductor package . This submission includes the copy number data, processed on two Affymetrix 50K copy number arrays and gene expression data processed on the Affymetrix U133 Plus 2 array. It also includes the Chromsome and Cytoband CINdex matrices.
2018-05-25 | GSE108475 | GEO
Project description:The REMBRANDT study – a large collection of genomic data from brain cancer patients
| PRJNA427347 | ENA
Project description:The REMBRANDT study – a large collection of genomic data from brain cancer patients [gene expression]
| PRJNA427368 | ENA
Project description:The Rembrandt brain cancer dataset and includes clinical and biospecimen data from this study for 671 patients collected from 14 contributing institutions in 2005. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes alongside clinical outcomes (overall survival) in this large brain cancer study. In 2015, the NCI retired the REMBRANDT data portal, and all molecular data including microarray gene expression, copy number, and clinical data, was migrated to the Georgetown Database of Cancer (G-DOC). The G-DOC platform can be accessed at: https://gdoc.georgetown.edu. G-DOC is a data integration platform that offers advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information. After migration of the REMBRANDT dataset into G-DOC, we applied our novel algorithm for copy number data analysis called Chromosomal Instability Index (CINdex), published as an open source Bioconductor package . This submission includes the copy number data, processed on two Affymetrix 50K copy number arrays and gene expression data processed on the Affymetrix U133 Plus2 array. It also includes the Chromosome and Cytoband CINdex matrices.
2018-05-25 | GSE108474 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-05-25 | GSE108476 | GEO
Project description:The Rembrandt brain cancer dataset includes 671 patients collected from 14 contributing institutions from 2004-2006. It is accessible for conducting clinical translational research using the open access Georgetown Database of Cancer (G-DOC) platform. In addition, the raw and processed genomics and transcriptomics data have also been made available via the public NCBI GEO repository as a super series GSE108476. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes in patients alongside clinical outcomes (overall survival) using this large brain cancer study.
| S-EPMC6091243 | biostudies-literature
Project description:This SuperSeries is composed of the following subset Series: GSE18642: Definitive SNP/CNV haplotype map of Asians determined using a collection of complete hydatidiform moles (Affymetrix) GSE18663: Definitive SNP/CNV haplotype map of Asians determined using a collection of complete hydatidiform moles (Illumina) Refer to individual Series
2010-05-17 | E-GEOD-18701 | biostudies-arrayexpress
Project description:Objectives: Despite recent advancements in diagnostic tools, the genomic landscape of hereditary hearing loss remains largely uncharacterized. One strategy to understand genome-wide aberrations includes the analysis of copy number variation that can be mapped using SNP-microarray technology. A growing collection of literature has begun to uncover the importance of copy number variation in hereditary hearing loss. This pilot study underpins a larger effort that involves the stage-wise analysis of hearing loss patients, many of whom have advanced to high-throughput sequencing analysis. Data description: Our data originate from Infinium HumanOmni1-Quad v1.0 SNP-microarrays (Illumina) that provide useful markers for genome-wide association studies and copy number variation analysis. This dataset comprises a cohort of 108 individuals (99 with hearing loss, 9 normal hearing family members) for the purpose of understanding the genetic contribution of copy number variations to hereditary hearing loss.
2018-03-06 | GSE111131 | GEO
Project description:A core task to understand the consequences of non-coding single nucleotide polymorphisms (SNP) is to identify their genotype specific binding of transcription factor (TF). Here, we generate a large-scale TF-SNP interaction map for a selection of 116 colorectal cancer (CRC) risk loci and validated TF binding to 10 putatively functional SNPs. Our data further revealed TF binding complexity adjacent to the 116 risk loci, adding an additional layer of understanding to regulatory networks associated with CRC relevant loci.
2017-02-23 | PXD004435 | Pride
Project description:The pathogenesis of diffuse large B cell lymphomas (DLBCL) is only partly understood. We analyzed 148 DLBCL by high resolution single nucleotide polymorphism (SNP)-chips to characterize genomic imbalances. Seventy-nine cases were of the germinal center B-cell like (GCB) type of DLBCL, 49 of the activated B-cell like (ABC) subtype and 20 were type 3 DLBCL. Twenty-four regions of recurrent genomic gains and 38 regions of recurrent genomic losses were identified over the whole cohort, with a median of 25 imbalances per case for ABC-DLBCL and 19 per case for GCB-DLBCL. Several recurrent copy number changes showed differential frequencies in the GCB- and ABC-DLBCL subgroups, including gains of HDAC7A predominantly in GCB-DLBCL (38% of cases) and losses of BACH2 and CASP8AP2 predominantly in ABC-DLBCL (35%), hinting at disparate pathogenetic mechanisms in these entities. Correlating gene expression and copy number revealed a strong gene dosage effect in all tumors, with 34% of probesets showing a concordant expression change in affected regions. Two new potential tumor suppressor genes emerging from the analysis, CASP3 and IL5RA, were sequenced in 10 and 16 candidate cases, respectively. However, no mutations were found, pointing to a potential haploinsufficiency effect of these genes, considering their reduced expression in cases with deletions. This work thus describes differences and similarities in the landscape of genomic aberrations in the DLBCL subgroups in a large collection of cases, confirming already known targets, but also discovering novel copy number changes with possible pathogenetic relevance. Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from cryopreserved lymph node biopsies of tumor samples.
2014-07-22 | E-GEOD-57277 | biostudies-arrayexpress