Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA142921 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE29016: Relation between smoking history and gene expression profiles in lung adenocarcinomas GSE29065: Landscape of somatic allelic imbalances and copy number alterations in human lung carcinoma Refer to individual Series
2012-10-09 | E-GEOD-29066 | biostudies-arrayexpress
Project description:Lung cancer is the worldwide leading cause of death from cancer. This GEO series correspond to one of the BAC aCGH data sets used as validation cohort for the study: Landscape of somatic allelic imbalances and copy number alterations in human lung cancer, Int J Cancer 2013.
2012-10-09 | GSE29065 | GEO
Project description:Lung cancer is the worldwide leading cause of death from cancer. This GEO series correspond to one of the BAC aCGH data sets used as validation cohort for the study: Landscape of somatic allelic imbalances and copy number alterations in human lung cancer, Int J Cancer 2012. Genomic profiling of 78 lung carcinomas using 32K BAC aCGH microarrays.
2012-10-09 | E-GEOD-29065 | biostudies-arrayexpress
Project description:A survey of the somatic allelic imbalances and copy number alterations in HER2-amplified breast cancer.
2011-12-02 | GSE31645 | GEO
Project description:Landscape of somatic allelic imbalances and copy number alterations in HER2-amplified breast cancer
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Project description:A survey of the somatic allelic imbalances and copy number alterations in HER2-amplified breast cancer. Genomic profiling of 26 breast tumors with amplification of HER2 using 1M and 2.5M Illumina SNP beadchips. Sample identifiers correspond to GSE21259 where sample annotations may be extracted.
2011-12-02 | E-GEOD-31645 | biostudies-arrayexpress
Project description:Characterizing genetic transitions of copy number alterations and allelic imbalances in metastatic process of the oral tongue carcinoma
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Project description:High-resolution microarray-based whole genome genotyping (WGG) techniques based on SNP analysis have successfully been applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Problems in data interpretation arise when WGG is applied on tumor tissue specimens, in which normal cell components and tumor subpopulations frequently exist. Such heterogeneity may lead to reduced detection of cancer cell specific genomic alterations. To circumvent problems with sample heterogeneity, we propose using a segmentation strategy derived from DNA copy number analysis for detection of LOH and allelic imbalance. We generated an experimental dilution series of a tumor cell line mixed with its paired normal cell line and simulated data for such dilutions to test the strategy. We also used data sets generated on both Affymetrix and Illumina WGG platforms, including paired tumor-normal samples and tumors previously characterized by FISH. We tested the segmentation strategy against several reported algorithms. We demonstrate high sensitivity and specificity of the segmentation strategy for detecting both minute and gross allelic imbalances originating from DNA copy number gain, loss, and neutral events in tumor specimens. For example, hemizygous copy number loss can be detected in samples containing only 20-25% tumor cells. Furthermore, the strategy can identify cell subpopulation specific events and accurately estimate the fraction of cells affected by an allelic imbalance. Thus, the segmentation strategy extends the usefulness of WGG platforms for investigation of allelic imbalances in heterogeneous tumor genomes.
2008-09-18 | GSE11976 | GEO
Project description:Somatic copy number alterations of 17 paired tumor and metastasis tissue samples were measured by Agilent array-based comparative genomic hybridization (CGH). Seven colon adenocarcinomas with paired liver metastasis and 10 liver carcinoma with metastasis to the lymph node, adrenal gland or lung were analyzed.
2015-01-22 | GSE41210 | GEO
Project description:A SNP microarray and FISH-based procedure to detect allelic imbalances in multiple myeloma: an integrated genomics approach reveals a wide dosage effect on gene and microRNA expression Multiple myeloma (MM) is characterized by marked genomic instability. Beyond structural rearrangements, a relevant role in its biology is represented by allelic imbalances leading to significant variations in ploidy status. To better elucidate the genomic complexity of MM, we analyzed a panel of 45 patients using combined FISH and microarray approaches. Using a self-developed procedure to infer exact local copy numbers for each sample, we identified a significant fraction of patients showing marked aneuploidy. A conventional clustering analysis showed that aneuploidy, chromosome 1 alterations, hyperdiploidy and recursive deletions at 1p and chromosomes 13, 14 and 22 were the main aberrations driving samples grouping. Then, we integrated mapping information with gene and microRNAs expression profiles: a multiclass analysis of the identified clusters showed a marked gene-dosage effect, particularly concerning 1q transcripts, also confirmed by correlating gene expression levels and local copy number alterations. A wide dosage effect affected also microRNAs, indicating that structural abnormalities in MM closely reflect in their expression imbalances. Finally, we identified several loci in which genes and microRNAs expression correlated with loss-of-heterozygosity occurrence. Our results provide insights into the composite network linking genome structure and gene/microRNA transcriptional features in MM. Keywords: Integrated genomics approach based on SNP microarray and FISH procedures to detect allelic imbalances in multiple myeloma. Pathological bone marrow specimens from 41 MM and four plasma cell leukemia (PCL) patients at diagnosis. 250 nanograms of genomic DNA was processed and, in accordance with the manufacturer's protocols, 40 micrograms of fragmented biotin-labelled DNA were hybridized on GeneChip Human Mapping 50K XbaI Arrays (Affymetrix Inc.). The arrays were scanned using the GeneChip Scanner 3000 7G. The images were acquired using Affymetrix GeneChip® Operating Software (GCOS version 1.4). Copy number values for individual SNPs were extracted and converted from CEL files into signal intensities using GTYPE 4.1 and Affymetrix Copy Number Analysis Tool (CNAT 4.0.1) softwares. Genomic Smoothing analysis was performed by using the smoothing window of 0 Mb, and inferred copy number states were derived from a Hidden Markov Model (HMM) based algorithm implemented in CNAT 4.0.1. Circular Binary Segmentation (Ohlsen et al., 2004) was applied using DNAcopy package for R Bioconductor on raw data. FBN procedure was finally applied to infer exact local copy number as described in the mentioned Reference.
2009-05-16 | E-GEOD-16121 | biostudies-arrayexpress