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Series GSE11976 Query DataSets for GSE11976
Status Public on Sep 18, 2008
Title Segmentation-based detection of allelic imbalance and LOH in cancer cells using whole genome SNP arrays
Organism Homo sapiens
Experiment type Genome variation profiling by SNP array
Summary 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.
 
Overall design The proposed segmentation strategy detects allelic imbalance originating from DNA copy number gain, loss or copy neutral events. Here we use Illumina 317K, 370K and 550K and Affymetrix 250K whole-genome single-nucleotide polymorphism arrays hybridized with tumor samples (breast, colon, leukemia, and urothelial) to evaluate the segmentation strategy against several other reported methods. The data sets include a simulated dilution series, an experimental dilution series of a breast cancer cell line as well as paired tumor-normal samples. This GEO series include the 12 sample experimental dilution series of the HCC1395 breast cancer cell line (CRL-2324: www.atcc.org) and 4 paired urothelial tumor-normal samples, all hybridized on Illumina HumanCNV 370k BeadChips.
Web link http://baseplugins.thep.lu.se/wiki/se.lu.onk.BAFsegmentation
 
Contributor(s) Staaf J, Lindgren D, Vallon-Christersson J, Isaksson A, Göransson H, Juliusson G, Rosenquist R, Höglund M, Borg A, Ringner M
Citation(s) 18796136
Submission date Jul 03, 2008
Last update date Jan 03, 2013
Contact name Johan Staaf
Organization name SCIBLU - Swegene Centre for Integrative Biology at Lund University
Street address Medicon Village
City Lund
ZIP/Postal code SE-223 81
Country Sweden
 
Platforms (1)
GPL6986 Illumina HumanCNV370-Duov1 DNA Analysis BeadChip (HumanCNV370v1)
Samples (20)
GSM302883 Lymphoblastoid cell line HCC1395BL (CRL2325)
GSM302884 Breast carcinoma cell line HCC1395 diluted with HCC1395BL (CRL2324_23pc)
GSM302885 Breast carcinoma cell line HCC1395 diluted with HCC1395BL (CRL2324_47pc)
Relations
BioProject PRJNA105853

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE11976_GEO_Export_Urothelial_INT_370k.txt 89.9 Mb (ftp)(http) TXT
GSE11976_GEO_export_DilutionSeries_INT_370k.txt 132.0 Mb (ftp)(http) TXT
GSE11976_RAW.tar 38.9 Mb (http)(custom) TAR
Raw data provided as supplementary file
Raw data are available on Series record
Processed data included within Sample table
Processed data provided as supplementary file

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