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Series GSE5460 Query DataSets for GSE5460
Status Public on May 26, 2007
Title Predicting Features of Breast Cancer with Gene Expression Patterns
Organism Homo sapiens
Experiment type Expression profiling by array
Summary Predictors built from gene expression data accurately predict ER, PR, and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data cannot be used to predict tumor size or lymphatic-vascular invasion.
Keywords: disease state analysis
Overall design Microarray data from the tumors of 129 patients were analyzed for the ability to predict biomarkers (ER, PR, HER2), histologic features (grade and lymphatic-vascular invasion), and stage-related information (tumor size and lymph node metastasis). Multiple statistical predictors were used and the prediction accuracy determined by error rates of prediction and by dimensional scaling and visualization of the states under study. Models to predict lymph node metastasis were built by combinations of molecular, histologic and anatomic features.

***GSM125119.CEL and GSM125120.CEL are corrupt***
Contributor(s) Lu X, Lu X, Wang ZC, Iglehart JD, Zhang X, Richardson AL
Citation(s) 18297396
Submission date Aug 04, 2006
Last update date Mar 25, 2019
Contact name James Dirk Iglehart
Organization name Dana Farber Cancer Institute
Department Cancer Biology
Lab Iglehart lab
Street address 44 Binney St.
City Boston
State/province MA
ZIP/Postal code 02115
Country USA
Platforms (1)
GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array
Samples (129)
GSM124994 C113
GSM124995 C114
GSM124996 C115
BioProject PRJNA95969

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
GSE5460_RAW.tar 1.0 Gb (http)(custom) TAR (of CEL)
Raw data provided as supplementary file

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