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Series GSE6532 Query DataSets for GSE6532
Status Public on Mar 01, 2007
Title Definition of clinically distinct molecular subtypes in estrogen receptor positive breast carcinomas using genomic grade
Organism Homo sapiens
Experiment type Expression profiling by array
Summary Purpose: A number of microarray studies have reported distinct molecular profiles of breast cancers (BC): basal-like, ErbB2-like and two to three luminal-like subtypes. These were associated with different clinical outcomes. However, although the basal and the ErbB2 subtypes are repeatedly recognized, identification of estrogen receptor (ER)-positive subtypes has been inconsistent. Refinement of their molecular definition is therefore needed.

Materials and methods: We have previously reported a gene-expression grade index (GGI) which defines histological grade based on gene expression profiles. Using this algorithm, we assigned ER-positive BC to either high or low genomic grade subgroups and compared these to previously reported ER-positive molecular classifications. As further validation, we classified 666 ER-positive samples into subtypes and assessed their clinical outcome.

Results: Two ER-positive molecular subgroups (high and low genomic grade) could be defined using the GGI. Despite tracking a single biological pathway, these were highly comparable to the previously described luminal A and B classification and significantly correlated to the risk groups produced using the 21-gene recurrence score. The two subtypes were associated with statistically distinct clinical outcome in both systemically untreated and tamoxifen-treated populations.

Conclusions: The use of genomic grade can identify two clinically distinct ER-positive molecular subtypes in a simple and highly reproducible manner across multiple datasets. This study emphasizes the important role of proliferation-related genes in predicting prognosis in ER-positive BC.
Keywords: disease state analysis
Overall design dataset of microarray experiments from primary breast tumors used to assess the reationship between GGI, molecular subtypes, and tamoxifen resistance.

No replicate, no reference sample.
Contributor(s) Loi S, Haibe-Kains B, Desmedt C, Lallemand F, Tutt AM, Gillet C, Ellis P, Harris A, Bergh J, Foekens JA, Klijn JG, Larsimont D, Buyse M, Bontempi G, Delorenzi M, Piccart MJ, Sotiriou C
Citation(s) 17401012, 18498629, 20479250, 19552798
Submission date Dec 14, 2006
Last update date Sep 26, 2019
Contact name Benjamin Haibe-Kains
Phone +14165818626
Organization name Princess Margaret Cancer Centre
Department Princess Margaret Research
Lab Bioinformatics and Computational Genomics
Street address 610 University Avenue
City Toronto
State/province Ontario
ZIP/Postal code M5G 2M9
Country Canada
Platforms (3)
GPL96 [HG-U133A] Affymetrix Human Genome U133A Array
GPL97 [HG-U133B] Affymetrix Human Genome U133B Array
GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array
Samples (741)
GSM65316 KIT_82A83
GSM65317 KIT_6B85
GSM65318 KIT_8B87
BioProject PRJNA98807

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
GSE6532_LUMINAL.RData.gz 134.1 Mb (ftp)(http) RDATA
GSE6532_LUMINAL_README.txt.gz 846 b (ftp)(http) TXT
GSE6532_LUMINAL_annot.txt.gz 1.1 Mb (ftp)(http) TXT
GSE6532_LUMINAL_demo.txt.gz 7.2 Kb (ftp)(http) TXT
GSE6532_RAW.tar 2.7 Gb (http)(custom) TAR (of CEL)

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