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| 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
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| 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
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| 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.
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| 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 |
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| Submission date |
Dec 14, 2006 |
| Last update date |
Sep 26, 2019 |
| Contact name |
Benjamin Haibe-Kains |
| E-mail(s) |
[email protected]
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| Phone |
+14165818626
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| Organization name |
Princess Margaret Cancer Centre
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| Department |
Princess Margaret Research
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| Lab |
Bioinformatics and Computational Genomics
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| Street address |
610 University Avenue
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| City |
Toronto |
| State/province |
Ontario |
| ZIP/Postal code |
M5G 2M9 |
| Country |
Canada |
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| 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 |
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| Samples (741)
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| Relations |
| BioProject |
PRJNA98807 |
| 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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