Dataset Information

A consensus prognostic gene expression classifier for ER positive breast cancer.

ABSTRACT:

Background

A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer.

Results

Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation.

Conclusion

The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors.

SUBMITTER: Teschendorff AE

PROVIDER: S-EPMC1794561 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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Publications

A consensus prognostic gene expression classifier for ER positive breast cancer.

Teschendorff Andrew E AE Naderi Ali A Barbosa-Morais Nuno L NL Pinder Sarah E SE Ellis Ian O IO Aparicio Sam S Brenton James D JD Caldas Carlos C

Genome biology 20061031 10

<h4>Background</h4>A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer.<h4>Results</h4>Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, co ...[more]

PMID: 17076897

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Project description:BACKGROUND: The role of miRNAs in familial breast cancer (fBC) is poorly investigated as also in the BRCA-like tumors. To identify a specific miRNA expression pattern which could allow a better fBC classification not only based on clinico-pathological and immunophenotypical parameters we analyzed miRNA profile in familial and sporadic samples. Moreover since BRCA1 tumors and sporadic triple negative (TN) breast tumors share similarities regarding clinical outcomes and some histological characteristics, we focused on TN and not TN cases. METHODS: The sample set included fresh frozen tissue samples, including 39 female fBCs (19 BRCA-related and 20 BRCAX) and 12 male fBC (BRCAX). Moreover, we considered TN and non TN (NTN), 21 BRCA-related and 27 sporadic BCs. MiRNA profiling was performed through GeneChip miRNA v.1.0 Array (Affymetrix). ANOVA, hierarchical and consensus clustering analyses allowed identification of pattern of expression of miRNAs and pathway enrichment analysis, considering validated target genes, was carried out to achieve a deeper biological understanding. RESULTS: ANOVA test led to the identification of 53 deregulated miRNAs; hierarchical and consensus clustering of female fBCs (fFBCs) and male fBCs (fMBCs) highlighted the presence of 3 sample clusters named FBC1, FBC2 and FBC3. We found a correlation between ER-status and the three sample clusters. The three clusters are distinct by a different expression of two clusters of miRNAs (CLU1 and CLU2), which resulted to be different in targeted pathways. In particular, CLU1 targets cellular pathways and CLU2 is involved in epigenetic activities. Considering TN and NTN BRCA-related and sporadic tumors, a hierarchical clustering identified two clusters of miRNAs, which were not so different from CLU1 and CLU2, both in miRNA content and targeted pathways. CONCLUSIONS: Our results highlighted the importance of miRNA regulation to better clarify similarities and differences between familial and sporadic BC groups.

Dataset Information

A consensus prognostic gene expression classifier for ER positive breast cancer.

Background

Results

Conclusion

Publications

A consensus prognostic gene expression classifier for ER positive breast cancer.

Similar Datasets

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