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A consensus prognostic gene expression classifier for ER positive breast cancer.
Andrew E Teschendorff1, Ali Naderi, Nuno L Barbosa-Morais
1Cancer Genomics Program, Department of Oncology, University of Cambridge, Hutchison/MRC Research Center, Hills Road, Cambridge CB2 2XZ, UK. aet21@cam.ac.uk.
Genome Biology
|November 2, 2006
Summary
This study developed a validated prognostic gene classifier for estrogen receptor (ER) positive breast cancer. The classifier shows promise in hybrid schemes, improving prognostic separation for ER-positive tumors.
Area of Science:
- Oncology
- Genomics
- Biostatistics
Background:
- Breast cancer prognosis remains challenging due to disease heterogeneity.
- A universally accepted prognostic gene expression classifier is currently lacking.
Purpose of the Study:
- To identify and validate a universally applicable prognostic molecular classifier for estrogen receptor (ER) positive breast cancer.
- To assess the performance of molecular classifiers in comparison to classical prognostic indices and in hybrid schemes.
Main Methods:
- Combined analysis of three major breast cancer microarray datasets.
- Application of a robust measure for prognostic separation.
- Validation in three independent external cohorts comprising 877 ER-positive samples.
Main Results:
- A prognostic molecular classifier was identified and validated across multiple datasets and platforms.
- The classifier demonstrated validity in 877 ER-positive breast cancer samples.
- Molecular classifiers may enhance, rather than replace, classical prognostic indices in hybrid models.
Conclusions:
- The presented prognostic molecular classifier is the first validated across 877 ER-positive breast cancer samples and three microarray platforms.
- Hybrid molecular-pathological classification schemes can improve prognostic separation.
- Further multi-institutional studies are required to ascertain the full value of molecular classifiers alongside standard prognostic factors.