Identification of a robust gene signature that predicts breast cancer outcome in independent data sets Journal Article
| Authors: | Korkola, J. E.; Blaveri, E.; DeVries, S.; Moore, D. H. 2nd; Hwang, E. S.; Chen, Y. Y.; Estep, A. L. H.; Chew, K. L.; Jensen, R. H.; Waldman, F. M. |
| Article Title: | Identification of a robust gene signature that predicts breast cancer outcome in independent data sets |
| Abstract: | Background: Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients. Methods: We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets. Results: We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62-65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors. Conclusion: This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients. © 2007 Korkola et al; licensee BioMed Central Ltd. |
| Keywords: | survival; cancer chemotherapy; cancer survival; controlled study; human tissue; treatment outcome; survival analysis; carrier protein; unclassified drug; major clinical study; genetics; cancer radiotherapy; disease free survival; lymph node metastasis; antineoplastic agent; cell cycle protein; breast cancer; cluster analysis; gene expression profiling; epidermal growth factor receptor 2; transcription factor; validation study; breast neoplasms; brca2 protein; survivin; cancer hormone therapy; molecular cloning; correlation analysis; statistical analysis; microarray analysis; oligonucleotide array sequence analysis; gene identification; nucleotide sequence; breast tumor; prediction and forecasting; predictive value of tests; receptors, estrogen; dna microarray; kaplan meier method; cancer classification; estrogen receptor; guanine nucleotide binding protein; antineoplastic hormone agonists and antagonists; protein derivative; aurora a kinase; insulin receptor; cd3d protein; cktsf1b1 protein; gmfb protein; gnal protein; gnas protein; kifap3 protein; ltbr protein; mad2l1 protein; ngb protein; nrg1 protein; protein myb; sodium proton exchange protein 3; transcription factor tal1; transcription factor znf217; ywhaq protein |
| Journal Title: | BMC Cancer |
| Volume: | 7 |
| ISSN: | 1471-2407 |
| Publisher: | Biomed Central Ltd |
| Date Published: | 2007-04-11 |
| Start Page: | 61 |
| Language: | English |
| DOI: | 10.1186/1471-2407-7-61 |
| PUBMED: | 17428335 |
| PROVIDER: | scopus |
| PMCID: | PMC1855059 |
| DOI/URL: | |
| Notes: | --- - "Cited By (since 1996): 15" - "Export Date: 17 November 2011" - "CODEN: BCMAC" - "Molecular Sequence Numbers: GENBANK: AA458968, AA495944;" - "Source: Scopus" |
