Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer

<p>Abstract</p> <p>Background</p> <p>Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures.<...

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Bibliographic Details
Main Authors: Klijn Jan GM, Smid Marcel, Martens John WM, Zhang Yi, Sieuwerts Anieta M, Yu Jack X, Wang Yixin, Foekens John A
Format: Article
Language:English
Published: BMC 2007-09-01
Series:BMC Cancer
Online Access:http://www.biomedcentral.com/1471-2407/7/182
Description
Summary:<p>Abstract</p> <p>Background</p> <p>Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures.</p> <p>Methods</p> <p>Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients.</p> <p>Results</p> <p>The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways.</p> <p>Conclusion</p> <p>We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different.</p>
ISSN:1471-2407