1. Bioinformatics Program, University of California San Diego, La Jolla, CA, USA
2. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
3. Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
4. Cancer Genetics Program, Moores Cancer Center, University of California San Diego, La Jolla, CA, USA

Correspondence to: Trey Ideker^1,2,4 Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA. Tel.: +1 858 822 4558; Fax: +1 858 534 5722; Email: trey@bioeng.ucsd.edu

Received 11 June 2007; Accepted 20 August 2007; Published online 16 October 2007

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation or the creation of derivative works without specific permission.

Abstract

Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.