Published online before print January 10, 2008

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Copyright © 2008 American Society for Investigative Pathology
American Journal of Pathology, doi:10.2353/ajpath.2008.061079

Accepted for publication November 1, 2007.

Article

Transcriptional Networks Inferred from Molecular Signatures of Breast Cancer

Ron Tongbai^*, Gila Idelman^*, Silje H. Nordgard, Wenwu Cui^*, Jonathan L. Jacobs^*, Cynthia M. Haggerty^*, Stephen J. Chanock, Anne-Lise Børresen-Dale, Gary Livingston^¶, Patrick Shaunessy^¶, Chih-Hung Chiang^¶, Vessela N. Kristensen, Sven Bilke^||, and Kevin Gardner^*@

From the Laboratory of Receptor Biology and Gene Expression,* the Section on Genomic Variation, Pediatric Oncology Branch, and the Genetics Branch,^|| National Cancer Institute, Bethesda, Maryland; the Computer Science Department,^¶ University of Massachusetts, Lowell, Massachusetts; the Department of Genetics, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical Center, Oslo, Norway; and the Faculty of Medicine, University of Oslo, Oslo, Norway

^@ To whom correspondence should be addressed. E-mail: gardnerk{at}mail.nih.gov.

	Abstract

Global genomic approaches in cancer research have provided new and innovative strategies for the identification of signatures that differentiate various types of human cancers. Computational analysis of the promoter composition of the genes within these signatures may provide a powerful method for deducing the regulatory transcriptional networks that mediate their collective function. In this study we have systematically analyzed the promoter composition of gene classes derived from previously established genetic signatures that recently have been shown to reliably and reproducibly distinguish five molecular subtypes of breast cancer associated with distinct clinical outcomes. Inferences made from the trends of transcription factor binding site enrichment in the promoters of these gene groups led to the identification of regulatory pathways that implicate discrete transcriptional networks associated with specific molecular subtypes of breast cancer. One of these inferred pathways predicted a role for nuclear factor-B in a novel feed-forward, self-amplifying, autoregulatory module regulated by the ERBB family of growth factor receptors. The existence of this pathway was verified in vivo by chromatin immunoprecipitation and shown to be deregulated in breast cancer cells overexpressing ERBB2. This analysis indicates that approaches of this type can provide unique insights into the differential regulatory molecular programs associated with breast cancer and will aid in identifying specific transcriptional networks and pathways as potential targets for tumor subtype-specific therapeutic intervention.