Affiliations: Laboratory of Cancer Biology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA | Department of Pathology and Laboratory Medicine, Center for Comparative Medicine, University of California, Davis, CA 95616, USA
Correspondence:
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Corresponding author: Jeffrey E. Green, Chief, Transgenic Oncogenesis and Genomics Section, Laboratory of Cancer Biology and Genetics, National Cancer Institute, Building 37, Room 4054, 37 Convent Dr., Bethesda, MD 20892, USA. Tel.: +1 301 435 5193; Fax: +1 301 496 8709; General laboratory office phone: +1 301 496 3430; +1 301 496 5391; E-mail: jegreen@nih.gov; http://rex.nci.nih.gov/RESEARCH/basic/lc/jg.htm
Abstract: Breast cancer is a heterogeneous disease, and much of the molecular basis for this heterogeneity is being unraveled using advanced genomic technologies. More recently, global transcriptional profiling has proven to be an effective new tool for characterizing human tumors. Genomic “signatures” have been developed that classify tumors with varying prognoses and responses to treatment. Recent studies have begun to extend the use of global transcriptional profiling to better characterize genetically engineered mouse (GEM) models of breast cancer, which will improve the ability to translate basic research advances into clinical advances. GEM models of mammary carcinoma have proven to be invaluable tools to gain insight into mechanisms underlying tumor initiation, progression, and therapeutic responses in an in vivo system where tumors spontaneously develop in an appropriate tissue environment. This review will discuss the use of transcriptional profiling of breast cancer in tumors from both human patients and GEM models to improve prognostic measures, examine mechanisms of tumor initiation and progression, identify novel therapeutic targets, and improve pre-clinical testing for drug development. Together, these advances offer a framework for classifying human tumors, identifying appropriate GEM models for specific experimental purposes, and utilizing the combined data to identify more specific and effective cancer therapies.
DOI: 10.3233/BD-2007-28105
Journal: Breast Disease, vol. 28, no. 1, pp. 39-51, 2007
