Comparing genetically engineered mouse mammary cancer models with human breast cancer by expression profiling

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.