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Transcriptomic classification of genetically engineered mouse models of breast cancer identifies human subtype counterparts

Adam D Pfefferle12, Jason I Herschkowitz3, Jerry Usary24, Joshua Chuck Harrell24, Benjamin T Spike5, Jessica R Adams6, Maria I Torres-Arzayus7, Myles Brown7, Sean E Egan68, Geoffrey M Wahl5, Jeffrey M Rosen9 and Charles M Perou124*

Author Affiliations

1 Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC 27599, USA

2 Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA

3 Department of Biomedical Sciences, University at Albany, Rensselaer, NY 12144, USA

4 Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA

5 Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92130, USA

6 Program in Developmental and Stem Cell Biology, Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada

7 Division of Molecular and Cellular Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02115, USA

8 Department of Molecular Genetics, The University of Toronto, Toronto, ON M5R 0A3, Canada

9 Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA

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Genome Biology 2013, 14:R125  doi:10.1186/gb-2013-14-11-r125

Published: 12 November 2013

Abstract

Background

Human breast cancer is a heterogeneous disease consisting of multiple molecular subtypes. Genetically engineered mouse models are a useful resource for studying mammary cancers in vivo under genetically controlled and immune competent conditions. Identifying murine models with conserved human tumor features will facilitate etiology determinations, highlight the effects of mutations on pathway activation, and should improve preclinical drug testing.

Results

Transcriptomic profiles of 27 murine models of mammary carcinoma and normal mammary tissue were determined using gene expression microarrays. Hierarchical clustering analysis identified 17 distinct murine subtypes. Cross-species analyses using three independent human breast cancer datasets identified eight murine classes that resemble specific human breast cancer subtypes. Multiple models were associated with human basal-like tumors including TgC3(1)-Tag, TgWAP-Myc and Trp53-/-. Interestingly, the TgWAPCre-Etv6 model mimicked the HER2-enriched subtype, a group of human tumors without a murine counterpart in previous comparative studies. Gene signature analysis identified hundreds of commonly expressed pathway signatures between linked mouse and human subtypes, highlighting potentially common genetic drivers of tumorigenesis.

Conclusions

This study of murine models of breast carcinoma encompasses the largest comprehensive genomic dataset to date to identify human-to-mouse disease subtype counterparts. Our approach illustrates the value of comparisons between species to identify murine models that faithfully mimic the human condition and indicates that multiple genetically engineered mouse models are needed to represent the diversity of human breast cancers. The reported trans-species associations should guide model selection during preclinical study design to ensure appropriate representatives of human disease subtypes are used.