Abstract
Recent advances in genome wide transcriptional analysis have provided greater insights into the etiology and heterogeneity of breast cancer. Molecular signatures have been developed that stratify the conventional estrogen receptor positive or negative categories into subtypes that are associated with differing clinical outcomes. It is thought that the expression patterns of the molecular subtypes primarily reflect cell-of-origin or tumor driver mutations. In this study however, using a genetically engineered mouse mammary tumor model we demonstrate that the PAM50 subtype signature of tumors driven by a common oncogenic event can be significantly influenced by the genetic background on which the tumor arises. These results have important implications for interpretation of “snapshot” expression profiles, as well as suggesting that incorporation of genetic background effects may allow investigation into phenotypes not initially anticipated in individual mouse models of cancer.
Highlights
The past decades have seen significant advances in our understanding of and ability to model breast cancer
The results indicate that mouse models may acquire additional characteristics, depending on genetic background, which may extend their utility for modeling human disease
Conserved modules were identified that were prognostic for either estrogen receptor positive (ER+) or estrogen receptor negative (ER–) human breast cancer patients
Summary
The past decades have seen significant advances in our understanding of and ability to model breast cancer. The advent of genome-wide expression profiling has led to the development of prognostic gene signatures [1] and improved molecular subtyping [2] that can stratify tumors into groups with different clinical outcomes These molecular subtypes express signatures similar to those of different cellular components of the mammary duct, including both luminal and basal cell types, and are thought to reflect contributions from the tumor cell type of origin and somatic mutations. Investigators can better focus on appropriate models for further characterization or translational studies of breast cancer subtypes of interest This improved understanding of breast cancer may permit more sophisticated targeting of particular somatic events associated with the different breast cancer subtypes [4] to the presumptive cell of origin in future mouse models to further improve our understanding of this pervasive disease
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