Genomics

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Identification of conserved gene expression features across human and murine mammary tumors


ABSTRACT: Mice have been used as models for human breast cancers for many years, however, it is still unclear which murine models faithfully represent human tumor phenotypes. To address this question, we used DNA microarrays to characterize 10 different murine mammary models and compared these data to the expression patterns from primary human breast tumors. Hierarchical clustering analysis of the murine samples showed that the WAP-Myc, MMTV-Neu, MMTV-PyMT, WAP-Int3, and C3(1)-Tag tumors were highly correlated within each model. Other models, including the WAP-T_121 , MMTV-Wnt1, and DMBA-induced tumor model, did not show this consistency and gave rise to tumors with potentially different cell types of origin. A combined clustering analysis of the murine tumors with 102 human breast tumors showed many shared expression features. These features included a proliferation signature, an Interferon-regulated pattern, and patterns reflective of the presence of lymphocytes and fibroblasts. Murine tumors could be categorized according to their presumed cellular origins; the C3(1)-Tag, BRCA1+/-; p53+/-;IR, and DMBA-treated models displayed expression characteristics of human basal-like breast tumors; the MMTV-Neu, MMTV-PyMT, and WAP-Myc models shared features with human luminal breast tumors including the high expression of GATA3 and XBP1. In some cases, single mouse models did not reproduce the entire expression pattern seen in a specific human subtype; rather portions of a subtype’s expression profile were captured/represented by different murine models. The presence of shared patterns of expression between mice and humans provides a common framework for the direct comparison and integration of animal models with human breast Keywords: reference x sample

OTHER RELATED OMICS DATASETS IN: PRJNA93005

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE3165 | GEO | 2007/05/01

SECONDARY ACCESSION(S): PRJNA93005

REPOSITORIES: GEO

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