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Transcriptomic dynamics of breast cancer progression in the MMTV-PyMT mouse model.

ABSTRACT: Malignant breast cancer with complex molecular mechanisms of progression and metastasis remains a leading cause of death in women. To improve diagnosis and drug development, it is critical to identify panels of genes and molecular pathways involved in tumor progression and malignant transition. Using the PyMT mouse, a genetically engineered mouse model that has been widely used to study human breast cancer, we profiled and analyzed gene expression from four distinct stages of tumor progression (hyperplasia, adenoma/MIN, early carcinoma and late carcinoma) during which malignant transition occurs.We found remarkable expression similarity among the four stages, meaning genes altered in the later stages showed trace in the beginning of tumor progression. We identified a large number of differentially expressed genes in PyMT samples of all stages compared with normal mammary glands, enriched in cancer-related pathways. Using co-expression networks, we found panels of genes as signature modules with some hub genes that predict metastatic risk. Time-course analysis revealed genes with expression transition when shifting to malignant stages. These may provide additional insight into the molecular mechanisms beyond pathways.Thus, in this study, our various analyses with the PyMT mouse model shed new light on transcriptomic dynamics during breast cancer malignant progression.

SUBMITTER: Cai Y

PROVIDER: S-EPMC5316186 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Transcriptomic dynamics of breast cancer progression in the MMTV-PyMT mouse model.

Cai Ying Y Nogales-Cadenas Ruben R Zhang Quanwei Q Lin Jhih-Rong JR Zhang Wen W O'Brien Kelly K Montagna Cristina C Zhang Zhengdong D ZD

BMC genomics 20170217 1

<h4>Background</h4>Malignant breast cancer with complex molecular mechanisms of progression and metastasis remains a leading cause of death in women. To improve diagnosis and drug development, it is critical to identify panels of genes and molecular pathways involved in tumor progression and malignant transition. Using the PyMT mouse, a genetically engineered mouse model that has been widely used to study human breast cancer, we profiled and analyzed gene expression from four distinct stages of ...[more]

PMID: 28212608

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Project description:Cancer metastases arise in part from disseminated tumor cells originating from the primary tumor and from residual disease persisting after therapy. The identification of biomarkers on micro-metastases, disseminated tumors, and residual disease may yield novel tools for early detection and treatment of these disease states prior to their development into metastases and recurrent tumors. Here we describe the molecular profiling of disseminated tumor cells in lungs, lung metastases, and residual tumor cells in the MMTV-PyMT breast cancer model. MMTV-PyMT mice were bred with actin-GFP mice, and focal hyperplastic lesions from pubertal MMTV-PyMT;actin-GFP mice were orthotopically transplanted into FVB/n mice to track single tumor foci. Tumor-bearing mice were treated with TAC chemotherapy (docetaxel, doxorubicin, cyclophosphamide), and residual and relapsed tumor cells were sorted and profiled by mRNA microarray analysis. Data analysis revealed enrichment of the Jak/Stat pathway, Notch pathway, and epigenetic regulators in residual tumors. Stat1 was significantly up-regulated in a DNA-damage-resistant population of residual tumor cells, and a pre-existing Stat1 sub-population was identified in untreated tumors. Tumor cells from adenomas, carcinomas, lung disseminated tumor cells, and lung metastases were also sorted from MMTV-PyMT transplant mice and profiled by mRNA microarray. Whereas disseminated tumors cells appeared similar to carcinoma cells at the mRNA level, lung metastases were genotypically very different from disseminated cells and primary tumors. Lung metastases were enriched for a number of chromatin-modifying genes and stem cell-associated genes. Histone analysis of H3K4 and H3K9 suggested that lung metastases had been reprogrammed during malignant progression. These data identify novel biomarkers of residual tumor cells and disseminated tumor cells and implicate pathways that may mediate metastasis formation and tumor relapse after therapy.

Dataset Information

Transcriptomic dynamics of breast cancer progression in the MMTV-PyMT mouse model.

Publications

Transcriptomic dynamics of breast cancer progression in the MMTV-PyMT mouse model.

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