Abstract 175: Transcriptomic and epigenetic dynamics of breast cancer progression in the MMTV-PyMT mouse model

Abstract

Abstract Using RNA-sequencing and ERRBS (enhanced reduced representation bisulphite sequencing), we identified genes involved in breast cancer malignant progression. Human breast cancers have complex molecular mechanisms of progression and metastasis. To improve diagnosis and drug development, it is critical to identify the genes and molecular pathways involved in tumor progression and malignant transition. Using the polyoma middle T antigen (PyMT) mouse model, we profiled gene expression from four different stages of tumor progression, including hyperplasia, adenoma/MIN (mammary intraepithelial neoplasia), early and late carcinoma. We identified a large number of differentially expressed genes in tumors compared with normal mammary gland, enriched in E2F targets and G2M checkpoint genes. Using the temporal information, we found Myc, Sox4 and Casp9 were associated with tumor progression. Using co-expression network analysis, we identified signature modules of panels of genes, revealing novel insights into molecular mechanisms of breast tumorigenesis. For example, Mcm6 and Ncapd2, hub genes in a DNA replication and cell cycle module, predict metastasis in human samples. We also found the Hells, Hmgb2 and Cit genes to be increased in expression in both mouse and human TCGA (The Cancer Genome Atlas) datasets. We complemented these studies by profiling DNA methylation using ERRBS in the same samples. We identified a large number of differentially methylated cytosines (DMCs), enriched in cis-regulatory elements as well as near genes in cancer-related pathways. A small subset of the DMCs was well conserved between the progressive stages of tumorigenesis, with the greatest shift occurring with the transition to malignancy. We also observed a global hypomethylation and a shift towards a subset of loci with hypermethylation with tumor progression. Expression of some tricarboxylic acid (TCA) cycle genes was negatively correlated with DNA methylation, which may reflect altered metabolic regulation in carcinoma. We then constructed a co-methylation network and discovered a module with decreased DNA methylation in tumor that was enriched with genes from WNT and MAPK signaling pathways. In addition, we compared our results to the human TCGA breast carcinoma dataset and found the epigenetic modules to be overall distinctive between the two species. Altogether, our gene expression, DNA methylation and network analyses with the PyMT mouse model shed new light on transcriptional and epigenetic dynamics during breast cancer malignant progression and reveal gene modules that may regulate this biological process. Citation Format: Ying Cai, Ruben Nogales-Cadenas, Quanwei Zhang, Jhih-rong Lin, Wen Zhang, Zhengdong Zhang. Transcriptomic and epigenetic dynamics of breast cancer progression in the MMTV-PyMT mouse model. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 175.