Genome-wide maps of Tamoxifen resistance MCF7 cell line
ABSTRACT: We report the ER alpha regulatory network of Tamoxifen resistance MCF7 cell line using the Chromatin immunoprecipitated high-throughput sequencing technology (ChIP-seq). By Integrating the gene expression data (previously reported) with the ChIP-seq data, we generated ER alpha regulatory network and pathways. For ER alpha regulatory network, hub TFs with enriched motifs were identified from ER alpha peak together with PolII peaks. We then scan the position weight matrix (PWM) of ER alpha peak region of certain gene to find out the regulatory relationship between hub TF and normal TF. For regulatory pathway, genes were grouped base on their expression value at 4 different time point. Then the hub TF that plays important role in each time point of each group was identified. This study provides a framework for the application of ChIP-seq and gene expression data for the construction of ER alpha regulatory network. Overall design: 4 different ChIP-seq dataset in Tamoxifen resistance MCF7 cell line
Project description:We report the ER alpha regulatory network of Tamoxifen resistance MCF7 cell line using the Chromatin immunoprecipitated high-throughput sequencing technology (ChIP-seq). By Integrating the gene expression data (previously reported) with the ChIP-seq data, we generated ER alpha regulatory network and pathways. For ER alpha regulatory network, hub TFs with enriched motifs were identified from ER alpha peak together with PolII peaks. We then scan the position weight matrix (PWM) of ER alpha peak region of certain gene to find out the regulatory relationship between hub TF and normal TF. For regulatory pathway, genes were grouped base on their expression value at 4 different time point. Then the hub TF that plays important role in each time point of each group was identified. This study provides a framework for the application of ChIP-seq and gene expression data for the construction of ER alpha regulatory network. 4 different ChIP-seq dataset in Tamoxifen resistance MCF7 cell line
Project description:Global profiling of in vivo protein-DNA interactions using ChIP-based technologies has evolved rapidly in recent years. Although many genome-wide studies have identified thousands of ER? binding sites and have revealed the associated transcription factor (TF) partners, such as AP1, FOXA1 and CEBP, little is known about ER? associated hierarchical transcriptional regulatory networks.In this study, we applied computational approaches to analyze three public available ChIP-based datasets: ChIP-seq, ChIP-PET and ChIP-chip, and to investigate the hierarchical regulatory network for ER? and ER? partner TFs regulation in estrogen-dependent breast cancer MCF7 cells. 16 common TFs and two common new TF partners (RORA and PITX2) were found among ChIP-seq, ChIP-chip and ChIP-PET datasets. The regulatory networks were constructed by scanning the ChIP-peak region with TF specific position weight matrix (PWM). A permutation test was performed to test the reliability of each connection of the network. We then used DREM software to perform gene ontology function analysis on the common genes. We found that FOS, PITX2, RORA and FOXA1 were involved in the up-regulated genes.We also conducted the ER? and Pol-II ChIP-seq experiments in tamoxifen resistance MCF7 cells (denoted as MCF7-T in this study) and compared the difference between MCF7 and MCF7-T cells. The result showed very little overlap between these two cells in terms of targeted genes (21.2% of common genes) and targeted TFs (25% of common TFs). The significant dissimilarity may indicate totally different transcriptional regulatory mechanisms between these two cancer cells.Our study uncovers new estrogen-mediated regulatory networks by mining three ChIP-based data in MCF7 cells and ChIP-seq data in MCF7-T cells. We compared the different ChIP-based technologies as well as different breast cancer cells. Our computational analytical approach may guide biologists to further study the underlying mechanisms in breast cancer cells or other human diseases.
Project description:Estrogens regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. Dynamic gene expression changes have been shown to characterize the breast cancer cell response to estrogens, the every molecular mechanism of which is still not well understood.We developed a modulated empirical Bayes model, and constructed a novel topological and temporal transcription factor (TF) regulatory network in MCF7 breast cancer cell line upon stimulation by 17?-estradiol stimulation. In the network, significant TF genomic hubs were identified including ER-alpha and AP-1; significant non-genomic hubs include ZFP161, TFDP1, NRF1, TFAP2A, EGR1, E2F1, and PITX2. Although the early and late networks were distinct (<5% overlap of ER? target genes between the 4 and 24 h time points), all nine hubs were significantly represented in both networks. In MCF7 cells with acquired resistance to tamoxifen, the ER? regulatory network was unresponsive to 17?-estradiol stimulation. The significant loss of hormone responsiveness was associated with marked epigenomic changes, including hyper- or hypo-methylation of promoter CpG islands and repressive histone methylations.We identified a number of estrogen regulated target genes and established estrogen-regulated network that distinguishes the genomic and non-genomic actions of estrogen receptor. Many gene targets of this network were not active anymore in anti-estrogen resistant cell lines, possibly because their DNA methylation and histone acetylation patterns have changed.
Project description:The TCF7L2 transcription factor (TF) is a member of Wnt signalling pathway, and may influence transcription of several genes by binding to distinct regulatory regions. Genome-wide studies have identified thousands of TCF7L2 binding sites and have revealed some associated TF partners. However, there is still a large uncharted region in the hierarchical regulatory network for TCF7L2 and the partner TFs in MCF7 cells. We analysed ChIP-seq data by searching for motifs in the enriched peak region based on TF-specific position weight matrix (PWM). We found association of FOXO1 and CAD with up-regulated genes, AP2?, PBF and AP1 with down-regulated genes. TCF7L2 and GATA3 were found to be associated with both up and down-regulated genes. Our study uncovers new TCF7L2 associated regulatory networks by mining ChIP-seq data in MCF7 cell, which may contribute to further study of the mechanisms related to Wnt pathway in breast cancer or other diseases.
Project description:Tamoxifen provided a successful treatment for ER-positive breast cancer for many years. However, HER2 overexpressing breast cancer cells respond poorly to tamoxifen therapy presumably by pass. The molecular mechanisms underlying development of tamoxifen resistance have not been well established. Recently, we reported that breast cancer cells with high levels of ER-?36, a variant of ER-?, were resistant to tamoxifen and knockdown of ER-?36 expression in tamoxifen resistant cells with the shRNA method restored tamoxifen sensitivity, indicating that gained ER-?36 expression is one of the underlying mechanisms of tamoxifen resistance. Here, we found that tamoxifen induced expression of ER-?36-EGFR/HER2 positive regulatory loops and tamoxifen resistant MCF7 cells (MCF7/TAM) expressed enhanced levels of the loops. Disruption of the ER-?36-EGFR/HER2 positive regulatory loops with the dual tyrosine kinase inhibitor Lapatinib or ER-?36 down-regulator Broussoflavonol B in tamoxifen resistant MCF7 cells restored tamoxifen sensitivity. In addition, we also found both Lapatinib and Broussoflavonol B increased the growth inhibitory activity of tamoxifen in tumorsphere cells derived from MCF7/TAM cells. Our results thus demonstrated that elevated expression of the ER-?36-EGFR/HER2 loops is one of the mechanisms by which ER-positive breast cancer cells escape tamoxifen therapy. Our results thus provided a rational to develop novel therapeutic approaches for tamoxifen resistant patients by targeting the ER-?36-EGFR/HER2 loops.
Project description:Estrogen receptor alpha positive (ER+) of breast cancer could develop resistance to antiestrogens including Tamoxifen. Our previous study showed that the E3 ubiquitin ligase HRD1 played an important role in anti-breast cancer. However, its role in chemotherapy resistance hasn't been reported. In this study, we found that HRD1 expression was downregulated in Tamoxifen-resistant breast cancer cell line MCF7/Tam compared to the Tamoxifen sensitive cell line MCF7. Moreover, S100A8 is the direct target of HRD1 by proteome analysis. Our data showed that HRD1 decreased the protein level of S100A8 through ubiquitination while HRD1 was regulated by acetylation of histone. More importantly, HRD1 knockdown significantly increased the cell survival of MCF7 cells to the Tamoxifen treatment. HRD1 overexpression sensitized MCF7/Tam cells to the Tamoxifen treatment in vitro and in vivo. In conclusion, the decrease of HRD1 expression contributed to Tamoxifen resistance in breast cancer.
Project description:Recent genome-wide profiling reveals highly complex regulation networks among ER? and its targets. We integrated estrogen (E2)-stimulated time-series ER? ChIP-seq and gene expression data to identify the ER?-centered transcription factor (TF) hubs and their target genes, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ER? core transcriptional network. The GO analyses revealed the distinct biological function associated with each of three embedded modules. The survival analysis showed the genes in each module were able to render a significant survival correlation in breast cancer patient cohorts. In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ER?-centered regulatory network and associated distinct biological functions, but also provides a reliable and effective genomic analytical approach for the analysis of dynamic regulatory network for any given TF.
Project description:Background:Drug resistance is frequently found in estrogen receptor-positive (ER+) breast cancer patients during and after prolonged tamoxifen treatment. Although tamoxifen rechallenge has been proposed for treating recurrent breast tumors, the clinical benefit of this treatment is still controversial. The aims of this study are to identify the possible tamoxifen cytotoxicity-resistant subpopulation of MCF7 cells and to determine the effects of tamoxifen rechallenge on these cells. Methods:Western blot analysis was used to determine the expression levels of various epithelial-mesenchymal transition- and cell survival/proliferation-related proteins in MCF7 and MCF7-derived, tamoxifen-mediated cytotoxicity-resistant MCF7-TAM12.5 breast cancer cells. Wound healing, Transwell migration, and invasion assays were used to examine the metastatic potential of cells. Clonogenic assays, trypan blue exclusion assays, and bromodeoxyuridine assays were used to examine clonogenicity and to determine the proliferation rate of cells. Results:We found that MCF7-TAM12.5 cells exhibited higher tolerance to tamoxifen-mediated cytotoxicity, higher metastatic potential, higher expression levels of XIAP, and lower expression levels of ER?/ER?/HER2/Smac than MCF7 cells. In addition, MCF7 cells endogenously expressed Bcl-2?, whereas MCF7-TAM12.5 cells only expressed Bcl-2?. Interestingly, tamoxifen rechallenge decreased the metastatic potential but increased the proliferation and clonogenicity of MCF7-TAM12.5 cells. At the molecular level, tamoxifen rechallenge upregulated the expression of phosphorylated Aurora A and Aurora B kinase in MCF7-TAM12.5 cells. Conclusion:Our findings further support the existence of highly heterogenetic cancer cell populations in ER+ breast tumors. It will be of clinical importance to determine the protein expression and the genetic profiles of tamoxifen-resistant/recurrent ER+ breast tumors to predict the potential effects of tamoxifen readministration in the future.
Project description:Estrogens play a pivotal role in breast cancer etiology, and endocrine therapy remains the main first line treatment for estrogen receptor-alpha (ER?)-positive breast cancer. ER are transcription factors whose activity is finely regulated by various regulatory complexes, including histone deacetylases (HDACs). Here, we investigated the role of HDAC9 in ER? signaling and response to antiestrogens in breast cancer cells. Various Michigan Cancer Foundation-7 (MCF7) breast cancer cell lines that overexpress class IIa HDAC9 or that are resistant to the partial antiestrogen 4-hydroxy-tamoxifen (OHTam) were used to study phenotypic changes in response to ER ligands by using transcriptomic and gene set enrichment analyses. Kaplan-Meier survival analyses were performed using public transcriptomic datasets from human breast cancer biopsies. In MCF7 breast cancer cells, HDAC9 decreased ER? mRNA and protein expression and inhibited its transcriptional activity. Conversely, HDAC9 mRNA was strongly overexpressed in OHTam-resistant MCF7 cells and in ER?-negative breast tumor cell lines. Moreover, HDAC9-overexpressing cells were less sensitive to OHTam antiproliferative effects compared with parental MCF7 cells. Several genes (including MUC1, SMC3 and S100P) were similarly deregulated in OHTam-resistant and in HDAC9-overexpressing MCF7 cells. Finally, HDAC9 expression was positively associated with genes upregulated in endocrine therapy-resistant breast cancers and high HDAC9 levels were associated with worse prognosis in patients treated with OHTam. These results demonstrate the complex interactions of class IIa HDAC9 with ER? signaling in breast cancer cells and its effect on the response to hormone therapy.
Project description:: Breast cancer (BRCA) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers expressed during estradiol and tamoxifen treatment of BRCA. The microarray dataset of E-MTAB-4975 from Array Express database was downloaded, and the differential expressed genes (DEGs) between estradiol-treated BRCA sample and tamoxifen-treated BRCA sample were identified by limma package. The pathway and gene ontology (GO) enrichment analysis, construction of protein-protein interaction (PPI) network, module analysis, construction of target genes-miRNA interaction network and target genes-transcription factor (TF) interaction network were performed using bioinformatics tools. The expression, prognostic values, and mutation of hub genes were validated by SurvExpress database, cBioPortal, and human protein atlas (HPA) database. A total of 856 genes (421 up-regulated genes and 435 down-regulated genes) were identified in T47D (overexpressing Split Ends (SPEN) + estradiol) samples compared to T47D (overexpressing Split Ends (SPEN) + tamoxifen) samples. Pathway and GO enrichment analysis revealed that the DEGs were mainly enriched in response to lysine degradation II (pipecolate pathway), cholesterol biosynthesis pathway, cell cycle pathway, and response to cytokine pathway. DEGs (MCM2, TCF4, OLR1, HSPA5, MAP1LC3B, SQSTM1, NEU1, HIST1H1B, RAD51, RFC3, MCM10, ISG15, TNFRSF10B, GBP2, IGFBP5, SOD2, DHF and MT1H) , which were significantly up- and down-regulated in estradiol and tamoxifen-treated BRCA samples, were selected as hub genes according to the results of protein-protein interaction (PPI) network, module analysis, target genes-miRNA interaction network and target genes-TF interaction network analysis. The SurvExpress database, cBioPortal, and Human Protein Atlas (HPA) database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. A comprehensive bioinformatics analysis was performed, and potential therapeutic applications of estradiol and tamoxifen were predicted in BRCA samples. The data may unravel the future molecular mechanisms of BRCA.