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Estrogen receptor α (ERα) drives growth in the majority of human breast cancers by binding to regulatory elements and inducing transcriptional events that promote tumor growth. ERα binding activity largely depends on access to binding sites on chromatin, which is facilitated in part by Pioneer Factors (PFs). Transcription factors operate in complexes through thousands of genomic binding sites in a combinatorial fashion to control the expression of genes. However, the extent of crosstalk and cooperation between ERα pioneer factors and more collaborative transcription factors in breast cancer still remains to be elucidated systematically. Methods: Here, we determined the genomic binding information of 40 transcription-related factors and histone modifications with ChIP-seq in ENCODE and integrated it with other genomic information (RNA-seq, ATAC-seq, Gene microarray, 450k methylation chip, GRO-seq), forming a multi-dimension network to illuminate ERα associated transcription. Results: We show that transcription factor, NR2F2 binds to most sites independently of estrogen. Perturbation of NR2F2 expression decreases ERα DNA binding, chromatin openning, and estrogen-dependent cell growth. In the genome-wide analysis, we show that most binding events of NR2F2 and known pioneer factors FOXA1, GATA3 occur together, covering 85% of the ERα binding sites. Regions bound by all the three TFs appeared to be the most active, to have the strongest ERα binding and to be enriched for the super enhancers. Conclusions: The ERα binds to pre-accessible sites containing ERE elements bound by the three transcription factors (NR2F2, FOXA1 and GATA3).The three genes were also identified to correlate with decreased metastatic potential in patient cohorts and co-regulate each other. Together, our results suggest that NR2F2 is a cofactor with FOXA1 and GATA3 in ERα-mediated transcription.
Pubmed ID: 31588232
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Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsSoftware ultrafast memory efficient tool for aligning sequencing reads. Bowtie is short read aligner.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE, documented on January 19, 2022.
View all literature mentionsA commercial organization which provides assay technologies to isolate DNA, RNA, and proteins from any biological sample. Assay technologies are then used to make specific target biomolecules, such as the DNA of a specific virus, visible for subsequent analysis.
View all literature mentionsSoftware tools for Motif Discovery and next-gen sequencing analysis. Used for analyzing ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and numerous other types of functional genomics sequencing data sets. Collection of command line programs for unix style operating systems written in Perl and C++.
View all literature mentionsSoftware tool for transcriptome assembly and differential expression analysis for RNA-Seq. Includes script called cuffmerge that can be used to merge together several Cufflinks assemblies. It also handles running Cuffcompare as well as automatically filtering a number of transfrags that are likely to be artifacts. If the researcher has a reference GTF file, the researcher can provide it to the script to more effectively merge novel isoforms and maximize overall assembly quality.
View all literature mentionsConsortium to build comprehensive parts list of functional elements in human genome. This includes elements that act at protein and RNA levels, and regulatory elements that control cells and circumstances in which gene is active. Data from 2012-present.
View all literature mentionsCell line MCF-7 is a Cancer cell line with a species of origin Homo sapiens (Human)
View all literature mentionsCell line MCF-7 is a Cancer cell line with a species of origin Homo sapiens (Human)
View all literature mentionsCell line ZR-75-1 is a Cancer cell line with a species of origin Homo sapiens (Human)
View all literature mentionsCell line MDA-MB-453 is a Cancer cell line with a species of origin Homo sapiens (Human)
View all literature mentionsCell line MDA-MB-231 is a Cancer cell line with a species of origin Homo sapiens (Human)
View all literature mentionsCell line HEK293T is a Transformed cell line with a species of origin Homo sapiens (Human)
View all literature mentionsCell line MDA-MB-435 is a Cancer cell line with a species of origin Homo sapiens (Human)
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