Luigi Cicatiello

Estrogens and Progesterone Promote Persistent CCND1 Gene Activation during G1 by Inducing Transcriptional Derepression via c-Jun/c-Fos/Estrogen Receptor (Progesterone Receptor) Complex Assembly to a Distal Regulatory Element and Recruitment of Cyclin D1 to Its Own Gene Promoter

ABSTRACT Transcriptional activation of the cyclin D1 gene (CCND1) plays a pivotal role in G1-phase progression, which is thereby controlled by multiple regulatory factors, including nuclear receptors (NRs). Appropriate CCND1 gene activity is essential for normal development and physiology of the mammary gland, where it is regulated by ovarian steroids through a mechanism(s) that is not fully elucidated. We report here that CCND1 promoter activation by estrogens in human breast cancer cells is mediated by recruitment of a c-Jun/c-Fos/estrogen receptor α complex to the tetradecanoyl phorbol acetate-responsive element of the gene, together with Oct-1 to a site immediately adjacent. This process coincides with the release from the same DNA region of a transcriptional repressor complex including Yin-Yang 1 (YY1) and histone deacetylase 1 and is sufficient to induce the assembly of the basal transcription machinery on the promoter and to lead to initial cyclin D1 accumulation in the cell. Later on in estrogen stimulation, the cyclin D1/Cdk4 holoenzyme associates with the CCND1 promoter, where E2F and pRb can also be found, contributing to the long-lasting gene enhancement required to drive G1-phase completion. Interestingly, progesterone triggers similar regulatory events through its own NRs, suggesting that the gene regulation cascade described here represents a crossroad for the transcriptional control of G1-phase progression by different classes of NRs.

Estrogen Receptor α Controls a Gene Network in Luminal-Like Breast Cancer Cells Comprising Multiple Transcription Factors and MicroRNAs

Luminal-like breast tumor cells express estrogen receptor alpha (ERalpha), a member of the nuclear receptor family of ligand-activated transcription factors that controls their proliferation, survival, and functional status. To identify the molecular determinants of this hormone-responsive tumor phenotype, a comprehensive genome-wide analysis was performed in estrogen stimulated MCF-7 and ZR-75.1 cells by integrating time-course mRNA expression profiling with global mapping of genomic ERalpha binding sites by chromatin immunoprecipitation coupled to massively parallel sequencing, microRNA expression profiling, and in silico analysis of transcription units and receptor binding regions identified. All 1270 genes that were found to respond to 17beta-estradiol in both cell lines cluster in 33 highly concordant groups, each of which showed defined kinetics of RNA changes. This hormone-responsive gene set includes several direct targets of ERalpha and is organized in a gene regulation cascade, stemming from ligand-activated receptor and reaching a large number of downstream targets via AP-2gamma, B-cell activating transcription factor, E2F1 and 2, E74-like factor 3, GTF2IRD1, hairy and enhancer of split homologue-1, MYB, SMAD3, RARalpha, and RXRalpha transcription factors. MicroRNAs are also integral components of this gene regulation network because miR-107, miR-424, miR-570, miR-618, and miR-760 are regulated by 17beta-estradiol along with other microRNAs that can target a significant number of transcripts belonging to one or more estrogen-responsive gene clusters.

Global analysis of estrogen receptor beta binding to breast cancer cell genome reveals an extensive interplay with estrogen receptor alpha for target gene regulation

BackgroundEstrogen receptors alpha (ERα) and beta (ERβ) are transcription factors (TFs) that mediate estrogen signaling and define the hormone-responsive phenotype of breast cancer (BC). The two receptors can be found co-expressed and play specific, often opposite, roles, with ERβ being able to modulate the effects of ERα on gene transcription and cell proliferation. ERβ is frequently lost in BC, where its presence generally correlates with a better prognosis of the disease. The identification of the genomic targets of ERβ in hormone-responsive BC cells is thus a critical step to elucidate the roles of this receptor in estrogen signaling and tumor cell biology.ResultsExpression of full-length ERβ in hormone-responsive, ERα-positive MCF-7 cells resulted in a marked reduction in cell proliferation in response to estrogen and marked effects on the cell transcriptome. By ChIP-Seq we identified 9702 ERβ and 6024 ERα binding sites in estrogen-stimulated cells, comprising sites occupied by either ERβ, ERα or both ER subtypes. A search for TF binding matrices revealed that the majority of the binding sites identified comprise one or more Estrogen Response Element and the remaining show binding matrixes for other TFs known to mediate ER interaction with chromatin by tethering, including AP2, E2F and SP1. Of 921 genes differentially regulated by estrogen in ERβ+ vs ERβ- cells, 424 showed one or more ERβ site within 10 kb. These putative primary ERβ target genes control cell proliferation, death, differentiation, motility and adhesion, signal transduction and transcription, key cellular processes that might explain the biological and clinical phenotype of tumors expressing this ER subtype. ERβ binding in close proximity of several miRNA genes and in the mitochondrial genome, suggests the possible involvement of this receptor in small non-coding RNA biogenesis and mitochondrial genome functions.ConclusionsResults indicate that the vast majority of the genomic targets of ERβ can bind also ERα, suggesting that the overall action of ERβ on the genome of hormone-responsive BC cells depends mainly on the relative concentration of both ERs in the cell.

Quantitative expression profiling of highly degraded RNA from formalin-fixed, paraffin-embedded breast tumor biopsies by oligonucleotide microarrays

Microarray-based gene expression profiling is well suited for parallel quantitative analysis of large numbers of RNAs, but its application to cancer biopsies, particularly formalin-fixed, paraffin-embedded (FFPE) archived tissues, is limited by the poor quality of the RNA recovered. This represents a serious drawback, as FFPE tumor tissue banks are available with clinical and prognostic annotations, which could be exploited for molecular profiling studies, provided that reliable analytical technologies are found. We applied and evaluated here a microarray-based cDNA-mediated annealing, selection, extension and ligation (DASL) assay for analysis of 502 mRNAs in highly degraded total RNA extracted from cultured cells or FFPE breast cancer (MT) biopsies. The study included quantitative and qualitative comparison of data obtained by analysis of the same RNAs with genome-wide oligonucleotide microarrays vs DASL arrays and, by DASL, before and after extensive in vitro RNA fragmentation. The DASL-based expression profiling assay applied to RNA extracted from MCF-7 cells, before or after 24 h stimulation with a mitogenic dose of 17β-estradiol, consistently allowed to detect hormone-induced gene expression changes following extensive RNA degradation in vitro. Comparable results where obtained with tumor RNA extracted from FFPE MT biopsies (6 to 19 years old). The method proved itself sensitive, reproducible and accurate, when compared to results obtained by microarray analysis of RNA extracted from snap-frozen tissue of the same tumor.

Time-course analysis of genome-wide gene expression data from hormone-responsive human breast cancer cells

BackgroundMicroarray experiments enable simultaneous measurement of the expression levels of virtually all transcripts present in cells, thereby providing a ‘molecular picture’ of the cell state. On the other hand, the genomic responses to a pharmacological or hormonal stimulus are dynamic molecular processes, where time influences gene activity and expression. The potential use of the statistical analysis of microarray data in time series has not been fully exploited so far, due to the fact that only few methods are available which take into proper account temporal relationships between samples.ResultsWe compared here four different methods to analyze data derived from a time course mRNA expression profiling experiment which consisted in the study of the effects of estrogen on hormone-responsive human breast cancer cells. Gene expression was monitored with the innovative Illumina BeadArray platform, which includes an average of 30-40 replicates for each probe sequence randomly distributed on the chip surface. We present and discuss the results obtained by applying to these datasets different statistical methods for serial gene expression analysis. The influence of the normalization algorithm applied on data and of different parameter or threshold choices for the selection of differentially expressed transcripts has also been evaluated. In most cases, the selection was found fairly robust with respect to changes in parameters and type of normalization. We then identified which genes showed an expression profile significantly affected by the hormonal treatment over time. The final list of differentially expressed genes underwent cluster analysis of functional type, to identify groups of genes with similar regulation dynamics.ConclusionsSeveral methods for processing time series gene expression data are presented, including evaluation of benefits and drawbacks of the different methods applied. The resulting protocol for data analysis was applied to characterization of the gene expression changes induced by estrogen in human breast cancer ZR-75.1 cells over an entire cell cycle.

Identification of new genes associated with breast cancer progression by gene expression analysis of predefined sets of neoplastic tissues

Gene expression profiles were studied by microarray analysis in 2 sets of archival breast cancer tissues from patients with distinct clinical outcome. Seventy‐seven differentially expressed genes were identified when comparing 30 cases with relapse and 30 cases without relapse within 72 months from surgery. These genes had a specific ontological distribution and some of them have been linked to breast cancer in previous studies: AIB1, the two keratin genes KRT5 and KRT15, RAF1, WIF1 and MSH6. Seven out of 77 differentially expressed genes were selected and analyzed by qRT‐PCR in 127 cases of breast cancer. The expression levels of 6 upregulated genes (CKMT1B, DDX21, PRKDC, PTPN1, SLPI, YWHAE) showed a significant association to both disease‐free and overall survival. Multivariate analysis using the significant factors (i.e., estrogen receptor and lymph node status) as covariates confirmed the association with survival. There was no correlation between the expression level of these genes and other clinical parameters. In contrast, SERPINA3, the only downregulated gene examined, was not associated with survival, but correlated with steroid receptor status. An indirect validation of our genes was provided by calculating their association with survival in 3 publicly available microarray datasets. CKMT1B expression was an independent prognostic marker in all 3 datasets, whereas other genes confirmed their association with disease‐free survival in at least 1 dataset. This work provides a novel set of genes that could be used as independent prognostic markers and potential drug targets for breast cancer.

Identification of a hormone-regulated dynamic nuclear actin network associated with estrogen receptor alpha in human breast cancer cell nuclei

Estrogen receptor α (ERα) is a modular protein of the steroid/nuclear receptor family of transcriptional regulators that upon binding to the hormone undergoes structural changes, resulting in its nuclear translocation and docking to specific chromatin sites. In the nucleus, ERα assembles in multiprotein complexes that act as final effectors of estrogen signaling to the genome through chromatin remodeling and epigenetic modifications, leading to dynamic and coordinated regulation of hormone-responsive genes. Identification of the molecular partners of ERα and understanding their combinatory interactions within functional complexes is a prerequisite to define the molecular basis of estrogen control of cell functions. To this end, affinity purification was applied to map and characterize the ERα interactome in hormone-responsive human breast cancer cell nuclei. MCF-7 cell clones expressing human ERα fused to a tandem affinity purification tag were generated and used to purify native nuclear ER-containing complexes by IgG-Sepharose affinity chromatography and glycerol gradient centrifugation. Purified complexes were analyzed by two-dimensional DIGE and mass spectrometry, leading to the identification of a ligand-dependent multiprotein complex comprising β-actin, myosins, and several proteins involved in actin filament organization and dynamics and/or known to participate in actin-mediated regulation of gene transcription, chromatin dynamics, and ribosome biogenesis. Time course analyses indicated that complexes containing ERα and actin are assembled in the nucleus early after receptor activation by ligands, and gene knockdown experiments showed that gelsolin and the nuclear isoform of myosin 1c are key determinants for assembly and/or stability of these complexes. Based on these results, we propose that the actin network plays a role in nuclear ERα actions in breast cancer cells, including coordinated regulation of target gene activity, spatial and functional reorganization of chromatin, and ribosome biogenesis.

Transcriptional activation of jun and actin genes by estrogen during mitogenic stimulation of rat uterine cells

Estrogens induce transcriptional activation of c-fos and c-myc proto-oncogenes during mitogenic stimulation of human, chicken, mouse and rat cells in vivo and in vitro. In this paper we show that 17 beta-estradiol injected into adult ovariectomized rats increases c-jun, jun-B and jun-D gene transcription in the uterus. Kinetics and amplitude of response are different for each gene, since c-jun is activated first, within 30 min after injection, followed by jun-D and jun-B, 60 and 90 min after injection, respectively. Maximal activation of jun-B marks a drop in transcription of all the jun genes. Furthermore, transcriptional activation by 17 beta-estradiol of the growth-regulated beta- and gamma-cytoskeletal actin genes is prevented by an inhibitor of protein synthesis, indicating that it is a secondary response to the hormone. These data support the hypothesis that during growth stimulation of target cells the estrogen receptor induces transcription of regulatory genes, triggering in this way a cascade of gene regulation events that results in progression through the cell cycle.

Molecular identification of ERα-positive breast cancer cells by the expression profile of an intrinsic set of estrogen regulated genes

Estrogens exert a key biological role in mammary gland epithelial cells and promote breast carcinogenesis and tumor progression. We recently identified a new large set of estrogen responsive genes from breast cancer (BC) cells by DNA microarray analysis of the gene expression profiles induced by 17β‐estradiol in ZR‐75.1 and MCF‐7 cells. The purpose of the present study was to test whether the expression pattern of hormone regulated genes from this set identifies estrogen receptor (ERα) positive, hormone responsive BC cells. To this aim, we carried out in silico metanalysis of ERα positive and ERα negative human BC cell line transcriptomes, focusing on two sets of 171 and 218 estrogen responsive genes, respectively. Results show that estrogen dependent gene activity in hormone responsive BC cells is significantly different from that of non‐responsive cells and, alone, allows to discriminate these two cellular phenotypes. Indeed, we have identified 61 genes whose expression profile specifically marks ERα positive BC cells, suggesting that this gene set may be exploited for phenotypic characterization of breast tumors. This possibility was tested with data obtained by gene expression profiling of BC surgical samples, where the ERα positive phenotypes were highlighted by the expression profile of a subset of 27 such hormone responsive genes and four additional BC marker genes, not including ERs. These results provide direct evidence that the expression pattern of a limited number of estrogen responsive genes can be exploited to assess the estrogen signaling status of BC cells both in vitro and ex‐vivo.

Effects of Oestrogen on MicroRNA Expression in Hormone-Responsive Breast Cancer Cells

Oestrogen receptor alpha (ERα) is a ligand-dependent transcription factor that mediates oestrogen effects in hormone-responsive cells. Following oestrogenic activation, ERα directly regulates the transcription of target genes via DNA binding. MicroRNAs (miRNAs) represent a class of small noncoding RNAs that function as negative regulators of protein-coding gene expression. They are found aberrantly expressed or mutated in cancer, suggesting their crucial role as either oncogenes or tumour suppressor genes. Here, we analysed changes in miRNA expression in response to oestrogen in hormone-responsive breast cancer MCF-7 and ZR-75.1 cells by microarray-mediated expression profiling. This led to the identification of 172 miRNAs up- or down-regulated by ERα in response to 17β-oestradiol, of which 52 are similarly regulated by the hormone in the two cell models investigated. To identify mechanisms by which ERα exerts its effects on oestrogen-responsive miRNA genes, the oestrogen-dependent miRNA expression profiles were integrated with global in vivo ERα binding site mapping in the genome by ChIP-Seq. In addition, data from miRNA and messenger RNA (mRNA) expression profiles obtained under identical experimental conditions were compared to identify relevant miRNA target transcripts. Results show that miRNAs modulated by ERα represent a novel genomic pathway to impact oestrogen-dependent processes that affect hormone-responsive breast cancer cell behaviour. MiRNome analysis in tumour tissues from breast cancer patients confirmed a strong association between expression of these small RNAs and clinical outcome of the disease, although this appears to involve only marginally the oestrogen-regulated miRNAs identified in this study.