This article has Open Peer Review reports available.
Breast tumor specific mutation in GATA3 affects physiological mechanisms regulating transcription factor turnover
© Adomas et al.; licensee BioMed Central Ltd. 2014
Received: 7 November 2013
Accepted: 3 April 2014
Published: 22 April 2014
The transcription factor GATA3 is a favorable prognostic indicator in estrogen receptor-α (ERα)-positive breast tumors in which it participates with ERα and FOXA1 in a complex transcriptional regulatory program driving tumor growth. GATA3 mutations are frequent in breast cancer and have been classified as driver mutations. To elucidate the contribution(s) of GATA3 alterations to cancer, we studied two breast cancer cell lines, MCF7, which carries a heterozygous frameshift mutation in the second zinc finger of GATA3, and T47D, wild-type at this locus.
Immunofluorescence staining and subcellular fractionation were employed to verify cellular localization of GATA3 in T47D and MCF7 cells. To test protein stability, cells were treated with translation inhibitor, cycloheximide or proteasome inhibitor, MG132, and GATA3 abundance was measured over time using immunoblot. GATA3 turn-over in response to hormone was determined by treating the cells with estradiol or ERα agonist, ICI 182,780. DNA binding ability of recombinant GATA3 was evaluated using electrophoretic mobility shift assay and heparin chromatography. Genomic location of GATA3 in MCF7 and T47D cells was assessed by chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq).
GATA3 localized in the nucleus in T47D and MCF7 cells, regardless of the mutation status. The truncated protein in MCF7 had impaired interaction with chromatin and was easily released from the nucleus. Recombinant mutant GATA3 was able to bind DNA to a lesser degree than the wild-type protein. Heterozygosity for the truncating mutation conferred protection from regulated turnover of GATA3, ERα and FOXA1 following estrogen stimulation in MCF7 cells. Thus, mutant GATA3 uncoupled protein-level regulation of master regulatory transcription factors from hormone action. Consistent with increased protein stability, ChIP-seq profiling identified greater genome-wide accumulation of GATA3 in MCF7 cells bearing the mutation, albeit with a similar distribution across the genome, comparing to T47D cells.
We propose that this specific, cancer-derived mutation in GATA3 deregulates physiologic protein turnover, stabilizes GATA3 binding across the genome and modulates the response of breast cancer cells to estrogen signaling.
Accumulation of somatic mutations is responsible for development of breast cancer, as 85% of affected women have no family history of the disease (http://www.breastcancer.org). Nearly 31,000 point mutations and small insertions or deletions (indels) in at least 170 previously reported and novel cancer genes have been implicated in the development of breast tumors . Whole exome sequencing places the zinc-finger transcription factor GATA3, with a 10% frequency of alterations, among the top three (together with p53 (TP53) and phosphoinositide-3-kinase (PIK3CA)) mutation driver genes in breast cancer [1, 2].
On the basis of mutation pattern, Vogelstein and colleagues  classify GATA3 as a tumor suppressor. Indeed, in mice xenograft studies GATA3 was positively correlated with survival and lack of metastasis . However, it has been also postulated that GATA3 defines a distinct class of cancer genes that are differentiation factors rather than conventional tumor suppressor genes, which affect the malignant phenotype by enforcing differentiation [5–7]. Specifically, conditional deletion of GATA3 is not sufficient to promote malignant progression, and is not tolerated in early tumors [5, 8]. GATA3 has been shown in mouse model of breast cancer to maintain tumor differentiation, suppress dissemination and inhibit metastasis [8, 9]. While GATA3 has been intensively studied in the immune system, where it functions in development and differentiation of T-cells , it is also an essential regulator of mammary-gland morphogenesis and luminal-cell differentiation [11, 12]. It is frequently up-regulated in breast cancer and has been identified as a favorable prognosis marker . GATA3 is involved in a positive cross-regulatory loop with estrogen receptor-α (ERα)  where they both serve as markers for luminal breast cancer [15, 16].
The interplay of GATA3, ERα, and FOXA1 has been a topic of multiple functional genomic studies. Kong and co-authors defined an enhanceosome consisting of co-localizing ERα-FOXA1-GATA3 which recruits RNA Pol II and p300 . The triple conjoint binding sites are highly represented at the locations involved in frequent long-range chromatin interactions and associated with genes that are most responsive to estrogen. In turn, Theodorou and colleagues silenced GATA3 and observed a global redistribution of FOXA1 and p300 cofactors, and active histone marks prior to estrogen stimulation . These global genomic changes alter the ERα-binding profile that subsequently occurs following estrogen treatment, demonstrating that GATA3 can act upstream of FOXA1 in mediating ERα binding by modulating enhancer composition.
Haploinsufficiency of GATA3 in humans results in HDR syndrome, a rare condition inherited as autosomal dominant trait, characterized by hypoparathyroidism, deafness, and renal dysplasia . Genomic alteration of GATA3 associated with HDR syndrome include large deletions removing the entire gene and flanking sequences, splice site mutations, indels, and point mutations resulting most often in frameshifts . Mutations in HDR patients localized in the second zinc finger (ZnF2) of GATA3 or adjacent amino acids result in loss of DNA binding, whereas those in the first zinc finger (ZnF1) lead to loss of interaction with a cofactor, FOG2, or altered DNA-binding affinity [20, 21]. Interestingly, while HDR GATA3 mutations are spread throughout the gene, breast cancer mutations cluster around ZnF2 and C-terminal domain [1, 22, 23]. Analysis of six different heterozygous GATA3 mutations from eight breast tumors has demonstrated loss or reduction of DNA binding ability, aberrant nuclear localization, decrease in transcription activation, and alterations in invasiveness, but not proliferation . However, it is unclear how those functional modifications contribute to the oncogenesis process in breast cancer.
The aim of the present study was to evaluate the effect of a breast cancer-specific mutation in GATA3 on biochemical properties and genomic location of the protein. We utilized two luminal breast cancer cell lines, MCF7 harboring a heterozygous frameshift mutation in ZnF2, and T47D carrying wild-type version GATA3. We observed that mutant GATA3 was expressed at elevated levels relative to wild-type protein and it accumulated in nuclei. Surprisingly, the mutation led to enhanced protein stability following challenge with estrogen receptor agonist or antagonist. This increased stability led to increased levels, but not to global redistribution, of GATA3 binding in the genome as determined by ChIP-seq. The data collectively support the hypothesis that the carboxyl terminus of GATA3 contains protein regulatory information that ensures appropriate turnover following ligand binding by ERα.
Human breast carcinoma cell lines MCF-7 and T47D were obtained from the American Type Culture Collection (Manassas, VA, USA) and cultured in DMEM/F-12 medium supplemented with 10% FBS at 37°C in 5% CO2. Protein stability was evaluated in the normal growth medium and cells were treated with 1 μM cycloheximide (CHX) and/or 1 μM MG132 (MG) for up to eight hours. For estrogen starvation assays, cells were grown for 72 hours in MEM medium containing 5% FBS and then for 24 hours in phenol red-free MEM supplemented with 5% charcoal-dextran stripped FBS. Cells were treated with 50 nM 17β-estradiol (E2) for 24 hours. The effect of ERα inhibitor, ICI 182,780 (ICI) was tested in normal growth medium. ICI was added at 100 nM concentration and cells were harvested 24 hours later. MG (EMD Biosciences, San Diego, CA, USA) was dissolved in DMSO, CHX (Cayman Chemical, Ann Arbor, MI, USA) in water, ICI (Tocris Bioscience Ellisville, MS, USA) and E2 (Sigma, St. Louis, MO, USA) in ethanol.
Cells were grown in 10 cm tissue culture dishes until they were 70-80% confluent. The cells were washed with PBS, collected by scraping and resuspended in buffer containing 0.15 M NaCl, 10 mM HEPES, pH 7.4, 1.5 mM MgCl2, 10 mM KCl, 0.5% NP-40, 0.5 mM DTT and protease inhibitors. The cytoplasmic fraction was separated by centrifugation at 2500 rpm for 10 min. The pellet was resuspended in nuclear extraction buffer containing 0.1, 0.2, 0.4 or 0.8 M NaCl, 25 mM HEPES, pH 7.4, 0.15 mM spermidine, 0.5 mM spermine, 5% glycerol, 1 mM EDTA and protease inhibitors. Samples were rotated for 30 min at +4°C and spun down in Optima Max centrifuge (Beckman Coulter, Brea, CA, USA) at 38,000 rpm for 45 min at +4°C. The nuclear fraction was collected and remaining pellet was dissolved in lysis buffer (8 M urea, 1% SDS, 0.125 M Tris, pH 6.8).
Whole cell lysates were obtained using 8 M urea lysis buffer (8 M urea, 1% SDS, 0.125 M Tris, pH 6.8). Protein extracts (15 μg) were resolved on SDS–PAGE gels and immunoblotted using the following antibodies: GATA3 (D13C9; Cell Signaling Technology, Danvers, MA), FOXA1 (ab23738; Abcam, Cambridge, MA, USA), ERα (sc-543; Santa Cruz Biotechnology, Santa Cruz, CA, USA) and actin (ab8226; Abcam). Signal intensity was analyzed using rectangular volume tool in Quantity One Analysis Software (Bio-Rad, Hercules, CA, USA) with global background subtraction.
Cells were grown on glass coverslips in six-well tissue culture dishes. They were fixed with 4% formaldehyde in PBS for 10 min, washed with PBS, and permeabilized with 0.1% Triton X-100 for 2 min, washed with PBS, and blocked with 5% BSA in PBS. The coverslips were incubated with the anti-GATA3 antibody (Cell Signaling Technology) for one hour, washed with PBS, incubated with the secondary antibody (Alexa Fluor 568, Life Technologies, Grand Island, NY, USA) for one hour, washed with PBS, and mounted on glass slides with mounting medium containing 4′,6-diamidino-2-phenylindole (DAPI). The slides were examined and photographed using a Zeiss Axiovert 200 M microscope equipped with an Axiocam MR digital camera controlled by AxioVision software (Zeiss, Thornwood, NY, USA).
Expression and purification of the DNA binding domain of GATA3
DNA binding domain (DBD) of GATA3 (amino acids 261 to 371) was cloned into the pET-15b vector to produce a hexahistidine tagged fusion protein. The expression vector was transformed into the E.coli BL21 (DE3) CodonPlus RIL cells, and the cells were cultured at 37°C. The bacterial cell lysate was centrifuged at 15,000 rpm for 20 min. The supernatant was mixed gently by the batch method with Ni-NTA beads (Qiagen, Valencia, CA, USA) at +4°C for 30 min. The beads were washed with 5 mM imidazole-containing buffer and GATA3-DBD was eluted with 500 mM imidazole-containing buffer. The fractions containing GATA3-DBD were subjected to MonoS column (GE Healthcare Life Sciences, Pittsburgh, PA, USA) chromatography. The binding domain was eluted with a 4-column volume linear gradient of 100–600 mM NaCl. The protein was further purified by Superdex 75 column (GE Healthcare) in a buffer containing 20 mM Tris–HCl pH 7.5, 0.3 M NaCl, 10% glycerol, 2 mM 2-mercaptoethanol, and 1 μM zinc sulfate. For the purification of GATA3 mutant (D336fs) DBD, the Ni-NTA beads were washed with the 20 mM imidazole-containing buffer. The fractions eluted from Ni-NTA beads were dialyzed against 20 mM Tris–HCl pH 7.5, 0.3 M NaCl, 10% glycerol, 2 mM 2-mercaptoethanol, and 1 μM zinc sulfate buffer, and concentrated with Amicon ultra-centrifuge filter (Millipore, Billerica, MA, USA).
Electrophoretic mobility shift assay (EMSA)
GATA protein (0.5, 1, 2 or 4 μM for the wild-type protein and 0.25, 0.5, 1 or 2 μM for the mutant protein) was incubated with 30 μM of 20 bp dsDNA (GATA3 recognition motif-containing oligonucleotide AATGTCCATCTGATAAGACG or GATA3 recognition motif-lacking oligonucleotide AATGTCAAACTTTTAAGACG) in 10 μl of a reaction buffer (28 mM Tris–HCl pH 7.5, 1 mM dithiothreitol, 0.8 mM 2-mercaptoethanol, 120 mM NaCl, 4% glycerol, and 0.4 μM zinc sulfate). After 10 min incubation at 37°C, the samples were analyzed by polyacrylamide gel electrophoresis, and the bands were visualized by ethidium bromide staining. In the competitive DNA binding assay, wild-type and mutated GATA3 DBDs were used individually or mixed in equimolar proportion. The reactions were performed with 15 μM of 20 bp GATA3 motif-containing oligonucleotide and 23 bp GATA3 motif-lacking DNA (CACTTTTTAACGTAATTTACTCT).
T47D and MCF7 nuclear extracts were prepared as described above, using nuclear extraction buffer containing 0.4 M NaCl. The extracts were applied to a 1 ml HiTrap Heparin Sepharose (GE Healthcare Life Sciences). The column was eluted with a 10 ml linear gradient of NaCl concentration from 0.1 to 1 M in 20 mM Hepes, pH 7.9 containing 20% glycerol, 0.2 mM EDTA, 0.1 mM PMSF, and 0.5 mM DTT. Separated fractions were analyzed by Western blot directed against anti-GATA3.
Chromatin immunoprecipitation (ChIP) analysis
GATA3 antibody was generated in rabbits using recombinant 6x histidine tag-fused GATA3 full-length wild-type protein. ChIP was performed as previously described  with the following modifications. T47D or MCF7 cells were cross-linked with 1% formaldehyde in DMEM F12 for 10 min at room temperature, quenched with glycine, and then sonicated using Bioruptor (Diagenode, Liège, Belgium) to generate 200 to 400 bp DNA fragments. Immunoprecipitation was performed with GATA3 serum, and normal rabbit serum (Santa Cruz Biotechnology, Dallas, TX, USA) was used as a control. The efficiency of the reaction was verified using SYBR-green (Bio-Rad) based Real-Time PCR and primers developed by Eeckhoute et al.  for GATA3 binding sites at ESR1 locus. Quantitation of precipitated DNA was done using a standard curve with 10, 1, 0.1, and 0.01% of input DNA.
ChIP-seq library construction
DNA immunoprecipitated by GATA3 antibody in four to five individual reactions performed at the same time was pooled for T47D and MCF7 cells separately and purified using MinElute PCR Purification kit (Qiagen). Total 100 μg of ChIP or input DNA, quantified with Qubit Fluorometer (Life Technologies, Grand Island, NY, USA) and dsDNA High Sensitivity Assay kit (Life Technologies), was used for library construction with the help of TruSeq RNA Sample Preparation kit (Illumina, San Diego, CA, USA). The library was prepared following the manufacturer’s instructions, starting with the end repair step, and amplified with twelve PCR cycles. Two sets of libraries (ChIP and input) were prepared for each of the cell lines from samples immunoprecipitated on separate occasions. The libraries were sequenced on a Genome Analyzer IIx (Illumina) as single end 36mers.
ChIP-seq data analysis
To ensure that low quality reads were excluded from the analysis, the raw sequence reads were filtered to remove any entries with a mean base quality score < 20. Filtered reads were aligned to the human genome (Genome Reference Consortium build 37/hg19; excluding haplotype chromosomes) via Bowtie (v0.12.8 with parameters –m 1 –v 2) ; only reads that were mapped to an unambiguous ‘best’ genomic location with no more than two mismatches were accepted. To limit PCR amplification bias, duplicate reads were removed using MarkDuplicates.jar from the Picard tools package (v1.62) (http://picard.sourceforge.net). Replicate libraries were in good agreement and were merged prior to downstream analysis. All alignments were extended at the 3’ end to a length of 180 bases (the average expected genomic fragment size for these libraries). ‘bedGraph’ files were generated from these uniquely-mapped, non-duplicated, extended reads for visualization of aggregate genomic coverage. Peak calling for regions of enriched GATA3 binding was performed with HOMER (v4.1; with default parameters and “-style factor -tbp 0 -inputtpb 0”)  using input (unchipped) data to model background.
GATA3 ChIP-seq data have been deposited in NCBI’s Gene Expression Omnibus  and are accessible through GEO Series accession number GSE51274 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51274).
Heterozygous mutation is present in GATA3gene in MCF7 cell line
Truncated GATA3 protein is easily released from the nucleus
The second-zinc finger frameshift mutation stabilizes GATA3 protein
Protein stability controlled by action of the 26S proteasome is integral to the biology of ERα , which is found in close proximity to GATA3 at many genomic locations in breast cancer cells [17, 18]. Inhibition of ubiquitin-proteasome pathway stabilizes GATA3 in developing T cells . To determine whether GATA3 protein turnover is regulated in a similar, proteasome-dependent manner in breast cancer, we treated cells with cycloheximide and a proteasome inhibitor, MG132. In both T47D and MCF7 cells, proteasome inhibition alleviated the effect of translation inhibition on wild-type and, to a lesser degree, mutated GATA3 (Figure 3B). These data indicate that GATA3 is regulated at the protein level by the proteosome and that the cancer-specific mutation results in increased protein stability.
GATA3 mutation uncouples turnover from the hormone response
Because the truncating mutation alters GATA3 protein level following hormone treatment, we asked whether the action of estrogen antagonists was likewise affected by this mutation. We treated cells grown in normal conditions (media plus FBS) with the ERα antagonist, ICI 182,780 (ICI). As expected, ERα expression was reduced in both cell lines (Figure 4C). While wild-type GATA3 protein levels were reduced following antagonist treatment in both T47D and MCF cells, the level of mutated GATA3 in MCF7 cells did not change. FOXA1 expression was not affected by ICI (Figure 4C). The GATA3 mRNA level remained mostly unaffected in cells treated with estradiol or ICI (Additional file 1: Figure S2). These experiments demonstrate that the truncation mutation in GATA3 stabilizes the protein in the face of agonist or antagonist binding by ERα, thus uncoupling physiologic, protein-level regulation from estradiol action.
DNA binding ability of mutated GATA3 is impaired
Genomic location of GATA3 in breast cancer cells
We explored the similarities in ChIP-seq between the two cell lines in terms of location relative to genomic features and intensity. In spite of the difference in number of GATA3 enriched regions, peak distribution relative to the closest transcription start site (TSS) was similar in T47D and MCF7 cells (Figure 7B). T47D cells had modestly higher frequency of enrichment in the range from -1 kb to +2 kb from TSS (Figure 7C, D). When the peaks were sorted by ChIP-seq signal, distribution in both cell lines was almost indistinguishable for peaks within 10 kb from the closest TSS as well as for those not associated with TSS – meaning that high-intensity peaks were distributed in a similar manner (Figure 7E). While these general indicators of pattern of enrichment appeared highly similar across the two cell lines, we observed a difference in overall signal intensity at peaks. Regardless of their localization relative to TSSs, bins of peaks in MCF7 exhibited a broader range of signal intensity than comparable bins in T47D (Figure 7F). This relationship was further corroborated by scrutiny of mean and median values for peaks grouped according to distance from TSS, where MCF7 invariably had higher mean and median values. At a gross level, the overall pattern of association of GATA3 with the genome appeared highly similar between the two cell lines. However, peak signal intensity after normalization for read depth was higher in MCF7 than in T47D.
Number of genes associated with GATA3 transcription factor binding in T47D and MC7 breast cancer cell lines within 10 and 50 kb from the closest transcription start site (TSS)
Number of genes*
+/−10 kb from TSS
+/−50 kb from TSS
Frequency of GATA3 recognition motif WGATAR in GATA3-peaks identified by ChIP-seq in T47D and MCF7 cells
Peaks containing WGATAR motif
T47D peaks overlapping with MCF7
MCF7 peaks overlapping with T47D
Large-scale genome sequencing projects have provided, and continue to provide, volumes of information on the mutational landscape of cancers. A current challenge for cancer biologists is to investigate the emerging genomic data in a mechanistic context, establishing the relationship of specific mutations to tumor biology and informing on clinical parameters including aggressiveness, response to therapy, and potential for metastasis. Here, we have initiated an attempt to address the mechanistic basis by which mutations in the transcription factor GATA3 may provide a growth advantage to breast cancer cells. The Cancer Genome Atlas Network (TCGA) recently reported a comprehensive study of human breast cancer: tumors from 507 patients were analyzed on multiple high information content platforms: whole exome sequencing, DNA copy number arrays, DNA methylation, mRNA array and sequencing, microRNA sequencing and reverse-phase protein arrays . Somatic mutations in GATA3 occurred in 58 cases (10.7%), predominantly in luminal A and B cancer subtypes, an additional 12 samples displayed copy number alterations (http://www.cbioportal.org). Strikingly, while mutations of GATA3 in the congenital disorder HDR syndrome are found throughout the protein , breast cancer specific mutations occur almost exclusively in exons 5 and 6 (TCGA). This clustering suggests regulatory roles for the carboxyl terminus of GATA3 and that impairment of these functions can provide a growth advantage to cancer cells.
Careful scrutiny of the TCGA mutation data revealed that six mutations were localized in the second zinc finger and five of them were frameshifts, similar to the mutation in MCF7 , making MCF7 a useful model to study a clinically relevant phenomenon. We confirmed the presence of a heterozygous guanine insertion in the fifth exon of GATA3 in the MCF7 genome and showed that although both full-length and truncated proteins were expressed, the mutated protein was present in the cells at a higher level. The D336 frameshift does not affect the N-terminal and C-terminal sequences flanking ZnF1 that are required for nuclear localization  and GATA3 proteins localized to the nucleus of MCF7 cells. Mutations in GATA3 ZnF2 impair DNA binding [20–22] suggesting that the same effect could be expected for MCF7-specific mutation. The biochemical fractionation assay identified a pool of truncated protein very loosely associated with chromatin (Figure 2B). However, the gel shift assay demonstrated that truncated GATA3 could bind DNA selectively, albeit with decreased affinity compared to wild-type (Figure 5). Consistent with the documented capacity of GATA3 to self-associate and to dimerize on DNA , we observed a pool of mutant protein that exhibited similar chromatin binding properties to wild-type GATA3. The data are consistent with formation of heterodimers between mutant and wild-type GATA3, potentially altering the association of the protein with its recognition elements in the genome.
ChIP-seq was utilized to assess the degree of overlap of GATA3 across the two cell lines used in our study. Surprisingly, the number of binding sites detected in MCF7 was substantially higher than in T47D cells. In spite of the large difference in genomic occupancy, detailed analysis of genes associated with GATA3 binding failed to identify any major functional differences between binding profiles in T47D and MCF7 cell lines (Additional file 1: Figure S6-S10). We speculated that the increased number of GATA3-enriched regions in MCF7 genome could have been due to compromised ability of the truncated protein to recognize the specific GATA binding motif, WGATAR. However, the proportion of GATA3 peaks containing the WGATAR motif was nearly identical in binding regions identified in T47D and MCF7 cells, as well as in cell-line specific regions (Table 2). This finding suggested that the heterozygous mutation did not affect binding specificity in MCF7 cells.
Although the number of GATA3 peaks was considerably lower in T47D than in MCF7 cells, progesterone receptor gene was an example of a locus featuring a greater number of bound regions in T47D than in MCF7. Remarkably, lack of PGR expression, as determined by immunohistochemical staining, was a common denominator for all five patients in the TCGA database carrying a frameshift mutation in ZnF2 of GATA3 (http://www.cbioportal.org). Even though both T47D and MCF7 cell lines are classified as PGR and ERα positive, and belong to luminal A breast cancer subtype , MCF7 has been also used as a model for luminal B subtype . The luminal B subtype is the more aggressive form of ERα-positive breast cancer that is less responsive to endocrine therapy . It is characterized by increased expression of proliferation-related genes and lower expression of ER-dependent genes, including PGR [38, 39]. In our model system, PGR mRNA level was approximately 20-fold lower in MCF7 than in T47D cells (Additional file 1: Figure S11). Loss of PGR expression is often considered as a marker for the gain of hormone-independent growth properties by ERα-positive breast cancers, through increased cross-talk between ERα and growth factor signaling pathways [38, 40]. In addition, the normal balance of the two known PGR isoforms, A or B, impacts biological properties of tumors .
Comparison of the biochemical properties of mutated GATA3 with wild type protein present in the T47D cell line demonstrated an increased half-life of truncated GATA3 in normal growth conditions and in response to ERα agonist and antagonist (Figures 3 and 4). GATA3 levels were proteasome-dependent (Figure 3B), similar to ERα, where rapid turnover of the receptor upon ligand binding is based on the ubiquitin-proteasome pathway . GATA3 is required for estrogen stimulation of cell cycle progression in breast cancer cells  and we showed that this truncating mutation present in MCF7 genome uncouples protein level regulation from hormonal signaling.
These findings strongly suggest that the carboxyl terminus of GATA3, a mutational hotspot in breast cancer, confers regulation on protein levels through as yet undefined mechanisms, resulting in increased stability of transcription factors resident on critical response elements in the breast cancer genome. We predict that mutations in GATA3 with similar characteristics to the mutation in MCF7 likely confer a growth advantage, particularly in pre-menopausal women, and are likely to occur early in tumor evolution.
This research was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences.
- Koboldt DC, Fulton RS, McLellan MD, Schmidt H, Kalicki-Veizer J, McMichael JF, Fulton LL, Dooling DJ, Ding L, Mardis ER, Wilson RK, Ally A, Balasundaram M, Butterfield YSN, Carlsen R, Carter C, Chu A, Chuah E, Chun HJE, Coope RJN, Dhalla N, Guin R, Hirst C, Hirst M, Holt RA, Lee D, Li HYI, Mayo M, Moore RA, Mungall AJ: Comprehensive molecular portraits of human breast tumours. Nature. 2012, 490: 61-70.View ArticleGoogle Scholar
- Stephens PJ, Tarpey PS, Davies H, Van Loo P, Greenman C, Wedge DC, Nik-Zainal S, Martin S, Varela I, Bignell GR, Yates LR, Papaemmanuil E, Beare D, Butler A, Cheverton A, Gamble J, Hinton J, Jia MM, Jayakumar A, Jones D, Latimer C, Lau KW, McLaren S, McBride DJ, Menzies A, Mudie L, Raine K, Rad R, Chapman MS, Teague J: The landscape of cancer genes and mutational processes in breast cancer. Nature. 2012, 486: 400-404.PubMedPubMed CentralGoogle Scholar
- Vogelstein B, Papadopoulos N, Velculescu VE, Zhou SB, Diaz LA, Kinzler KW: Cancer genome landscapes. Science. 2013, 339: 1546-1558.View ArticlePubMedPubMed CentralGoogle Scholar
- Yan W, Cao QJ, Arenas RB, Bentley B, Shao R: GATA3 inhibits breast cancer metastasis through the reversal of epithelial-mesenchymal transition. J Biol Chem. 2010, 285: 14042-14051.View ArticlePubMedPubMed CentralGoogle Scholar
- Chou J, Provot S, Werb Z: GATA3 in development and cancer differentiation: cells GATA have it!. J Cell Physiol. 2010, 222: 42-49.View ArticlePubMedPubMed CentralGoogle Scholar
- Zheng R, Blobel GA: GATA transcription factors and cancer. Genes Cancer. 2010, 1: 1178-1188.View ArticlePubMedPubMed CentralGoogle Scholar
- Chou J, Lin JH, Brenot A, Kim JW, Provot S, Werb Z: GATA3 suppresses metastasis and modulates the tumour microenvironment by regulating microRNA-29b expression. Nat Cell Biol. 2013, 15: 201-213.View ArticlePubMedPubMed CentralGoogle Scholar
- Kouros-Mehr H, Bechis SK, Slorach EM, Littlepage LE, Egeblad M, Ewald AJ, Pai SY, Ho IC, Werb Z: GATA-3 links tumor differentiation and dissemination in a luminal breast cancer model. Cancer Cell. 2008, 13: 141-152.View ArticlePubMedPubMed CentralGoogle Scholar
- Dydensborg AB, Rose AAN, Wilson BJ, Grote D, Paquet M, Giguere V, Siegel PM, Bouchard M: GATA3 inhibits breast cancer growth and pulmonary breast cancer metastasis. Oncogene. 2009, 28: 2634-2642.View ArticlePubMedGoogle Scholar
- Ho IC, Tai T-S, Pai S-Y: GATA3 and the T-cell lineage: essential functions before and after T-helper-2-cell differentiation. Nat Rev Immunol. 2009, 9: 125-135.View ArticlePubMedPubMed CentralGoogle Scholar
- Asselin-Labat ML, Sutherland KD, Barker H, Thomas R, Shackleton M, Forrest NC, Hartley L, Robb L, Grosveld FG, van der Wees J, Deb S, Fox SB, Smyth GK, Lindeman GJ, Visvader JE: Gata-3 is an essential regulator of mammary-gland morphogenesis and luminal-cell differentiation. Nat Cell Biol. 2007, 9: 201-209.View ArticlePubMedGoogle Scholar
- Kouros-Mehr H, Slorach EM, Sternlicht MD, Werb Z: GATA-3 maintains the differentiation of the luminal cell fate in the mammary gland. Cell. 2006, 127: 1041-1055.View ArticlePubMedPubMed CentralGoogle Scholar
- Mehra R, Varambally S, Ding L, Shen R, Sabel MS, Ghosh D, Chinnaiyan AM, Kleer CG: Identification of GATA3 as a breast cancer prognostic marker by global gene expression meta-analysis. Cancer Res. 2005, 65: 11259-11264.View ArticlePubMedGoogle Scholar
- Eeckhoute J, Keeton EK, Lupien M, Krum SA, Carroll JS, Brown M: Positive Cross-Regulatory Loop Ties GATA-3 to Estrogen Receptor α Expression in Breast Cancer. Cancer Res. 2007, 67: 6477-6483.View ArticlePubMedGoogle Scholar
- Oh DS, Troester MA, Usary J, Hu Z, He X, Fan C, Wu J, Carey LA, Perou CM: Estrogen-regulated genes predict survival in hormone receptor–positive breast cancers. J Clin Oncol. 2006, 24: 1656-1664.View ArticlePubMedGoogle Scholar
- Sørlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, Demeter J, Perou CM, Lønning PE, Brown PO, Børresen-Dale A-L, Botstein D: Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. 2003, 100: 8418-8423.View ArticlePubMedPubMed CentralGoogle Scholar
- Kong SL, Li G, Loh SL, Sung WK, Liu ET: Cellular reprogramming by the conjoint action of ERalpha, FOXA1, and GATA3 to a ligand-inducible growth state. Mol Syst Biol. 2011, 7: 526-View ArticlePubMedPubMed CentralGoogle Scholar
- Theodorou V, Stark R, Menon S, Carroll JS: GATA3 acts upstream of FOXA1 in mediating ESR1 binding by shaping enhancer accessibility. Genome Res. 2013, 23: 12-22.View ArticlePubMedPubMed CentralGoogle Scholar
- Van Esch H, Groenen P, Nesbit MA, Schuffenhauer S, Lichtner P, Vanderlinden G, Harding B, Beetz R, Bilous RW, Holdaway I, Shaw NJ, Fryns J-P, Van de Ven W, Thakker RV, Devriendt K: GATA3 haplo-insufficiency causes human HDR syndrome. Nature. 2000, 406: 419-422.View ArticlePubMedGoogle Scholar
- Nesbit MA, Bowl MR, Harding B, Ali A, Ayala A, Crowe C, Dobbie A, Hampson G, Holdaway I, Levine MA, McWilliams R, Rigden S, Sampson J, Williams AJ, Thakker RV: Characterization of GATA3 mutations in the hypoparathyroidism, deafness, and renal dysplasia (HDR) syndrome. J Biol Chem. 2004, 279: 22624-22634.View ArticlePubMedGoogle Scholar
- Ali A, Christie PT, Grigorieva IV, Harding B, Van Esch H, Ahmed SF, Bitner-Glindzicz M, Blind E, Bloch C, Christin P, Clayton P, Gecz J, Gilbert-Dussardier B, Guillen-Navarro E, Hackett A, Halac I, Hendy GN, Lalloo F, Mache CJ, Mughal Z, Ong AC, Rinat C, Shaw N, Smithson SF, Tolmie J, Weill J, Nesbit MA, Thakker RV: Functional characterization of GATA3 mutations causing the hypoparathyroidism-deafness-renal (HDR) dysplasia syndrome: insight into mechanisms of DNA binding by the GATA3 transcription factor. Hum Mol Genet. 2007, 16: 265-275.View ArticlePubMedGoogle Scholar
- Gaynor KU, Grigorieva IV, Allen MD, Esapa CT, Head RA, Gopinath P, Christie PT, Nesbit MA, Jones JL, Thakker RV: GATA3 mutations found in breast cancers may be associated with aberrant nuclear localization, reduced transactivation and cell invasiveness. Horm Canc. 2013, 4: 123-139.View ArticleGoogle Scholar
- Usary J, Llaca V, Karaca G, Presswala S, Karaca M, He X, Langerod A, Karesen R, Oh DS, Dressler LG, Lonning PE, Strausberg RL, Chanock S, Borresen-Dale AL, Perou CM: Mutation of GATA3 in human breast tumors. Oncogene. 2004, 23: 7669-7678.View ArticlePubMedGoogle Scholar
- Fujita N, Wade PA: Use of bifunctional cross-linking reagents in mapping genomic distribution of chromatin remodeling complexes. Methods. 2004, 33: 81-85.View ArticlePubMedGoogle Scholar
- Langmead B, Trapnell C, Pop M, Salzberg S: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10: R25-View ArticlePubMedPubMed CentralGoogle Scholar
- Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK: Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010, 38: 576-589.View ArticlePubMedPubMed CentralGoogle Scholar
- Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30: 207-210.View ArticlePubMedPubMed CentralGoogle Scholar
- Nawaz Z, Lonard DM, Dennis AP, Smith CL, O’Malley BW: Proteasome-dependent degradation of the human estrogen receptor. Proc Natl Acad Sci USA. 1996, 96: 1858-1862.View ArticleGoogle Scholar
- Yamashita M, Shinnakasu R, Asou H, Kimura M, Hasegawa A, Hashimoto K, Hatano N, Ogata M, Nakayama T: Ras-ERK MAPK cascade regulates GATA3 stability and Th2 differentiation through ubiquitin-proteasome pathway. J Biol Chem. 2005, 280: 29409-29419.View ArticlePubMedGoogle Scholar
- Bernardo GM, Lozada KL, Miedler JD, Harburg G, Hewitt SC, Mosley JD, Godwin AK, Korach KS, Visvader JE, Kaestner KH: FOXA1 is an essential determinant of ERα expression and mammary ductal morphogenesis. Development. 2010, 137: 2045-2054.View ArticlePubMedPubMed CentralGoogle Scholar
- Carroll JS, Meyer CA, Song J, Li W, Geistlinger TR, Eeckhoute J, Brodsky AS, Keeton EK, Fertuck KC, Hall GF, Wang Q, Bekiranov S, Sementchenko V, Fox EA, Silver PA, Gingeras TR, Liu XS, Brown M: Genome-wide analysis of estrogen receptor binding sites. Nat Genet. 2006, 38: 1289-1297.View ArticlePubMedGoogle Scholar
- Bates DL, Chen Y, Kim G, Guo L, Chen L: Crystal structures of multiple GATA zinc fingers bound to DNA reveal new insights into DNA recognition and self-association by GATA. J Mol Biol. 2008, 381: 1292-1306.View ArticlePubMedPubMed CentralGoogle Scholar
- Raouf A, Zhao Y, To K, Stingl J, Delaney A, Barbara M, Iscove N, Jones S, McKinney S, Emerman J, Aparicio S, Marra M, Eaves C: Transcriptome analysis of the normal human mammary cell commitment and differentiation process. Cell Stem Cell. 2008, 3: 109-118.View ArticlePubMedGoogle Scholar
- Lin CY, Strom A, Vega VB, Kong SL, Yeo AL, Thomsen JS, Chan WC, Doray B, Bangarusamy DK, Ramasamy A, Vergara LA, Tang S, Chong A, Bajic VB, Miller LD, Gustafsson JA, Liu ET: Discovery of estrogen receptor alpha target genes and response elements in breast tumor cells. Genome Biol. 2004, 5: R66-View ArticlePubMedPubMed CentralGoogle Scholar
- Prat A, Parker JS, Karginova O, Fan C, Livasy C, Herschkowitz JI, He X, Perou CM: Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 2010, 12: R68-View ArticlePubMedPubMed CentralGoogle Scholar
- Holliday D, Speirs V: Choosing the right cell line for breast cancer research. Breast Cancer Res. 2011, 13: 215-View ArticlePubMedPubMed CentralGoogle Scholar
- Loi S, Sotiriou C, Haibe-Kains B, Lallemand F, Conus N, Piccart M, Speed T, McArthur G: Gene expression profiling identifies activated growth factor signaling in poor prognosis (Luminal-B) estrogen receptor positive breast cancer. BMC Med Genomics. 2009, 2: 37-View ArticlePubMedPubMed CentralGoogle Scholar
- Creighton CJ: The molecular profile of luminal B breast cancer. Biol Targets Ther. 2012, 6: 289-297.View ArticleGoogle Scholar
- Tran B, Bedard P: Luminal-B breast cancer and novel therapeutic targets. Breast Cancer Res. 2011, 13: 221-View ArticlePubMedPubMed CentralGoogle Scholar
- Cui X, Schiff R, Arpino G, Osborne CK, Lee AV: Biology of progesterone receptor loss in breast cancer and its implications for endocrine therapy. J Clin Oncol. 2005, 23: 7721-7735.View ArticlePubMedGoogle Scholar
- Richer JK, Jacobsen BM, Manning NG, Abel MG, Wolf DM, Horwitz KB: Differential gene regulation by the two progesterone receptor isoforms in human breast cancer cells. J Biol Chem. 2002, 277: 5209-5218.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/14/278/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.