Estrogen receptor alpha (ERα) is a ligand-activated transcription factor that controls key cellular pathways via protein-protein interactions involving multiple components of transcriptional coregulator and signal transduction complexes. Natural and synthetic ERα ligands are classified as agonists (17β-estradiol/E(2)), selective estrogen receptor modulators (SERMs: Tamoxifen/Tam and Raloxifene/Ral), and pure antagonists (ICI 182,780-Fulvestrant/ICI), according to the response they elicit in hormone-responsive cells. Crystallographic analyses reveal ligand-dependent ERα conformations, characterized by specific surface docking sites for functional protein-protein interactions, whose identification is needed to understand antiestrogen effects on estrogen target tissues, in particular breast cancer (BC). Tandem affinity purification (TAP) coupled to mass spectrometry was applied here to map nuclear ERα interactomes dependent upon different classes of ligands in hormone-responsive BC cells. Comparative analyses of agonist (E(2))- vs antagonist (Tam, Ral or ICI)-bound ERα interacting proteins reveal significant differences among ER ligands that relate with their biological activity, identifying novel functional partners of antiestrogen-ERα complexes in human BC cell nuclei. In particular, the E(2)-dependent nuclear ERα interactome is different and more complex than those elicited by Tam, Ral, or ICI, which, in turn, are significantly divergent from each other, a result that provides clues to explain the pharmacological specificities of these compounds.
Estrogen receptor β (ERβ) is a member of the nuclear receptor family of homeostatic regulators that is frequently lost in breast cancer (BC), where its presence correlates with a better prognosis and a less aggressive clinical outcome of the disease. In contrast to ERα, its closest homolog, ERβ shows significant estrogen-independent activities, including the ability to inhibit cell cycle progression and regulate gene transcription in the absence of the ligand. Investigating the nature and extent of this constitutive activity of ERβ in BC MCF-7 and ZR-75.1 cells by means of microRNA (miRNA) sequencing, we identified 30 miRNAs differentially expressed in ERβ+ versus ERβ− cells in the absence of ligand, including up-regulated oncosuppressor miRs such miR-30a. In addition, a significant fraction of >1,600 unique proteins identified in MCF-7 cells by iTRAQ quantitative proteomics were either increased or decreased by ERβ, revealing regulation of multiple cell pathways by ligand-free receptors. Transcriptome analysis showed that for a large number of proteins regulated by ERβ, the corresponding mRNAs are unaffected, including a large number of putative targets of ERβ-regulated miRNAs, indicating a central role of miRNAs in mediating BC cell proteome regulation by ERβ. Expression of a mimic of miR-30a-5p, a direct target and downstream effector of ERβ in BC, led to the identification of several target transcripts of this miRNA, including 11 encoding proteins whose intracellular concentration was significantly affected by unliganded receptor. These results demonstrate a significant effect of ligand-free ERβ on BC cell functions via modulation of the cell proteome and suggest that miRNA regulation might represent a key event in the control of the biological and clinical phenotype of hormone-responsive BC by this nuclear receptor. Estrogen receptor β (ERβ) is a member of the nuclear receptor family of homeostatic regulators that is frequently lost in breast cancer (BC), where its presence correlates with a better prognosis and a less aggressive clinical outcome of the disease. In contrast to ERα, its closest homolog, ERβ shows significant estrogen-independent activities, including the ability to inhibit cell cycle progression and regulate gene transcription in the absence of the ligand. Investigating the nature and extent of this constitutive activity of ERβ in BC MCF-7 and ZR-75.1 cells by means of microRNA (miRNA) sequencing, we identified 30 miRNAs differentially expressed in ERβ+ versus ERβ− cells in the absence of ligand, including up-regulated oncosuppressor miRs such miR-30a. In addition, a significant fraction of >1,600 unique proteins identified in MCF-7 cells by iTRAQ quantitative proteomics were either increased or decreased by ERβ, revealing regulation of multiple cell pathways by ligand-free receptors. Transcriptome analysis showed that for a large number of proteins regulated by ERβ, the corresponding mRNAs are unaffected, including a large number of putative targets of ERβ-regulated miRNAs, indicating a central role of miRNAs in mediating BC cell proteome regulation by ERβ. Expression of a mimic of miR-30a-5p, a direct target and downstream effector of ERβ in BC, led to the identification of several target transcripts of this miRNA, including 11 encoding proteins whose intracellular concentration was significantly affected by unliganded receptor. These results demonstrate a significant effect of ligand-free ERβ on BC cell functions via modulation of the cell proteome and suggest that miRNA regulation might represent a key event in the control of the biological and clinical phenotype of hormone-responsive BC by this nuclear receptor. Estrogen receptors (ERs) 1The abbreviations used are:ERestrogen receptorBCbreast canceriTRAQisobaric tags for relative and absolute quantitationmiRNAmicroRNAmiRNA-Seqnext-generation microRNA sequencingUTRuntranslated regionwtwild typeSCXstrong cation exchange chromatography. 1The abbreviations used are:ERestrogen receptorBCbreast canceriTRAQisobaric tags for relative and absolute quantitationmiRNAmicroRNAmiRNA-Seqnext-generation microRNA sequencingUTRuntranslated regionwtwild typeSCXstrong cation exchange chromatography. α and β, ligand-inducible nuclear receptors, regulate the transcription of target genes such as those involved in cell cycle control, including proto-oncogenes and cyclin genes (1Cicatiello L. Addeo R. Sasso A.R. Altucci L. Belsito Petrizzi V. Borgo R. Cancemi M. Caporali S. Caristi S. Scafoglio C. Teti D. Bresciani F. Perillo B. Weisz A. Estrogens promote persistent G1 activation of the CCND1 gene by inducing transcriptional de-repression via c-Jun/c-Fos/ER complex assembly to a distal regulatory element and recruitment of cyclin D1 to its own gene promoter.Mol. Cell. Biol. 2004; 24: 7260-7274Crossref PubMed Scopus (142) Google Scholar, 2Heldring N. Pike A. Andersson S. Matthews J. Cheng G. Hartman J. Tujague M. Strom A. Treuter E. Warner M. Gustafsson J.A. Estrogen receptors: how do they signal and what are their targets.Physiol. Rev. 2007; 87: 905-931Crossref PubMed Scopus (1326) Google Scholar, 3Weisz A. Rosales R. Identification of an estrogen response element upstream of the human c-fos proto-oncogene that binds the estrogen receptor and the AP-1 transcription factor.Nucleic Acids Res. 1990; 18: 5097-5106Crossref PubMed Scopus (267) Google Scholar). ERs act through genomic and nongenomic signal transduction pathways (4Klinge C.M. Jernigan S.C. Mattingly K.A. Risinger K.E. Zhang J. Estrogen response element-dependent regulation of transcriptional activation of estrogen receptors alpha and beta by coactivators and corepressors.J. Mol. Endocrinol. 2004; 33: 387-410Crossref PubMed Scopus (154) Google Scholar, 5Leclercq G. Molecular forms of the estrogen receptor in breast cancer.J. Steroid Biochem. Mol. Biol. 2002; 80: 259-272Crossref PubMed Scopus (33) Google Scholar, 6Manavathi B. Kumar R. Steering estrogen signals from the plasma membrane to the nucleus: two sides of the coin.J. Cell. Physiol. 2006; 207: 594-604Crossref PubMed Scopus (128) Google Scholar, 7Pettersson K. Gustafsson J.A. Role of estrogen receptor beta in estrogen action.Annu. Rev. Physiol. 2001; 63: 165-192Crossref PubMed Scopus (432) Google Scholar) that integrate with one another to mediate the mitogenic actions of estrogens, hormones involved in the pathophysiology of the mammary gland through their ability to modulate cell growth and differentiation. Although the two receptor subtypes share a similar mechanism of action, they show substantial differences in their ability to control gene transcription, including specific properties defining subtype-specific functions and features (8Grober O.M. Mutarelli M. Giurato G. Ravo M. Cicatiello L. De Filippo M.R. Ferraro L. Nassa G. Papa M.F. Paris O. Tarallo R. Luo S. Schroth G.P. Benes V. Weisz A. Global analysis of estrogen receptor beta binding to breast cancer cell genome reveals an extensive interplay with estrogen receptor alpha for target gene regulation.BMC Genomics. 2011; 12: 36Crossref PubMed Scopus (126) Google Scholar, 9McInerney E.M. Tsai M.J. O'Malley B.W. Katzenellenbogen B.S. Analysis of estrogen receptor transcriptional enhancement by a nuclear hormone receptor coactivator.Proc. Natl. Acad. Sci. U.S.A. 1996; 93: 10069-10073Crossref PubMed Scopus (219) Google Scholar, 10Watanabe T. Inoue S. Ogawa S. Ishii Y. Hiroi H. Ikeda K. Orimo A. Muramatsu M. Agonistic effect of tamoxifen is dependent on cell type, ERE-promoter context, and estrogen receptor subtype: functional difference between estrogen receptors alpha and beta.Biochem. Biophys. Res. Commun. 1997; 236: 140-145Crossref PubMed Scopus (204) Google Scholar). Indeed, upon hormonal activation they recruit different molecular partners in large multiprotein complexes exerting different effects in breast cancer (BC) cells (11Ambrosino C. Tarallo R. Bamundo A. Cuomo D. Franci G. Nassa G. Paris O. Ravo M. Giovane A. Zambrano N. Lepikhova T. Janne O.A. Baumann M. Nyman T.A. Cicatiello L. Weisz A. Identification of a hormone-regulated dynamic nuclear actin network associated with estrogen receptor alpha in human breast cancer cell nuclei.Mol. Cell. Proteomics. 2010; 9: 1352-1367Abstract Full Text Full Text PDF PubMed Scopus (57) Google Scholar, 12Nassa G. Tarallo R. Ambrosino C. Bamundo A. Ferraro L. Paris O. Ravo M. Guzzi P.H. Cannataro M. Baumann M. Nyman T.A. Nola E. Weisz A. A large set of estrogen receptor beta-interacting proteins identified by tandem affinity purification in hormone-responsive human breast cancer cell nuclei.Proteomics. 2011; 11: 159-165Crossref PubMed Scopus (33) Google Scholar, 13Nassa G. Tarallo R. Guzzi P.H. Ferraro L. Cirillo F. Ravo M. Nola E. Baumann M. Nyman T.A. Cannataro M. Ambrosino C. Weisz A. Comparative analysis of nuclear estrogen receptor alpha and beta interactomes in breast cancer cells.Mol. Biosyst. 2011; 7: 667-676Crossref PubMed Scopus (0) Google Scholar, 14Tarallo R. Bamundo A. Nassa G. Nola E. Paris O. Ambrosino C. Facchiano A. Baumann M. Nyman T.A. Weisz A. Identification of proteins associated with ligand-activated estrogen receptor alpha in human breast cancer cell nuclei by tandem affinity purification and nano LC-MS/MS.Proteomics. 2011; 11: 172-179Crossref PubMed Scopus (35) Google Scholar). As a consequence, whereas ERα is mitogenic for hormone-responsive BC cells, ERβ inhibits cell proliferation by increasing the expression of growth-inhibitory genes and interfering with the activation of cell cycle and anti-apoptotic genes by ERα in response to 17β-estradiol (8Grober O.M. Mutarelli M. Giurato G. Ravo M. Cicatiello L. De Filippo M.R. Ferraro L. Nassa G. Papa M.F. Paris O. Tarallo R. Luo S. Schroth G.P. Benes V. Weisz A. Global analysis of estrogen receptor beta binding to breast cancer cell genome reveals an extensive interplay with estrogen receptor alpha for target gene regulation.BMC Genomics. 2011; 12: 36Crossref PubMed Scopus (126) Google Scholar, 15Chang E.C. Frasor J. Komm B. Katzenellenbogen B.S. Impact of estrogen receptor beta on gene networks regulated by estrogen receptor alpha in breast cancer cells.Endocrinology. 2006; 147: 4831-4842Crossref PubMed Scopus (274) Google Scholar). Interestingly, ERβ is frequently lost in BC, and this is believed to represent a critical stage in estrogen-dependent tumor progression (16Bardin A. Boulle N. Lazennec G. Vignon F. Pujol P. Loss of ERbeta expression as a common step in estrogen-dependent tumor progression.Endocr. Relat. Cancer. 2004; 11: 537-551Crossref PubMed Scopus (359) Google Scholar, 17Roger P. Sahla M.E. Makela S. Gustafsson J.A. Baldet P. Rochefort H. Decreased expression of estrogen receptor beta protein in proliferative preinvasive mammary tumors.Cancer Res. 2001; 61: 2537-2541PubMed Google Scholar). The presence of ERβ generally correlates with a better prognosis of the disease (18Sugiura H. Toyama T. Hara Y. Zhang Z. Kobayashi S. Fujii Y. Iwase H. Yamashita H. Expression of estrogen receptor beta wild-type and its variant ERbetacx/beta2 is correlated with better prognosis in breast cancer.Jpn. J. Clin. Oncol. 2007; 37: 820-828Crossref PubMed Scopus (70) Google Scholar) and is a biomarker of a less aggressive clinical phenotype (19Novelli F. Milella M. Melucci E. Di Benedetto A. Sperduti I. Perrone-Donnorso R. Perracchio L. Venturo I. Nistico C. Fabi A. Buglioni S. Natali P.G. Mottolese M. A divergent role for estrogen receptor-beta in node-positive and node-negative breast cancer classified according to molecular subtypes: an observational prospective study.Breast Cancer Res. 2008; 10: R74Crossref PubMed Scopus (88) Google Scholar, 20Shaaban A.M. Green A.R. Karthik S. Alizadeh Y. Hughes T.A. Harkins L. Ellis I.O. Robertson J.F. Paish E.C. Saunders P.T. Groome N.P. Speirs V. Nuclear and cytoplasmic expression of ERbeta1, ERbeta2, and ERbeta5 identifies distinct prognostic outcome for breast cancer patients.Clin. Cancer Res. 2008; 14: 5228-5235Crossref PubMed Scopus (196) Google Scholar). Unlike ERα, which is mainly inactive in the absence of estrogens, ERβ is able to regulate gene expression in the absence of steroid ligands, as unliganded receptor can translocate to the nucleus (21Leung Y.K. Ho S.M. Estrogen receptor beta: switching to a new partner and escaping from estrogen.Sci. Signal. 2011; 4: pe19Crossref PubMed Scopus (18) Google Scholar, 22Vivar O.I. Zhao X. Saunier E.F. Griffin C. Mayba O.S. Tagliaferri M. Cohen I. Speed T.P. Leitman D.C. Estrogen receptor beta binds to and regulates three distinct classes of target genes.J. Biol. Chem. 2010; 285: 22059-22066Abstract Full Text Full Text PDF PubMed Scopus (87) Google Scholar, 23Ivanova M.M. Mazhawidza W. Dougherty S.M. Klinge C.M. Sex differences in estrogen receptor subcellular location and activity in lung adenocarcinoma cells.Am. J. Respir. Cell Mol. Biol. 2010; 42: 320-330Crossref PubMed Scopus (46) Google Scholar), where it recruits transcriptional co-regulators and modulates gene activity (24Tremblay A. Tremblay G.B. Labrie F. Giguere V. Ligand-independent recruitment of SRC-1 to estrogen receptor beta through phosphorylation of activation function AF-1.Mol. Cell. 1999; 3: 513-519Abstract Full Text Full Text PDF PubMed Scopus (387) Google Scholar, 25Tremblay A. Giguere V. Contribution of steroid receptor coactivator-1 and CREB binding protein in ligand-independent activity of estrogen receptor beta.J. Steroid Biochem. Mol. Biol. 2011; 77: 19-27Crossref Scopus (63) Google Scholar). estrogen receptor breast cancer isobaric tags for relative and absolute quantitation microRNA next-generation microRNA sequencing untranslated region wild type strong cation exchange chromatography. estrogen receptor breast cancer isobaric tags for relative and absolute quantitation microRNA next-generation microRNA sequencing untranslated region wild type strong cation exchange chromatography. The different regulatory roles of the two ERs in BC have recently been shown to relate also to the specific modulation of microRNA (miRNA) expression. Both ERs were found to bind in vivo to miRNA gene regulatory sites in BC cell chromatin upon activation by E2 (8Grober O.M. Mutarelli M. Giurato G. Ravo M. Cicatiello L. De Filippo M.R. Ferraro L. Nassa G. Papa M.F. Paris O. Tarallo R. Luo S. Schroth G.P. Benes V. Weisz A. Global analysis of estrogen receptor beta binding to breast cancer cell genome reveals an extensive interplay with estrogen receptor alpha for target gene regulation.BMC Genomics. 2011; 12: 36Crossref PubMed Scopus (126) Google Scholar, 26Cicatiello L. Mutarelli M. Grober O.M. Paris O. Ferraro L. Ravo M. Tarallo R. Luo S. Schroth G.P. Seifert M. Zinser C. Chiusano M.L. Traini A. De Bortoli M. Weisz A. Estrogen receptor alpha controls a gene network in luminal-like breast cancer cells comprising multiple transcription factors and microRNAs.Am. J. Pathol. 2010; 176: 2113-2130Abstract Full Text Full Text PDF PubMed Scopus (138) Google Scholar) and to control BC cell miRNome activity in vitro and in vivo (27Ferraro L. Ravo M. Nassa G. Tarallo R. De Filippo M.R. Giurato G. Cirillo F. Stellato C. Silvestro S. Cantarella C. Rizzo F. Cimino D. Friard O. Biglia N. De Bortoli M. Cicatiello L. Nola E. Weisz A. Effects of oestrogen on microRNA expression in hormone-responsive breast cancer cells.Horm. Cancer. 2012; 3: 65-78Crossref PubMed Scopus (46) Google Scholar, 28Paris O. Ferraro L. Grober O.M. Ravo M. De Filippo M.R. Giurato G. Nassa G. Tarallo R. Cantarella C. Rizzo F. Di Benedetto A. Mottolese M. Benes V. Ambrosino C. Nola E. Weisz A. Direct regulation of microRNA biogenesis and expression by estrogen receptor beta in hormone-responsive breast cancer.Oncogene. 2012; 31: 4196-4206Crossref PubMed Scopus (82) Google Scholar). This result is particularly intriguing in view of the known master regulatory role of these RNAs in normal and transformed cells. miRNAs are small (20 to 25 nucleotides) noncoding RNAs synthesized in the nucleus by RNA polymerase II or III as long primary transcripts (pri-miRNAs) that are then processed by a microprocessor complex comprising Drosha and DiGeorge syndrome critical region protein 8 proteins (29Han J. Lee Y. Yeom K.H. Kim Y.K. Jin H. Kim V.N. The Drosha-DGCR8 complex in primary microRNA processing.Genes Dev. 2004; 18: 3016-3027Crossref PubMed Scopus (1581) Google Scholar) to ∼70-nucleotide stem-loop RNAs (pre-miRNAs). Pre-miRNAs are exported from nucleus to cytoplasm by exportin 5 and Ran-GTP (30Kim V.N. Han J. Siomi M.C. Biogenesis of small RNAs in animals.Nat. Rev. Mol. Cell Biol. 2009; 10: 126-139Crossref PubMed Scopus (2581) Google Scholar) and cleaved by Dicer/TRBP endoribonucleases to generate ∼22-nucleotide mature miRNAs (31Chendrimada T.P. Gregory R.I. Kumaraswamy E. Norman J. Cooch N. Nishikura K. Shiekhattar R. TRBP recruits the Dicer complex to Ago2 for microRNA processing and gene silencing.Nature. 2005; 436: 740-744Crossref PubMed Scopus (1591) Google Scholar, 32Okamura K. Phillips M.D. Tyler D.M. Duan H. Chou Y.T. Lai E.C. The regulatory activity of microRNA* species has substantial influence on microRNA and 3′ UTR evolution.Nat. Struct. Mol. Biol. 2008; 15: 354-363Crossref PubMed Scopus (429) Google Scholar) that, in turn, are incorporated into RNA-induced silencing complexes and bind to the 3′ untranslated regions (UTRs) of target mRNAs determining gene silencing by either inhibition of translation or mRNA degradation (33Newman M.A. Hammond S.M. Emerging paradigms of regulated microRNA processing.Genes Dev. 2010; 24: 1086-1092Crossref PubMed Scopus (176) Google Scholar). In this way miRNA regulates a wide variety of physiological and pathological cellular pathways at a post-transcriptional level, including cell proliferation, differentiation, and homeostasis, as well as neoplastic transformation (34Garzon R. Calin G.A. Croce C.M. MicroRNAs in cancer.Ann. Rev. Med. 2009; 60: 167-179Crossref PubMed Scopus (1602) Google Scholar). Interestingly, in solid tumors such as prostate, colon, stomach, pancreas, lung, and breast, the spectrum of expressed miRNAs (miRNome) is different from that of the corresponding normal tissues (35Volinia S. Calin G.A. Liu C.G. Ambs S. Cimmino A. Petrocca F. Visone R. Iorio M. Roldo C. Ferracin M. Prueitt R.L. Yanaihara N. Lanza G. Scarpa A. Vecchione A. Negrini M. Harris C.C. Croce C.M. A microRNA expression signature of human solid tumors defines cancer gene targets.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 2257-2261Crossref PubMed Scopus (4942) Google Scholar), suggesting the involvement of miRNAs in the transformed cell biology. Indeed, altered miRNA expression contributes to tumorigenesis, as some of them can function as either tumor suppressors or oncogenes (36Croce C.M. Causes and consequences of microRNA dysregulation in cancer.Nat. Rev. Genet. 2009; 10: 704-714Crossref PubMed Scopus (2582) Google Scholar). We investigated here the ability of unliganded ERβ to influence hormone-responsive BC cell proteome via miRNAs. By combining massively parallel next-generation miRNA sequencing, gene expression profiling, and isobaric tags for relative and absolute quantitation (iTRAQ) quantitative proteomics for comparative analyses of MCF-7 cells expressing or not expressing ERβ, we observed a remarkable effect of this ER subtype on miRNome composition in the absence of steroidal ligands. This, in turn, results in significant quantitative and qualitative changes of the cell proteome that, in most cases, are not accompanied by comparable changes of the corresponding transcriptome. These results demonstrate that ERβ controls many BC cell functions via miRNA-mediated post-transcriptional regulation of the cell proteome also in the absence of estrogen ligands. Stable cell clones expressing either C-TAP-ERβ or Ν-TAP-ERβ (ERβ+) have been previously described (12Nassa G. Tarallo R. Ambrosino C. Bamundo A. Ferraro L. Paris O. Ravo M. Guzzi P.H. Cannataro M. Baumann M. Nyman T.A. Nola E. Weisz A. A large set of estrogen receptor beta-interacting proteins identified by tandem affinity purification in hormone-responsive human breast cancer cell nuclei.Proteomics. 2011; 11: 159-165Crossref PubMed Scopus (33) Google Scholar). To generate ZRFlagβ cells expressing Flag-ERβ, the full-length coding region of human ERβ cDNA was PCR amplified, creating BamHI sites at both ends that were then used to excise the insert for cloning into the p3XFLAG-CMV expression vector (Sigma Aldrich) to obtain p3XFLAG-ERβ-CMV plasmid. p3XFLAG-ERβ-CMV was transfected into ZR75.1 cells by lipofection (Lipofectamine 2000, Invitrogen). Stable Flag-ERβ-expressing clones were isolated after antibiotic selection using 200 μg/ml G418. All cell lines were maintained in Dulbecco's modified Eagle's medium (DMEM) (Sigma-Aldrich) supplemented with 10% FBS (HyClone) and antibiotics (100 U/ml penicillin, 100 mg/ml streptomycin, 250 ng/ml Amfotericin-B). Steroid deprivation (starvation) was performed by culturing in DMEM without phenol red and 5% dextran-coated charcoal stripped serum for 5 days, as described elsewhere (12Nassa G. Tarallo R. Ambrosino C. Bamundo A. Ferraro L. Paris O. Ravo M. Guzzi P.H. Cannataro M. Baumann M. Nyman T.A. Nola E. Weisz A. A large set of estrogen receptor beta-interacting proteins identified by tandem affinity purification in hormone-responsive human breast cancer cell nuclei.Proteomics. 2011; 11: 159-165Crossref PubMed Scopus (33) Google Scholar). ERβ+ or ERβ− cells were harvested by scraping in cold PBS, collected by centrifugation at 1,000 × g, and resuspended in three volumes with respect to the cell pellet of hypotonic buffer (20 mm HEPES pH 7.4, 5 mm NaF, 10 μm Na molybdate, 0.1 mm EDTA, 1 mm DTT, 1 mm PMSF, 1X protease inhibitor mixture). Cell lysis was induced by incubation on ice for 15 min; then 0.5% Triton X-100 was added and a cytosolic fraction was prepared by spinning the samples for 30 s at 4 °C at 15,000 × g. Nuclei were purified by stratification on 25% sucrose cushion in hypotonic buffer and centrifuged for 15 min at 4 °C at 9,000 × g to remove cytosolic contaminants. The obtained nuclear pellets were then dissolved in one volume of nuclear lysis buffer (20 mm HEPES, pH 7.4, 25% glycerol, 420 mm NaCl, 1.5 mm MgCl2, 0.2 mm EDTA, 1 mm DTT, 1X Sigma-Aldrich protease inhibitor mixture, and 1 mm PMSF), incubated for 30 min at 4 °C with gentle shaking, and centrifuged for 30 min at 4 °C at 15,000 × g. Finally, nuclear extracts were diluted with 2X volumes of nuclear lysis buffer without NaCl for a final salt concentration of 140 mm (11Ambrosino C. Tarallo R. Bamundo A. Cuomo D. Franci G. Nassa G. Paris O. Ravo M. Giovane A. Zambrano N. Lepikhova T. Janne O.A. Baumann M. Nyman T.A. Cicatiello L. Weisz A. Identification of a hormone-regulated dynamic nuclear actin network associated with estrogen receptor alpha in human breast cancer cell nuclei.Mol. Cell. Proteomics. 2010; 9: 1352-1367Abstract Full Text Full Text PDF PubMed Scopus (57) Google Scholar). SDS-PAGE and Western blot analyses were performed using standard protocols as previously described (11Ambrosino C. Tarallo R. Bamundo A. Cuomo D. Franci G. Nassa G. Paris O. Ravo M. Giovane A. Zambrano N. Lepikhova T. Janne O.A. Baumann M. Nyman T.A. Cicatiello L. Weisz A. Identification of a hormone-regulated dynamic nuclear actin network associated with estrogen receptor alpha in human breast cancer cell nuclei.Mol. Cell. Proteomics. 2010; 9: 1352-1367Abstract Full Text Full Text PDF PubMed Scopus (57) Google Scholar). Protein samples from cytosolic and nuclear extracts were denatured, separated on 10% polyacrylamide and 0.1% SDS (SDS-PAGE), and electro-transferred onto a nitrocellulose membrane (Whatman GmbH-Schleicher & Schuell, Dassel, Germany). The membrane was blocked using 5% (w/v) fat-free milk powder in 1× TBS supplemented with 0.1% (v/v) Tween-20 (TBS-T). The following primary antibodies were used: rabbit anti-TAP (CAB1001, Thermo Scientific-Pierce), rabbit anti-ERα (sc-543, Santa Cruz Biotechnology, Dallas, TX), mouse anti-α-tubulin (T6199, Sigma Aldrich), mouse anti-β-actin (A1978, Sigma Aldrich), and mouse M2 anti-Flag (F1804, Sigma Aldrich). After extensive washing with the TBS–Tween-20 mixture, the immunoblotted proteins were incubated with the appropriate horseradish-peroxidase-conjugated secondary antibodies (GE Healthcare) and detected via enhanced chemiluminescence (Pierce ECL Western blotting Substrate, Thermo Scientific) and exposure to a medical x-ray film (FujiFilm, Dusseldorf, Germany). Hormone-starved MCF-7 cells expressing or not expressing ERβ (3,000 per well) were seeded in 96-well plates and the proliferation rate was monitored for 10 days. At the established time (every 2 days) cells were washed in phosphate-buffered saline (PBS) and fixed with 12.5% glutaraldehyde for 20 min at room temperature. This was followed by two washes with distilled water, incubation with 0.05% methylene blue for 30 min, accurate rinsing, and incubation with 0.33 m HCl for 18 h. Absorption was measured at 620 nm. Hormone-starved ZR75.1 cells expressing or not expressing ERβ (3,000/well) were seeded in 96-well plates and the proliferation rate was assayed at 3, 6, and 12 days. At the established time a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide cell proliferation assay (Invitrogen) was performed. At each time point 200 μl of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (final concentration of 1 mg/ml) reagent was added to each well, incubated for 4 h in a humidified 5% CO2 incubator at 37 °C, and resuspended in 100 μl of 0.4 m HCl in 2-propanol. Absorbance was then measured at 570 and 620 nm (background) wavelengths. ERβ+ and ERβ− cells (1.5 × 105 cells per dish) were starved in 60-mm culture dishes for 5 days and collected in PBS containing 50 μg/ml propidiumiodide, 0.1% (v/v) sodium citrate, 0.1% (v/v) Nonidet P-40. Cell samples were incubated in the dark for at least 15 min at room temperature, or overnight at 4 °C, and analyzed by a FACScalibur flow cytometer using the CellQuest software package (BD Biosciences) according to standard protocols suggested by the manufacturer. Data analysis was performed with Modfit software (Verity Software, Topsham, ME). Results shown were obtained from several independent experiments. Wild-type (wt) MCF-7, C-TAP-ERβ (Ct-ERβ), and Ν-TAP-ERβ (Nt-ERβ) clones were starved as described above. Then 5 × 105 cells per dish were seeded in 60-mm culture dishes and transfected by using 25 μg of polyethylenimine per dish (Polysciences, Inc., Eppelheim, Germany) with 2.5 μg of DNA per dish, including 300 ng of ERE-tk-Luc, pSG-Δ2-NLS-LacZ vector (β-galactosidase) co-transfected as an internal control for transfection efficiency, and carrier DNA (Bluescribe M13+). At 48 h after transfection, cells were washed with cold PBS and lysed in 100 μl of lysis buffer (Promega, Madison, WI). Luciferase activity was measured in extracts using the luciferase assay reagent (Promega), according to the manufacturer's instructions, and values were expressed as relative light units normalized to the β-galactosidase activity. Average luciferase activity was calculated from the data obtained from three independent replicates. The identification of miRNA expression profiles was performed by means of next-generation sequencing with sequencing-by-synthesis technology; this allowed a dynamic range of detection (from very low to highly abundant RNAs) and measurement of relatively limited differences in expression between samples. For MCF7 miRNA profiling, 7.5 μg of total RNA was used in a library preparation according to the Illumina TruSeq small RNA sample preparation protocol (Rev. B, Illumina, San Diego, CA). Libraries were multiplexed by using Illumina indices set A with indices 10 (RPI10) and 11 (RPI11) for ERβ− and ERβ+ cells, respectively. Sized miRNA libraries were gel purified and sequenced on GAIIx (Illumina) at a concentration of 10 pm for 36 plus 7 additional cycles for index sequencing. For ZR75.1 small RNA sequencing, an updated version of the protocol, recommending library preparation from 1 μg of RNA input, was applied (Rev. E). In this case samples were indexed by using indices 2 (RPI2) and 3 (RPI3) for ERβ+ and ERβ− cells, respectively, and sequenced on a HiSeq1500 sequencer (Illumina) at a concentration of 10 pm for 50 plus 7 additional cycles for index sequencing. Raw sequencing data were filtered following several criteria. Because the sequence of the adapter was known, a perl script was used to trim the adaptors from the raw data. Sequence reads were then filtered for quality and clustered to unique sequences to remove redundancy, retaining their individual read count information. Unique sequences 18 nucleotides or more in length were mapped, without any mismatch, on miRNA annotation according to miRBase version 18 using iMir, a modular pipeline for comprehensive analysis of small RNA sequencing data, comprising specific tools for adapter trimming, quality filtering, identification of known small noncoding RNAs, novel miRNA prediction, differential expression analysis, and target prediction (37Giurato G. De Filippo M.R. Rinaldi A. Hashim A. Nassa G. Ravo M. Rizzo F. Tarallo R. Weisz A. iMir: an integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq.BMC Bioinformatics. 2013; 14: 362Crossref PubMed Scopus (50) Google Scholar). The pipeline created has proven to be efficient and flexible enough to allow a user to select the preferred combination of analytical steps. It detects the reads corresponding to known miRNAs, giving a precise estimation of their expression level. The miRBase (38Kozomara A. Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data.Nucleic Acids Res. 2011; 39: D152-D157Crossref PubMed Scopus (2948) Google Scholar, 39Griffiths-Jones S. Saini H.K. van Dongen S. Enright A.J. miR
The Publisher regrets that this article is an accidental duplication of an article that has already been published, http://dx.doi.org/10.1016/j.ijmyco.2016.11.012. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
Abstract Adenomyoepithelioma (AME) is a rare biphasic proliferative breast lesion, which may resemble salivary gland epithelial-myoepithelial carcinomas (EMCs). Most AMEs have an indolent clinical course, but malignant transformation and local and distant recurrences have been reported. We sought to define the mutational landscape of AMEs and investigate the functional impact of recurrent likely pathogenic mutations identified in these tumors. Nineteen AMEs were subjected to whole-exome massively parallel sequencing (MPS, n=7) or targeted capture MPS using MSK-IMPACT assay (n=12). Somatic genetic alterations and the cancer cell fraction of mutations were defined using state-of-the-art bioinformatics algorithms. Selected genes (i.e. HRAS and PIK3CA) were subjected to Sanger sequencing in a series of 17 additional AMEs (total n=36). Non-tumorigenic mammary epithelial cells (i.e. MCF10A, MCF10A with the PIK3CAH1047R mutation and MCF12A), which are estrogen receptor (ER)-negative, were utilized for 2D and 3D functional studies. Of 36 cases, 22 were ER-positive and 14 were ER-negative. MPS analysis revealed a low mutation burden and HRASQ61 and PIK3CA hotspot mutations in 6/19 (32%) and 11/19 (58%) AMEs, respectively. All HRASQ61 and all but one PIK3CA mutations were clonal. ER-positive and ER-negative AMEs were fundamentally histologically and genetically distinct. Whilst ER-positive AMEs displayed recurrent PIK3CA mutations (50%, 11/22) but lacked HRAS mutations, ER-negative AMEs displayed, in addition to PIK3CA mutations (57%, 8/14), recurrent HRASQ61 mutations (57%, 8/14). HRASQ61 mutations co-occurred with PIK3CA mutations (50%, 4/8), PIK3R1 deletions (12.5%, 1/8) and/or CDKN2A homozygous deletions (25%, 2/8). HRASQ61 mutations, but not PIK3CA mutations, were significantly associated with ER-negativity (100% vs 21%), concurrent carcinoma (50% vs 7%), axillary metastases (38% vs 0%), high proliferation (63% vs 4%), necrosis (63% vs 11%) and nuclear pleomorphism (75% vs 29%). In vitro forced HRASQ61R expression in MCF10A and MCF12A cells resulted in increased proliferation and transformation. In 3D organotypic cell cultures, forced HRASQ61R resulted in a highly disorganized growth pattern, a partial loss of epithelial phenotype and acquisition of aberrant myoepithelial differentiation, which was more overt in PIK3CA-mutant MCF10A cells. In conclusion, AMEs are phenotypically and genetically heterogeneous. Whilst PIK3CA hotspot mutations occur across the spectrum of lesions, HRASQ61 hotspot mutations are restricted to ER-negative AMEs, which should arguably be classified as breast EMCs. Our genomic and functional analyses are consistent with the notion that HRASQ61 mutations are driver events in the pathogenesis of ER-negative AMEs and may be sufficient for the acquisition of myoepithelial differentiation in breast cells. Citation Format: Felipe C. Geyer, Kathleen A. Burke, Anqi Li, Anastasios D. Papanastatiou, Fresia Pareja, Anne S. Schulteis, Charlotte K. Ng, Salvatore Piscuoglio, Marcia Edelweiss, Luciano G. Martelotto, Pier Selenica, Maria R. Filippo, Gabriel S. Macedo, Achim Jungbluth, Hannah Y. Wen, Juan Palazzo, Zsuzsanna Varga, Emad Rakha, Ian O. Ellis, Brian Rubin, Britta Weigelt, Jorge S. Reis-Filho. Massively parallel sequencing analysis of breast adenomyoepitheliomas reveals the heterogeneity of the disease and identifies a subset driven by HRAS hotspot mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3379. doi:10.1158/1538-7445.AM2017-3379
Abstract Background:All carcinomas contain a variable proportion of benign stromal and immune cells, limiting the sensitivity in the identification of somatic genetic alterations. The average tumor purity of squamous cell carcinomas (SCC) of the lung is only around 50%. Low tumor content in tumor samples represents a challenge in studying intratumoral genomic heterogeneity in cancer research. Here, we applied flow-sorting to enrich for tumor cell nuclei to investigate the clonal relationship of primary SCC and matched metastases. Methods:Tumor tissues from 16 patients with primary SCC of the lung and matched metastases were used. We implemented a flow-sorting based approach to enrich for tumor nuclei as followed: after extraction of nuclei from snap-frozen or FFPE tissue, they were flow-sorted by DNA content (ploidy) and cytokeratin expression of the adherent cytoplasm, using a pan-cytokeratin (pCK) antibody. Isolated diploid and aneuploid pCK-positive tumor cell populations were subjected to whole exome sequencing (WES). DNA from diploid pCK-negative populations was used as germline control. Mutational profile and copy number aberrations (CNA) were determined to infer the clonal relationship and evolution between the primary tumors and their metastases. Results:Our flow-sorting based approach was able to enrich tumor content from 20%-50% to more than 80%-90% and to distinguish between aneuploid and diploid tumor cell populations from bulk tumor tissues. It enabled the identification of somatic mutations and CNA in both, aneuploid and diploid tumor cell populations, including potential subclonal driver mutations in ARID1A and KDM6A at low allelic frequencies. Shared and private mutations were observed in matched longitudinal tumor samples of individual patients and in synchronous distant metastases. Ploidy did not change significantly between primary tumors and relapse or distant metastases. Conclusion:We present a flow-sorting based method to enrich for tumor cell nuclei to facilitate genomic analysis, which also enabled separate analysis of aneuploid and diploid tumor populations. Our results show that the enrichment approach can be used to decode the clonal evolutionary relationship between primary tumors and their matched metastases, even in samples with low tumor cell content. Citation Format: Arthur Krause, Maria R. De Filippo, Thomas Lorber, Tanja Dietsche, Valeria Perrina, Christian Ruiz, Michael T. Barrett, Salvatore Piscuoglio, Charlotte K. Ng, Lukas Bubendorf. Enriching tumor purity using a unique flow-sorting approach to elucidate clonal evolution in matched samples of squamous cell carcinoma of the lung [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4699.
Synchronous early-stage endometrioid endometrial carcinomas (EECs) and endometrioid ovarian carcinomas (EOCs) are associated with a favorable prognosis and have been suggested to represent independent primary tumors rather than metastatic disease. We subjected sporadic synchronous EECs/EOCs from five patients to whole-exome massively parallel sequencing, which revealed that the EEC and EOC of each case displayed strikingly similar repertoires of somatic mutations and gene copy number alterations. Despite the presence of mutations restricted to the EEC or EOC in each case, we observed that the mutational processes that shaped their respective genomes were consistent. High-depth targeted massively parallel sequencing of sporadic synchronous EECs/EOCs from 17 additional patients confirmed that these lesions are clonally related. In an additional Lynch Syndrome case, however, the EEC and EOC were found to constitute independent cancers lacking somatic mutations in common. Taken together, sporadic synchronous EECs/EOCs are clonally related and likely constitute dissemination from one site to the other.
Abstract Background: Massively parallel sequencing studies have identified large numbers of mutations of unknown biologic significance. There is a pressing need for computational methods to predict and distinguish neutral from potentially pathogenic mutations accurately, to help identify those mutations worth exploring experimentally and clinically. Although various bioinformatic algorithms are available, they are based on different methodologies and assumptions, and their predictions for the same mutations are not always concordant. In this study, we sought to benchmark the performance of 17 prediction algorithms using functionally validated and pathognomonic mutations. Methods: We curated the literature for functionally validated and pathognomonic mutations as our positive dataset (i.e. pathogenic mutations). For the negative dataset (i.e. neutral mutations), we retrieved variants from the dbSNP database, including only those with minor allele frequency >25%. We compiled a total of 7975 mutations (875 pathogenic and 7100 neutral). The performance of each prediction algorithm, namely accuracy, specificity, sensitivity, positive predictive value (PPV) and negative predictive value (NPV), were defined using the positive and negative datasets described above. Confidence intervals were calculated by sub-sampling 2/3 of the functionally pathogenic mutations and equal number of neutral mutations 500 times. To reduce the bias introduced by mutations included in the COSMIC database, we excluded those found in COSMIC v67, resulting in 6048 mutations (212 pathogenic and 5835 neutral), and re-evaluated the performance of each prediction algorithm. Results: Our analysis revealed that the overall accuracy varied considerably, with a median of 87% (range 78%-97%). In terms of accuracy, FATHMM (cancer) statistically outperformed all other prediction algorithms (97%, 95% confidence interval (CI) 96%-98%), followed by MutationTaster 2 (94%, 95% CI 93%-95%). Sensitivity and specificity also varied (median 85%, range 77%-96% and median 89%, range 71%-100%, respectively). The most sensitive prediction algorithm, FATHMM (cancer) (96%, 95% CI 95%-97%) statistically outperformed all others. The most specific prediction algorithm was CHASM (breast) (100%, 95% CI 94%-100%). While CHASM (breast) had the highest PPV (100%, 95% CI 99%-100%), FATHMM (cancer) had statistically better NPV than all other prediction algorithms (96%, 95% CI 95%-97%). When COSMIC mutations were removed, FATHMM (cancer) remained the most accurate (93%, 95% CI 91%-95%) though the difference was not statistically significant. In this context, CanDrA (breast) was the most sensitive prediction algorithm (95%, 95% CI 93%-97%) and had the highest NPV (93%, 95% CI 90%-96%), while CHASM (breast) was the most specific prediction algorithm (100%, 95% CI 99%-100%) and had the best PPV (99%, 95% CI 97%-100%). Conclusions: Our results demonstrate that functional prediction algorithms varied in performance. Using this dataset of mutations, FATHMM (cancer) outperformed all other prediction algorithms in terms of accuracy, sensitivity and NPV, and remained the most accurate even when mutations catalogued in the COSMIC database were excluded. Citation Format: Maria R De Filippo, Charlotte KY Ng, Jorge S Reis-Filho, Britta Weigelt. Benchmarking mutation function prediction algorithms using validated cancer driver and passenger mutations [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-03-09.
In the original article, there was an error. The concentration and administration schedule of Rapalink-1 reported for the in vivo experiment was not correct. The error appeared both in the "Materials and Methods" and in the "Results" sections, where it is incorrectly reported as "1.5 mg/g […] every 5 days" and "1.5 mg/g/6 days", respectively. Figure 5C in the original article reported the correct administration schedule.A correction has been made to the "Materials and Methods" section, "Animals Maintenance and in vivo Experiment" sub-section:"Group 1 received 3.5 μl/g of vehicle (20% DMSO, 40% PEG-300 and 40% PBS) i.p. once a week while group 2 received Rapalink-1 (1.5 mg/Kg) resuspended in vehicle, i.p. every 5-7 days."A correction of the same error has been made to the "Results" section, "Treatment of LAPC9 in vivo With Rapalink-1 Delays Tumor Growth" sub-section:"We then assessed the effect of in vivo on LAPC9 PDX model, comparing the treatment to vehicle only, a schematic of treatment schedule is reported ( Figure 5C)."The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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