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BMC Cancer
Published in: BMC Cancer 1/2017

Open Access 01-12-2017 | Research article

Guanylate-binding protein-1 is a potential new therapeutic target for triple-negative breast cancer

Authors: Melissa Quintero, Douglas Adamoski, Larissa Menezes dos Reis, Carolline Fernanda Rodrigues Ascenção, Krishina Ratna Sousa de Oliveira, Kaliandra de Almeida Gonçalves, Marília Meira Dias, Marcelo Falsarella Carazzolle, Sandra Martha Gomes Dias

Published in: BMC Cancer | Issue 1/2017

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Abstract

Background

Triple-negative breast cancer (TNBC) is characterized by a lack of estrogen and progesterone receptor expression (ESR and PGR, respectively) and an absence of human epithelial growth factor receptor (ERBB2) amplification. Approximately 15–20% of breast malignancies are TNBC. Patients with TNBC often have an unfavorable prognosis. In addition, TNBC represents an important clinical challenge since it does not respond to hormone therapy.

Methods

In this work, we integrated high-throughput mRNA sequencing (RNA-Seq) data from normal and tumor tissues (obtained from The Cancer Genome Atlas, TCGA) and cell lines obtained through in-house sequencing or available from the Gene Expression Omnibus (GEO) to generate a unified list of differentially expressed (DE) genes. Methylome and proteomic data were integrated to our analysis to give further support to our findings. Genes that were overexpressed in TNBC were then curated to retain new potentially druggable targets based on in silico analysis. Knocking-down was used to assess gene importance for TNBC cell proliferation.

Results

Our pipeline analysis generated a list of 243 potential new targets for treating TNBC. We finally demonstrated that knock-down of Guanylate-Binding Protein 1 (GBP1 ), one of the candidate genes, selectively affected the growth of TNBC cell lines. Moreover, we showed that GBP1 expression was controlled by epidermal growth factor receptor (EGFR) in breast cancer cell lines.

Conclusions

We propose that GBP1 is a new potential druggable therapeutic target for treating TNBC with enhanced EGFR expression.
Appendix
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Literature
1.
Isakov O, Shomron N. Deep Sequencing Data Analysis : Challenges and Solutions. Bioinforma. - Trends Methodol. 2011. p. 655–679.
2.
Mardis ER. The impact of next-generation sequencing technology on genetics. Cell. 2008;24:133–41.
3.
Li B, Ruotti V, Stewart RM, Thomson JA, Dewey CN. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics. 2009;26:493–500.
4.
5.
Griffith M, Griffith OL, Mwenifumbo J, Goya R, Morrissy AS, Morin RD, et al. Alternative expression analysis by RNA sequencing. Nat Methods. 2010;7:843–50.CrossRefPubMed
6.
Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Baren JV, et al. Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms. Nat Biotechnol. 2010;28:511–5.CrossRefPubMedPubMedCentral
7.
Wang L, Xi Y, Yu J, Dong L, Yen L, Li WA. Statistical method for the detection of alternative splicing using RNA-Seq. PLoS One. 2010;5
8.
Reis-Filho JS, Tutt ANJ. Triple negative tumours: a critical review. Histopathology. 2007;52:108–18.CrossRef
9.
Haffty BG, Yang Q, Reiss M, Kearney T, Higgins SA, Weidhaas J, et al. Locoregional relapse and distant metastasis in conservatively managed triple negative early-stage breast cancer. J Clin Oncol. 2006;24:5652–7.CrossRefPubMed
10.
Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka C a, et al. Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res. 2007;13:4429–34.CrossRefPubMed
11.
Fisher B, Costantino JP, Wickerham DL, Cecchini RS, Cronin WM, Robidoux A, et al. Tamoxifen for the prevention of breast cancer: current status of the National Surgical Adjuvant Breast and bowel project P-1 study. J Natl Cancer Inst. 2005;97:1652–62.CrossRefPubMed
12.
Baselga J, Norton L, Albanell J, Kim Y, Mendelsohn J. Recombinant humanized anti-HER2 antibody ( Herceptin ™ ) enhances the antitumor activity of paclitaxel and doxorubicin against HER2 / neu overexpressing human breast cancer Xenografts recombinant humanized anti-HER2 antibody ( Herceptinâ „ ¢ ) enhances th. Cancer Res 1998;58:2825–31.
13.
Oakman C, Viale G, Di Leo A. Management of triple negative breast cancer. Breast. 2010;19:312–21.CrossRefPubMed
14.
Criscitiello C, Azim H a, Schouten PC, Linn SC, Sotiriou C. Understanding the biology of triple-negative breast cancer. Ann Oncol. 2012;23(Suppl 6):vi13–8.CrossRefPubMed
15.
Bianchini G, Balko JM, Mayer IA, Sanders ME, Gianni L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat Rev Clin Oncol. 2016;13:674–90.CrossRefPubMedPubMedCentral
16.
Cheng YSE, Colonno RJ, Yin FH. Interferon induction of fibroblast proteins with guanylate binding activity. J Biol Chem. 1983;258:7746–50.PubMed
17.
Guenzi E, Töpolt K, Cornali E, Lubeseder-Martellato C, Jörg A, Matzen K, et al. The helical domain of GBP-1 mediates the inhibition of endothelial cell proliferation by inflammatory cytokines. EMBO J. 2001;20:5568–77.CrossRefPubMedPubMedCentral
18.
Lan Q, Wang A, Cheng Y, Mukasa A, Ma J, Hong L, et al. Guanylate binding protein-1 mediates EGFRvIII and promotes glioblastoma growth in vivo but not in vitro. Oncotarget. 2014:7.
19.
Li M, Mukasa A, Inda M d-M, Zhang J, Chin L, Cavenee W, et al. Guanylate binding protein 1 is a novel effector of EGFR-driven invasion in glioblastoma. J Exp Med. 2011;208:2657–73.CrossRefPubMedPubMedCentral
20.
Li L, Ma G, Jing C, Liu Z. Guanylate-binding protein 1 (GBP1) promotes lymph node metastasis in human esophageal squamous cell carcinoma. Discov Med. 2015;20:369–78.PubMed
21.
Duan Z, Foster R, Brakora K a, Yusuf RZ, Seiden MV. GBP1 overexpression is associated with a paclitaxel resistance phenotype. Cancer Chemother Pharmacol. 2006;57:25–33.CrossRefPubMed
22.
De Donato M, Mariani M, Petrella L, Martinelli E, Zannoni GF, Vellone V, et al. Class III β-tubulin and the cytoskeletal gateway for drug resistance in ovarian cancer. J Cell Physiol. 2012;227:1034–41.CrossRefPubMed
23.
Fukumoto M, Amanuma T, Kuwahara Y, Shimura T, Suzuki M, Mori S, et al. Guanine nucleotide-binding protein 1 is one of the key molecules contributing to cancer cell radioresistance. Cancer Sci. 2014;105:1351–9.CrossRefPubMedPubMedCentral
24.
Hastie E, Cataldi M, Moerdyk-Schauwecker MJ, Felt SA, Steuerwald N, Grdzelishvili VZ. Novel biomarkers of resistance of pancreatic cancer cells to oncolytic vesicular stomatitis virus. Oncotarget. 2016;7:61601–618.CrossRefPubMedPubMedCentral
25.
Varley KE, Gertz J, Roberts BS, Davis NS, Bowling KM, Kirby MK, et al. Recurrent read-through fusion transcripts in breast cancer. Breast Cancer Res Treat. 2014;146:287–97.CrossRefPubMedPubMedCentral
26.
27.
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013;41:D991–5.CrossRefPubMed
28.
Kodama Y, Shumway M, Leinonen R. The sequence read archive: explosive growth of sequencing data. Nucleic Acids Res. 2012;40:2011–3.
29.
Leinonen R, Sugawara H, Shumway M. International nucleotide sequence database collaboration. The sequence read archive. Nucleic Acids Res. 2011;39:D19–21.CrossRefPubMed
30.
31.
Jiang H, Lei R, Ding S-W, Zhu S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics. 2014;15:182.CrossRefPubMedPubMedCentral
32.
Del Fabbro C, Scalabrin S, Morgante M, Giorgi FM. An extensive evaluation of read trimming effects on Illumina NGS data analysis. PLoS One. 2013;8:e85024.CrossRefPubMedPubMedCentral
33.
Dobin A, Davis C a, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.CrossRefPubMed
34.
35.
Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics. 2010;11:94.CrossRefPubMedPubMedCentral
36.
Curtis C, Shah SP, Chin S-F, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012:1–7.
37.
Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies.
38.
Vincent KM, Findlay SD, Postovit LM. Assessing breast cancer cell lines as tumour models by comparison of mRNA expression profiles. Breast cancer res. Breast Cancer Res. 2015;17:114.CrossRefPubMedPubMedCentral
39.
Fraley C, Raftery AE, Murphy TB, Scrucca L. mclust version 4 for R : normal mixture modeling for model-based clustering, classification, and density estimation. . TechRep 597. 2012.
40.
41.
Gautier L, Cope L, Bolstad BM, Irizarry RA. Affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004;20:307–15.CrossRefPubMed
42.
43.
Smyth GK. Limma : Linear Models for Microarray Data. :397–420.
44.
Kolde R. Pheatmap: pretty Heatmaps. R package. CRAN; 2012.
45.
Chen H, Boutros PC. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC bioinformatics. BioMed Central Ltd. 2011;12:35.
46.
Vu VQ. ggbiplot: A ggplot2 based biplot. 2011.
47.
Carlson M. org.Hs.eg.db: Genome wide annotation for Human.
48.
Blake JA, Christie KR, Dolan ME, Drabkin HJ, Hill DP, Ni L, et al. Gene ontology consortium: going forward. Nucleic Acids Res. 2015;43:D1049–56.CrossRef
49.
50.
R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R foundation for statistical Computing; 2011.
51.
Carlson M. GO.db: A set of annotation maps describing the entire Gene Ontology.
52.
Kovalchik S. RISmed: download content from NCBI databases. 2015.
53.
Bulusu KC, Tym JE, Coker EA, Schierz AC, Al-Lazikani B. canSAR: updated cancer research and drug discovery knowledgebase. Nucleic Acids Res. 2014;42:D1040–7.CrossRefPubMed
54.
Hussein HA, Borrel A, Geneix C, Petitjean M, Regad L, Camproux A-C. PockDrug-server: a new web server for predicting pocket druggability on holo and apo proteins. Nucleic Acids Res. 2015;43:W436–42.CrossRefPubMedPubMedCentral
55.
Pidsley R, Y Wong CC, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 2013;14:293.
56.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015;43:e47–e47.
57.
58.
Triche T. FDb.InfiniumMethylation.hg19: Annotation package for Illumina Infinium DNA methylation probes. 2014.
59.
Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, et al. Software for computing and annotating genomic ranges. Prlic A, editor. PLoS Comput Biol 2013;9:e1003118.
60.
Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, et al. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19:1639–45.CrossRefPubMedPubMedCentral
61.
Hu, Yan C, Hsu C-H, Chen Q-R, Niu K, Komatsoulis G, et al. OmicCircos: a simple-to-use R package for the circular visualization of multidimensional Omics data. Cancer Inform 2014;13:13.
62.
Li J, Lu Y, Akbani R, Ju Z, Roebuck PL, Liu W, et al. TCPA: a resource for cancer functional proteomics data. Nat. Methods. Nat Publ Group. 2013;10:1046–7.
63.
Akbani R, Ng PKS, Werner HMJ, Shahmoradgoli M, Zhang F, Ju Z, et al. A pan-cancer proteomic perspective on the cancer genome atlas. Nat Commun. 2014;5:3887.CrossRefPubMedPubMedCentral
64.
Mertins P, Mani DR, Ruggles KV, Gillette MA, Clauser KR, Wang P, et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature. Nat Publ Group. 2016;534:55–62.
65.
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) method. Methods Elsevier. 2001;25:402–8.
66.
Roukos V, Pegoraro G, Voss TC, Misteli T. Cell cycle staging of individual cells by fluorescence microscopy. Nat Protoc. 2015;10:334–48.CrossRefPubMed
67.
Riccardi C, Nicoletti I. Analysis of apoptosis by propidium iodide staining and flow cytometry. Nat Protoc. 2006;1:1458–61.CrossRefPubMed
68.
69.
Anders S, Huber W. Differential expression analysis for sequence count data. Genome biol. BioMed Central Ltd. 2010;11:R106.CrossRef
70.
Stirzaker C, Zotenko E, Clark SJ. Genome-wide DNA Methylation profiling in triple negative breast cancer reveals epigenetic signatures with important clinical value. Mol. Cell. Oncol. Taylor & Francis; 2015;3:00–00.
71.
Mathe A, Wong-Brown M, Locke WJ, Stirzaker C, Braye SG, Forbes JF, et al. DNA methylation profile of triple negative breast cancer-specific genes comparing lymph node positive patients to lymph node negative patients. Sci. Rep. Nat Publ Group. 2016;6:33435.
72.
Rechache NS, Wang Y, Stevenson HS, Killian JK, Edelman DC, Merino M, et al. DNA methylation profiling identifies global methylation differences and markers of adrenocortical tumors. J Clin Endocrinol Metab. 2012;97:1004–13.CrossRef
73.
Lim DHK, Maher ERDNA. Methylation: a form of epigenetic control of gene expression. Obstet Gynaecol. 2010;12:37–42.
74.
Halling-Brown MD, Bulusu KC, Patel M, Tym JE, Al-Lazikani B. canSAR: an integrated cancer public translational research and drug discovery resource. Nucleic Acids Res. 2012;40:947–56.CrossRef
75.
Rodriguez-Acebes S, Proctor I, Loddo M, Wollenschlaeger A, Rashid M, Falzon M, et al. Targeting DNA replication before it starts. Am J Pathol American Society for Investigative Pathology. 2010;177:2034–45.
76.
Sainsbury R, Proctor I, Rodriguez S, Loddo M, Tudzarova S, Stoeber K, et al. Targeting DNA replication before it starts: Cdc7 as a therapeutic target in p53 mutant Her2 and triple negative breast cancer. Breast Cancer Res. 2010;12:P50.CrossRefPubMedCentral
77.
Zou M, Liu Z, Zhang X-S, Wang Y. NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data. Bioinformatics. 2015;31:3330–8.CrossRefPubMed
78.
Honda K, Taniguchi T. IRFs: master regulators of signalling by toll-like receptors and cytosolic pattern-recognition receptors. Nat. Rev Immunol. 2006;6:644–58.CrossRef
79.
Ramsauer K, Farlik M, Zupkovitz G, Seiser C, Kröger A, Hauser H, et al. Distinct modes of action applied by transcription factors STAT1 and IRF1 to initiate transcription of the IFN-gamma-inducible gbp2 gene. Proc Natl Acad Sci U S A. 2007;104:2849–54.CrossRefPubMedPubMedCentral
80.
Zhao C, Denison C, Huibregtse JM, Gygi S, Krug RM. Human ISG15 conjugation targets both IFN-induced and constitutively expressed proteins functioning in diverse cellular pathways. Proc Natl Acad Sci U S A. 2005;102:10200–5.CrossRefPubMedPubMedCentral
81.
Chuang J-Y, Wang S-A, Yang W-B, Yang H-C, Hung C-Y, T-P S, et al. Sp1 phosphorylation by cyclin-dependent kinase 1/cyclin B1 represses its DNA-binding activity during mitosis in cancer cells. Oncogene. 2012;31:4946–59.CrossRefPubMedPubMedCentral
82.
Park HS, Jang MH, Kim EJ, Kim HJ, Lee HJ, Kim YJ, et al. High EGFR gene copy number predicts poor outcome in triple-negative breast cancer. Mod. Pathol Nat Publ Group. 2014:1–11.
83.
Herold CI, Anders CK. New targets for triple-negative breast cancer. Oncology. 2013, 2016;9
84.
Jiang Y-Z, Liu Y-R, X-E X, Jin X, Hu X, K-D Y, et al. Transcriptome analysis of triple-negative breast cancer reveals an integrated mRNA-lncRNA signature with predictive and prognostic value. Cancer Res. 2016;76:2105–14.CrossRefPubMed
85.
Burns MB, Lackey L, Carpenter MA, Rathore A, Land AM, Leonard B, et al. APOBEC3B is an enzymatic source of mutation in breast cancer. Nature. Nat Publ Group. 2013;494:366–70.
86.
Craig DW, O’Shaughnessy J a, Kiefer J a, Aldrich J, Sinari S, Moses TM, et al. Genome and Transcriptome sequencing in prospective metastatic triple-negative breast cancer uncovers therapeutic vulnerabilities. Mol Cancer Ther. 2013;12:104–16.CrossRefPubMed
87.
Eswaran J, Cyanam D, Mudvari P, Reddy SDN, Pakala SB, Nair SS, et al. Transcriptomic landscape of breast cancers through mRNA sequencing. Sci Rep. 2012;2:264.CrossRefPubMedPubMedCentral
88.
89.
Donato MDE, Mariani M, Petrella L, Martinelli E, Zannoni GF, Vellone V, et al. Class III b -tubulin and the cytoskeletal gateway for drug resistance in ovarian. Cancer. 2011:1034–41.
90.
Andreoli M, Persico M, Kumar A, Orteca N, Kumar V, Pepe A, et al. Identification of the first inhibitor of the GBP1:PIM1 interaction. Implications for the development of a new class of anticancer agents against paclitaxel resistant. Cancer Cells. 2014;
91.
Ahn SG, Kim SJ, Kim C, Jeong J. Molecular classification of triple-negative breast cancer. J. Breast Cancer. 2016;19:223–30.CrossRef
92.
Sasa M, Bando Y, Takahashi M, Hirose T, Nagao T. Screening for basal marker expression is necessary for decision of therapeutic strategy for triple-negative breast cancer. J Surg Oncol. 2008;97:30–4.CrossRefPubMed
93.
Viale G, Rotmensz N, Maisonneuve P, Bottiglieri L, Montagna E, Luini A, et al. Invasive ductal carcinoma of the breast with the “triple-negative” phenotype: prognostic implications of EGFR immunoreactivity. Breast Cancer Res Treat. 2009;116:317–28.CrossRefPubMed
94.
Carey L, Winer E, Viale G, Cameron D, Gianni L. Triple-negative breast cancer: disease entity or title of convenience? Nat Rev Clin Oncol. 2010;7:683–92.CrossRefPubMed
95.
Gazinska P, Grigoriadis A, Brown JP, Millis RR, Mera A, Gillett CE, et al. Comparison of basal-like triple-negative breast cancer defined by morphology, immunohistochemistry and transcriptional profiles. Mod Pathol. 2013;26:955–66.CrossRefPubMed
96.
Vokes EE, Chu E. Anti-EGFR Therapies: clinical experience in colorectal, lung, and head and neck cancers. Oncology (Williston Park) 2006;20:15–25.
97.
Baselga J, Phase II. Tumor Pharmacodynamic study of Gefitinib in patients with advanced breast cancer. J Clin Oncol. 2005;23:5323–33.CrossRefPubMed
98.
NL S, W X, 3rd BH, H H, EC D, a D, et al. study of the biologic effects of lapatinib, a reversible inhibitor of ErbB1 and ErbB2 tyrosine kinases, on tumor growth and survival pathways in patients with advanced malignancies. J Clin Oncol 2005;23:2502–12.
99.
Brand TM, Iida M, Dunn EF, Luthar N, Kostopoulos KT, Corrigan KL, et al. Nuclear epidermal growth factor receptor is a functional molecular target in triple-negative breast cancer. Mol Cancer Ther. 2014;13:1356–68.CrossRefPubMedPubMedCentral
Metadata
Title
Guanylate-binding protein-1 is a potential new therapeutic target for triple-negative breast cancer
Authors
Melissa Quintero
Douglas Adamoski
Larissa Menezes dos Reis
Carolline Fernanda Rodrigues Ascenção
Krishina Ratna Sousa de Oliveira
Kaliandra de Almeida Gonçalves
Marília Meira Dias
Marcelo Falsarella Carazzolle
Sandra Martha Gomes Dias
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2017
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-017-3726-2

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