Volume 26, Issue 2 (3-2022)                   IBJ 2022, 26(2): 160-174 | Back to browse issues page

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Dariushnejad H, Ghorbanzadeh V, Akbari S, Hashemzadeh P. Design of a Novel Recombinant Multi-Epitope Vaccine against Triple-Negative Breast Cancer. IBJ 2022; 26 (2) :160-174
URL: http://ibj.pasteur.ac.ir/article-1-3447-en.html
Background: Triple-negative breast cancer (TNBC) is determined by the absence of ERBB2, estrogen and progesterone receptors’ expression. Cancer vaccines, as the novel immunotherapy strategies, have emerged as promising tools   for treating the advanced stage of TNBC. The aim of this study was to evaluate Carcinoembryonic antigen (CEA), Metadherin (MTDH), and Mucin 1 (MUC-1) proteins as vaccine candidates against TNBC.
Methods: In this research, a novel vaccine was designed against TNBC by using different immunoinformatics and bioinformatics approaches. Effective immunodominant epitopes were chosen from three antigenic proteins, namely CEA, MTDH, and MUC-1. Recombinant TLR4 agonists were utilized as an adjuvant to stimulate immune responses. Following the selection of antigens and adjuvants, appropriate linkers were chosen to generate the final recombinant protein. To achieve an excellent 3D model, the best predicted 3D model was required to be refined and validated. To demonstrate whether the vaccine/TLR4 complex is stable or not, we performed docking analysis and dynamic molecular simulation.
Result: Immunoinformatics and bioinformatics evaluations of the designed construct demonstrated that this vaccine candidate could effectively be used as a therapeutic armament against TNBC.
Conclusion: Bioinformatics studies revealed that the designed vaccine has an acceptable quality. Investigating the effectiveness of this vaccine can be confirmed by supplementary in vitro and in vivo studies.

1. Esteva FJ, Hubbard-Lucey VM, Tang J, Pusztai L. Immunotherapy and targeted therapy combinations in metastatic breast cancer. The lancet oncology 2019; 20(3): e175-e186. [DOI:10.1016/S1470-2045(19)30026-9]
2. Katz H, Alsharedi M. Immunotherapy in triple-negative breast cancer. Medical oncology 2018; 35(1): 13. [DOI:10.1007/s12032-017-1071-6]
3. Crown J, O'shaughnessy J, Gullo G. Emerging targeted therapies in triple-negative breast cancer. Annals of oncology 2012; 23(6): vi56-vi65. [DOI:10.1093/annonc/mds196]
4. O'Neill S, Porter RK, McNamee N, Martinez VG, O'Driscoll L. 2-Deoxy-D-Glucose inhibits aggressive triple-negative breast cancer cells by targeting glycolysis and the cancer stem cell phenotype. Scientific reports 2019; 9(1): 3788. [DOI:10.1038/s41598-019-39789-9]
5. de Paula Peres L, da Luz FAC, dos Anjos Pultz B, Brígido AC , Agenor de Araújo R , Ricardo Goulart L , Barbosa Silva MJ . Peptide vaccines in breast cancer: The immunological basis for clinical response. Biotechnology advances 2015; 33(8): 1868-1877. [DOI:10.1016/j.biotechadv.2015.10.013]
6. Pol JG, Bridle BW, Lichty BD. Detection of Tumor Antigen-Specific T-Cell Responses After Oncolytic Vaccination. Methods in molecular biology 2020: 2058:191-211. [DOI:10.1007/978-1-4939-9794-7_12]
7. Shahid F, Ashfaq UA, Javaid A, Khalid H. Immunoinformatics guided rational design of a next generation multi epitope based peptide (MEBP) vaccine by exploring Zika virus proteome. Infection, genetics and evolution 2020; 80: 104199. [DOI:10.1016/j.meegid.2020.104199]
8. Fikes JD, Sette A. Design of multi-epitope, analogue-based cancer vaccines. Expert opinion on biological therapy 2003; 3(6): 985-993. [DOI:10.1517/14712598.3.6.985]
9. Bayraktar S, Batoo S, Okuno S, Gluke S. Immunotherapy in breast cancer. Journal of carcinogenesis 2019; 18: 2. [DOI:10.4103/jcar.JCar_2_19]
10. Brown DM, Ruoslahti E. Metadherin, a cell surface protein in breast tumors that mediates lung metastasis. Cancer cell 2004 ; 5(4): 365-374. [DOI:10.1016/S1535-6108(04)00079-0]
11. Criscitiello C. Tumor-associated antigens in breast cancer. Breast care 2012; 7(4): 262-266. [DOI:10.1159/000342164]
12. Vermaelen K. Strategies to Improve Cancer Vaccine Efficacy. Frontiers in immunology 2019; 10: 8. [DOI:10.3389/fimmu.2019.00008]
13. Bairoch A, Apweiler R, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O'Donovan C, Redaschi N, L Yeh L-S. The universal protein resource (UniProt). Nucleic acids research 2005; 33: D154-D159. [DOI:10.1093/nar/gki070]
14. Park BS, Song DH, Kim HM, Choi B-S, Lee H, Lee J-O. The structural basis of lipopolysaccharide recognition by the TLR4-MD-2 complex. Nature 2009; 458(7242): 1191-1195. [DOI:10.1038/nature07830]
15. Bhasin M, Raghava G. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 2004; 22(23-34): 3195-3204. [DOI:10.1016/j.vaccine.2004.02.005]
16. Liu I-H, Lo Y-S, Yang J-M. PAComplex: a web server to infer peptide antigen families and binding models from TCR-pMHC complexes. Nucleic acids research 2011; 39: W254-W260. [DOI:10.1093/nar/gkr434]
17. Reche PA, Glutting J-P, Reinherz EL. Prediction of MHC class I binding peptides using profile motifs. Human immunology 2002; 63(9): 701-709. [DOI:10.1016/S0198-8859(02)00432-9]
18. Rammensee H-G, Bachmann J, Emmerich NPN, Bachor OA, Stevanović S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 1999; 50(3-4): 213-219. [DOI:10.1007/s002510050595]
19. Guan P, Doytchinova IA, Zygouri C, Flower DR. MHCPred: a server for quantitative prediction of peptide-MHC binding. Nucleic acids research 2003; 31: 3621-3624. [DOI:10.1093/nar/gkg510]
20. Kangueane P, Sakharkar MK. T-Epitope Designer: A HLA-peptide binding prediction server. Bioinformation 2005; 1(1): 21. [DOI:10.6026/97320630001021]
21. Wang P, Sidney J, Dow C, Mothé B, Sette A, Peters B. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. Plos computational biology 2008; 4(4): e1000048. [DOI:10.1371/journal.pcbi.1000048]
22. Hashemzadeh P, Ghorbanzadeh V, Otaghsara SMV, et al. Novel predicted B-cell epitopes of PSMA for development of prostate cancer vaccine. International journal of peptide research and therapeutics 2020; 26(3): 15231525. [DOI:10.1007/s10989-019-09954-9]
23. EL‐Manzalawy Y, Dobbs D, Honavar V. Predicting linear B‐cell epitopes using string kernels. Journal of molecular recognition 2008; 21(4): 243-255. [DOI:10.1002/jmr.893]
24. Larsen JEP, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome research 2006; 2: 2. [DOI:10.1186/1745-7580-2-2]
25. Saha S, Raghava G. AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic acids research 2006; 34: W202-W209. [DOI:10.1093/nar/gkl343]
26. Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v. 2-a server for in silico prediction of allergens. Journal of molecular modeling 2014; 20(6): 2278. [DOI:10.1007/s00894-014-2278-5]
27. Dimitrov I, Naneva L, Doytchinova I, Bangov I. AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics 2013; 30(6): 846-851. [DOI:10.1093/bioinformatics/btt619]
28. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics 2007; 8: 4. [DOI:10.1186/1471-2105-8-4]
29. Magnan CN, Zeller M, Kayala MA, Vigil A, Randall A, Felgner P-L, Baldi P. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics 2010; 26(23): 2936-2943. [DOI:10.1093/bioinformatics/btq551]
30. Magnan CN, Randall A, Baldi P. SOLpro: accurate sequence-based prediction of protein solubility. Bioinformatics 2009; 25(17): 2200-2207. [DOI:10.1093/bioinformatics/btp386]
31. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A. Protein identification and analysis tools on the ExPASy server. The proteomics protocols handbook 2005: 571-607. [DOI:10.1385/1-59259-890-0:571]
32. Buchan DW, Jones DT. The PSIPRED protein analysis workbench: 20 years on. Nucleic acids research 2019; 47(W1): W402-W407. [DOI:10.1093/nar/gkz297]
33. Peng J, Xu J. RaptorX: exploiting structure information for protein alignment by statistical inference. Proteins 2011; 79(10): 161-171. [DOI:10.1002/prot.23175]
34. Heo L, Park H, Seok C. GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic acids research 2013; 41: W384-W388. [DOI:10.1093/nar/gkt458]
35. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic acids research 2007; 35: W407-W410. [DOI:10.1093/nar/gkm290]
36. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein science 1993; 2(9): 1511-1519. [DOI:10.1002/pro.5560020916]
37. Lovell SC, Davis IW, Arendall 3 WB, de Bakker PIW, Michael Word J, Prisant MG, Richardson JS, Richardson DC. Structure validation by Cα geometry: Phi, psi and Cbeta deviation. Proteins 2003; 50(3): 437-450. [DOI:10.1002/prot.10286]
38. Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic acids research 2018; 46: W363-W367. [DOI:10.1093/nar/gky473]
39. Pierce BG, Hourai Y, Weng Z. Accelerating protein docking in ZDOCK using an advanced 3D convolution library. Plos one 2011; 6(9): e24657. [DOI:10.1371/journal.pone.0024657]
40. Awan FM, Obaid A, Ikram A, Janjua Ha. Mutation-structure-function relationship based integrated strategy reveals the potential impact of deleterious missense mutations in autophagy related proteins on hepatocellular carcinoma (HCC): A comprehensive informatics approach. International journal of molecular sciences 2017; 18(1): 139. [DOI:10.3390/ijms18010139]
41. Tama F, Brooks CL. Symmetry, form, and shape: guiding principles for robustness in macromolecular machines. Annual review of biophysics and biomolecular structure 2006; 35: 115-133. [DOI:10.1146/annurev.biophys.35.040405.102010]
42. López-Blanco JR, Aliaga JI, Quintana-Ortí ES, Chacon P. iMODS: internal coordinates normal mode analysis server. Nucleic acids research 2014; 42: W271-W276. [DOI:10.1093/nar/gku339]
43. Grote A, Hiller K, Scheer M, Münch R, Nörtemann B, Hempel DC, Jahn D. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic acids research 2005; 33: W526-W531. [DOI:10.1093/nar/gki376]
44. Huynh M-m, Jayanthan A, Pambid MR, Los G , Dunn SE. RSK2: a promising therapeutic target for the treatment of triple-negative breast cancer. Expert opinion on therapeutic targets 2020; 24(1):1-5. [DOI:10.1080/14728222.2020.1709824]
45. Li L, Zheng X, Zhou Q, Villanueva N, Nian W, Liu X. Metabolomics-Based Discovery of Molecular Signatures for Triple Negative Breast Cancer in Asian Female Population. Scientific reports 2020; 10(1): 1-12. [DOI:10.1038/s41598-019-57068-5]
46. Emens LA. Breast cancer immunotherapy: facts and hopes. Clinical cancer research 2018; 24(3): 511-520. [DOI:10.1158/1078-0432.CCR-16-3001]
47. Soria-Guerra RE, Nieto-Gomez R, Govea-Alonso DO, Rosales-Mendoza S. An overview of bioinformatics tools for epitope prediction: implications on vaccine development. Journal of biomedical informatics 2015; 53: 405-414. [DOI:10.1016/j.jbi.2014.11.003]
48. Childers NK, Miller KL, Tong G, Llarena JC, Greenway T, Ulrich JT, Michalek SM. Adjuvant activity of monophosphoryl lipid A for nasal and oral immunization with soluble or liposome-associated antigen. Infection and immunity 2000; 68(10): 5509-5516. [DOI:10.1128/IAI.68.10.5509-5516.2000]
49. Cluff CW. Monophosphoryl lipid A (MPL) as an adjuvant for anti-cancer vaccines: clinical results. Advances in experimental medicine and biology 2009; 667:111-123. [DOI:10.1007/978-1-4419-1603-7_10]
50. Didierlaurent AM, Morel S, Lockman L, Giannini SL, Bisteau MI, Carlsen H, Kielland A, Vosters O, Vanderheyde N, Schiavetti F, Larocque D, Van Mechelen M, Garçon N. AS04, an aluminum salt-and TLR4 agonist-based adjuvant system, induces a transient localized innate immune response leading to enhanced adaptive immunity. Journal of immunology 2009; 183(10): 6186-6197. [DOI:10.4049/jimmunol.0901474]
51. Steinhagen F, Kinjo T, Bode C, Klinman DM. TLR-based immune adjuvants. Vaccine 2011; 29(17): 3341-3355. [DOI:10.1016/j.vaccine.2010.08.002]
52. Shirota H, Tross D, Klinman DM. CpG oligonucleotides as cancer vaccine adjuvants. Vaccine 2015; 3(2): 390-407. [DOI:10.3390/vaccines3020390]
53. Duthie MS, Windish HP, Fox CB, Reed SG. Use of defined TLR ligands as adjuvants within human vaccines. Immunological reviews 2011; 239(1): 178-196. [DOI:10.1111/j.1600-065X.2010.00978.x]
54. Chen X, Zaro JL, Shen W-C. Fusion protein linkers: property, design and functionality. Advanced drug delivery reviews 2013; 65(10): 1357-1369. [DOI:10.1016/j.addr.2012.09.039]
55. Reddy Chichili VP, Kumar V, Sivaraman J. Linkers in the structural biology of protein-protein interactions. Protein science 2013; 22: 153-167. [DOI:10.1002/pro.2206]
56. Minhas V, Shrestha A, Wadhwa N, Singh R , Gupta SK. Novel Sperm and Gonadotropin-releasing Hormone-based Recombinant Fusion Protein: Achievement of 100% Contraceptive Efficacy by Co-immunization of Male and Female Mice. Molecular reproduction and development 2016; 83(12): 1048-1059. [DOI:10.1002/mrd.22743]
57. de Oliveira LMF, Morale MG, Chaves AAM, Cavalher AM, Lopes AS, de Oliveira Diniz M, Schanoski AS , Lopes de Melo R, Carlos de Souza Ferreira L, S de Oliveira ML, Demasi M, Lee Ho P. Design, immune responses and anti-tumor potential of an HPV16 E6E7 multi-epitope vaccine. Plos one 2015;10(9): 0139686. [DOI:10.1371/journal.pone.0138686]
58. Ashokan K, Pillai M. In silico characterization of silk fibroin protein using computational tools and servers. Asian journal of experimental sciences 2008; 22(3): 265-274.
59. Makrides SC. Strategies for achieving high-level expression of genes in Escherichia coli. Microbiological reviews 1996; 60(30): 512-538. [DOI:10.1128/mr.60.3.512-538.1996]

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