Login Register Cart 0
Generic placeholder image

Recent Patents on Anti-Cancer Drug Discovery

Editor-in-Chief

ISSN (Print): 1574-8928
ISSN (Online): 2212-3970

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

The Anti-Proliferative Effect of a Newly-Produced Anti-PSCA-Peptide Antibody by Multiple Bioinformatics Tools, on Prostate Cancer Cells

Author(s): Milad Chizari, Sajad Fani-Kheshti, Jaleh Taeb, Mohammad M. Farajollahi* and Monireh Mohsenzadegan*

Volume 16, Issue 1, 2021

Published on: 10 November, 2020

Page: [73 - 83] Pages: 11

DOI: 10.2174/1574892815999201110212411

Price: $65

Abstract

Background: Prostate Stem Cell Antigen (PSCA) is a small cell surface protein, overexpressed in 90% of prostate cancers. Determination of epitopes that elicit an appropriate response to the antibody generation is vital for diagnostic and immunotherapeutic purposes for prostate cancer treatment. Presently, bioinformatics B-cell prediction tools can predict the location of epitopes, which is uncomplicated, faster, and more cost-effective than experimental methods.

Objective: We aimed to predict a novel linear peptide for Prostate Stem Cell Antigen (PSCA) protein in order to generate anti-PSCA-peptide (p) antibody and to investigate its effect on prostate cancer cells.

Methods: In the current study, a novel linear peptide for PSCA was predicted using in silico methods that utilize a set of linear B-cell epitope prediction tools. Polyclonal antibody (anti-PSCA-p antibody “Patent No. 99318”) against PSCA peptide was generated. The antibody reactivity was determined by the Enzyme-Linked Immunosorbent Assay (ELISA) and its specificity by immunocytochemistry (ICC), immunohistochemistry (IHC), and Western Blotting (WB) assays. The effect of the anti-PSCA-p antibody on PSCA-expressing prostate cancer cell line was assessed by Methylthiazolyldiphenyl- Tetrazolium bromide (MTT) assay.

Results: New peptide-fragment of PSCA sequence as “N-CVDDSQDYYVGKKN-C” (PSCA-p) was selected and synthesized. The anti-PSCA-p antibody against the PSCA-p showed immunoreactivity with PSCA-p specifically bound to PC-3 cells. Also, the anti-PSCA-p antibody strongly stained the prostate cancer tissues as compared to Benign Prostatic Hyperplasia (BPH) and normal tissues (P < 0.001). As the degree of malignancy increased, the staining intensity was also elevated in prostate cancer tissue (P < 0.001). Interestingly, the anti-PSCA-p antibody showed anti-proliferative effects on PC-3 cells (31%) with no growth inhibition effect on PSCA-negative cells.

Conclusion: In this study, we developed a new peptide sequence (PSCA-p) of PSCA. The PSCA-p targeting by anti-PSCA-p antibody inhibited the proliferation of prostate cancer cells, suggesting the potential of PSCA-p immunotherapy for future prostate cancer studies.

Keywords: Antibody, B-cell linear epitope, immunotherapy, peptide mapping, prostate cancer, PSCA-peptide.

« Previous
[1]
Sela-Culang I, Kunik V, Ofran Y. The structural basis of antibody-antigen recognition. Front Immunol 2013; 4: 302.
[http://dx.doi.org/10.3389/fimmu.2013.00302] [PMID: 24115948]
[2]
Deng X, Storz U, Doranz BJ, Eds. Enhancing antibody patent protection using epitope mapping information. MAbs Taylor & Francis. 2018.
[3]
Sela-Culang I, Ofran Y, Peters B. Antibody specific epitope prediction-emergence of a new paradigm. Curr Opin Virol 2015; 11: 98-102.
[http://dx.doi.org/10.1016/j.coviro.2015.03.012] [PMID: 25837466]
[4]
Jespersen MC, Mahajan S, Peters B, Nielsen M, Marcatili P. Antibody specific B-cell epitope predictions: Leveraging information from antibody-antigen protein complexes. Front Immunol 2019; 10: 298.
[http://dx.doi.org/10.3389/fimmu.2019.00298] [PMID: 30863406]
[5]
Kozlova EEG, Cerf L, Schneider FS, et al. Computational B-cell epitope identification and production of neutralizing murine antibodies against Atroxlysin-I. Sci Rep 2018; 8(1): 14904.
[http://dx.doi.org/10.1038/s41598-018-33298-x] [PMID: 30297733]
[6]
Yasser E-M, Dobbs D, Honavar VG. In silico prediction of linear B-cell epitopes on proteins. Prediction of Protein Secondary Structure Springer. 2017; pp. pp. 255-264.
[7]
Wang H-W, Pai T-W. Machine learning-based methods for prediction of linear B-cell epitopes. Immunoinformatics Springer. 2014; pp. pp. 217-236.
[8]
El-Manzalawy Y, Honavar V. Recent advances in B-cell epitope prediction methods. Immunome Res 2010; 6(2)(Suppl. 2): S2.
[http://dx.doi.org/10.1186/1745-7580-6-S2-S2] [PMID: 21067544]
[9]
Kringelum JV, Lundegaard C, Lund O, Nielsen M. Reliable B cell epitope predictions: Impacts of method development and improved benchmarking. PLOS Comput Biol 2012; 8(12): e1002829.
[http://dx.doi.org/10.1371/journal.pcbi.1002829] [PMID: 23300419]
[10]
Lee B-S, Huang J-S, Jayathilaka LP, Lee J, Gupta S. Antibody production with synthetic peptides. High-Resolution Imaging of Cellular Proteins Springer. 2016; pp. pp. 25-47.
[11]
Saeki N, Gu J, Yoshida T, Wu X. Prostate stem cell antigen: A Jekyll and Hyde molecule? Clin Cancer Res 2010; 16(14): 3533-8.
[http://dx.doi.org/10.1158/1078-0432.CCR-09-3169] [PMID: 20501618]
[12]
Raff AB, Gray A, Kast WM. Prostate stem cell antigen: A prospective therapeutic and diagnostic target. Cancer Lett 2009; 277(2): 126-32.
[http://dx.doi.org/10.1016/j.canlet.2008.08.034] [PMID: 18838214]
[13]
Yang X, Guo Z, Liu Y, et al. Prostate stem cell antigen and cancer risk, mechanisms and therapeutic implications. Expert Rev Anticancer Ther 2014; 14(1): 31-7.
[http://dx.doi.org/10.1586/14737140.2014.845372] [PMID: 24308679]
[14]
Taeb J, Asgari M, Abolhasani M, Farajollahi MM, Madjd Z. Expression of Prostate Stem Cell Antigen (PSCA) in prostate cancer: A tissue microarray study of Iranian patients. Pathol Res Pract 2014; 210(1): 18-23.
[http://dx.doi.org/10.1016/j.prp.2013.09.012] [PMID: 24183365]
[15]
Reiter RE, Gu Z, Watabe T, et al. Prostate stem cell antigen: A cell surface marker overexpressed in prostate cancer. Proc Natl Acad Sci USA 1998; 95(4): 1735-40.
[http://dx.doi.org/10.1073/pnas.95.4.1735] [PMID: 9465086]
[16]
Madjd Z, Karimi A, Molanae S, Asadi-Lari M. BRCA1 protein expression level and CD44+ phenotype in breast cancer patients. Cell J 2011; 13(3): 155-62.
[PMID: 23508738]
[17]
Voskuil J. Commercial antibodies and their validation. F1000 Res 2014; 3: 232.
[http://dx.doi.org/10.12688/f1000research.4966.1] [PMID: 25324967]
[18]
Brown MC, Joaquim TR, Chambers R, et al. Impact of immunization technology and assay application on antibody performance a systematic comparative evaluation. PLoS One 2011; 6(12): e28718.
[http://dx.doi.org/10.1371/journal.pone.0028718] [PMID: 22205963]
[19]
Arora S, Ayyar BV, O’Kennedy R. Affinity chromatography for antibody purification. Protein Downstream Processing Springer. 2014; pp. pp. 497-516.
[20]
Ogishi M, Yotsuyanagi H. Quantitative prediction of the landscape of T cell epitope immunogenicity in sequence space. Front Immunol 2019; 10: 827.
[http://dx.doi.org/10.3389/fimmu.2019.00827] [PMID: 31057550]
[21]
Stave JW, Lindpaintner K. Antibody and antigen contact residues define epitope and paratope size and structure. J Immunol 2013; 191(3): 1428-35.
[http://dx.doi.org/10.4049/jimmunol.1203198] [PMID: 23797669]
[22]
Gomes AR, Byregowda SM, Veeregowda BM, Balamurugan V. An overview of heterologous expression host systems for the production of recombinant proteins. Adv Anim Vet Sci 2016; 4(7): 346-56.
[http://dx.doi.org/10.14737/journal.aavs/2016/4.7.346.356]
[23]
Sanchez-Trincado JL, Gomez-Perosanz M, Reche PA. Fundamentals and methods for T-and B-cell epitope prediction. J Immunol Res 2017; 2017: 2680160.
[24]
Forsström B, Axnäs BB, Rockberg J, Danielsson H, Bohlin A, Uhlen M. Dissecting antibodies with regards to linear and conformational epitopes. PLoS One 2015; 10(3): e0121673.
[http://dx.doi.org/10.1371/journal.pone.0121673] [PMID: 25816293]
[25]
Larsen JEP, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res 2006; 2(1): 2.
[http://dx.doi.org/10.1186/1745-7580-2-2] [PMID: 16635264]
[26]
Emini EA, Hughes JV, Perlow DS, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol 1985; 55(3): 836-9.
[http://dx.doi.org/10.1128/JVI.55.3.836-839.1985] [PMID: 2991600]
[27]
Karplus P, Schulz G. Prediction of chain flexibility in proteins. Naturwissenschaften 1985; 72(4): 212-3.
[http://dx.doi.org/10.1007/BF01195768]
[28]
Parker JM, Guo D, Hodges RS. New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: Correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry 1986; 25(19): 5425-32.
[http://dx.doi.org/10.1021/bi00367a013] [PMID: 2430611]
[29]
Chou PY, Fasman GD. Prediction of the secondary structure of proteins from their amino acid sequence. Adv Enzymol Relat Areas Mol Biol 1978; 47: 45-148.
[PMID: 364941]
[30]
Saha S, Raghava GPS. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 2006; 65(1): 40-8.
[http://dx.doi.org/10.1002/prot.21078] [PMID: 16894596]
[31]
Dobbs D, Honavar Honavar V, EL-Manzalawy Y. Predicting linear B-cell epitopes using string kernels. J Mol Recognit 2008; 21(4): 243-55.
[http://dx.doi.org/10.1002/jmr.893]
[32]
Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res 2017; 45(W1): W24-9.
[http://dx.doi.org/10.1093/nar/gkx346] [PMID: 28472356]
[33]
Wang H-W, Lin Y-C, Pai T-W, Chang H-T. Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification. BioMed Res Int 2011; 2011: 432830.
[34]
Ansari HR, Raghava GP. Identification of conformational B-cell epitopes in an antigen from its primary sequence. Immunome Res 2010; 6(1): 6.
[http://dx.doi.org/10.1186/1745-7580-6-6] [PMID: 20961417]
[35]
Nejatollahi F, Abdi S, Asgharpour M. Antiproliferative and apoptotic effects of a specific antiprostate stem cell single chain antibody on human prostate cancer cells. J Oncol 2013; 2013: 839831.
[http://dx.doi.org/10.1155/2013/839831]
[36]
Mohsenzadegan M, Farajollahi M. Anti-NGEP-P antibody. US99455, 2019.
[37]
Mohsenzadegan M, Madjd Z, Asgari M, et al. Reduced expression of NGEP is associated with high-grade prostate cancers: A tissue microarray analysis. Cancer Immunol Immunother 2013; 62(10): 1609-18.
[http://dx.doi.org/10.1007/s00262-013-1463-1] [PMID: 23955683]
[38]
Mohsenzadegan M, Shekarabi M, Madjd Z, et al. Study of NGEP expression pattern in cancerous tissues provides novel insights into prognostic marker in prostate cancer. Biomarkers Med 2015; 9(4): 391-401.
[http://dx.doi.org/10.2217/bmm.14.106] [PMID: 25808443]
[39]
Mohsenzadegan M, Tajik N, Madjd Z, Shekarabi M, Farajollahi MM. Study of NGEP expression in androgen sensitive prostate cancer cells: A potential target for immunotherapy. Med J Islam Repub Iran 2015; 29: 159.
[PMID: 26000254]
[40]
Chizari M, Fani Kheshti S, Mohsenzadegan M, Farajollahi M. Anti-PSCA-P antibody. Patent No. 99318, 2019.
[41]
Robert E. Anti-PSCA antibodies. US6790939, 2000.
[42]
Robert E. PSCA antibodies and hybridomas producing them. US6258939, 1998.

Rights & Permissions Print Cite
Article Metrics
26
© 2024 Bentham Science Publishers | Privacy Policy