MUC1 glycopeptide epitopes predicted by computational glycomics

Song,Wei; Delyria,Elizabeth  S.; Chen,Jieqing; Huang,Wei; Lee,Jun  Soo; Mittendorf,Elizabeth   A.; Ibrahim,Nuhad; Radvanyi,Laszlo  G.; Li,Yunsen; Lu,Hongzhou; Xu,Huaxi; Shi,Yinqiang; Wang,Lai-Xi; Ross,Jeremy   A.; Rodrigues,Silas  P.; Almeida,Igor  C.; Yang,Xifeng; Qu,Jin; Schocker,Nathaniel  S.; Michael,Katja; Zhou,Dapeng

doi:10.3892/ijo.2012.1645

December 2012 Volume 41 Issue 6

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December 2012 Volume 41 Issue 6

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Article Open Access

MUC1 glycopeptide epitopes predicted by computational glycomics

Authors:
- Wei Song
- Elizabeth S. Delyria
- Jieqing Chen
- Wei Huang
- Jun Soo Lee
- Elizabeth A. Mittendorf
- Nuhad Ibrahim
- Laszlo G. Radvanyi
- Yunsen Li
- Hongzhou Lu
- Huaxi Xu
- Yinqiang Shi
- Lai-Xi Wang
- Jeremy A. Ross
- Silas P. Rodrigues
- Igor C. Almeida
- Xifeng Yang
- Jin Qu
- Nathaniel S. Schocker
- Katja Michael
- Dapeng Zhou
View Affiliations / Copyright

Affiliations: Department of Melanoma Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA, Institute of Human Virology and Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MA, USA, Department of Surgical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA, Department of Breast Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA, Institutes of Biology and Medical Sciences, Soochow University, Suzhou, P.R. China, Shanghai Public Health Clinical Center, Shanghai, P.R. China, School of Medical Science and Laboratory Medicine, Jiangsu University, Zhenjiang, P.R. China, Department of Gastric Cancer and Soft Tissue Sarcomas, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China, Department of Biological Sciences, Border Biomedical Research Center, University of Texas at El Paso, El Paso, TX, USA, BioLegend Inc., San Diego, CA, USA, College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, P.R. China, Department of Chemistry, University of Texas at El Paso, El Paso, TX, USA

Copyright: © Song et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY_NC 3.0].
Pages: 1977-1984
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Published online on: September 27, 2012

https://doi.org/10.3892/ijo.2012.1645
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Abstract

Bioinformatic tools and databases for glycobiology and glycomics research are playing increasingly important roles in functional studies. However, to verify hypotheses generated by computational glycomics with empirical functional assays is only an emerging field. In this study, we predicted glycan epitopes expressed by a cancer-derived mucin, MUC1, by computational glycomics. MUC1 is expressed by tumor cells with a deficiency in glycosylation. Although numerous diagnostic reagents and cancer vaccines have been designed based on abnormally glycosylated MUC1 sequences, the glycan and peptide sequences responsible for immune responses in vivo are poorly understood. The immunogenicity of synthetic MUC1 glycopeptides bearing Tn or sialyl-Tn antigens have been studied in mouse models, while authentic glyco-epitopes expressed by tumor cells remain unclear. To examine the immunogenicity of authentic cancer derived MUC1 glyco-epitopes, we expressed membrane bound forms of MUC1 tandem repeats in Jurkat, a mutant cancer cell line deficient of mucin-type core-1 β1-3 galactosyltransferase activity, and immunized mice with cancer cells expressing authentic MUC1 glyco-epitopes. Antibody responses to individual glyco-epitopes were determined by chemically synthesized candidate MUC1 glycopeptides predicted through computational glycomics. Monoclonal antibodies can be generated toward chemically synthesized glycopeptide sequences. With RPAPGS(Tn)TAPPAHG as an example, a monoclonal antibody 16A, showed 25-fold higher binding to glycosylated peptide (EC50=9.278±1.059 ng/ml) compared to its non-glycosylated form (EC50=247.3±16.29 ng/ml) as measured by ELISA experiments with plate-bound peptides. A library of monoclonal antibodies toward authentic MUC1 glycopeptide epitopes may be a valuable tool for studying glycan and peptide sequences in cancer, as well as reagents for diagnosis and therapy.

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Journals

International Journal of Molecular Medicine

International Journal of Oncology

Molecular Medicine Reports

Oncology Reports

Experimental and Therapeutic Medicine

Oncology Letters

Biomedical Reports

Molecular and Clinical Oncology

World Academy of Sciences Journal

International Journal of Functional Nutrition

International Journal of Epigenetics

Medicine International

MUC1 glycopeptide epitopes predicted by computational glycomics

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Abstract

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