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Molecular Mimics of the Tumour Antigen MUC1

Overview of attention for article published in PLOS ONE, November 2012
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Title
Molecular Mimics of the Tumour Antigen MUC1
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0049728
Pubmed ID
Authors

Tharappel C. James, Ursula Bond

Abstract

A key requirement for the development of cancer immunotherapy is the identification of tumour-associated antigens that are differentially or exclusively expressed on the tumour and recognized by the host immune system. However, immune responses to such antigens are often muted or lacking due to the antigens being recognized as "self", and further complicated by the tumour environment and regulation of immune cells within. In an effort to circumvent the lack of immune responses to tumour antigens, we have devised a strategy to develop potential synthetic immunogens. The strategy, termed mirror image phage display, is based on the concept of molecular mimicry as demonstrated by the idiotype/anti-idiotype paradigm in the immune system. Here as 'proof of principle' we have selected molecular mimics of the well-characterised tumour associated antigen, the human mucin1 protein (MUC1) from two different peptide phage display libraries. The putative mimics were compared in structure and function to that of the native antigen. Our results demonstrate that several of the mimic peptides display T-cell stimulation activity in vitro when presented by matured dendritic cells. The mimic peptides and the native MUC1 antigenic epitopes can cross-stimulate T-cells. The data also indicate that sequence homology and/or chemical properties to the original epitope are not the sole determining factors for the observed immunostimulatory activity of the mimic peptides.

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The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 43%
Student > Doctoral Student 3 14%
Researcher 3 14%
Student > Bachelor 2 10%
Student > Master 1 5%
Other 1 5%
Unknown 2 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 33%
Medicine and Dentistry 5 24%
Biochemistry, Genetics and Molecular Biology 4 19%
Immunology and Microbiology 1 5%
Chemistry 1 5%
Other 1 5%
Unknown 2 10%