In silico prediction of cancer immunogens:current state of the art


Cancer kills 8 million annually worldwide. Although survival rates in prevalent cancers continue to increase, many cancers have no effective treatment, prompting the search for new and improved protocols. Immunotherapy is a new and exciting addition to the anti-cancer arsenal. The successful and accurate identification of aberrant host proteins acting as antigens for vaccination and immunotherapy is a key aspiration for both experimental and computational research. Here we describe key elements of in silico prediction, including databases of cancer antigens and bleeding-edge methodology for their prediction. We also highlight the role dendritic cell vaccines can play and how they can act as delivery mechanisms for epitope ensemble vaccines. Immunoinformatics can help streamline the discovery and utility of Cancer Immunogens.

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Divisions: College of Health & Life Sciences > Aston Pharmacy School
College of Health & Life Sciences
College of Health & Life Sciences > Chronic and Communicable Conditions
Additional Information: Copyright: The Authors This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated. Funding: Aston University, the National Science Fund, Bulgaria, and the Medical Research Council of the Medical University of Sofia, Bulgaria.
Uncontrolled Keywords: Cancer immunogens,Databases of cancer immunogens,Prediction of cancer immunogens,Dendritic cell-based vaccines,Multi-epitope vaccines
Publication ISSN: 1365-2567
Last Modified: 08 Dec 2023 10:41
Date Deposited: 22 Mar 2018 08:13
Full Text Link: https://bmcimmu ... 2865-018-0248-x
Related URLs: https://bmcimmu ... 2865-018-0248-x (Publisher URL)
PURE Output Type: Article
Published Date: 2018-03-15
Accepted Date: 2018-03-06
Authors: Doytchinova, Irini A.
Flower, Darren R. (ORCID Profile 0000-0002-8542-7067)



Version: Published Version

License: Creative Commons Attribution

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