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

BMC Immunol. 2018 Mar 15;19(1):11. doi: 10.1186/s12865-018-0248-x.

Abstract

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.

Keywords: Cancer immunogens; Databases of cancer immunogens; Dendritic cell-based vaccines; Multi-epitope vaccines; Prediction of cancer immunogens.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Antigens, Neoplasm / immunology*
  • Antigens, Neoplasm / therapeutic use
  • Cancer Vaccines / immunology*
  • Cancer Vaccines / therapeutic use
  • Clinical Trials as Topic
  • Computational Biology / methods
  • Computer Simulation*
  • Dendritic Cells / immunology
  • Humans
  • Immunologic Factors / immunology*
  • Immunologic Factors / therapeutic use
  • Immunotherapy / methods
  • Neoplasms / immunology*
  • Neoplasms / therapy

Substances

  • Antigens, Neoplasm
  • Cancer Vaccines
  • Immunologic Factors