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

Irini A Doytchinova1, Darren R Flower2

  • 1Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st, 1000, Sofia, Bulgaria.

BMC Immunology
|March 17, 2018
PubMed

Insights

Immunotherapy offers a promising new approach to cancer treatment. This study explores in silico methods for identifying cancer antigens, crucial for developing effective cancer vaccines and immunotherapies.

Area of Science:

  • Oncology
  • Immunology
  • Bioinformatics

Background:

  • Cancer remains a leading cause of mortality worldwide, necessitating novel therapeutic strategies.
  • While survival rates for some cancers improve, many lack effective treatments, driving research into new protocols.
  • Cancer immunotherapy represents a significant advancement in anti-cancer treatments.

Purpose of the Study:

  • To describe key elements of in silico prediction for identifying cancer antigens.
  • To highlight databases and advanced methodologies for cancer antigen prediction.
  • To emphasize the role of dendritic cell vaccines and epitope ensemble vaccines in cancer immunotherapy.

Main Methods:

  • Utilizing in silico prediction tools and databases for cancer antigen identification.
  • Exploring bleeding-edge computational methodologies for accurate antigen prediction.
  • Investigating the application of immunoinformatics in streamlining cancer immunogen discovery.

Main Results:

  • The study outlines essential components for in silico prediction of cancer antigens.
  • It presents advanced computational methods for identifying potential therapeutic targets.
  • Databases and prediction tools are highlighted for their utility in cancer research.

Conclusions:

  • Accurate identification of aberrant host proteins as antigens is vital for effective cancer vaccines and immunotherapy.
  • Immunoinformatics provides a powerful framework to accelerate the discovery and application of cancer immunogens.
  • Dendritic cell vaccines can serve as effective delivery systems for epitope ensemble vaccines.
Keywords:
Cancer immunogensDatabases of cancer immunogensDendritic cell-based vaccinesMulti-epitope vaccinesPrediction of cancer immunogens

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