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Related Concept Videos

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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
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T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...
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T Cell Epitope Prediction and Its Application to Immunotherapy.

Anna-Lisa Schaap-Johansen1, Milena Vujović1, Annie Borch1

  • 1Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.

Frontiers in Immunology
|October 4, 2021
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Summary

This review explores computational tools for predicting T cell epitopes, focusing on cancer-specific neoepitopes for immunotherapy. It details tool mechanisms, data requirements, and application advantages and disadvantages.

Keywords:
T cellT cell receptorTCRepitope predictionneoantigensneoepitope prediction

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • T cells are critical for adaptive immunity, distinguishing self from non-self peptides.
  • Epitope prediction is essential for understanding immune responses and developing therapies.
  • Neoepitopes, derived from cancer-specific mutations, are promising targets for cancer immunotherapy.

Purpose of the Study:

  • To review computational tools for T cell epitope prediction.
  • To focus on tools identifying cancer-specific neoepitopes for immunotherapy.
  • To analyze the methodologies, data inputs, and limitations of these predictive tools.

Main Methods:

  • Review of existing literature on epitope prediction algorithms.
  • Analysis of computational tools for identifying T cell epitopes, particularly neoepitopes.
  • Comparative assessment of data requirements, performance metrics, and usability of different tools.

Main Results:

  • Several computational tools exist for epitope prediction, varying in their underlying algorithms and data sources.
  • Tools for neoepitope identification are crucial for personalized cancer vaccine development.
  • Each tool presents unique strengths and weaknesses regarding accuracy, speed, and applicability.

Conclusions:

  • Computational epitope prediction tools are advancing rapidly, aiding cancer immunotherapy research.
  • Understanding the capabilities and limitations of these tools is vital for effective clinical translation.
  • Further development is needed to enhance the accuracy and broaden the application of neoepitope prediction tools.