T Cell Epitope Prediction and Its Application to Immunotherapy

Abstract

T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications.

Keywords: T cell; T cell receptor; TCR; epitope prediction; neoantigens; neoepitope prediction.

Figure 1

Graphic representation showing genomic aberrations, which can lead to the occurrence of cancer-specific peptides (neoepitopes). The left panel shows gene fusions, which is the rearrangement of two genes leading to the encoding and translation of a potentially novel immunogenic peptide. The right upper panel shows single nucleotide variations (SNV) and the right lower panel shows insertions and deletions (indels), that may cause the creation of immunogenic cancer-specific peptides. For further detail see the main text.

Figure 2

T-cell interaction with a pMHC complex rendered in PyMOL (PDB code: 6TRO). Here MHC I is shown as colored in beige. The TCR is colored blue white. The CDR3 variable regions of the T-cell have been colored in different colors, these are as follows: CDR3 α colored in yellow, CDR3β colored in orange. The bound peptide is colored in green, with the anchor residues are colored in red.