Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires

Abstract

High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual's TCR repertoire and directly queries their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining.be.

Current challenges for epitope-agnostic TCR interaction prediction and a new perspective derived from image classification

“…Previous work has tackled this problem from an epitopespecific angle, and demonstrated that the amino acid sequences of the TCR CDR3 region contain sufficient information to predict epitope recognition using epitope-specific models (10,11,14,(16)(17)(18). These types of predictive models have now been made accessible to immunology researchers via web tools such as TCRex (19). One shortcoming of an epitope-specific approach is that a unique model needs to be trained for every epitope (or for a set of epitopes in the case of Bi et al (17)), which requires sufficient training examples of TCRs with the same epitope specificity.…”

Uncovering the Tumor Antigen Landscape: What to Know about the Discovery Process

“…An interesting work that brings peptide–TCR recognition to the same side is that of Gielis et al, who identified epitope-specific TCR sequences using a random forest algorithm [ 213 ]. However, it can only predict TCR sequences based on the peptides present in the training database.…”

Methods for sequence and structural analysis of B and T cell receptor repertoires

“…Though their focus is more on immunogenicity of peptides presented to MHC molecules, they also observed correlation between individual TCR-pMHC affinities and the features important for immunogenicity score. Gielis et al [77] applied random forest-based classifiers for epitope specific TCRs to repertoire level analysis. Their models successfully detected the increase of epitope specific TCRs upon vaccination in two Yellow Fever vaccination studies.…”