Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides

Abstract

Background: Targeting epitopes derived from neo-antigens (or "neo-epitopes") represents a promising immunotherapy approach with limited off-target effects. However, most peptides predicted using MHC binding prediction algorithms do not induce a CD8 + T cell response, and there is a crucial need to refine the predictions to readily identify the best antigens that could mediate T-cell responses. Such a response requires a high enough number of epitopes bound to the target MHC. This number is correlated with both the peptide-MHC binding affinity and the number of peptides reaching the ER. Beyond this, the response may be affected by the properties of the neo-epitope mutated residues.

Methods: Herein, we analyzed several experimental datasets from cancer patients to elaborate better predictive algorithms for T-cell reactivity to neo-epitopes.

Results: Indeed, potent classifiers for epitopes derived from neo-antigens in melanoma and other tumors can be developed based on biochemical properties of the mutated residue, the antigen expression level and the peptide processing stage. Among MHC binding peptides, the present classifiers can remove half of the peptides falsely predicted to activate T cells while maintaining the absolute majority of reactive peptides.

Conclusions: The classifier properties further highlight the contribution of the quantity of peptides reaching the ER and the mutation type to CD8 + T cell responses. These classifiers were then validated on neo-antigens obtained from other datasets, confirming the validity of our prediction. Algorithm Availability: http://peptibase.cs.biu.ac.il/Tcell_predictor/ or by request from the authors as a standalone code.

Keywords: MHC-binding peptides; Machine Learning; Neoantigen; T cell activation.

Fig. 1

Existing methods and proposed new classifier (a) Current approaches for neo-antigen detection involve three main stages: RNA sequencing, detection of mutations in tumor cells and the computation of MHC binding peptides in such mutated regions. We propose a new stage (b) the detection among the MHC binding peptides of those that manage to induce a T cell response

Fig. 2

a. -log 10 of p value for Kolmogorov Smirnov test for similarity between distribution of positive and negative peptides (peptides inducing and not inducing a T cell response). b. Average values for positive and negative groups of all measures with significant differences between groups. c. Histogram of sum of log expression, TAP binding score and cleavage score. One can clearly see a difference between the groups. d. Correlation heatmap of positive and negative groups for all measures. Only correlations with a p value below 0.005 were plotted Rows with no significant correlations were removed. The row and columns are the same properties

Fig. 3

Subplots of ROC curves (a) Leave one out test for each one of the datasets. The AUC for the test on melanoma dataset is 0.86. b-d In the ROC curve for three different patients, the prediction was with the classifiers used to generate the test in (a). The horizonal dashed line in (a) indicates the threshold of 90% of the data to be true positive

Fig. 4

Experimental validation of T cell response. TIL culture of patient 1 recognized 3 neoantigens, but not the corresponding wildtype peptides. Following pulsing with 10 μg/ml of 25-mer mutant or wt peptide overnight, EBV-transformed autologous B cells B-LCL were co-cultured with T-cells from TIL culture from patient 1. 16 h after the beginning of the co-culture, these cells were co-stained for CD137 (41BB) and CD8+ and analyzed by flow cytometry. The double positive population is indicated in quadrant Q2