Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting

Abstract

CD8+ T cell recognition of peptide epitopes plays a central role in immune responses against pathogens and tumors. However, the rules that govern which peptides are truly recognized by existing T cell receptors (TCRs) remain poorly understood, precluding accurate predictions of neo-epitopes for cancer immunotherapy. Here, we capitalize on recent (neo-)epitope data to train a predictor of immunogenic epitopes (PRIME), which captures molecular properties of both antigen presentation and TCR recognition. PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition that we experimentally validate. Analysis of cancer genomics data reveals that recurrent mutations tend to be less frequent in patients where they are predicted to be immunogenic, providing further evidence for immunoediting in human cancer. PRIME will facilitate identification of pathogen epitopes in infectious diseases and neo-epitopes in cancer immunotherapy.

Keywords: TCR recognition; immunoediting; immunogenicity; neo-epitope predictions; tumor immunology.

Graphical abstract

Figure 1

Immunogenicity predictions beyond binding and presentation on HLA-I molecules (A) Summary of the data used to benchmark existing tools and develop our new immunogenicity predictor. (B) Allelic coverage in the immunogenic and non-immunogenic mutated peptides tested in cancer neo-epitope studies for the 15 most frequent HLA-I alleles. “Other (n)” indicates the cumulative frequency of the n remaining HLA-I alleles. (C) Ability of existing predictors to distinguish between immunogenic and non-immunogenic mutated peptides in cancer neo-epitope studies. The x axis shows the p values of the Wilcoxon test between immunogenic and non-immunogenic peptides for the scores of each predictor (see Figure S1C). (D) Graphical representation of the immunogenicity predictor combining affinity to HLA-I and TCR binding signals. PΩ stands for the position of last epitope residue. Residues at MIA positions (P4 to PΩ−1 in this example with HLA-B∗44:03) are shown in yellow, and other epitope residues are shown in blue. The HLA is shown in gray and the TCR in light blue and light green. (E) AUC and PRAUC values of the 10-fold cross-validation (left), the leave-one-study-out cross-validation (middle), and the leave-one-allele-out cross-validation (right). (F) AUC and PRAUC values obtained based on mouse neo-epitopes from Capietto et al.

Figure 2

PRIME runs faster than other HLA-I ligand predictors Computational efficiency of PRIME and different HLA-I ligand predictors for predictions with 6 HLA-I alleles.

Figure 3

PRIME correlates with structural avidity Correlation between structural avidity (i.e., monomeric pHLA-TCR dissociation kinetics; t_1/2) and the predictions of the different tools. Pearson correlation coefficients and p values are shown above each plot. Each point corresponds to the average log(t_1/2) of different clones (Table S4A).

Figure 4

PRIME reveals molecular determinants of TCR recognition (A) Ranking of the coefficients in PRIME corresponding to amino acids at MIA positions. (B) IFNγ-ELISpot signals obtained after stimulating naive CD8+ T cells from three healthy donors (d1, d2, and d3) with all P5 variants of the HIV (ALIRILQQL) and CMV (NLVPMVATV) epitopes (two technical replicates). (C) Spearman correlation coefficients between immunogenicity predictions and IFNγ ELISpot signals for P5 variants of the HIV and CMV epitopes from each donor. Stars indicate Spearman correlation p values smaller than 0.05. (D) Functional avidity of CD8+ T cell responses (EC₅₀) measured for five variants of the CMV epitopes used to vaccinate HLA-A∗02:01 transgenic mice (4–7 biological replicates). Data were renormalized for each epitope based on the signal at 10³ nM. No response was detected for the peptide with K at P5 (values of 0 are shown).

Figure 5

TCR-pHLA structures provide molecular interpretations for the predictions of PRIME (A) HLA-A^∗02:01 binding of the PMEL_{209(2M)–217} epitope (IMDQVPFSV) and the F7A substitution (thermal stability measured by differential scanning fluorimetry). Solid lines represent bi-Gaussian fits to the data. The measured T_m values are 61.9°C ± 0.2°C for the epitope and 61.7°C ± 0.2°C for the F7A-modified epitope (fitted values with standard fitting errors). (B) Recognition of the PMEL_{209(2M)–217} WT and F7A mutant by the SILv44 TCR. Solid line for the WT represents a 1:1 fit to the data, yielding a K_D of 140 ± 20 μM (average and standard deviation of three independent measurements). No detectable binding was observed for the F7A modified epitope. (C) Crystal structure of the SILv44 TCR in complex with PMEL_{209(2M)–217} presented by HLA-A^∗02:01. (D) Crystal structure of a TCR in complex with the HHAT_68–76 L75F neo-epitope (KQWLVWLFL; PDB: 6UK4). (E) Frequencies of 9-mer epitope residues with sidechains directly contacting TCR residues in TCR-pHLA X-ray structures, normalized by the amino acid frequencies in the source proteins.

Figure 6

PRIME provides insight into immunoediting in human cancer (A) Proposed framework to study immunoediting acting on cancer mutations. For each mutation, patients are stratified based on whether the mutation would be immunogenic ($P^{+}$, yellow rectangle) or would not ($P^{-}$, red rectangle). The actual frequency of the mutation is compared between these two groups ($f^{+}=\left(|P^{+}\cap M|/\left|P^{+}\right|\right)$ and $f^{-}=\left(|P^{-}\cap M|/\left|P^{-}\right|\right)$, where $M$ stands for the subset of patients where the mutation is observed). These frequencies will be further compared to those obtained after shuffling the HLA-I alleles between patients (i.e., $f_r^{+}$ and $f_r^{-}$). (B) Frequency of KRAS G12D mutation in patients where it would give rise to neo-epitopes ($f^{+}$) and where it would not ($f^{-}$), together with the example of HLA-I alleles of a patient with the mutation in $P^{+}$(upper row, with the two predicted epitopes) and one in $P^{-}$ (lower row). (C) Top: average value of $f^{+}-f^{-}$ for mutations observed N times ($N=\left|M\right|)$ in the TCGA cohort. The color scale shows the number of distinct mutations for all values of the mutation occurrence (N > 0). Bottom: average value of $f^{+}-f^{-}$ for mutations observed at least N^min times in the TCGA cohort is shown. The red circles and error bars correspond to randomized HLA-I alleles (mean and standard deviation of $<f_r^{+}-f_r^{-}>$). (D) Top: average value of $f^{+}-f^{-}$ for mutations observed N times in colorectal tumors of TCGA. Bottom: average value of $f^{+}-f^{-}$ for mutations observed at least N^min times in colorectal tumors of TCGA is shown. The insets in (C) and (D) show the boxplots of the frequencies $f^{+}$ and $f^{-}$ for all mutations observed at least N^min times in our TCGA samples (N^min = 35 in C and N^min = 10 in D). r and p values shown in the top plots in (C) and (D) correspond to the Pearson correlation coefficient (STAR methods). p values shown in the insets of the bottom plots in (C) and (D) correspond to paired Wilcoxon tests. All frequencies are shown in percentages (%).