- Research article
- Open Access
Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides
© The Author(s). 2019
- Received: 4 December 2018
- Accepted: 22 April 2019
- Published: 22 May 2019
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.
Herein, we analyzed several experimental datasets from cancer patients to elaborate better predictive algorithms for T-cell reactivity to neo-epitopes.
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.
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.
- T cell activation
- Machine Learning
- MHC-binding peptides
CD8 T cell activation by either exogenous or endogenous epitopes is induced by binding of the T cell receptor to epitopes presented on host MHC class I proteins. Such peptides are usually the product of cytosolic protein or DRIP eventually digested by the proteasome . The cellular TAP apparatus transfers cleaved peptides from the cytosol to the ER, where they can bind the MHC protein (for TAP dependent MHC binding). The probability of each of the above steps is often determined by the peptide linear sequence. This strongly simplifies the prediction of these stages by computational tools. We and others have developed multiple such tools for MHC binding, TAP binding and proteasomal cleavage [2, 3]. However, recent evidence suggests that most peptides predicted or measured to bind the MHC do not necessarily induce a T cell response when tested in vitro with a given patient’s T cells. Understanding the underlying reasons for this lack of optimal predictions represents an important challenge in T cell based anti-tumor treatments.
Progress in the understanding of immune components and their function has led to the implementation of successful immunotherapeutic approaches  based on checkpoint inhibitors or the adoptive transfer of tumor–specific T-cells. These strategies were shown to mediate regression of large tumor masses and remission in terminally-ill patients with different malignancies . T-cells targeting cancer cells can recognize antigens which can be classified into two broad categories. The first class are non-mutated proteins or “tumor associated antigens” whose restricted tissue expression pattern probably allows for an immune response in patients . The second class, on which we focus herein, are antigens derived from mutated proteins that could be recognized as foreign, commonly termed “neo-antigens”. These antigens have been shown to be relevant in efficient CPI (Check-point inhibitors) treatment and in T-cell based therapies . Neoantigens can be used to identify tumor specific T-cells  or generate vaccines . While advances in genomic sequencing have enabled a better characterization of DNA mutations and these antigens, an in silico approach to predict potent T-cell epitopes is lacking . Experimental verifications are therefore needed , and these have revealed that most MHC-binding peptides are not recognized by T-cells. This could be the result of “holes” in the T cell repertoire or from properties of the peptides or the antigen from which they originated (e.g., protein expression level or biochemical properties of the peptides). Mass spectrometry can be applied for direct identification of epitopes , though the yield is often low and necessitates large amounts of tumor cells which are not always available.
Thus, we sought here to devise a novel algorithm based on experimentally acquired data to predict which MHC binding peptide would also induce a T cell response.
Several groups have studied specific class I epitopes in neo-antigens and have also shown that epitopes from low expression proteins or from poorly preprocessed proteins do not induce a response [18, 19]. Other studies focused on the difference of the activation of the immune system between the W.T. and the mutant peptide by sequence property (with known algorithms), and involved other measures of the mutant peptide. Such methods report an Area Under Curve (AUC) of 0.63 .
Beyond MHC binding, the next candidate for affecting T cell response is the number of peptides reaching the Endoplasmic Reticulum (ER). This number determines the number of candidate MHC binding peptides, and in viral responses, we have shown it to be tightly related to the escape mutations . We thus test the effect of the amount of such predecessors on the T cell response. Moreover, the response may be affected by the properties of the mutations producing the neo-antigen. Such properties were thus also added to the analysis.
Correlation between neo-antigen features and T cell activation
Summary of the datasets used
Total no. of predicted samples
Confirmed Positive samples
Confirmed Negative samples
Melanoma Patient 1
Melanoma Patient 2
Melanoma Patient 3
Multiple tools were previously developed to predict T cell activation by MHC binding peptides [2, 3], the main one being the IEDB Class I Immunogenicity tool . However, when we tested the quality of this predictor in the specific context of neo-antigen datasets, the results obtained were an AUC of 0.51 (i.e., random).
First column is the score name, second column is the description of the score
The average expression level by cell line in melanoma tissue
The absolute difference in size between the W.T. amino acid and the mutant
By Dalton units
The absolute difference in hydrophobicity index between the W.T. amino acid and the mutant
Kyte J, Doolittle RF
The absolute difference in charge between the W.T. amino acid and the mutant
Values at ph = 7.4
Categorical variable for the polarity change between the W.T. amino acid the mutant
Values at ph = 7.4
Estimated cleavage probability of a full peptide.
Vider et al.
Estimated TAP binding energy
Peters et al.
It is important to note that the MHC binding level was not included in the current analysis, since in all studied peptides (both positive and negative) were predicted to bind MHC. We repeated the analysis for a combined dataset containing the Me. and the epitopes from the Tantigen database of peptides from different tumors, with similar results (data not shown). We focused on MHC binding peptides and thus eliminated peptides that had low score/chance to bind to the specific MHC of this patient. It is worthy to note that including non-MHC binding peptides in the negative set would significantly increase the precision of a resulting classifier (e.g. Kim et al. ), but the goal of the current analysis is to add another layer of prediction beyond MHC binding, and thus such peptides were removed.
Beyond the number of peptides reaching the ER, we tested whether the properties of the mutation could affect the T cell response. While there were no significant differences in the average of each studied property, the correlation between these properties (listed in Table 2) differed between the positive and negative groups (i.e., peptides inducing/not inducing a response) - Fig. 2d.
Machine learning based classifier
We examined each patient individually (Fig. 3b-d), and obtained AUC values of 0.73, 0.67 and 0.65 for the three melanoma patients tested (Fig. 3). The origin of the large differences between this experiment and the first two datasets is the completely different experimental setups. The significant AUC in the validation is evidence for the robustness of our prediction to variation in the experimental settings.
Fraction of Negatives kept when loosing 50% of positives
Fraction of positives kept when loosing 50% of negatives
Identification of missense mutations using deep-sequencing,
detection of MHC binding peptide appropriate for the host, and sometimes,
prediction which of these peptides are properly processed by the Proteasome and bind TAP.
While highly precise algorithms exists for these three stages [2, 3, 33, 34], the resulting predicted MHC binding neo-peptides do not induce a CD8 T cell response in the majority of cases [4, 35, 36]. This can be due to the fact that either T-cells specific for these epitopes never existed to begin with, were eliminated during negative selection, underwent anergy or ceased to exist. Another possibility is that the epitope itself is not potent enough to generate a detectable reactivity.
In the present work, we have shown that it is possible to predict the immunogenicity of neoepitopes based on a fourth layer for this analysis, which is a prediction of the peptide passing the filters above that can induce such a T-cell response, using the properties of the protein and of the mutation. We propose to add this layer to peptide prediction pipelines to improve current methodologies.
A crucial element affecting the response is the expression level of the protein carrying the peptide. We have here shown that such an association is also critical for T cell stimulation/activation by neo-antigens . Another important element is the charge difference of the mutation itself. Such a difference may represent the important effect of charge on T cell-epitope binding .
An alternative approach would be to optimize the MHC binding ignoring the other elements. It has been shown that immunogenicity is associated with strong MHC binding [38–40]. However, this approach limits the scope of the possible neo-antigen to excellent binders, and such binders are not always found for all HLA alleles.
While CD8+ T-cells are considered a central driver of the anti-tumor activity of the immune response, recent reports suggest that the adoptive transfer of CD4+ T-cells can lead to tumor cytotoxicity and clinical response, both in animal models and in patient studies . There are currently limited prediction algorithms for MHC class II epitope presentation and pre-processing [42, 43].
In conclusion, we have developed and used herein a novel predictive algorithm that would enable the more precise identification of neo-epitopes that can facilitate CD8+ T-cell activation. We trust that this algorithm will contribute to the design of more precise and potent immunotherapies targeting neo-epitopes.
Me. dataset: we previously described our experimental screening methodology . Briefly, following exomic sequencing and RNA-Seq analysis of tumor samples collected from eight metastatic melanoma patients, we predicted candidate nine and ten amino acid peptides containing mutated residues derived from proteins with a minimum FPKM of 1 using the IEDB prediction algorithm available. T cells were tested for reactivity to T2 cells pulsed with predicted epitopes in cytokine release assays at an effector to target ratio of 1:1. Epitopes were deemed positives if yielding repetitive (n > 3) significant cytokine secretion of at least 3 times above background.
M.T. dataset: we download from the Tantigen database published neoepitope epitopes that were published in previous papers . Those epitopes are from a wide variety of tissues, not limited only for melanoma tissue and not only for a specific MHC class. We combined the first dataset (Me.) and the epitopes from Tantigen to one dataset.
Prediction of neoantigens
Analysis of whole exome sequencing identified non-synonymous mutations from tumor and matched normal tissue. RNA-seq narrowed down the selection using threshold of expression level. The NetMHCpan predication algorithm was applied to further eliminate peptides by prediction of the Ic50 for to the specific HLA-molecules of the patient.
Detection of neoantigen specific –T-cells
For each candidate, neoantigen 2 × 10e6 EBV-transformed autologous B cells B-LCL were pulsed with 10 μg/ml of a 25-mer mutant peptide in a 96-well plate and incubated overnight at 37 °C and then washed twice. Co-culture assays were performed by adding 0.5 × 10e5 T cells to each well, followed by an overnight incubation at 37 °C. Reactivity of neo-antigen specific T cells was determined by CD137 expression measured by flow cytometric analysis. WT peptides served as control.
We used the Mann Whitney score to compare the median values of each measure in the distributions. To further test for differences in the distribution not apparent in the median, we used the Kolmogorov-Smirnov Distance.
Learning and evaluation
The TCR binds to the epitope and to the MHC. The vast majority of presented epitopes do not induce a T cell` response. We developed a predictor of T cell activation for MHC binding peptidesbased on the biochemical character of the peptide presented and on the expression level of the antigen. We calculated the difference in the following parameters between the unmutated peptide. and the mutant at the mutation site: Charge, hydrophobicity, size, polarity.
We used a Random Forest algorithm (Matlab) with the following hyper-parameters 5000 estimators, min leaf size (minimum number of observations per tree leaf) 10, cost matrix (penalty) [[0, 0.15], [1, 0]]. To evaluate the performance, we used a leave one out method on the positives (the amount of the positives is much smaller than the negatives). In order to get accurate and unbiased results, we maximized the positives in the train). We had 35 positives samples and 450 negatives samples??that?? we??learned?? 35 times on 34 positives and 435 negatives and validated on the remains. The ROC curve is the score for the all data obtained from the validation method. The negative fraction in the test was 1:12.
The main measure we used to examine the performance is the area under the curve (also known as c-statistics) obtained from the surface under the curve describing the FPR (false positive rate) vs the TPR (true positive rate).
No funding to declare.
Availability of data and materials
Code is available as server and standalone version.
HB developed the code, SY, EMS, CJC performed the analysis on human patient samples. HB performed the analysis on the patients used for the ML development. YL led the research and YL and CJC wrote the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Consent for publication
All authors agreed to submit the current manuscript to JITC.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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