An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes

Bjerregaard, Anne-Mette; Nielsen, Morten; Jurtz, Vanessa Isabell; Barra, Carolina; Hadrup, Sine Reker; Szállási, Zoltán; Eklund, Aron C.

doi:10.3389/fimmu.2017.01566

Cited by 104 publications

(114 citation statements)

References 30 publications

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“…The newest version of this algorithm, NetMHCpan-4.0, is taking length preferences for naturally eluted ligands directly into consideration during training, and which provides a simpler implementation of length-specific scores, and shows similar improvements in the identification of neo-epitopes. 44,54,55 …”

Section: Discussionmentioning

confidence: 99%

Predicting T cell recognition of MHC class I restricted neoepitopes

et al. 2018

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Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope’s immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used.Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4

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Section: Discussionmentioning

confidence: 99%

Predicting T cell recognition of MHC class I restricted neoepitopes

et al. 2018

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“…Much effort has been dedicated to the development of methods for accurate and cost‐effective identification of immunogenic peptides . The prediction of peptide binding affinity to MHC molecules is one such method, which has enabled the establishment of a clear correlation between MHC binding affinity and peptide immunogenicity.…”

Section: Discussionmentioning

confidence: 99%

“…The prediction of peptide binding affinity to MHC molecules is one such method, which has enabled the establishment of a clear correlation between MHC binding affinity and peptide immunogenicity. However, only a small proportion of the peptides measured or predicted to bind to an MHC molecule are immunogenic, underlining the fact that additional features are needed to efficiently and accurately describe peptide immunogenicity . Within recent years, this has, in concurrence with the huge advancements made within the field of MS, created an interest in examining whether a larger fraction of immunogenic peptides can be identified when incorporating knowledge about immunological hotspots of high epitope concentration.…”

Section: Discussionmentioning

confidence: 99%

“…One such method is in silico MHC binding prediction, and the development of such tools has progressed substantially over the last decades, with current state‐of‐the‐art methods having accuracies comparable to the experimental variation between independent experimental laboratory measurements . However, despite the established fact that there is a correlation between binding to MHC and peptide immunogenicity, predicting the immunogenicity of predicted MHC class I binders remains a central challenge . A peptide predicted to bind an MHC class I molecule is not naturally a T cell epitope, and only a small proportion of the peptides measured or predicted to bind to an MHC molecule are immunogenic .…”

Section: Introductionmentioning

confidence: 99%

“…However, despite the established fact that there is a correlation between binding to MHC and peptide immunogenicity, predicting the immunogenicity of predicted MHC class I binders remains a central challenge . A peptide predicted to bind an MHC class I molecule is not naturally a T cell epitope, and only a small proportion of the peptides measured or predicted to bind to an MHC molecule are immunogenic . This realization has spurred interest in investigating additional determinants of peptide immunogenicity with features such as antigen processing and MHC binding stability being of interest for evaluation …”

Section: Introductionmentioning

confidence: 99%

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Predicted MHC peptide binding promiscuity explains MHC class I ‘hotspots’ of antigen presentation defined by mass spectrometry eluted ligand data

Jappe

Kringelum²,

Trolle³

et al. 2018

Immunology

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Peptides that bind to and are presented by MHC class I and class II molecules collectively make up the immunopeptidome. In the context of vaccine development, an understanding of the immunopeptidome is essential, and much effort has been dedicated to its accurate and cost-effective identification. Current state-of-the-art methods mainly comprise in silico tools for predicting MHC binding, which is strongly correlated with peptide immunogenicity. However, only a small proportion of the peptides that bind to MHC molecules are, in fact, immunogenic, and substantial work has been dedicated to uncovering additional determinants of peptide immunogenicity. In this context, and in light of recent advancements in mass spectrometry (MS), the existence of immunological hotspots has been given new life, inciting the hypothesis that hotspots are associated with MHC class I peptide immunogenicity. We here introduce a precise terminology for defining these hotspots and carry out a systematic analysis of MS and in silico predicted hotspots. We find that hotspots defined from MS data are largely captured by peptide binding predictions, enabling their replication in silico. This leads us to conclude that hotspots, to a great degree, are simply a result of promiscuous HLA binding, which disproves the hypothesis that the identification of hotspots provides novel information in the context of immunogenic peptide prediction. Furthermore, our analyses demonstrate that the signal of ligand processing, although present in the MS data, has very low predictive power to discriminate between MS and in silico defined hotspots.

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Cancer Immunotherapies Ignited by a Thorough Machine Learning‐Based Selection of Neoantigens

Jurczak,

Druchok

2024

Advanced Biology

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Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy for cancer immunotherapies. However, not all somatic mutations result in immunogenicity, hence, efficient tools to predict the immunogenicity of neoepitopes are needed. A pipeline is presented that provides a comprehensive solution for the identification of neoepitopes based on genomic sequencing data. The pipeline consists of a data pre‐processing step and three machine learning predictive steps. The pre‐processing step analyzes genomic data for different types of alterations, produces a list of all possible antigens, and determines the human leukocyte antigen (HLA) type and T‐cell receptor (TCR) repertoire. The first predictive step performs a classification into antigens and neoantigens, selecting neoantigens for further consideration. The next step predicts the strength of binding between neoantigens and available major histocompatibility complexes of class I (MHC‐I). The third step is engaged to predict the likelihood of inducing an immune response. Neoepitopes satisfying all three predictive stages are assumed to be potent candidates to ensure immunogenicity. The predictive pipeline is used in two regimes: selecting neoantigens from patients' sequencing data and generating novel neoantigen candidates. Two different techniques — Monte Carlo and Reinforcement Learning – are implemented to facilitate the generative regime.

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An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes

Cited by 104 publications

References 30 publications

Predicting T cell recognition of MHC class I restricted neoepitopes

Predicting T cell recognition of MHC class I restricted neoepitopes

Predicted MHC peptide binding promiscuity explains MHC class I ‘hotspots’ of antigen presentation defined by mass spectrometry eluted ligand data

Cancer Immunotherapies Ignited by a Thorough Machine Learning‐Based Selection of Neoantigens

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