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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.

SUBMITTER: Schmidt J

PROVIDER: S-EPMC7897774 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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

Schmidt Julien J Smith Angela R AR Magnin Morgane M Racle Julien J Devlin Jason R JR Bobisse Sara S Cesbron Julien J Bonnet Victor V Carmona Santiago J SJ Huber Florian F Ciriello Giovanni G Speiser Daniel E DE Bassani-Sternberg Michal M Coukos George G Baker Brian M BM Harari Alexandre A Gfeller David D

Cell reports. Medicine 20210206 2

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 recogn ...[more]

PMID: 33665637

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Project description:Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunotherapies. However, the underlying principles of this "convergence" of adaptive immunity among different individuals remain poorly understood. To quantitatively describe epitope immunogenicity, here we propose a supervised machine learning framework generating probabilistic estimates of immunogenicity, termed "immunogenicity scores," based on the numerical features computed through sequence-based simulation approximating the molecular scanning process of peptides presented onto major histocompatibility complex (MHC) by the human T cell receptor (TCR) repertoire. Notably, overlapping sets of intermolecular interaction parameters were commonly utilized in MHC-I and MHC-II prediction. Moreover, a similar simulation of individual TCR-peptide interaction using the same set of interaction parameters yielded correlates of TCR affinity. Pathogen-derived epitopes and tumor-associated epitopes with positive T cell reactivity generally had higher immunogenicity scores than non-immunogenic counterparts, whereas thymically expressed self-epitopes were assigned relatively low scores regardless of their immunogenicity annotation. Immunogenicity score dynamics among single amino acid mutants delineated the landscape of position- and residue-specific mutational impacts. Simulation of position-specific immunogenicity score dynamics detected residues with high escape potential in multiple epitopes, consistent with known escape mutations in the literature. This study indicates that targeting of epitopes by human adaptive immunity is to some extent directed by defined thermodynamic principles. The proposed framework also has a practical implication in that it may enable to more efficiently prioritize epitope candidates highly prone to T cell recognition in multiple individuals, warranting prospective validation across different cohorts.

Dataset Information

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

Publications

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

Similar Datasets

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