The role of antigen expression in shaping the repertoire of HLA presented ligands

VACCIMEL, an allogeneic melanoma vaccine, efficiently triggers T cell immune responses against neoantigens and alloantigens, as well as against tumor-associated antigens

VACCIMEL is a therapeutic cancer vaccine composed of four irradiated allogeneic human melanoma cell lines rationally selected to cover a wide range of melanoma tumor-associated antigens (TAA). We previously demonstrated that vaccination in the adjuvant setting prolonged the distant-metastasis-free survival of cutaneous melanoma patients and that T cells reactive to TAA and the patient's private neoantigens increased during treatment. However, immune responses directed to vaccine antigens that may arise from VACCIMEL's somatic mutations and human polymorphisms remain unexplored. To study these immunogens, we performed whole-exome sequencing of paired tumor and germinal samples from four vaccinated patients and the vaccine cells. VACCIMEL variants were called by comparing the vaccine and the patient's exomes, and non-synonymous coding variants were used to predict T cell epitopes. Candidates were ranked based on their mRNA expression in VACCIMEL, predicted peptide-HLA (pHLA) presentation, and pHLA stability. Then, the immune responses to prioritized epitope candidates were tested using IFNγ ELISpot assays on vaccinated patients' PBMC samples. The comparison of the vaccine with the patients' germinal exomes revealed on average 9481 coding non-synonymous variants, suggesting that VACCIMEL offers a high number of potential antigens. Between 0,05 and 0,2% of these variants were also found in the tumors of three vaccinated patients; however, one patient with a high tumor mutational burden (TMB) shared 19,5% somatic variants. The assessment of T cell responses showed that vaccinated patients mounted highly diverse responses against VACCIMEL peptides. Notably, effector T cells targeting the patient's tumor antigens, comprising neoantigens and TAA, were found in higher frequencies than T cells targeting VACCIMEL-exclusive antigens. On the other hand, we observed that the immunogenic epitopes are not conserved across patients, despite sharing HLA and that immune responses fluctuate over time. Finally, a positive correlation between VACCIMEL antigen expression and the intensity of the T cell responses was found. Our results demonstrate that the immune system simultaneously responds to a high number of antigens, either vaccinal or private, proving that immune responses against epitopes not expressed in the patient's tumors were not detrimental to the immune recognition of neoantigens and TAA.

Towards designing improved cancer immunotherapy targets with a peptide-MHC-I presentation model, HLApollo

Based on the success of cancer immunotherapy, personalized cancer vaccines have emerged as a leading oncology treatment. Antigen presentation on MHC class I (MHC-I) is crucial for the adaptive immune response to cancer cells, necessitating highly predictive computational methods to model this phenomenon. Here, we introduce HLApollo, a transformer-based model for peptide-MHC-I (pMHC-I) presentation prediction, leveraging the language of peptides, MHC, and source proteins. HLApollo provides end-to-end treatment of MHC-I sequences and deconvolution of multi-allelic data, using a negative-set switching strategy to mitigate misassigned negatives in unlabelled ligandome data. HLApollo shows a 12.65% increase in average precision (AP) on ligandome data and a 4.1% AP increase on immunogenicity test data compared to next-best models. Incorporating protein features from protein language models yields further gains and reduces the need for gene expression measurements. Guided by clinical use, we demonstrate pan-allelic generalization which effectively captures rare alleles in underrepresented ancestries.

Epstein-Barr virus and immune status imprint the immunogenomics of non-Hodgkin lymphomas occurring in immune-suppressed environments

Non-Hodgkin lymphomas (NHL) commonly occur in immunodeficient patients, both those infected by human immunodeficiency virus (HIV) and those who have been transplanted, and are often driven by Epstein-Barr virus (EBV) with cerebral localization, raising the question of tumor immunogenicity, a critical issue for treatment responses. We investigated the immunogenomics of 68 lymphoproliferative disorders from 51 immunodeficient (34 post-transplant, 17 HIV+) and 17 immunocompetent patients. Overall, 72% were large B-cell lymphoma and 25% were primary central nervous system lymphoma, while 40% were EBV+. Tumor whole-exome and RNA sequencing, along with a bioinformatics pipeline allowed analysis of tumor mutational burden, tumor landscape and tumor microenvironment and prediction of tumor neoepitopes. Both tumor mutational burden (2.2 vs. 3.4/Mb, P=0.001) and numbers of neoepitopes (40 vs. 200, P=0.00019) were lower in EBV+ than in EBV- NHL, regardless of the immune status. In contrast both EBV and the immune status influenced the tumor mutational profile, with HNRNPF and STAT3 mutations observed exclusively in EBV+ and immunodeficient NHL, respectively. Peripheral blood T-cell responses against tumor neoepitopes were detected in all EBV- cases but in only half of the EBV+ ones, including responses against IgH-derived MHC-class-II restricted neoepitopes. The tumor microenvironment analysis showed higher CD8 T-cell infiltrates in EBV+ versus EBV- NHL, together with a more tolerogenic profile composed of regulatory T cells, type-M2 macrophages and an increased expression of negative immune-regulators. Our results highlight that the immunogenomics of NHL in patients with immunodeficiency primarily relies on the tumor EBV status, while T-cell recognition of tumor- and IgH-specific neoepitopes is conserved in EBV- patients, offering potential opportunities for future T-cell-based immune therapies.

Gene and protein sequence features augment HLA class I ligand predictions

The sensitivity of malignant tissues to T cell-based immunotherapies depends on the presence of targetable human leukocyte antigen (HLA) class I ligands. Peptide-intrinsic factors, such as HLA class I affinity and proteasomal processing, have been established as determinants of HLA ligand presentation. However, the role of gene and protein sequence features as determinants of epitope presentation has not been systematically evaluated. We perform HLA ligandome mass spectrometry to evaluate the contribution of 7,135 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turnover, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of such "hard-coded" sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as experimental measures of gene expression. Our study highlights the value of gene and protein features for HLA ligand predictions.

FASTMAP-a flexible and scalable immunopeptidomics pipeline for HLA- and antigen-specific T-cell epitope mapping based on artificial antigen-presenting cells

The study of peptide repertoires presented by major histocompatibility complex (MHC) molecules and the identification of potential T-cell epitopes contribute to a multitude of immunopeptidome-based treatment approaches. Epitope mapping is essential for the development of promising epitope-based approaches in vaccination as well as for innovative therapeutics for autoimmune diseases, infectious diseases, and cancer. It also plays a critical role in the immunogenicity assessment of protein therapeutics with regard to safety and efficacy concerns. The main challenge emerges from the highly polymorphic nature of the human leukocyte antigen (HLA) molecules leading to the requirement of a peptide mapping strategy for a single HLA allele. As many autoimmune diseases are linked to at least one specific antigen, we established FASTMAP, an innovative strategy to transiently co-transfect a single HLA allele combined with a disease-specific antigen into a human cell line. This approach allows the specific identification of HLA-bound peptides using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Using FASTMAP, we found a comparable spectrum of endogenous peptides presented by the most frequently expressed HLA alleles in the world's population compared to what has been described in literature. To ensure a reliable peptide mapping workflow, we combined the HLA alleles with well-known human model antigens like coagulation factor VIII, acetylcholine receptor subunit alpha, protein structures of the SARS-CoV-2 virus, and myelin basic protein. Using these model antigens, we have been able to identify a broad range of peptides that are in line with already published and in silico predicted T-cell epitopes of the specific HLA/model antigen combination. The transient co-expression of a single affinity-tagged MHC molecule combined with a disease-specific antigen in a human cell line in our FASTMAP pipeline provides the opportunity to identify potential T-cell epitopes/endogenously processed MHC-bound peptides in a very cost-effective, fast, and customizable system with high-throughput potential.

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition

Background

Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition.

Method

To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy.

Results

We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity.

Conclusion

Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.

A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes

Accurate prediction of immunogenicity for neo-epitopes arising from a cancer associated mutation is a crucial step in many bioinformatics pipelines that predict outcome of checkpoint blockade treatments or that aim to design personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive analysis of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications. The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), extracts the predicted ICORE from the full neo-epitope as input, i.e. the nested peptide with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank). Key additional features integrated into the model include assessment of the BLOSUM mutation score of the neo-epitope, and antigen expression levels of the wild-type counterpart which is often reflecting a neo-epitope's abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.

In Silico Tools for Predicting Novel Epitopes

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.

Contemplating immunopeptidomes to better predict them

The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.

Estimating tissue-specific peptide abundance from public RNA-Seq data

Several novel MHC class I epitope prediction tools additionally incorporate the abundance levels of the peptides' source antigens and have shown improved performance for predicting immunogenicity. Such tools require the user to input the MHC alleles and peptide sequences of interest, as well as the abundance levels of the peptides' source proteins. However, such expression data is often not directly available to users, and retrieving the expression level of a peptide's source antigen from public databases is not trivial. We have developed the Peptide eXpression annotator (pepX), which takes a peptide as input, identifies from which proteins the peptide can be derived, and returns an estimate of the expression level of those source proteins from selected public databases. We have also investigated how the abundance level of a peptide can be best estimated in cases when it can originate from multiple transcripts and proteins and found that summing up transcript-level expression values performs best in distinguishing ligands from decoy peptides.