A multi-omics systems vaccinology resource to develop and test computational models of immunity.

Systems vaccinology studies have identified factors affecting individual vaccine responses, but comparing these findings is challenging due to varying study designs. To address this lack of reproducibility, we established a community resource for comparing Bordetella pertussis booster responses and to host annual contests for predicting patients' vaccination outcomes. We report here on our experiences with the "dry-run" prediction contest. We found that, among 20+ models adopted from the literature, the most successful model predicting vaccination outcome was based on age alone. This confirms our concerns about the reproducibility of conclusions between different vaccinology studies. Further, we found that, for newly trained models, handling of baseline information on the target variables was crucial. Overall, multiple co-inertia analysis gave the best results of the tested modeling approaches. Our goal is to engage community in these prediction challenges by making data and models available and opening a public contest in August 2024.

A functional identification platform reveals frequent, spontaneous neoantigen-specific T cell responses in patients with cancer.

The clinical impact of tumor-specific neoantigens as both immunotherapeutic targets and biomarkers has been impeded by the lack of efficient methods for their identification and validation from routine samples. We have developed a platform that combines bioinformatic analysis of tumor exomes and transcriptional data with functional testing of autologous peripheral blood mononuclear cells (PBMCs) to simultaneously identify and validate neoantigens recognized by naturally primed CD4+ and CD8+ T cell responses across a range of tumor types and mutational burdens. The method features a human leukocyte antigen (HLA)-agnostic bioinformatic algorithm that prioritizes mutations recognized by patient PBMCs at a greater than 40% positive predictive value followed by a short-term in vitro functional assay, which allows interrogation of 50 to 75 expressed mutations from a single 50-ml blood sample. Neoantigens validated by this method include both driver and passenger mutations, and this method identified neoantigens that would not have been otherwise detected using an in silico prediction approach. These findings reveal an efficient approach to systematically validate clinically actionable neoantigens and the T cell receptors that recognize them and demonstrate that patients across a variety of human cancers have a diverse repertoire of neoantigen-specific T cells.

Intra-tumoral T cells in pediatric brain tumors display clonal expansion and effector properties.

Brain tumors in children are a devastating disease in a high proportion of patients. Owing to inconsistent results in clinical trials in unstratified patients, the role of immunotherapy remains unclear. We performed an in-depth survey of the single-cell transcriptomes and clonal relationship of intra-tumoral T cells from children with brain tumors. Our results demonstrate that a large fraction of T cells in the tumor tissue are clonally expanded with the potential to recognize tumor antigens. Such clonally expanded T cells display enrichment of transcripts linked to effector function, tissue residency, immune checkpoints and signatures of neoantigen-specific T cells and immunotherapy response. We identify neoantigens in pediatric brain tumors and show that neoantigen-specific T cell gene signatures are linked to better survival outcomes. Notably, among the patients in our cohort, we observe substantial heterogeneity in the degree of clonal expansion and magnitude of T cell response. Our findings suggest that characterization of intra-tumoral T cell responses may enable selection of patients for immunotherapy, an approach that requires prospective validation in clinical trials.

Late-rising CD4 T cells resolve mouse cytomegalovirus persistent replication in the salivary gland.

Conventional antiviral memory CD4 T cells typically arise during the first two weeks of acute infection. Unlike most viruses, cytomegalovirus (CMV) exhibits an extended persistent replication phase followed by lifelong latency accompanied with some gene expression. We show that during mouse CMV (MCMV) infection, CD4 T cells recognizing an epitope derived from the viral M09 protein only develop after conventional memory T cells have already peaked and contracted. Ablating these CD4 T cells by mutating the M09 genomic epitope in the MCMV Smith strain, or inducing them by introducing the epitope into the K181 strain, resulted in delayed or enhanced control of viral persistence, respectively. These cells were shown to be unique compared to their conventional memory counterparts; producing higher IFNγ and IL-2 and lower IL-10 levels. RNAseq analyses revealed them to express distinct subsets of effector genes as compared to classical CD4 T cells. Additionally, when M09 cells were induced by epitope vaccination they significantly enhanced protection when compared to conventional CD4 T cells alone. These data show that late-rising CD4 T cells are a unique memory subset with excellent protective capacities that display a development program strongly differing from the majority of memory T cells.

PEPMatch: a tool to identify short peptide sequence matches in large sets of proteins.

BACKGROUND: Numerous tools exist for biological sequence comparisons and search. One case of particular interest for immunologists is finding matches for linear peptide T cell epitopes, typically between 8 and 15 residues in length, in a large set of protein sequences. Both to find exact matches or matches that account for residue substitutions. The utility of such tools is critical in applications ranging from identifying conservation across viral epitopes, identifying putative epitope targets for allergens, and finding matches for cancer-associated neoepitopes to examine the role of tolerance in tumor recognition. RESULTS: We defined a set of benchmarks that reflect the different practical applications of short peptide sequence matching. We evaluated a suite of existing methods for speed and recall and developed a new tool, PEPMatch. The tool uses a deterministic k-mer mapping algorithm that preprocesses proteomes before searching, achieving a 50-fold increase in speed over methods such as the Basic Local Alignment Search Tool (BLAST) without compromising recall. PEPMatch's code and benchmark datasets are publicly available. CONCLUSIONS: PEPMatch offers significant speed and recall advantages for peptide sequence matching. While it is of immediate utility for immunologists, the developed benchmarking framework also provides a standard against which future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org , and the source code can be found at https://github.com/IEDB/PEPMatch .

Transcriptomes and metabolism define mouse and human MAIT cell populations.

Mucosal-associated invariant T (MAIT) cells are a subset of T lymphocytes that respond to microbial metabolites. We defined MAIT cell populations in different organs and characterized the developmental pathway of mouse and human MAIT cells in the thymus using single-cell RNA sequencing and phenotypic and metabolic analyses. We showed that the predominant mouse subset, which produced IL-17 (MAIT17), and the subset that produced IFN-γ (MAIT1) had not only greatly different transcriptomes but also different metabolic states. MAIT17 cells in different organs exhibited increased lipid uptake, lipid storage, and mitochondrial potential compared with MAIT1 cells. All these properties were similar in the thymus and likely acquired there. Human MAIT cells in lung and blood were more homogeneous but still differed between tissues. Human MAIT cells had increased fatty acid uptake and lipid storage in blood and lung, similar to human CD8 T resident memory cells, but unlike mouse MAIT17 cells, they lacked increased mitochondrial potential. Although mouse and human MAIT cell transcriptomes showed similarities for immature cells in the thymus, they diverged more strikingly in the periphery. Analysis of pet store mice demonstrated decreased lung MAIT17 cells in these so-called "dirty" mice, indicative of an environmental influence on MAIT cell subsets and function.

Linked CD4+/CD8+ T cell neoantigen vaccination overcomes immune checkpoint blockade resistance and enables tumor regression.

Therapeutic benefit to immune checkpoint blockade (ICB) is currently limited to the subset of cancers thought to possess a sufficient tumor mutational burden (TMB) to allow for the spontaneous recognition of neoantigens (NeoAg) by autologous T cells. We explored whether the response to ICB of an aggressive low-TMB squamous cell tumor could be improved through combination immunotherapy using functionally defined NeoAg as targets for endogenous CD4+ and CD8+ T cells. We found that, whereas vaccination with CD4+ or CD8+ NeoAg alone did not offer prophylactic or therapeutic immunity, vaccines containing NeoAg recognized by both subsets overcame ICB resistance and led to the eradication of large established tumors that contained a subset of PD-L1+ tumor-initiating cancer stem cells (tCSC), provided the relevant epitopes were physically linked. Therapeutic CD4+/CD8+ T cell NeoAg vaccination produced a modified tumor microenvironment (TME) with increased numbers of NeoAg-specific CD8+ T cells existing in progenitor and intermediate exhausted states enabled by combination ICB-mediated intermolecular epitope spreading. We believe that the concepts explored herein should be exploited for the development of more potent personalized cancer vaccines that can expand the range of tumors treatable with ICB.

A systems vaccinology resource to develop and test computational models of immunity.

Computational models that predict an individual's response to a vaccine offer the potential for mechanistic insights and personalized vaccination strategies. These models are increasingly derived from systems vaccinology studies that generate immune profiles from human cohorts pre- and post-vaccination. Most of these studies involve relatively small cohorts and profile the response to a single vaccine. The ability to assess the performance of the resulting models would be improved by comparing their performance on independent datasets, as has been done with great success in other areas of biology such as protein structure predictions. To transfer this approach to system vaccinology studies, we established a prototype platform that focuses on the evaluation of Computational Models of Immunity to Pertussis Booster vaccinations (CMI-PB). A community resource, CMI-PB generates experimental data for the explicit purpose of model evaluation, which is performed through a series of annual data releases and associated contests. We here report on our experience with the first such 'dry run' for a contest where the goal was to predict individual immune responses based on pre-vaccination multi-omic profiles. Over 30 models adopted from the literature were tested, but only one was predictive, and was based on age alone. The performance of new models built using CMI-PB training data was much better, but varied significantly based on the choice of pre-vaccination features used and the model building strategy. This suggests that previously published models developed for other vaccines do not generalize well to Pertussis Booster vaccination. Overall, these results reinforced the need for comparative analysis across models and datasets that CMI-PB aims to achieve. We are seeking wider community engagement for our first public prediction contest, which will open in early 2024.

Linked CD4 + /CD8 + T cell neoantigen vaccination overcomes immune checkpoint blockade resistance and enables tumor regression.

Therapeutic benefit to immune checkpoint blockade (ICB) is currently limited to the subset of cancers thought to possess a sufficient tumor mutational burden (TMB) to allow for the spontaneous recognition of neoantigens (NeoAg) by autologous T cells. We explored whether the response of an aggressive low TMB squamous cell tumor to ICB could be improved through combination immunotherapy using functionally defined NeoAg as targets for endogenous CD4 + and CD8 + T cells. We found that, whereas vaccination with CD4 + or CD8 + NeoAg alone did not offer prophylactic or therapeutic immunity, vaccines containing NeoAg recognized by both subsets overcame ICB resistance and led to the eradication of large established tumors that contained a subset of PD-L1 + tumor-initiating cancer stem cells (tCSC), provided the relevant epitopes were physically linked. Therapeutic CD4 + /CD8 + T cell NeoAg vaccination produced a modified tumor microenvironment (TME) with increased numbers of NeoAg-specific CD8 + T cells existing in progenitor and intermediate exhausted states enabled by combination ICB-mediated intermolecular epitope spreading. The concepts explored herein should be exploited for the development of more potent personalized cancer vaccines that can expand the range of tumors treatable with ICB.

IEDB-3D 2.0: Structural data analysis within the Immune Epitope Database.

The Immune Epitope Database (IEDB) catalogs T cell, B cell, and major histocompatibility complex ligand information in the context of infectious disease, allergy, autoimmunity, and transplantation. An important component of this information is three-dimensional structural data on T cell receptors, antibodies, and pairwise residue interactions between immune receptors and antigens, which we refer to as IEDB-3D. Such data is highly valuable for mechanically understanding receptor:ligand interactions. Here, we present IEDB-3D 2.0, which comprises a complete overhaul of how we obtain and present 3D structural data. A new 3D viewer experience that utilizes iCn3D has been implemented to replace outdated java-based technology. In addition, we have designed a new epitope mapping system that matches each epitope available in the IEDB with its antigen structural data. Finally, immunogenicity data retrieved from the IEDB's ImmunomeBrowser can now be used to highlight immunogenic regions of an antigen directly in iCn3D. Overall, the IEDB-3D 2.0 provides an updated tool platform to visualize epitope data cataloged in the IEDB.

Coronavirus Immunotherapeutic Consortium Database.

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has seen multiple anti-SARS-CoV-2 antibodies being generated globally. It is difficult, however, to assemble a useful compendium of these biological properties if they are derived from experimental measurements performed at different sites under different experimental conditions. The Coronavirus Immunotherapeutic Consortium (COVIC) circumvents these issues by experimentally testing blinded antibodies side by side for several functional activities. To collect these data in a consistent fashion and make it publicly available, we established the COVIC database (COVIC-DB, https://covicdb.lji.org/). This database enables systematic analysis and interpretation of this large-scale dataset by providing a comprehensive view of various features such as affinity, neutralization, in vivo protection and effector functions for each antibody. Interactive graphs enable direct comparisons of antibodies based on select functional properties. We demonstrate how the COVIC-DB can be utilized to examine relationships among antibody features, thereby guiding the design of therapeutic antibody cocktails. Database URL  https://covicdb.lji.org/.

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.

Tumor cells fail to present MHC-II-restricted epitopes derived from oncogenes to CD4+ T cells.

CD4+ T cells play a critical role in antitumor immunity via recognition of peptide antigens presented on MHC class II (MHC-II). Although some solid cancers can be induced to express MHC-II, the extent to which this enables direct recognition by tumor-specific CD4+ T cells is unclear. We isolated and characterized T cell antigen receptors (TCRs) from naturally primed CD4+ T cells specific for 2 oncoproteins, HPV-16 E6 and the activating KRASG12V mutation, from patients with head and neck squamous cell carcinoma and pancreatic ductal adenocarcinoma, respectively, and determined their ability to recognize autologous or human leukocyte antigen-matched antigen-expressing tumor cells. We found in both cases that the TCRs were capable of recognizing peptide-loaded target cells expressing the relevant MHC-II or B cell antigen-presenting cells (APCs) when the antigens were endogenously expressed and directed to the endosomal pathway but failed to recognize tumor cells expressing the source protein even after induction of surface MHC-II expression by IFN-γ or transduction with CIITA. These results suggest that priming and functional recognition of both a nuclear (E6) and a membrane-associated (KRAS) oncoprotein are predominantly confined to crosspresenting APCs rather than via direct recognition of tumor cells induced to express MHC-II.

Predicting the Success of Fmoc-Based Peptide Synthesis.

Synthetic peptides are commonly used in biomedical science for many applications in basic and translational research. While peptide synthesis is generally easy and reliable, the chemical nature of some amino acids as well as the many steps and chemical compounds involved can render the synthesis of some peptide sequences difficult. Identification of these problematic sequences and mitigation of issues they may present can be important for the reliable use of peptide reagents in several contexts. Here, we assembled a large dataset of peptides that were synthesized using standard Fmoc chemistry and whose identity was validated using mass spectrometry. We analyzed the mass spectra to identify errors in peptide syntheses and sought to develop a computational tool to predict the likelihood that any given peptide sequence would be synthesized accurately. Our model, named Peptide Synthesis Score (PepSySco), is able to predict the likelihood that a peptide will be successfully synthesized based on its amino acid sequence.

A comprehensive analysis of the IEDB MHC class-I automated benchmark.

In 2014, the Immune Epitope Database automated benchmark was created to compare the performance of the MHC class I binding predictors. However, this is not a straightforward process due to the different and non-standardized outputs of the methods. Additionally, some methods are more restrictive regarding the HLA alleles and epitope sizes for which they predict binding affinities, while others are more comprehensive. To address how these problems impacted the ranking of the predictors, we developed an approach to assess the reliability of different metrics. We found that using percentile-ranked results improved the stability of the ranks and allowed the predictors to be reliably ranked despite not being evaluated on the same data. We also found that given the rate new data are incorporated into the benchmark, a new method must wait for at least 4 years to be ranked against the pre-existing methods. The best-performing tools with statistically indistinguishable scores in this benchmark were NetMHCcons, NetMHCpan4.0, ANN3.4, NetMHCpan3.0 and NetMHCpan2.8. The results of this study will be used to improve the evaluation and display of benchmark performance. We highly encourage anyone working on MHC binding predictions to participate in this benchmark to get an unbiased evaluation of their predictors.

Distinguishing cell-cell complexes from dual lineage cells using single-cell transcriptomics is not trivial.

In their recent correspondence, Jie et al. strongly defend that the DE cell population they discovered are always dual lineage co-expressing cells and not complexes of B cells and T cells, which we have previously described as frequently present in single-cell RNA sequencing data. Here, we respond to the specific arguments made in their correspondence. Specifically, we demonstrate that the presence of a gene signature in a given cell population is not enough to ascertain that it does not contain cell-cell complexes, or that it represents a biologically distinct cell type. We also show that the gene signature of DE cells contains several genes from the myeloid lineage, suggesting either that their DE cells are a triple-lineage co-expressing cell, or a three-component cell aggregate. Finally, we identify multiple transcriptomic features of DE cells that correspond to B cell-T cell complexes, namely the presence of lower average expression of B- and T-cell specific genes, and a higher number of detected genes per cell. Taken together, our results demonstrate that solely based on their scRNAseq profile, it is not possible to ascertain that DE cells are dual expressing cells and not cell-cell complexes.

Targeting the tumor mutanome for personalized vaccination in a TMB low non-small cell lung cancer.

BACKGROUND: Cancer is characterized by an accumulation of somatic mutations, of which a significant subset can generate cancer-specific neoepitopes that are recognized by autologous T cells. Such neoepitopes are emerging as important targets for cancer immunotherapy, including personalized cancer vaccination strategies. METHODS: We used whole-exome and RNA sequencing analysis to identify potential neoantigens for a patient with non-small cell lung cancer. Thereafter, we assessed the autologous T-cell reactivity to the candidate neoantigens using a long peptide approach in a cultured interferon gamma ELISpot and tracked the neoantigen-specific T-cells in the tumor by T-cell receptor (TCR) sequencing. In parallel, identified gene variants were incorporated into a Modified Vaccinia Ankara-based vaccine, which was evaluated in the human leucocyte antigen A*0201 transgenic mouse model (HHD). RESULTS: Sequencing revealed a tumor with a low mutational burden: 2219 sequence variants were identified from the primary tumor, of which 23 were expressed in the transcriptome, involving 18 gene products. We could demonstrate spontaneous T-cell responses to 5/18 (28%) mutated gene variants, and further analysis of the TCR repertoire of neoantigen-specific CD4+ and CD8+ T cells revealed TCR clonotypes that were expanded in both blood and tumor tissue. Following vaccination of HHD mice, de novo T-cell responses were generated to 4/18 (22%) mutated gene variants; T cells reactive against two variants were also evident in the autologous setting. Subsequently, we determined the major histocompatibility complex restriction of the T-cell responses and used in silico prediction tools to determine the likely neoepitopes. CONCLUSIONS: Our study demonstrates the feasibility of efficiently identifying tumor-specific neoantigens that can be targeted by vaccination in tumors with a low mutational burden, promising successful clinical exploitation, with trials currently underway.

Transcriptomics of Acute DENV-Specific CD8+ T Cells Does Not Support Qualitative Differences as Drivers of Disease Severity.

While several lines of evidence suggest a protective role of T cells against disease associated with Dengue virus (DENV) infection, their potential contribution to immunopathology in the acute phase of DENV infection remains controversial, and it has been hypothesized that the more severe form of the disease (dengue hemorrhagic fever, DHF) is associated with altered T cell responses. To address this question, we determined the transcriptomic profiles of DENV-specific CD8+ T cells in a cohort of 40 hospitalized dengue patients with either a milder form of the disease (dengue fever, DF) or a more severe disease form (dengue hemorrhagic fever, DHF). We found multiple transcriptomic signatures, one associated with DENV-specific interferon-gamma responding cells and two other gene signatures, one specifically associated with the acute phase and the other with the early convalescent phase. Additionally, we found no differences in quantity and quality of DENV-specific CD8+ T cells based on disease severity. Taken together with previous findings that did not detect altered DENV-specific CD4 T cell responses, the current analysis argues against alteration in DENV-specific T cell responses as being a correlate of immunopathology.

Developmentally distinct CD4+ Treg lineages shape the CD8+ T cell response to acute Listeria infection.

SignificanceThe CD4+ Treg response following acute Listeria infection is heterogeneous and deploys two distinct modes of suppression coinciding with initial pathogen exposure and resolution of infection. This bimodal suppression of CD8+ T cells during priming and contraction is mediated by separate Treg lineages. These findings make a significant contribution to our understanding of the functional plasticity inherent within Tregs, which allows these cells to serve as a sensitive and dynamic cellular rheostat for the immune system to prevent autoimmune pathology in the face of inflammation attendant to acute infection, enable expansion of the pathogen-specific response needed to control the infection, and reestablish immune homeostasis after the threat has been contained.

Combined assessment of MHC binding and antigen abundance improves T cell epitope predictions.

Many steps of the MHC class I antigen processing pathway can be predicted using computational methods. Here we show that epitope predictions can be further improved by considering abundance levels of peptides' source proteins. We utilized biophysical principles and existing MHC binding prediction tools in concert with abundance estimates of source proteins to derive a function that estimates the likelihood of a peptide to be an MHC class I ligand. We found that this combination improved predictions for both naturally eluted ligands and cancer neoantigen epitopes. We compared the use of different measures of antigen abundance, including mRNA expression by RNA-Seq, gene translation by Ribo-Seq, and protein abundance by proteomics on a dataset of SARS-CoV-2 epitopes. Epitope predictions were improved above binding predictions alone in all cases and gave the highest performance when using proteomic data. Our results highlight the value of incorporating antigen abundance levels to improve epitope predictions.