KEYNOTE - D36: personalized immunotherapy with a neoepitope vaccine, EVX-01 and pembrolizumab in advanced melanoma.

Despite improvements made with checkpoint inhibitor (CPI) therapy, a need for new approaches to improve outcomes for patients with unresectable or metastatic melanoma remains. EVX-01, a personalized neoepitope vaccine, combined with pembrolizumab treatment, holds the potential to fulfill this need. Here we present the rationale and novel design behind the KEYNOTE - D36 trial: an open label, single arm, phase II trial aiming to establish the clinical proof of concept and evaluate the safety of EVX-01 in combination with pembrolizumab in CPI naive patients with unresectable or metastatic melanoma. The primary objective is to evaluate if EVX-01 improves best overall response after initial stable disease or partial response to pembrolizumab treatment, in patients with advanced melanoma. The novel end points ensure a decisive readout which may prove helpful before making major investments in phase III trials with limited phase I data. Clinical Trial Registration: NCT05309421 (ClinicalTrials.gov).

Drugs targeting the immune system have improved the outcomes for patients with advanced melanoma. However, a significant proportion of patients do not benefit and there is a need for better therapeutic agents to be used alone or in combination with immune modulating agents. This article summarizes the rationale and design of a new trial with a personalized vaccine (EVX-01) that may improve outcomes for patients with advanced melanoma (unresectable stage III or IV melanoma). The EVX-01 vaccine aims to stimulate the patient's immune system to generate T cells that target specific molecules that can only be found on the surface of the individual patients' cancer cells (i.e. neoepitopes), resulting in cancer cell death. The trial will investigate if the personalized EVX-01 vaccine together with checkpoint inhibitor therapy works better for patients with advanced melanoma, than checkpoint inhibitor therapy alone.

Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data.

Major histocompatibility complex (MHC) class II antigen presentation is a key component in eliciting a CD4+ T cell response. Precise prediction of peptide-MHC (pMHC) interactions has thus become a cornerstone in defining epitope candidates for rational vaccine design. Current pMHC prediction tools have, so far, primarily focused on inference from in vitro binding affinity. In the current study, we collate a large set of MHC class II eluted ligands generated by mass spectrometry to guide the prediction of MHC class II antigen presentation. We demonstrate that models developed on eluted ligands outperform those developed on pMHC binding affinity data. The predictive performance can be further enhanced by combining the eluted ligand and pMHC affinity data in a single prediction model. Furthermore, by including ligand data, the peptide length preference of MHC class II can be accurately learned by the prediction model. Finally, we demonstrate that our model significantly outperforms the current state-of-the-art prediction method, NetMHCIIpan, on an external dataset of eluted ligands and appears superior in identifying CD4+ T cell epitopes.

An automated benchmarking platform for MHC class II binding prediction methods.

Motivation: Computational methods for the prediction of peptide-MHC binding have become an integral and essential component for candidate selection in experimental T cell epitope discovery studies. The sheer amount of published prediction methods-and often discordant reports on their performance-poses a considerable quandary to the experimentalist who needs to choose the best tool for their research. Results: With the goal to provide an unbiased, transparent evaluation of the state-of-the-art in the field, we created an automated platform to benchmark peptide-MHC class II binding prediction tools. The platform evaluates the absolute and relative predictive performance of all participating tools on data newly entered into the Immune Epitope Database (IEDB) before they are made public, thereby providing a frequent, unbiased assessment of available prediction tools. The benchmark runs on a weekly basis, is fully automated, and displays up-to-date results on a publicly accessible website. The initial benchmark described here included six commonly used prediction servers, but other tools are encouraged to join with a simple sign-up procedure. Performance evaluation on 59 data sets composed of over 10 000 binding affinity measurements suggested that NetMHCIIpan is currently the most accurate tool, followed by NN-align and the IEDB consensus method. Availability and implementation: Weekly reports on the participating methods can be found online at: http://tools.iedb.org/auto_bench/mhcii/weekly/. Contact: mniel@bioinformatics.dtu.dk. Supplementary information: Supplementary data are available at Bioinformatics online.

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

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.

The Length Distribution of Class I-Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele-Specific Binding Preference.

HLA class I-binding predictions are widely used to identify candidate peptide targets of human CD8(+) T cell responses. Many such approaches focus exclusively on a limited range of peptide lengths, typically 9 aa and sometimes 9-10 aa, despite multiple examples of dominant epitopes of other lengths. In this study, we examined whether epitope predictions can be improved by incorporating the natural length distribution of HLA class I ligands. We found that, although different HLA alleles have diverse length-binding preferences, the length profiles of ligands that are naturally presented by these alleles are much more homogeneous. We hypothesized that this is due to a defined length profile of peptides available for HLA binding in the endoplasmic reticulum. Based on this, we created a model of HLA allele-specific ligand length profiles and demonstrate how this model, in combination with HLA-binding predictions, greatly improves comprehensive identification of CD8(+) T cell epitopes.

Automated benchmarking of peptide-MHC class I binding predictions.

MOTIVATION: Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study. RESULTS: The benchmark has run for 15 weeks and includes evaluation of 44 datasets covering 17 MHC alleles and more than 4000 peptide-MHC binding measurements. Inspection of the results allows the end-user to make educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB. AVAILABILITY AND IMPLEMENTATION: Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto_bench/mhci/weekly. All prediction tool developers are invited to participate in the benchmark. Sign-up instructions are available at http://tools.iedb.org/auto_bench/mhci/join. CONTACT: mniel@cbs.dtu.dk or bpeters@liai.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NetTepi: an integrated method for the prediction of T cell epitopes.

Multiple factors determine the ability of a peptide to elicit a cytotoxic T cell lymphocyte response. Binding to a major histocompatibility complex class I (MHC-I) molecule is one of the most essential factors, as no peptide can become a T cell epitope unless presented on the cell surface in complex with an MHC-I molecule. As such, peptide-MHC (pMHC) binding affinity predictors are currently the premier methods for T cell epitope prediction, and these prediction methods have been shown to have high predictive performances in multiple studies. However, not all MHC-I binders are T cell epitopes, and multiple studies have investigated what additional factors are important for determining the immunogenicity of a peptide. A recent study suggested that pMHC stability plays an important role in determining if a peptide can become a T cell epitope. Likewise, a T cell propensity model has been proposed for identifying MHC binding peptides with amino acid compositions favoring T cell receptor interactions. In this study, we investigate if improved accuracy for T cell epitope discovery can be achieved by integrating predictions for pMHC binding affinity, pMHC stability, and T cell propensity. We show that a weighted sum approach allows pMHC stability and T cell propensity predictions to enrich pMHC binding affinity predictions. The integrated model leads to a consistent and significant increase in predictive performance and we demonstrate how this can be utilized to decrease the experimental workload of epitope screens. The final method, NetTepi, is publically available at www.cbs.dtu.dk/services/NetTepi .

DNA based neoepitope vaccination induces tumor control in syngeneic mouse models.

Recent findings have positioned tumor mutation-derived neoepitopes as attractive targets for cancer immunotherapy. Cancer vaccines that deliver neoepitopes via various vaccine formulations have demonstrated promising preliminary results in patients and animal models. In the presented work, we assessed the ability of plasmid DNA to confer neoepitope immunogenicity and anti-tumor effect in two murine syngeneic cancer models. We demonstrated that neoepitope DNA vaccination led to anti-tumor immunity in the CT26 and B16F10 tumor models, with the long-lasting presence of neoepitope-specific T-cell responses in blood, spleen, and tumors after immunization. We further observed that engagement of both the CD4+ and CD8+ T cell compartments was essential to hamper tumor growth. Additionally, combination therapy with immune checkpoint inhibition provided an additive effect, superior to either monotherapy. DNA vaccination offers a versatile platform that allows the encoding of multiple neoepitopes in a single formulation and is thus a feasible strategy for personalized immunotherapy via neoepitope vaccination.

Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.

Identifying the specific human leukocyte antigen (HLA) allele combination of an individual is crucial in organ donation, risk assessment of autoimmune and infectious diseases and cancer immunotherapy. However, due to the high genetic polymorphism in this region, HLA typing requires specialized methods. We investigated the performance of five next-generation sequencing (NGS) based HLA typing tools with a non-restricted license namely HLA*LA, Optitype, HISAT-genotype, Kourami and STC-Seq. This evaluation was done for the five HLA loci, HLA-A, -B, -C, -DRB1 and -DQB1 using whole-exome sequencing (WES) samples from 829 individuals. The robustness of the tools to lower depth of coverage (DOC) was evaluated by subsampling and HLA typing 230 WES samples at DOC ranging from 1X to 100X. The HLA typing accuracy was measured across four typing resolutions. Among these, we present two clinically-relevant typing resolutions (P group and pseudo-sequence), which specifically focus on the peptide binding region. On average, across the five HLA loci examined, HLA*LA was found to have the highest typing accuracy. For the individual loci, HLA-A, -B and -C, Optitype's typing accuracy was the highest and HLA*LA had the highest typing accuracy for HLA-DRB1 and -DQB1. The tools' robustness to lower DOC data varied widely and further depended on the specific HLA locus. For all Class I loci, Optitype had a typing accuracy above 95% (according to the modification of the amino acids in the functionally relevant portion of the HLA molecule) at 50X, but increasing the DOC beyond even 100X could still improve the typing accuracy of HISAT-genotype, Kourami, and STC-seq across all five HLA loci as well as HLA*LA's typing accuracy for HLA-DQB1. HLA typing is also used in studies of ancient DNA (aDNA), which is often based on sequencing data with lower quality and DOC. Interestingly, we found that Optitype's typing accuracy is not notably impaired by short read length or by DNA damage, which is typical of aDNA, as long as the DOC is sufficiently high.

Personalized therapy with peptide-based neoantigen vaccine (EVX-01) including a novel adjuvant, CAF®09b, in patients with metastatic melanoma.

The majority of neoantigens arise from unique mutations that are not shared between individual patients, making neoantigen-directed immunotherapy a fully personalized treatment approach. Novel technical advances in next-generation sequencing of tumor samples and artificial intelligence (AI) allow fast and systematic prediction of tumor neoantigens. This study investigates feasibility, safety, immunity, and anti-tumor potential of the personalized peptide-based neoantigen vaccine, EVX-01, including the novel CD8+ T-cell inducing adjuvant, CAF®09b, in patients with metastatic melanoma (NTC03715985). The AI platform PIONEERTM was used for identification of tumor-derived neoantigens to be included in a peptide-based personalized therapeutic cancer vaccine. EVX-01 immunotherapy consisted of 6 administrations with 5-10 PIONEERTM-predicted neoantigens as synthetic peptides combined with the novel liposome-based Cationic Adjuvant Formulation 09b (CAF®09b) to strengthen T-cell responses. EVX-01 was combined with immune checkpoint inhibitors to augment the activity of EVX-01-induced immune responses. The primary endpoint was safety, exploratory endpoints included feasibility, immunologic and objective responses. This interim analysis reports the results from the first dose-level cohort of five patients. We documented a short vaccine manufacturing time of 48-55 days which enabled the initiation of EVX-01 treatment within 60 days from baseline biopsy. No severe adverse events were observed. EVX-01 elicited long-lasting EVX-01-specific T-cell responses in all patients. Competitive manufacturing time was demonstrated. EVX-01 was shown to be safe and able to elicit immune responses targeting tumor neoantigens with encouraging early indications of a clinical and meaningful antitumor efficacy, warranting further study.

Thermostability profiling of MHC-bound peptides: a new dimension in immunopeptidomics and aid for immunotherapy design.

The features of peptide antigens that contribute to their immunogenicity are not well understood. Although the stability of peptide-MHC (pMHC) is known to be important, current assays assess this interaction only for peptides in isolation and not in the context of natural antigen processing and presentation. Here, we present a method that provides a comprehensive and unbiased measure of pMHC stability for thousands of individual ligands detected simultaneously by mass spectrometry (MS). The method allows rapid assessment of intra-allelic and inter-allelic differences in pMHC stability and reveals profiles of stability that are broader than previously appreciated. The additional dimensionality of the data facilitated the training of a model which improves the prediction of peptide immunogenicity, specifically of cancer neoepitopes. This assay can be applied to any cells bearing MHC or MHC-like molecules, offering insight into not only the endogenous immunopeptidome, but also that of neoepitopes and pathogen-derived sequences.

Toxoplasma gondii peptide ligands open the gate of the HLA class I binding groove.

HLA class I presentation of pathogen-derived peptide ligands is essential for CD8+ T-cell recognition of Toxoplasma gondii infected cells. Currently, little data exist pertaining to peptides that are presented after T. gondii infection. Herein we purify HLA-A*02:01 complexes from T. gondii infected cells and characterize the peptide ligands using LCMS. We identify 195 T. gondii encoded ligands originating from both secreted and cytoplasmic proteins. Surprisingly, T. gondii ligands are significantly longer than uninfected host ligands, and these longer pathogen-derived peptides maintain a canonical N-terminal binding core yet exhibit a C-terminal extension of 1-30 amino acids. Structural analysis demonstrates that binding of extended peptides opens the HLA class I F' pocket, allowing the C-terminal extension to protrude through one end of the binding groove. In summary, we demonstrate that unrealized structural flexibility makes MHC class I receptive to parasite-derived ligands that exhibit unique C-terminal peptide extensions.

Ebolavirus comparative genomics.

The 2014 Ebola outbreak in West Africa is the largest documented for this virus. To examine the dynamics of this genome, we compare more than 100 currently available ebolavirus genomes to each other and to other viral genomes. Based on oligomer frequency analysis, the family Filoviridae forms a distinct group from all other sequenced viral genomes. All filovirus genomes sequenced to date encode proteins with similar functions and gene order, although there is considerable divergence in sequences between the three genera Ebolavirus, Cuevavirus and Marburgvirus within the family Filoviridae. Whereas all ebolavirus genomes are quite similar (multiple sequences of the same strain are often identical), variation is most common in the intergenic regions and within specific areas of the genes encoding the glycoprotein (GP), nucleoprotein (NP) and polymerase (L). We predict regions that could contain epitope-binding sites, which might be good vaccine targets. This information, combined with glycosylation sites and experimentally determined epitopes, can identify the most promising regions for the development of therapeutic strategies.This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).