NetMHCcons: a consensus method for the major histocompatibility complex class I predictions

Prevalence of PRKDC mutations and association with response to immune checkpoint inhibitors in solid tumors

Predictive biomarkers of response to immune checkpoint inhibitors (ICI) help to identify cancer patients who will benefit from immunotherapy. Protein kinase, DNA-activated, catalytic subunit (PRKDC) is an important gene for DNA double-strand break (DSB) repair and central T-cell tolerance. We aimed to investigate the association between PRKDC mutations and tumor mutation burden (TMB), tumor microenvironment (TME), and response to ICI. Whole-exome sequencing data of 4023 solid tumor samples from the Cancer Genome Atlas (TCGA) and panel-based sequencing data of 3877 solid tumor samples from Geneplus-Beijing, China, were used to analyze the TMB. The mRNA expression data of 3541 solid tumor samples from TCGA were used to explore the effect of PRKDC mutations on the TME. Four ICI-treated cohorts were analyzed for verifying the correlation between PRKDC mutations and the response to ICI. In both the TCGA and Geneplus datasets, we found that the TMB in PRKDC mutation samples was significantly higher than in PRKDC wild-type samples (P < 0.05 and P < 0.0001, respectively). Further, TCGA datasets showed that PRKDC mutation samples were associated with a significantly increased expression of CD8+ T cells, NK cells, immune checkpoint, chemokines, etc. compared to PRKDC wild-type samples (P < 0.05). In ICI-treated cohorts, we also found the PRKDC mutations were associated with increased survival (median PFS, not reached vs. 6.8 months, HR, 0.2893; 95% CI, 0.1255-0.6672; P = 0.0650, Hellmann cohort; median OS, 1184 days vs. 250 days, HR, 0.5126; 95% CI, 0.2715-0.9679; P = 0.1020, Allen cohort), and the increase was significant in multivariate analysis (HR, 0.361; 95% CI, 0.155-0.841; P = 0.018, Allen cohort; HR, 0.240 95% CI, 0.058-0.998; P = 0.050, Hellmann cohort). In summary, we found that PRKDC mutation often appeared to co-exist with deficiency in some other DNA damage repair mechanism and is nonetheless one of the important factors associated with increased TMB, inflamed TME, and better response to ICI.

A novel neoantigen discovery approach based on chromatin high order conformation

Background

High-throughput sequencing technology has yielded reliable and ultra-fast sequencing for DNA and RNA. For tumor cells of cancer patients, when combining the results of DNA and RNA sequencing, one can identify potential neoantigens that stimulate the immune response of the T cell. However, when the somatic mutations are abundant, it is computationally challenging to efficiently prioritize the identified neoantigen candidates according to their ability of activating the T cell immuno-response.

Methods

Numerous prioritization or prediction approaches have been proposed to address this issue but none of them considers the original DNA loci of the neoantigens from the perspective of 3D genome. Based on our previous discoveries, we propose to investigate the distribution of neoantigens with different immunogenicity abilities in 3D genome and propose to adopt this important information into neoantigen prediction.

Results

We retrospect the DNA origins of the immuno-positive and immuno-negative neoantigens in the context of 3D genome and discovered that DNA loci of the immuno-positive neoantigens and immuno-negative neoantigens have very different distribution pattern. Specifically, comparing to the background 3D genome, DNA loci of the immuno-positive neoantigens tend to locate at specific regions in the 3D genome. We thus used this information into neoantigen prediction and demonstrated the effectiveness of this approach.

Conclusion

We believe that the 3D genome information will help to increase the precision of neoantigen prioritization and discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

Design an Efficient Multi-Epitope Peptide Vaccine Candidate Against SARS-CoV-2: An in silico Analysis

BACKGROUND

To date, no specific vaccine or drug has been proven to be effective against SARS-CoV-2 infection. Therefore, we implemented an immunoinformatic approach to design an efficient multi-epitopes vaccine against SARS-CoV-2.

RESULTS

The designed-vaccine construct consists of several immunodominant epitopes from structural proteins of spike, nucleocapsid, membrane, and envelope. These peptides promote cellular and humoral immunity and interferon-gamma responses. Also, these epitopes have a high antigenic capacity and are not likely to cause allergies. To enhance the vaccine immunogenicity, we used three potent adjuvants: Flagellin of Salmonella enterica subsp. enterica serovar Dublin, a driven peptide from high mobility group box 1 as HP-91, and human beta-defensin 3 protein. The physicochemical and immunological properties of the vaccine structure were evaluated. The tertiary structure of the vaccine protein was predicted and refined by Phyre2 and Galaxi refine and validated using RAMPAGE and ERRAT. Results of ElliPro showed 246 sresidues from vaccine might be conformational B-cell epitopes. Docking of the vaccine with toll-like receptors (TLR) 3, 5, 8, and angiotensin-converting enzyme 2 approved an appropriate interaction between the vaccine and receptors. Prediction of mRNA secondary structure and in silico cloning demonstrated that the vaccine can be efficiently expressed in Escherichia coli.

CONCLUSION

Our results demonstrated that the multi-epitope vaccine might be potentially antigenic and induce humoral and cellular immune responses against SARS-CoV-2. This vaccine can interact appropriately with the TLR3, 5, and 8. Also, it has a high-quality structure and suitable characteristics such as high stability and potential for expression in Escherichia coli .

A Pan-Specific GRU-Based Recurrent Neural Network for Predicting HLA-I-Binding Peptides

Human leukocyte antigens (HLAs) play a critical role in human-acquired immune responses by the recognition of non-self-peptides derived from exogenous bacteria, fungi, virus, and so forth. The accurate prediction of HLA-binding peptides is thus extremely useful for the mechanistic research of cell-mediated immunity and related epitope-based vaccine design. In this work, a simple pan-specific gated recurrent unit (GRU)-based recurrent neural network model was successfully proposed for predicting HLA-I-binding peptides. In comparison with the available six allele-specific, four pan-specific, and two ensemble-based prediction models, the GRU model achieves the highest area under the receiver operating characteristic curve (AUC) scores for 21 of 64 entries of the test benchmark datasets. Besides, the GRU model also achieves satisfactory performance on other 24 entries, of which the AUC scores differ by less than 0.1 from the highest scores. Overall, taking the advantages of the GRU network and auto-embedding techniques into account, the established pan-specific GRU model is more simple and direct and shows satisfactory prediction performance for HLA-I-binding peptides with varying lengths.

Pediatric patients with acute lymphoblastic leukemia generate abundant and functional neoantigen-specific CD8+ T cell responses

Cancer arises from the accumulation of genetic alterations, which can lead to the production of mutant proteins not expressed by normal cells. These mutant proteins can be processed and presented on the cell surface by major histocompatibility complex molecules as neoepitopes, allowing CD8⁺ T cells to mount responses against them. For solid tumors, only an average 2% of neoepitopes predicted by algorithms have detectable endogenous antitumor T cell responses. This suggests that low mutation burden tumors, which include many pediatric tumors, are poorly immunogenic. Here, we report that pediatric patients with acute lymphoblastic leukemia (ALL) have tumor-associated neoepitope-specific CD8⁺ T cells, responding to 86% of tested neoantigens and recognizing 68% of the tested neoepitopes. These responses include a public neoantigen from the ETV6-RUNX1 fusion that is targeted in seven of nine tested patients. We characterized phenotypic and transcriptional profiles of CD8⁺ tumor-infiltrating lymphocytes (TILs) at the single-cell level and found a heterogeneous population that included highly functional effectors. Moreover, we observed immunodominance hierarchies among the CD8⁺ TILs restricted to one or two putative neoepitopes. Our results indicate that robust antitumor immune responses are induced in pediatric ALL despite their low mutation burdens and emphasize the importance of immunodominance in shaping cellular immune responses. Furthermore, these data suggest that pediatric cancers may be amenable to immunotherapies aimed at enhancing immune recognition of tumor-specific neoantigens.

Mutational landscape and patterns of clonal evolution in relapsed pediatric acute lymphoblastic leukemia

Relapse of acute lymphoblastic leukemia (ALL) remains a leading cause of childhood death. Prior studies have shown clonal mutations at relapse often arise from relapse-fated subclones that exist at diagnosis. However, the genomic landscape, evolutionary trajectories and mutational mechanisms driving relapse are incompletely understood. In an analysis of 92 cases of relapsed childhood ALL, incorporating multimodal DNA and RNA sequencing, deep digital mutational tracking and xenografting to formally define clonal structure, we identify 50 significant targets of mutation with distinct patterns of mutational acquisition or enrichment. CREBBP, NOTCH1, and Ras signaling mutations rose from diagnosis subclones, whereas variants in NCOR2, USH2A and NT5C2 were exclusively observed at relapse. Evolutionary modeling and xenografting demonstrated that relapse-fated clones were minor (50%), major (27%) or multiclonal (18%) at diagnosis. Putative second leukemias, including those with lineage shift, were shown to most commonly represent relapse from an ancestral clone rather than a truly independent second primary leukemia. A subset of leukemias prone to repeated relapse exhibited hypermutation driven by at least three distinct mutational processes, resulting in heightened neoepitope burden and potential vulnerability to immunotherapy. Finally, relapse-driving sequence mutations were detected prior to relapse using deep digital PCR at levels comparable to orthogonal approaches to monitor levels of measurable residual disease. These results provide a genomic framework to anticipate and circumvent relapse by earlier detection and targeting of relapse-fated clones.

Uncovering the Tumor Antigen Landscape: What to Know about the Discovery Process

According to the latest available data, cancer is the second leading cause of death, highlighting the need for novel cancer therapeutic approaches. In this context, immunotherapy is emerging as a reliable first-line treatment for many cancers, particularly metastatic melanoma. Indeed, cancer immunotherapy has attracted great interest following the recent clinical approval of antibodies targeting immune checkpoint molecules, such as PD-1, PD-L1, and CTLA-4, that release the brakes of the immune system, thus reviving a field otherwise poorly explored. Cancer immunotherapy mainly relies on the generation and stimulation of cytotoxic CD8 T lymphocytes (CTLs) within the tumor microenvironment (TME), priming T cells and establishing efficient and durable anti-tumor immunity. Therefore, there is a clear need to define and identify immunogenic T cell epitopes to use in therapeutic cancer vaccines. Naturally presented antigens in the human leucocyte antigen-1 (HLA-I) complex on the tumor surface are the main protagonists in evocating a specific anti-tumor CD8+ T cell response. However, the methodologies for their identification have been a major bottleneck for their reliable characterization. Consequently, the field of antigen discovery has yet to improve. The current review is intended to define what are today known as tumor antigens, with a main focus on CTL antigenic peptides. We also review the techniques developed and employed to date for antigen discovery, exploring both the direct elution of HLA-I peptides and the in silico prediction of epitopes. Finally, the last part of the review analyses the future challenges and direction of the antigen discovery field.

Understanding the B and T cell epitopes of spike protein of severe acute respiratory syndrome coronavirus-2: A computational way to predict the immunogens

The 2019 novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak has caused a large number of deaths, with thousands of confirmed cases worldwide. The present study followed computational approaches to identify B- and T-cell epitopes for the spike (S) glycoprotein of SARS-CoV-2 by its interactions with the human leukocyte antigen alleles. We identified 24 peptide stretches on the SARS-CoV-2 S protein that are well conserved among the reported strains. The S protein structure further validated the presence of predicted peptides on the surface, of which 20 are surface exposed and predicted to have reasonable epitope binding efficiency. The work could be useful for understanding the immunodominant regions in the surface protein of SARS-CoV-2 and could potentially help in designing some peptide-based diagnostics. Also, identified T-cell epitopes might be considered for incorporation in vaccine designs.

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Determination of variability and average solvent accessibility.

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Identification of the B- and T-cell epitopes for spike glycoprotein of SARS-CoV-2.

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Interactions of B and T cell epitopes with the human leukocyte antigen alleles.

IEDB-AR: immune epitope database-analysis resource in 2019

The Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis.

CD8+ T-cell-Mediated Immunoediting Influences Genomic Evolution and Immune Evasion in Murine Gliomas

PURPOSE

Cancer immunoediting shapes tumor progression by the selection of tumor cell variants that can evade immune recognition. Given the immune evasion and intratumor heterogeneity characteristic of gliomas, we hypothesized that CD8⁺ T cells mediate immunoediting in these tumors.

EXPERIMENTAL DESIGN

We developed retrovirus-induced PDGF⁺ Pten ^-/- murine gliomas and evaluated glioma progression and tumor immunogenicity in the absence of CD8⁺ T cells by depleting this immune cell population. Furthermore, we characterized the genomic alterations present in gliomas that developed in the presence and absence of CD8⁺ T cells.

RESULTS

Upon transplantation, gliomas that developed in the absence of CD8⁺ T cells engrafted poorly in recipients with intact immunity but engrafted well in those with CD8⁺ T-cell depletion. In contrast, gliomas that developed under pressure from CD8⁺ T cells were able to fully engraft in both CD8⁺ T-cell-depleted mice and immunocompetent mice. Remarkably, gliomas developed in the absence of CD8⁺ T cells exhibited increased aneuploidy, MAPK pathway signaling, gene fusions, and macrophage/microglial infiltration, and showed a proinflammatory phenotype. MAPK activation correlated with macrophage/microglia recruitment in this model and in the human disease.

CONCLUSIONS

Our studies indicate that, in these tumor models, CD8⁺ T cells influence glioma oncogenic pathways, tumor genotype, and immunogenicity. This suggests immunoediting of immunogenic tumor clones through their negative selection by CD8⁺ T cells during glioma formation.

A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.

Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data

Major histocompatibility complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyze and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Furthermore, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0 demonstrated improved performance beyond current state-of-the-art predictors for ligand and CD4+ T cell epitope prediction. These results suggest that NNAlign_MA and NetMHCIIpan-4.0 are powerful tools for analysis of immunopeptidome MS data, prediction of T cell epitopes, and development of personalized immunotherapies.

Molecular Analysis of Clinically Defined Subsets of High-Grade Serous Ovarian Cancer

The diversity and heterogeneity within high-grade serous ovarian cancer (HGSC), which is the most lethal gynecologic malignancy, is not well understood. Here, we perform comprehensive multi-platform omics analyses, including integrated analysis, and immune monitoring on primary and metastatic sites from highly clinically annotated HGSC samples based on a laparoscopic triage algorithm from patients who underwent complete gross resection (R0) or received neoadjuvant chemotherapy (NACT) with excellent or poor response. We identify significant distinct molecular abnormalities and cellular changes and immune cell repertoire alterations between the groups, including a higher rate of NF1 copy number loss, and reduced chromothripsis-like patterns, higher levels of strong-binding neoantigens, and a higher number of infiltrated T cells in the R0 versus the NACT groups.

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for Clinical Pharmacologists

Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These "omics" data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno-oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non-small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.

pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens

Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector-based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at http://www.pvactools.org.

neoANT-HILL: an integrated tool for identification of potential neoantigens

Background

Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives.

Results

We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity.

Conclusion

neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available through github at https://github.com/neoanthill/neoANT-HILL.

HLA associations in inflammatory arthritis: emerging mechanisms and clinical implications

Our understanding of the mechanisms underlying HLA associations with inflammatory arthritis continues to evolve. Disease associations have been refined, and interactions of HLA genotype with other genes and environmental risk factors in determining disease risk have been identified. This Review provides basic information on the genetics and molecular function of HLA molecules, as well as general features of HLA associations with disease. Evidence is discussed regarding the various peptide-dependent and peptide-independent mechanisms by which HLA alleles might contribute to the pathogenesis of three types of inflammatory arthritis: rheumatoid arthritis, spondyloarthritis and systemic juvenile idiopathic arthritis. Also discussed are HLA allelic associations that shed light on the genetic heterogeneity of inflammatory arthritides and on the relationships between adult and paediatric forms of arthritis. Clinical implications range from improved diagnosis and outcome prediction to the possibility of using HLA associations in developing personalized strategies for the treatment and prevention of these diseases.

High-Throughput Identification of MHC Class I Binding Peptides Using an Ultradense Peptide Array

Rational vaccine development and evaluation requires identifying and measuring the magnitude of epitope-specific CD8 T cell responses. However, conventional CD8 T cell epitope discovery methods are labor intensive and do not scale well. In this study, we accelerate this process by using an ultradense peptide array as a high-throughput tool for screening peptides to identify putative novel epitopes. In a single experiment, we directly assess the binding of four common Indian rhesus macaque MHC class I molecules (Mamu-A1*001, -A1*002, -B*008, and -B*017) to ∼61,000 8-mer, 9-mer, and 10-mer peptides derived from the full proteomes of 82 SIV and simian HIV isolates. Many epitope-specific CD8 T cell responses restricted by these four MHC molecules have already been identified in SIVmac239, providing an ideal dataset for validating the array; up to 64% of these known epitopes are found in the top 192 SIVmac239 peptides with the most intense MHC binding signals in our experiment. To assess whether the peptide array identified putative novel CD8 T cell epitopes, we validated the method by IFN-γ ELISPOT assay and found three novel peptides that induced CD8 T cell responses in at least two Mamu-A1*001-positive animals; two of these were validated by ex vivo tetramer staining. This high-throughput identification of peptides that bind class I MHC will enable more efficient CD8 T cell response profiling for vaccine development, particularly for pathogens with complex proteomes for which few epitope-specific responses have been defined.

Intratumoral heterogeneity and clonal evolution in liver cancer

Clonal evolution of a tumor ecosystem depends on different selection pressures that are principally immune and treatment mediated. We integrate RNA-seq, DNA sequencing, TCR-seq and SNP array data across multiple regions of liver cancer specimens to map spatio-temporal interactions between cancer and immune cells. We investigate how these interactions reflect intra-tumor heterogeneity (ITH) by correlating regional neo-epitope and viral antigen burden with the regional adaptive immune response. Regional expression of passenger mutations dominantly recruits adaptive responses as opposed to hepatitis B virus and cancer-testis antigens. We detect different clonal expansion of the adaptive immune system in distant regions of the same tumor. An ITH-based gene signature improves single-biopsy patient survival predictions and an expression survey of 38,553 single cells across 7 regions of 2 patients further reveals heterogeneity in liver cancer. These data quantify transcriptomic ITH and how the different components of the HCC ecosystem interact during cancer evolution.

Immune-mediated selection pressures impact the clonal evolution of tumours. Here, in hepatocellular carcinoma the authors map spatio-temporal interactions between tumor and immune cells, highlighting the regulatory substrate of intra-tumoural heterogeneity that correlates with regional adaptive immune responses.

DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing

Although scRNA-seq is now ubiquitously adopted in studies of intratumor heterogeneity, detection of somatic mutations and inference of clonal membership from scRNA-seq is currently unreliable. We propose DENDRO, an analysis method for scRNA-seq data that clusters single cells into genetically distinct subclones and reconstructs the phylogenetic tree relating the subclones. DENDRO utilizes transcribed point mutations and accounts for technical noise and expression stochasticity. We benchmark DENDRO and demonstrate its application on simulation data and real data from three cancer types. In particular, on a mouse melanoma model in response to immunotherapy, DENDRO delineates the role of neoantigens in treatment response.

The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases

The science that investigates the ensembles of all peptides associated to human leukocyte antigen (HLA) molecules is termed "immunopeptidomics" and is typically driven by mass spectrometry (MS) technologies. Recent advances in MS technologies, neoantigen discovery and cancer immunotherapy have catalyzed the launch of the Human Immunopeptidome Project (HIPP) with the goal of providing a complete map of the human immunopeptidome and making the technology so robust that it will be available in every clinic. Here, we provide a long-term perspective of the field and we use this framework to explore how we think the completion of the HIPP will truly impact the society in the future. In this context, we introduce the concept of immunopeptidome-wide association studies (IWAS). We highlight the importance of large cohort studies for the future and how applying quantitative immunopeptidomics at population scale may provide a new look at individual predisposition to common immune diseases as well as responsiveness to vaccines and immunotherapies. Through this vision, we aim to provide a fresh view of the field to stimulate new discussions within the community, and present what we see as the key challenges for the future for unlocking the full potential of immunopeptidomics in this era of precision medicine.

OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction

OpenVax is a computational workflow for identifying somatic variants, predicting neoantigens, and selecting the contents of personalized cancer vaccines. It is a Dockerized end-to-end pipeline that takes as input raw tumor/normal sequencing data. It is currently used in three clinical trials (NCT02721043, NCT03223103, and NCT03359239). In this chapter, we describe how to install and use OpenVax, as well as how to interpret the generated results.

The SysteMHC Atlas: a Computational Pipeline, a Website, and a Data Repository for Immunopeptidomic Analyses

Mass spectrometry has emerged as the method of choice for the exploration of the immunopeptidome. Insights from the immunopeptidome promise novel cancer therapeutic approaches and a better understanding of the basic mechanisms of our immune system. To meet the computational demands from the steady gain in popularity and reach of mass spectrometry-based immunopeptidomics analysis, we created the SysteMHC Atlas project, a first-of-its-kind computational pipeline and resource repository dedicated to standardizing data analysis and public dissemination of immunopeptidomic datasets.

NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions

The set of peptides presented on a cell's surface by MHC molecules is known as the immunopeptidome. Current mass spectrometry technologies allow for identification of large peptidomes, and studies have proven these data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. Immunopeptidomes are usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in the system under investigation. Motif deconvolution -the process of associating each ligand to its presenting MHC molecule(s)- is therefore a critical and challenging step in the analysis of MS-eluted MHC ligand data. Here, we describe NNAlign_MA, a computational method designed to address this challenge and fully benefit from large, poly-specific data sets of MS-eluted ligands. NNAlign_MA simultaneously performs the tasks of (1) clustering peptides into individual specificities; (2) automatic annotation of each cluster to an MHC molecule; and (3) training of a prediction model covering all MHCs present in the training set. NNAlign_MA was benchmarked on large and diverse data sets, covering class I and class II data. In all cases, the method was demonstrated to outperform state-of-the-art methods, effectively expanding the coverage of alleles for which accurate predictions can be made, resulting in improved identification of both eluted ligands and T-cell epitopes. Given its high flexibility and ease of use, we expect NNAlign_MA to serve as an effective tool to increase our understanding of the rules of MHC antigen presentation and guide the development of novel T-cell-based therapeutics.

Identification and ranking of recurrent neo-epitopes in cancer

Background

Immune escape is one of the hallmarks of cancer and several new treatment approaches attempt to modulate and restore the immune system’s capability to target cancer cells. At the heart of the immune recognition process lies antigen presentation from somatic mutations. These neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority.

Methods

We carefully screen TCGA data sets for recurrent somatic amino acid exchanges and apply MHC class I binding predictions.

Results

We propose a method for in silico selection and prioritization of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. While the percentage of patients carrying a specific neo-epitope and HLA-type combination is relatively small, the sheer number of new patients leads to surprisingly high reoccurence numbers. We identify 769 epitopes which are expected to occur in 77629 patients per year.

Conclusion

While our candidate list will definitely contain false positives, the results provide an objective order for wet-lab testing of reusable neo-epitopes. Thus recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options.

Machine Learning for Cancer Immunotherapies Based on Epitope Recognition by T Cell Receptors

In the last years, immunotherapies have shown tremendous success as treatments for multiple types of cancer. However, there are still many obstacles to overcome in order to increase response rates and identify effective therapies for every individual patient. Since there are many possibilities to boost a patient's immune response against a tumor and not all can be covered, this review is focused on T cell receptor-mediated therapies. CD8+ T cells can detect and destroy malignant cells by binding to peptides presented on cell surfaces by MHC (major histocompatibility complex) class I molecules. CD4+ T cells can also mediate powerful immune responses but their peptide recognition by MHC class II molecules is more complex, which is why the attention has been focused on CD8+ T cells. Therapies based on the power of T cells can, on the one hand, enhance T cell recognition by introducing TCRs that preferentially direct T cells to tumor sites (so called TCR-T therapy) or through vaccination to induce T cells in vivo. On the other hand, T cell activity can be improved by immune checkpoint inhibition or other means that help create a microenvironment favorable for cytotoxic T cell activity. The manifold ways in which the immune system and cancer interact with each other require not only the use of large omics datasets from gene, to transcript, to protein, and to peptide but also make the application of machine learning methods inevitable. Currently, discovering and selecting suitable TCRs is a very costly and work intensive in vitro process. To facilitate this process and to additionally allow for highly personalized therapies that can simultaneously target multiple patient-specific antigens, especially neoepitopes, breakthrough computational methods for predicting antigen presentation and TCR binding are urgently required. Particularly, potential cross-reactivity is a major consideration since off-target toxicity can pose a major threat to patient safety. The current speed at which not only datasets grow and are made available to the public, but also at which new machine learning methods evolve, is assuring that computational approaches will be able to help to solve problems that immunotherapies are still facing.

Neoantigen prediction from genomic and transcriptomic data

Tumor cells acquire distinct genetic characteristics as a means to survive and proliferate indefinitely. Changes in the genetic code can also translate in changes at the protein level, therefore creating a distinguishable signature unique for tumor cells, and absent in normal tissues. The presence of discernable moieties in tumors is particularly attractive because it represents a therapeutic opportunity to target tumor cells with specificity, while sparing non-transformed cells. In this sense neoantigens, short peptides containing a mutated sequence, are seen attractive therapeutic targets because of their confinement within tumor cells. Neoantigens can be recognized with high affinity and specificity by tumor-targeting T cells, which consequently can initiate a potent anti-tumor immune response. While this is feasible and it has been tested in numerous cancer types including melanoma, colon and lung cancer, to mention a few, there are technical challenges in identifying immunogenic neoantigens. In this manuscript we address the topic of neoantigen identification from tumor samples, offering a technical overview of the bioinformatic methods utilized to profile the neoantigenic load of tumor samples obtained from clinical specimens. This is meant to guide readers through the steps of neoantigen identification using genomic data, by suggesting tools and methods that can provide, with a high degree of confidence, reliable results for downstream in vitro and in vivo applications.

DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity

Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.

Substantial Influence of ERAP2 on the HLA-B*40:02 Peptidome: Implications for HLA-B*27-Negative Ankylosing Spondylitis

HLA-B*40:02 is one of a few major histocompatibility complex class I (MHC-I) molecules associated with ankylosing spondylitis (AS) independently of HLA-B*27. The endoplasmic reticulum aminopeptidase 2 (ERAP2), an enzyme that process MHC-I ligands and preferentially trims N-terminal basic residues, is also a risk factor for this disease. Like HLA-B*27 and other AS-associated MHC-I molecules, HLA-B*40:02 binds a relatively high percentage of peptides with ERAP2-susceptible residues. In this study, the effects of ERAP2 depletion on the HLA-B*40:02 peptidome were analyzed. ERAP2 protein expression was knocked out by CRISPR in the transfectant cell line C1R-B*40:02, and the differences between the peptidomes from the wild-type and ERAP2-KO cells were determined by label-free quantitative comparisons. The qualitative changes dependent on ERAP2 affected about 5% of the peptidome, but quantitative changes in peptide amounts were much more substantial, reflecting a significant influence of this enzyme on the generation/destruction balance of HLA-B*40:02 ligands. As in HLA-B*27, a major effect was on the frequencies of N-terminal residues. In this position, basic and small residues were increased, and aliphatic/aromatic ones decreased in the ERAP2 knockout. Other peptide positions were also affected. Because most of the non-B*27 MHC-I molecules associated with AS risk bind a relatively high percentage of peptides with N-terminal basic residues, we hypothesize that the non-epistatic association of ERAP2 with AS might be related to the processing of peptides with these residues, thus affecting the peptidomes of AS-associated MHC-I molecules.

Protecting Tumors by Preventing Human Papilloma Virus Antigen Presentation: Insights from Emerging Bioinformatics Algorithms

Recent developments in bioinformatics technologies have led to advances in our understanding of how oncogenic viruses such as the human papilloma virus drive cancer progression and evade the host immune system. Here, we focus our review on understanding how these emerging bioinformatics technologies influence our understanding of how human papilloma virus (HPV) drives immune escape in cancers of the head and neck, and how these new informatics approaches may be generally applicable to other virally driven cancers. Indeed, these tools enable researchers to put existing data from genome wide association studies, in which high risk alleles have been identified, in the context of our current understanding of cellular processes regulating neoantigen presentation. In the future, these new bioinformatics approaches are highly likely to influence precision medicine-based decision making for the use of immunotherapies in virally driven cancers.

Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes

Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity using approaches modeled from tumor-specific antigen data. Here, we describe peptide-intrinsic biochemical features associated with neoantigen and minor histocompatibility mismatch antigen immunogenicity and present a gradient boosting algorithm for predicting tumor antigen immunogenicity. This algorithm was validated in two murine tumor models and demonstrated the capacity to select for therapeutically active antigens. Immune correlates of neoantigen immunogenicity were studied in a pan-cancer data set from The Cancer Genome Atlas and demonstrated an association between expression of immunogenic neoantigens and immunity in colon and lung adenocarcinomas. Lastly, we present evidence for expression of an out-of-frame neoantigen that was capable of driving antitumor cytotoxic T-cell responses. With the growing clinical importance of tumor vaccine therapies, our approach may allow for better selection of therapeutically relevant tumor-specific antigens, including nonclassic out-of-frame antigens capable of driving antitumor immunity.

A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands

The diversity of peptides displayed by class I human leukocyte antigen (HLA) plays an essential role in T cell immunity. The peptide repertoire is extended by various posttranslational modifications, including proteasomal splicing of peptide fragments from distinct regions of an antigen to form nongenomically templated cis-spliced sequences. Previously, it has been suggested that a fraction of the immunopeptidome constitutes such cis-spliced peptides; however, because of computational limitations, it has not been possible to assess whether trans-spliced peptides (i.e., the fusion of peptide segments from distinct antigens) are also bound and presented by HLA molecules, and if so, in what proportion. Here, we have developed and applied a bioinformatic workflow and demonstrated that trans-spliced peptides are presented by HLA-I, and their abundance challenges current models of proteasomal splicing that predict cis-splicing as the most probable outcome. These trans-spliced peptides display canonical HLA-binding sequence features and are as frequently identified as cis-spliced peptides found bound to a number of different HLA-A and HLA-B allotypes. Structural analysis reveals that the junction between spliced peptides is highly solvent exposed and likely to participate in T cell receptor interactions. These results highlight the unanticipated diversity of the immunopeptidome and have important implications for autoimmunity, vaccine design, and immunotherapy.

Integrative genomic analyses of APOBEC-mutational signature, expression and germline deletion of APOBEC3 genes, and immunogenicity in multiple cancer types

BACKGROUND

Although APOBEC-mutational signature is found in tumor tissues of multiple cancers, how a common germline APOBEC3A/B deletion affects the mutational signature remains unclear.

METHODS

Using data from 10 cancer types generated as part of TCGA, we performed integrative genomic and association analyses to assess inter-relationship of expressions for isoforms APOBEC3A and APOBEC3B, APOBEC-mutational signature, germline APOBEC3A/B deletions, neoantigen loads, and tumor infiltration lymphocytes (TILs).

RESULTS

We found that expression level of the isoform uc011aoc transcribed from the APOBEC3A/B chimera was associated with a greater burden of APOBEC-mutational signature only in breast cancer, while germline APOBEC3A/B deletion led to an increased expression level of uc011aoc in multiple cancer types. Furthermore, we found that the deletion was associated with elevated APOBEC-mutational signature, neoantigen loads and relative composition of T cells (CD8+) in TILs only in breast cancer. Additionally, we also found that APOBEC-mutational signature significantly contributed to neoantigen loads and certain immune cell abundances in TILs across cancer types.

CONCLUSIONS

These findings reveal new insights into understanding the genetic, biological and immunological mechanisms through which APOBEC genes may be involved in carcinogenesis, and provide potential genetic biomarker for the development of disease prevention and cancer immunotherapy.

Late-differentiated effector neoantigen-specific CD8+ T cells are enriched in peripheral blood of non-small cell lung carcinoma patients responding to atezolizumab treatment

BACKGROUND

There is strong evidence that immunotherapy-mediated tumor rejection can be driven by tumor-specific CD8+ T cells reinvigorated to recognize neoantigens derived from tumor somatic mutations. Thus, the frequencies or characteristics of tumor-reactive, mutation-specific CD8+ T cells could be used as biomarkers of an anti-tumor response. However, such neoantigen-specific T cells are difficult to reliably identify due to their low frequency in peripheral blood and wide range of potential epitope specificities.

METHODS

Peripheral blood mononuclear cells (PBMC) from 14 non-small cell lung cancer (NSCLC) patients were collected pre- and post-treatment with the anti-PD-L1 antibody atezolizumab. Using whole exome sequencing and RNA sequencing we identified tumor neoantigens that are predicted to bind to major histocompatibility complex class I (MHC-I) and utilized mass cytometry, together with cellular 'barcoding', to profile immune cells from patients with objective response to therapy (n = 8) and those with progressive disease (n = 6). In parallel, a highly-multiplexed combinatorial tetramer staining was used to screen antigen-specific CD8+ T cells in peripheral blood for 782 candidate tumor neoantigens and 71 known viral-derived control peptide epitopes across all patient samples.

RESULTS

No significant treatment- or response associated phenotypic difference were measured in bulk CD8+ T cells. Multiplexed peptide-MHC multimer staining detected 20 different neoantigen-specific T cell populations, as well as T cells specific for viral control antigens. Not only were neoantigen-specific T cells more frequently detected in responding patients, their phenotypes were also almost entirely distinct. Neoantigen-specific T cells from responder patients typically showed a differentiated effector phenotype, most like Cytomegalovirus (CMV) and some types of Epstein-Barr virus (EBV)-specific CD8+ T cells. In contrast, more memory-like phenotypic profiles were observed for neoantigen-specific CD8+ T cells from patients with progressive disease.

CONCLUSION

This study demonstrates that neoantigen-specific T cells can be detected in peripheral blood in non-small cell lung cancer (NSCLC) patients during anti-PD-L1 therapy. Patients with an objective response had an enrichment of neoantigen-reactive T cells and these cells showed a phenotype that differed from patients without a response. These findings suggest the ex vivo identification, characterization, and longitudinal follow-up of rare tumor-specific differentiated effector neoantigen-specific T cells may be useful in predicting response to checkpoint blockade.

TRIAL REGISTRATION

POPLAR trial NCT01903993 .

In silico epitope prediction and immunogenic analysis for penton base epitope-focused vaccine against hydropericardium syndrome in chicken

Certain strains of fowl adenovirus serotype 4 (FAdV-4) of the family Adenoviridae are recognized to be the causative agents of Hydropericardium Syndrome (HPS) in broiler chicken. Despite the significantly spiking mortality in broilers due to HPS, not much effort has been made to design an effective vaccine against FAdV-4. The combination of immuno- and bioinformatics tools for immunogenic epitope prediction is the most recent concept of vaccine design. It reduces the time and effort required for hunting a potent vaccine candidate and is economical. Previously, we have reported the penton base protein of FAdV-4 to be a candidate for subunit vaccine against HPS. In the present study, we have computationally pre-screened promising B- and T-cell epitopes of the penton base. Multiple methods were employed for linear B-cell epitope identification; BepiPred and five other methods based on physicochemical properties of the amino acids. The penton base was homology modeled by means of Modeller 9.17 and after refinement of the model (by GalaxyRefine web server) ElliPro web tool was used to predict the discontinuous epitopes. NetMHCcons 1.1 and NetMHCIIpan 3.1 servers were used for the likelihood of peptide binding to Major Histocompatibility Complex (MHC) class I & II molecules respectively for T-cell epitope forecast. As a result, we identified the peptide stretch of 1-225  as the most promiscuous B- and T-cell epitope region in penton base Full Length (FL) protein sequence. Escherichia coli based expression vectors were generated containing cloned peptide stretch 1-225 (penton base^1-225) and penton base FL gene sequence. The recombinant penton base^1-225 and penton base FL proteins were expressed and purified using Escherichia coli-based expression system. Purification yield of penton base^1-225 was 3-fold higher compared to penton base FL. These proteins were injected in chickens to determine their competence in protection against HPS. The results showed equal protection level of the two proteins and the commercial inactivated vaccine against FAdV-4 infection. The results suggest the peptide stretch 1-225 of penton base as a valuable candidate for developing an epitope-driven vaccine to combat HPS.

Structure Based Prediction of Neoantigen Immunogenicity

The development of immunological therapies that incorporate peptide antigens presented to T cells by MHC proteins is a long sought-after goal, particularly for cancer, where mutated neoantigens are being explored as personalized cancer vaccines. Although neoantigens can be identified through sequencing, bioinformatics and mass spectrometry, identifying those which are immunogenic and able to promote tumor rejection remains a significant challenge. Here we examined the potential of high-resolution structural modeling followed by energetic scoring of structural features for predicting neoantigen immunogenicity. After developing a strategy to rapidly and accurately model nonameric peptides bound to the common class I MHC protein HLA-A2, we trained a neural network on structural features that influence T cell receptor (TCR) and peptide binding energies. The resulting structurally-parameterized neural network outperformed methods that do not incorporate explicit structural or energetic properties in predicting CD8+ T cell responses of HLA-A2 presented nonameric peptides, while also providing insight into the underlying structural and biophysical mechanisms governing immunogenicity. Our proof-of-concept study demonstrates the potential for structure-based immunogenicity predictions in the development of personalized peptide-based vaccines.

Best practices for bioinformatic characterization of neoantigens for clinical utility

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

The SysteMHC Atlas project

Mass spectrometry (MS)-based immunopeptidomics investigates the repertoire of peptides presented at the cell surface by major histocompatibility complex (MHC) molecules. The broad clinical relevance of MHC-associated peptides, e.g. in precision medicine, provides a strong rationale for the large-scale generation of immunopeptidomic datasets and recent developments in MS-based peptide analysis technologies now support the generation of the required data. Importantly, the availability of diverse immunopeptidomic datasets has resulted in an increasing need to standardize, store and exchange this type of data to enable better collaborations among researchers, to advance the field more efficiently and to establish quality measures required for the meaningful comparison of datasets. Here we present the SysteMHC Atlas (https://systemhcatlas.org), a public database that aims at collecting, organizing, sharing, visualizing and exploring immunopeptidomic data generated by MS. The Atlas includes raw mass spectrometer output files collected from several laboratories around the globe, a catalog of context-specific datasets of MHC class I and class II peptides, standardized MHC allele-specific peptide spectral libraries consisting of consensus spectra calculated from repeat measurements of the same peptide sequence, and links to other proteomics and immunology databases. The SysteMHC Atlas project was created and will be further expanded using a uniform and open computational pipeline that controls the quality of peptide identifications and peptide annotations. Thus, the SysteMHC Atlas disseminates quality controlled immunopeptidomic information to the public domain and serves as a community resource toward the generation of a high-quality comprehensive map of the human immunopeptidome and the support of consistent measurement of immunopeptidomic sample cohorts.

Redundancy and Complementarity between ERAP1 and ERAP2 Revealed by their Effects on the Behcet's Disease-associated HLA-B*51 Peptidome

The endoplasmic reticulum aminopeptidases ERAP1 and ERAP2 trim peptides to be loaded onto HLA molecules, including the main risk factor for Behçet's disease HLA-B*51. ERAP1 is also a risk factor among HLA-B*51-positive individuals, whereas no association is known with ERAP2. This study addressed the mutual relationships between both enzymes in the processing of an HLA-bound peptidome, interrogating their differential association with Behçet's disease. CRISPR/Cas9 was used to generate knock outs of ERAP1, ERAP2 or both from transfectant 721.221-HLA-B*51:01 cells. The surface expression of HLA-B*51 was reduced in all cases. The effects of depleting each or both enzymes on the B*51:01 peptidome were analyzed by quantitative label-free mass spectrometry. Substantial quantitative alterations of peptide length, subpeptidome balance, N-terminal residue usage, affinity and presentation of noncanonical ligands were observed. These effects were often different in the presence or absence of the other enzyme, revealing their mutual dependence. In the absence of ERAP1, ERAP2 showed similar and significant processing of B*51:01 ligands, indicating functional redundancy. The high overlap between the peptidomes of wildtype and double KO cells indicates that a large majority of B*51:01 ligands are present in the ER even in the absence of ERAP1/ERAP2. These results indicate that both enzymes have distinct, but complementary and partially redundant effects on the B*51:01 peptidome, leading to its optimization and maximal surface expression. The distinct effects of both enzymes on the HLA-B*51 peptidome provide a basis for their differential association with Behçet's disease and suggest a pathogenetic role of the B*51:01 peptidome.

Editing the immunopeptidome of melanoma cells using a potent inhibitor of endoplasmic reticulum aminopeptidase 1 (ERAP1)

The efficacy of cancer immunotherapy, including treatment with immune-checkpoint inhibitors, often is limited by ineffective presentation of antigenic peptides that elicit T-cell-mediated anti-tumor cytotoxic responses. Manipulation of antigen presentation pathways is an emerging approach for enhancing the immunogenicity of tumors in immunotherapy settings. ER aminopeptidase 1 (ERAP1) is an intracellular enzyme that trims peptides as part of the system that generates peptides for binding to MHC class I molecules (MHC-I). We hypothesized that pharmacological inhibition of ERAP1 in cells could regulate the cellular immunopeptidome. To test this hypothesis, we treated A375 melanoma cells with a recently developed potent ERAP1 inhibitor and analyzed the presented MHC-I peptide repertoire by isolating MHC-I, eluting bound peptides, and identifying them using capillary chromatography and tandem mass spectrometry (LC-MS/MS). Although the inhibitor did not reduce cell-surface MHC-I expression, it induced qualitative and quantitative changes in the presented peptidomes. Specifically, inhibitor treatment altered presentation of about half of the total 3204 identified peptides, including about one third of the peptides predicted to bind tightly to MHC-I. Inhibitor treatment altered the length distribution of eluted peptides without change in the basic binding motifs. Surprisingly, inhibitor treatment enhanced the average predicted MHC-I binding affinity, by reducing presentation of sub-optimal long peptides and increasing presentation of many high-affinity 9-12mers, suggesting that baseline ERAP1 activity in this cell line is destructive for many potential epitopes. Our results suggest that chemical inhibition of ERAP1 may be a viable approach for manipulating the immunopeptidome of cancer.

Alternative mRNA splicing in cancer immunotherapy

Immunotherapies are yielding effective treatments for several previously untreatable cancers. Still, the identification of suitable antigens specific to the tumour that can be targets for cancer vaccines and T cell therapies is a challenge. Alternative processing of mRNA, a phenomenon that has been shown to alter the proteomic diversity of many cancers, may offer the potential of a broadened target space. Here, we discuss the promise of analysing mRNA processing events in cancer cells, with an emphasis on mRNA splicing, for the identification of potential new targets for cancer immunotherapy. Further, we highlight the challenges that must be overcome for this new avenue to have clinical applicability.

Computational Analysis of HLA-presentation of Non-synonymous Recipient Mismatches Indicates Effect on the Risk of Chronic Graft-vs.-Host Disease After Allogeneic HSCT

Genetic mismatches in protein coding genes between allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipient and donor can elicit an alloimmunity response via peptides presented by the recipient HLA receptors as minor histocompatibility antigens (mHAs). While the impact of individual mHAs on allo-HSCT outcome such as graft-vs.-host and graft-vs.-leukemia effects has been demonstrated, it is likely that established mHAs constitute only a small fraction of all immunogenic non-synonymous variants. In the present study, we have analyzed the genetic mismatching in 157 exome-sequenced sibling allo-HSCT pairs to evaluate the significance of polymorphic HLA class I associated peptides on clinical outcome. We applied computational mismatch estimation approaches based on experimentally verified HLA ligands available in public repositories, published mHAs, and predicted HLA-peptide affinites, and analyzed their associations with chronic graft-vs.-host disease (cGvHD) grades. We found that higher estimated recipient mismatching consistently increased the risk of severe cGvHD, suggesting that HLA-presented mismatching influences the likelihood of long-term complications in the patient. Furthermore, computational approaches focusing on estimation of HLA-presentation instead of all non-synonymous mismatches indiscriminately may be beneficial for analysis sensitivity and could help identify novel mHAs.

Therapy-Induced MHC I Ligands Shape Neo-Antitumor CD8 T Cell Responses during Oncolytic Virus-Based Cancer Immunotherapy

Oncolytic viruses (OVs), known for their cancer-killing characteristics, also overturn tumor-associated defects in antigen presentation through the MHC class I pathway and induce protective neo-antitumor CD8 T cell responses. Nonetheless, whether OVs shape the tumor MHC-I ligandome remains unknown. Here, we investigated if an OV induces the presentation of novel MHC I-bound tumor antigens (termed tumor MHC-I ligands). Using comparative mass spectrometry (MS)-based MHC-I ligandomics, we determined differential tumor MHC-I ligand expression following treatment with oncolytic reovirus in a murine ovarian cancer model. In vitro, we found that reovirus changes the tumor ligandome of cancer cells. Concurrent multiplexed quantitative proteomics revealed that the reovirus-induced changes in tumor MHC-I ligand presentation were mostly independent of their source proteins. In an in vivo model, tumor MHC-I ligands induced by reovirus were detectable not only in tumor tissues but also the spleens (a source of antigen-presenting cells) of tumor-bearing mice. Most importantly, therapy-induced MHC-I ligands stimulated antigen-specific IFNγ responses in antitumor CD8 T cells from mice treated with reovirus. These data show that therapy-induced MHC-I ligands may shape underlying neo-antitumor CD8 T cell responses. As such, they should be considered in strategies promoting the efficacy of OV-based cancer immunotherapies.

ACME: pan-specific peptide–MHC class I binding prediction through attention-based deep neural networks

Motivation

Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual binding affinities are needed to increase precision and reduce the number of false positive predictions.

Results

We present ACME (Attention-based Convolutional neural networks for MHC Epitope binding prediction), a new pan-specific algorithm to accurately predict the binding affinities between peptides and MHC class I molecules, even for those new alleles that are not seen in the training data. Extensive tests have demonstrated that ACME can significantly outperform other state-of-the-art prediction methods with an increase of the Pearson correlation coefficient between predicted and measured binding affinities by up to 23 percentage points. In addition, its ability to identify strong-binding peptides has been experimentally validated. Moreover, by integrating the convolutional neural network with attention mechanism, ACME is able to extract interpretable patterns that can provide useful and detailed insights into the binding preferences between peptides and their MHC partners. All these results have demonstrated that ACME can provide a powerful and practically useful tool for the studies of peptide–MHC class I interactions.

Availability and implementation

ACME is available as an open source software at https://github.com/HYsxe/ACME.

Supplementary information

Supplementary data are available at Bioinformatics online.

Analysis of Apical Membrane Antigen (AMA)-1 characteristics using bioinformatics tools in order to vaccine design against Plasmodium vivax

Plasmodium vivax, an intracellular protozoan, causes malaria which is characterized by fever, anemia, respiratory distress, liver and spleen enlargement. In spite of attempts to design an efficient vaccine, there is not a vaccine against P. vivax. Notable advances have recently achieved in the development of malaria vaccines targeting the surface antigens such as Apical Membrane Antigens (AMA)-1. AMA-1 is a micronemal protein synthesized during the erythrocyte-stage of Plasmodium species and plays a significant role in the invasion process of the parasite into host cells. P. vivax AMA-1 (PvAMA-1) can induce strong cellular and humoral responses, indicating that it can be an ideal candidate of vaccine against malaria. Identification and prediction of proteins characteristics increase our knowledge about them and leads to develop vaccine and diagnostic studies. In the present study several valid bioinformatics tools were applied to analyze the various characteristics of AMA-1 such as physical and chemical properties, secondary and tertiary structures, B- cell and T-cell prediction and other important features in order to introduce potential epitopes for designing a high-efficient vaccine. The results demonstrated that this protein had 57 potential PTM sites and only one transmembrane domain on its sequence. Also, multiple hydrophilic regions and classical high hydrophilic domains were predicted. Secondary structure prediction revealed that the proportions of random coil, alpha-helix and extended strand in the AMA-1 sequence were 53.74%, 27.22%, and 19.4%, respectively. Moreover, 5 disulfide bonds were predicted at positions 14-21aa, 162-192aa, 208-220aa, 247-265aa and 354-363aa. The data obtained from B-cell and T-cell epitopes prediction showed that there were several potential epitopes on AMA-1 that can be proper targets for diagnostic and vaccine studies. The current study presented interesting basic and theoretical information regarding PvAMA-1, being important for further studies in order to design a high-efficiency vaccine against malaria.

Large-scale in-silico identification of a tumor-specific antigen pool for targeted immunotherapy in triple-negative breast cancer

Since the advent of cetuximab, clinical cancer treatment has evolved from the standard, relatively nonspecific chemo- and radiotherapy with significant cytotoxic side effects towards immunotherapeutic approaches with selective, target-mechanism-based effects. Antibody therapies as the most successful form of cancer immunotherapy led to approved treatments for specific cancer types with increased patient survival. Thus, the identification of tumor antigens with high immunogenicity is in central focus now. In this study, we applied computational methods to comprehensively discover overexpressed molecular targets with high therapeutic relevance for clinical, immunotherapeutic cancer treatment in triple-negative breast cancer (TNBC). By actively modeling potential negative side effects utilizing expression data of 29 different, normal human tissues, we were able to develop a highly-specific coverage of TNBC patients with RNA targets. We identified here more than 400 potential tumor-specific antigens suitable for targeted therapy, including several already identified as potential targets for TNBC and other solid tumors. A specific cocktail of MAGEB4, CT83, TLX3, ACTL8, PRDM13 achieved almost 94% patient coverage in TNBC. Overall, these results show that our approach can identify and prioritize TNBC targets suitable for targeted therapy. Therefore, our method has the potential to lead to new and more effective immunotherapeutic cancer treatment.

Genomic Signatures Predict the Immunogenicity of BRCA-Deficient Breast Cancer

PURPOSE

Breast cancers with BRCA1/2 alterations have a relatively high mutational load, suggesting that immune checkpoint blockade may be a potential treatment option. However, the degree of immune cell infiltration varies widely, and molecular features contributing to this variability remain unknown.

EXPERIMENTAL DESIGN

We hypothesized that genomic signatures might predict immunogenicity in BRCA1/2 breast cancers. Using The Cancer Genome Atlas (TCGA) genomic data, we compared breast cancers with (89) and without (770) either germline or somatic BRCA1/2 alterations. We also studied 35 breast cancers with germline BRCA1/2 mutations from Penn using WES and IHC.

RESULTS

We found that homologous recombination deficiency (HRD) scores were negatively associated with expression-based immune indices [cytolytic index (P = 0.04), immune ESTIMATE (P = 0.002), type II IFN signaling (P = 0.002)] despite being associated with a higher mutational/neoantigen burden, in BRCA1/2 mutant breast cancers. Further, absence of allele-specific loss of heterozygosity (LOH negative; P = 0.01) or subclonality (P = 0.003) of germline and somatic BRCA1/2 mutations, respectively, predicted for heightened cytolytic activity. Gene set analysis found that multiple innate and adaptive immune pathways that converge on NF-κB may contribute to this heightened immunogenicity. IHC of Penn breast cancers demonstrated increased CD45⁺ (P = 0.039) and CD8⁺ infiltrates (P = 0.037) and increased PDL1 expression (P = 0.012) in HRD-low or LOH-negative cancers. Triple-negative cancers with low HRD had far greater CD8⁺ T cells (P = 0.0011) and Perforin 1 expression (P = 0.014) compared with hormone receptor-positive HRD-high cancers.

CONCLUSIONS

HRD scores and hormone receptor subtype are predictive of immunogenicity in BRCA1/2 breast cancers and may inform the design of optimal immune therapeutic strategies.

A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma

Immunologic responses to anti-PD-1 therapy in melanoma patients occur rapidly with pharmacodynamic T cell responses detectable in blood by 3 weeks. It is unclear, however, whether these early blood-based observations translate to the tumor microenvironment. We conducted a study of neoadjuvant/adjuvant anti-PD-1 therapy in stage III/IV melanoma. We hypothesized that immune reinvigoration in the tumor would be detectable at 3 weeks and that this response would correlate with disease-free survival. We identified a rapid and potent anti-tumor response, with 8 of 27 patients experiencing a complete or major pathological response after a single dose of anti-PD-1, all of whom remain disease free. These rapid pathologic and clinical responses were associated with accumulation of exhausted CD8 T cells in the tumor at 3 weeks, with reinvigoration in the blood observed as early as 1 week. Transcriptional analysis demonstrated a pretreatment immune signature (neoadjuvant response signature) that was associated with clinical benefit. In contrast, patients with disease recurrence displayed mechanisms of resistance including immune suppression, mutational escape, and/or tumor evolution. Neoadjuvant anti-PD-1 treatment is effective in high-risk resectable stage III/IV melanoma. Pathological response and immunological analyses after a single neoadjuvant dose can be used to predict clinical outcome and to dissect underlying mechanisms in checkpoint blockade.