Prediction of proteasome cleavage motifs by neural networks

RNA vaccines for cancer: Principles to practice

Vaccines are the most impactful medicines to improve health. Though potent against pathogens, vaccines for cancer remain an unfulfilled promise. However, recent advances in RNA technology coupled with scientific and clinical breakthroughs have spurred rapid discovery and potent delivery of tumor antigens at speed and scale, transforming cancer vaccines into a tantalizing prospect. Yet, despite being at a pivotal juncture, with several randomized clinical trials maturing in upcoming years, several critical questions remain: which antigens, tumors, platforms, and hosts can trigger potent immunity with clinical impact? Here, we address these questions with a principled framework of cancer vaccination from antigen detection to delivery. With this framework, we outline features of emergent RNA technology that enable rapid, robust, real-time vaccination with somatic mutation-derived neoantigens-an emerging "ideal" antigen class-and highlight latent features that have sparked the belief that RNA could realize the enduring vision for vaccines against cancer.

NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population

MOTIVATION

Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity.

RESULTS

Here, we present NeoAgDT, a two-step approach consisting of: (i) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (ii) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally validated neoantigens over ranking-based approaches in a study of seven patients.

AVAILABILITY AND IMPLEMENTATION

The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.

MHCSeqNet2-improved peptide-class I MHC binding prediction for alleles with low data

MOTIVATION

The binding of a peptide antigen to a Class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient's immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models.

RESULTS

In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data.

AVAILABILITY AND IMPLEMENTATION

The source code and installation instruction for MHCSeqNet2 are available at https://github.com/cmb-chula/MHCSeqNet2.

Widespread amyloid aggregates formation by Zika virus proteins and peptides

Viral pathogenesis typically involves numerous molecular mechanisms. Protein aggregation is a relatively unknown characteristic of viruses, despite the fact that viral proteins have been shown to form terminally misfolded forms. Zika virus (ZIKV) is a neurotropic one with the potential to cause neurodegeneration. Its protein amyloid aggregation may link the neurodegenerative component to the pathogenicity associated with the viral infection. Therefore, we investigated protein aggregation in the ZIKV proteome as a putative pathogenic route and one of the alternate pathways. We discovered that it contains numerous anticipated aggregation-prone regions in this investigation. To validate our prediction, we used a combination of supporting experimental techniques routinely used for morphological characterization and study of amyloid aggregates. Several ZIKV proteins and peptides, including the full-length envelope protein, its domain III (EDIII) and fusion peptide, Pr N-terminal peptide, NS1 β-roll peptide, membrane-embedded signal peptide 2K, and cytosolic region of NS4B protein, were shown to be highly aggregating in our study. Because our findings show that viral proteins can form amyloids in vitro, we need to do a thorough functional study of these anticipated APRs to understand better the role of amyloids in the pathophysiology of ZIKV infection.

Designing a novel and combinatorial multi-antigenic epitope-based vaccine "MarVax" against Marburg virus-a reverse vaccinology and immunoinformatics approach

CONTEXT

Marburg virus (MARV) is a member of the Filoviridae family and causes Marburg virus disease (MVD) among humans and primates. With fatality rates going up to 88%, there is currently no commercialized cure or vaccine to combat the infection. The National Institute of Allergy and Infectious Diseases (NIAID) classified MARV as priority pathogen A, which presages the need for a vaccine candidate which can provide stable, long-term adaptive immunity. The surface glycoprotein (GP) and fusion protein (FP) mediate the adherence, fusion, and entry of the virus into the host cell via the TIM-I receptor. Being important antigenic determinants, studies reveal that GP and FP are prone to evolutionary mutations, underscoring the requirement of a vaccine construct capable of eliciting a robust and sustained immune response. In this computational study, a reverse vaccinology approach was employed to design a combinatorial vaccine from conserved and antigenic epitopes of essential viral proteins of MARV, namely GP, VP24, VP30, VP35, and VP40 along with an endogenous protein large polymerase (L).

METHODS

Epitopes for T-cell and B-cell were predicted using TepiTool and ElliPro, respectively. The surface-exposed TLRs like TLR2, TLR4, and TLR5 were used to screen high-binding affinity epitopes using the protein-peptide docking platform MdockPeP. The best binding epitopes were selected and assembled with linkers to design a recombinant multi-epitope vaccine construct which was then modeled in Robetta. The in silico biophysical and biochemical analyses of the recombinant vaccine were performed. The docking and MD simulation of the vaccine using WebGro and CABS-Flex against TLRs support the stable binding of vaccine candidates. A virtual immune simulation to check the immediate and long-term immunogenicity was carried out using the C-ImmSim server.

RESULTS

The biochemical characteristics and docking studies with MD simulation establish the recombinant protein vaccine construct MarVax as a stable, antigenic, and potent vaccine molecule. Immune simulation studies reveal 1-year passive immunity which needs to be validated by in vivo studies.

Neo-intline: integrated pipeline enables neoantigen design through the in-silico presentation of T-cell epitope

Neoantigen vaccines are one of the most effective immunotherapies for personalized tumour treatment. The current immunogen design of neoantigen vaccines is usually based on whole-genome sequencing (WGS) and bioinformatics prediction that focuses on the prediction of binding affinity between peptide and MHC molecules, ignoring other peptide-presenting related steps. This may result in a gap between high prediction accuracy and relatively low clinical effectiveness. In this study, we designed an integrated in-silico pipeline, Neo-intline, which started from the SNPs and indels of the tumour samples to simulate the presentation process of peptides in-vivo through an integrated calculation model. Validation on the benchmark dataset of TESLA and clinically validated neoantigens illustrated that neo-intline could outperform current state-of-the-art tools on both sample level and melanoma level. Furthermore, by taking the mouse melanoma model as an example, we verified the effectiveness of 20 neoantigens, including 10 MHC-I and 10 MHC-II peptides. The in-vitro and in-vivo experiments showed that both peptides predicted by Neo-intline could recruit corresponding CD4+ T cells and CD8+ T cells to induce a T-cell-mediated cellular immune response. Moreover, although the therapeutic effect of neoantigen vaccines alone is not sufficient, combinations with other specific therapies, such as broad-spectrum immune-enhanced adjuvants of granulocyte-macrophage colony-stimulating factor (GM-CSF) and polyinosinic-polycytidylic acid (poly(I:C)), or immune checkpoint inhibitors, such as PD-1/PD-L1 antibodies, can illustrate significant anticancer effects on melanoma. Neo-intline can be used as a benchmark process for the design and screening of immunogenic targets for neoantigen vaccines.

Integrated Clinical, Molecular and Immunological Characterization of Pulmonary Sarcomatoid Carcinomas Reveals an Immune Escape Mechanism That May Influence Therapeutic Strategies

Pulmonary sarcomatoid carcinoma (PSC) has highly aggressive biological behaviour and poor clinical outcomes, raising expectations for new therapeutic strategies. We characterized 179 PSC by immunohistochemistry, next-generation sequencing and in silico analysis using a deep learning algorithm with respect to clinical, immunological and molecular features. PSC was more common in men, older ages and smokers. Surgery was an independent factor (p < 0.01) of overall survival (OS). PD-L1 expression was detected in 82.1% of all patients. PSC patients displaying altered epitopes due to processing mutations showed another PD-L1-independent immune escape mechanism, which also significantly influenced OS (p < 0.02). The effect was also maintained when only advanced tumour stages were considered (p < 0.01). These patients also showed improved survival with a significant correlation for immunotherapy (p < 0.05) when few or no processing mutations were detected, although this should be interpreted with caution due to the small number of patients studied. Genomic alterations for which there are already approved drugs were present in 35.4% of patients. Met exon 14 skipping was found more frequently (13.7%) and EGFR mutations less frequently (1.7%) than in other NSCLC. In summary, in addition to the divergent genomic landscape of PSC, the specific immunological features of this prognostically poor subtype should be considered in therapy stratification.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

Amyloidogenic proteins in the SARS-CoV and SARS-CoV-2 proteomes

The phenomenon of protein aggregation is associated with a wide range of human diseases. Our knowledge of the aggregation behaviour of viral proteins, however, is still rather limited. Here, we investigated this behaviour in the SARS-CoV and SARS-CoV-2 proteomes. An initial analysis using a panel of sequence-based predictors suggested the presence of multiple aggregation-prone regions (APRs) in these proteomes and revealed a strong aggregation propensity in some SARS-CoV-2 proteins. We then studied the in vitro aggregation of predicted aggregation-prone SARS-CoV and SARS-CoV-2 proteins and protein regions, including the signal sequence peptide and fusion peptides 1 and 2 of the spike protein, a peptide from the NSP6 protein, and the ORF10 and NSP11 proteins. Our results show that these peptides and proteins can form amyloid aggregates. We used circular dichroism spectroscopy to reveal the presence of β-sheet rich cores in aggregates and X-ray diffraction and Raman spectroscopy to confirm the formation of amyloid structures. Furthermore, we demonstrated that SARS-CoV-2 NSP11 aggregates are toxic to mammalian cell cultures. These results motivate further studies about the possible role of aggregation of SARS proteins in protein misfolding diseases and other human conditions.

Advances of mRNA vaccine in tumor: a maze of opportunities and challenges

High-frequency mutations in tumor genomes could be exploited as an asset for developing tumor vaccines. In recent years, with the tremendous breakthrough in genomics, intelligence algorithm, and in-depth insight of tumor immunology, it has become possible to rapidly target genomic alterations in tumor cell and rationally select vaccine targets. Among a variety of candidate vaccine platforms, the early application of mRNA was limited by instability low efficiency and excessive immunogenicity until the successful development of mRNA vaccines against SARS-COV-2 broken of technical bottleneck in vaccine preparation, allowing tumor mRNA vaccines to be prepared rapidly in an economical way with good performance of stability and efficiency. In this review, we systematically summarized the classification and characteristics of tumor antigens, the general process and methods for screening neoantigens, the strategies of vaccine preparations and advances in clinical trials, as well as presented the main challenges in the current mRNA tumor vaccine development.

An unexplored angle: T cell antigen discoveries reveal a marginal contribution of proteasome splicing to the immunogenic MHC class I antigen pool

In the current era of T cell–based immunotherapies, it is crucial to understand which types of MHC-presented T cell antigens are produced by tumor cells. In addition to linear peptide antigens, chimeric peptides are generated through proteasome-catalyzed peptide splicing (PCPS). Whether such spliced peptides are abundantly presented by MHC is highly disputed because of disagreement in computational analyses of mass spectrometry data of MHC-eluted peptides. Moreover, such mass spectrometric analyses cannot elucidate how much spliced peptides contribute to the pool of immunogenic antigens. In this Perspective, we explain the significance of knowing the contribution of spliced peptides for accurate analyses of peptidomes on one hand, and to serve as a potential source of targetable tumor antigens on the other hand. Toward a strategy for mass spectrometry independent estimation of the contribution of PCPS to the immunopeptidome, we first reviewed methodologies to identify MHC-presented spliced peptide antigens expressed by tumors. Data from these identifications allowed us to compile three independent datasets containing 103, 74, and 83 confirmed T cell antigens from cancer patients. Only 3.9%, 1.4%, and between 0% and 7.2% of these truly immunogenic antigens are produced by PCPS, therefore providing a marginal contribution to the pool of immunogenic tumor antigens. We conclude that spliced peptides will not serve as a comprehensive source to expand the number of targetable antigens for immunotherapies.

The Importance of Being Presented: Target Validation by Immunopeptidomics for Epitope-Specific Immunotherapies

Presentation of tumor-specific or tumor-associated peptides by HLA class I molecules to CD8⁺ T cells is the foundation of epitope-centric cancer immunotherapies. While often in silico HLA binding predictions or in vitro immunogenicity assays are utilized to select candidates, mass spectrometry-based immunopeptidomics is currently the only method providing a direct proof of actual cell surface presentation. Despite much progress in the last decade, identification of such HLA-presented peptides remains challenging. Here we review typical workflows and current developments in the field of immunopeptidomics, highlight the challenges which remain to be solved and emphasize the importance of direct target validation for clinical immunotherapy development.

Harnessing Fuzzy Rule Based System for Screening Major Histocompatibility Complex Class I Peptide Epitopes from the Whole Proteome: An Implementation on the Proteome of Leishmania donovani

The development of peptide-based vaccines is enhanced by immunoinformatics, which predicts the patterns that B cells and T cells recognize. Although several tools are available for predicting the Major histocompatibility complex (MHC-I) binding peptides, the wide variants of human leucocyte antigen allele make it challenging to choose a peptide that will induce an immune response in a majority of people. In addition, for a peptide to be considered a potential vaccine candidate, factors such as T cell affinity, proteasome cleavage, and similarity to human proteins also play a major role. Identifying peptides that satisfy the earlier cited measures across the entire proteome is, therefore, challenging. Hence, the fuzzy inference system (FIS) is proposed to detect each peptide's potential as a vaccine candidate and assign it either a very high, high, moderate, or low ranking. The FIS includes input features from 6 modules (binding of 27 major alleles, T cell propensity, pro-inflammatory response, proteasome cleavage, transporter associated with antigen processing, and similarity with human peptide) and rules derived from an observation of features on positive samples. On validation of experimentally verified peptides, a balanced accuracy of ∼80% was achieved, with a Mathew's correlation coefficient score of 0.67 and an F-1 score of 0.74. In addition, the method was implemented on complete proteome of Leishmania donovani, which contains ∼4,800,000 peptides. Lastly, a searchable database of the ranked results of the L. donovani proteome was made and is available online (MHC-FIS-LdDB). It is hoped that this method will simplify the identification of potential MHC-I binding candidates from a large proteome.

Identification of neoantigens for individualized therapeutic cancer vaccines

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.

Proteasomal Processing Immune Escape Mechanisms in Platinum-Treated Advanced Bladder Cancer

In recent years, the number and type of treatment options in advanced bladder cancer (BC) have been rapidly evolving. To select an effective therapy and spare unnecessary side effects, predictive biomarkers are urgently needed. As the host’s anti-cancer immune response is by far the most effective system to impede malignant tumor growth, immune system-based biomarkers are promising. We have recently described altered proteasomal epitope processing as an effective immune escape mechanism to impair cytotoxic T-cell activity. By altering the neoantigens’ characteristics through different proteasomal peptide cleavage induced by non-synonymous somatic mutations, the ability for T-cell activation was decreased (“processing escapes”). In the present study, we analyzed primary chemo-naïve tissue samples of 26 adjuvant platinum-treated urothelial BC patients using a targeted next-generation sequencing panel followed by the epitope determination of affected genes, a machine-learning based prediction of epitope processing and proteasomal cleavage and of HLA-affinity as well as immune activation. Immune infiltration (immunohistochemistries for CD8, granzyme B, CD45/LCA) was digitally quantified by a pathologist and clinico-pathological and survival data were collected. We detected 145 epitopes with characteristics of a processing escape associated with a higher number of CD8-positive but lower number of granzyme B-positive cells and no association with PD-L1-expression. In addition, a high prevalence of processing escapes was associated with unfavorable overall survival. Our data indicate the presence of processing escapes in advanced BC, potentially creating a tumor-promoting pro-inflammatory environment with lowered anti-cancerous activity and independence from PD-L1-expression. The data also need to be prospectively validated in BC treated with immune therapy.

Development of antigen-prediction algorithm for personalized neoantigen vaccine using human leukocyte antigen transgenic mouse

Immunotherapy is currently recognized as the fourth modality in cancer therapy. CTLs can detect cancer cells via complexes involving human leukocyte antigen (HLA) class I molecules and peptides derived from tumor antigens, resulting in antigen-specific cancer rejection. The peptides may be predicted in silico using machine learning-based algorithms. Neopeptides, derived from neoantigens encoded by somatic mutations in cancer cells, are putative immunotherapy targets, as they have high tumor specificity and immunogenicity. Here, we used our pipeline to select 278 neoepitopes with high predictive "SCORE" from the tumor tissues of 46 patients with hepatocellular carcinoma or metastasis of colorectal carcinoma. We validated peptide immunogenicity and specificity by in vivo vaccination with HLA-A2, A24, B35, and B07 transgenic mice using ELISpot assay, in vitro and in vivo killing assays. We statistically evaluated the power of our prediction algorithm and demonstrated the capacity of our pipeline to predict neopeptides (area under the curve = 0.687, p < 0.0001). We also analyzed the potential of long peptides containing the predicted neoepitopes to induce CTLs. Our study indicated that the short peptides predicted using our algorithm may be intrinsically present in tumor cells as cleavage products of long peptides. Thus, we empirically demonstrated that the accuracy and specificity of our prediction tools may be potentially improved in vivo using the HLA transgenic mouse model. Our data will help to feedback algorithms to improve in silico prediction, potentially allowing researchers to predict peptides for personalized immunotherapy.

Digital gene expression analysis of NSCLC-patients reveals strong immune pressure, resulting in an immune escape under immunotherapy

BACKGROUND

Immune checkpoint inhibitors (ICIs) are currently one of the most promising therapy options in the field of oncology. Although the first pivotal ICI trial results were published in 2011, few biomarkers exist to predict their therapy outcome. PD-L1 expression and tumor mutational burden (TMB) were proven to be sometimes-unreliable biomarkers. We have previously suggested the analysis of processing escapes, a qualitative measurement of epitope structure alterations under immune system pressure, to provide predictive information on ICI response. Here, we sought to further validate this approach and characterize interactions with different forms of immune pressure.

METHODS

We identified a cohort consisting of 48 patients with advanced non-small cell lung cancer (NSCLC) treated with nivolumab as ICI monotherapy. Tumor samples were subjected to targeted amplicon-based sequencing using a panel of 22 cancer-associated genes covering 98 mutational hotspots. Altered antigen processing was predicted by NetChop, and MHC binding verified by NetMHC. The NanoString nCounter® platform was utilized to provide gene expression data of 770 immune-related genes. Patient data from 408 patients with NSCLC were retrieved from The Cancer Genome Atlas (TCGA) as a validation cohort.

RESULTS

The two immune escape mechanisms of PD-L1 expression (TPS score) (n = 18) and presence of altered antigen processing (n = 10) are mutually non-exclusive and can occur in the same patient (n = 6). Both mechanisms have exclusive influence on different genes and pathways, according to differential gene expression analysis and gene set enrichment analysis, respectively. Interestingly, gene expression patterns associated with altered processing were enriched in T cell and NK cell immune activity. Though both mechanisms influence different genes, they are similarly linked to increased immune activity.

CONCLUSION

Pressure from the immune system will lay the foundations for escape mechanisms, leading to acquisition of resistance under therapy. Both PD-L1 expression and altered antigen processing are induced similarly by pronounced immunoactivity but in different context. The present data help to deepen our understanding of the underlying mechanisms behind those immune escapes.

Identification of Neoantigens in Two Murine Gastric Cancer Cell Lines Leading to the Neoantigen-Based Immunotherapy

Simple Summary

Despite the success of immune checkpoint inhibitors (ICI) for treating a variety of solid cancers, most gastric cancer patients are resistant to ICI monotherapies. Combinations of ICI with other therapies may be able to overcome this resistance. In order to develop combination immunotherapies, immunologically well-characterized preclinical gastric cancer models are required. To this end, in the present study, we characterized two murine gastric cancer cell lines, namely, YTN2 which spontaneously regresses, and YTN16 which grows progressively. Although anti-CTLA-4 monotherapy eradicated most YTN16 tumors, these were resistant to either anti-PD-1 or anti-PD-L1 treatment. Furthermore, we identified neoantigens in YTN2 and YTN16 tumors and conducted neoantigen-based immunotherapy for these tumors. In addition, the information on neoantigens facilitates the evaluation of tumor-specific immune responses induced by the combination therapies. These immunologically well-characterized gastric cancer models will contribute to the development of novel combination immunotherapies.

Abstract

To develop combination immunotherapies for gastric cancers, immunologically well-characterized preclinical models are crucial. Here, we leveraged two transplantable murine gastric cancer cell lines, YTN2 and YTN16, derived from the same parental line but differing in their susceptibility to immune rejection. We established their differential sensitivity to immune checkpoint inhibitors (ICI) and identified neoantigens. Although anti-CTLA-4 mAbs eradicated YTN16 tumors in 4 of 5 mice, anti-PD-1 and anti-PD-L1 mAbs failed to eradicate YTN16 tumors. Using whole-exome and RNA sequencing, we identified two and three neoantigens in YTN2 and YTN16, respectively. MHC class I ligandome analysis detected the expression of only one of these neoantigens, mutated Cdt1, but the exact length of MHC binding peptide was determined. Dendritic cell vaccine loaded with neoepitope peptides and adoptive transfer of neoantigen-specific CD8⁺ T cells successfully inhibited the YTN16 tumor growth. Targeting mutated Cdt1 had better efficacy for controlling the tumor. Therefore, mutated Cdt1 was the dominant neoantigen in these tumor cells. More mCdt1 peptides were bound to MHC class I and presented on YTN2 surface than YTN16. This might be one of the reasons why YTN2 was rejected while YTN16 grew in immune-competent mice.

Development of synthetic antigen vaccines for COVID-19

The current pandemic called COVID-19 caused by the SARS-CoV-2 virus brought the need for the search for fast alternatives to both control and fight the SARS-CoV-2 infection. Therefore, a race for a vaccine against COVID-19 took place, and some vaccines have been approved for emergency use in several countries in a record time. Ongoing prophylactic research has sought faster, safer, and precise alternatives by redirecting knowledge of other vaccines, and/or the development of new strategies using available tools, mainly in the areas of genomics and bioinformatics. The current review highlights the development of synthetic antigen vaccines, focusing on the usage of bioinformatics tools for the selection and construction of antigens on the different vaccine constructions under development, as well as strategies to optimize vaccines for COVID-19.

A T-Cell Epitope-Based Multi-Epitope Vaccine Designed Using Human HLA Specific T Cell Epitopes Induces a Near-Sterile Immunity against Experimental Visceral Leishmaniasis in Hamsters

Visceral leishmaniasis is a neglected tropical disease affecting 12 million people annually. Even in the second decade of the 21st century, it has remained without an effective vaccine for human use. In the current study, we designed three multiepitope vaccine candidates by the selection of multiple IFN-γ inducing MHC-I and MHC-II binder T-cell specific epitopes from three previously identified antigen genes of Leishmania donovani from our lab by an immuno-informatic approach using IFNepitope, the Immune Epitope Database (IEDB) T cell epitope identification tools, NET-MHC-1, and NET MHC-2 webservers. We tested the protective potential of these three multiepitope proteins as a vaccine in a hamster model of visceral leishmaniasis. The immunization data revealed that the vaccine candidates induced a very high level of Th1 biased protective immune response in-vivo in a hamster model of experimental visceral leishmaniasis, with one of the candidates inducing a near-sterile immunity. The vaccinated animals displayed highly activated monocyte macrophages with the capability of clearing intracellular parasites due to increased respiratory burst. Additionally, these proteins induced activation of polyfunctional T cells secreting INF-γ, TNF-α, and IL-2 in an ex-vivo stimulation of human peripheral blood mononuclear cells, further supporting the protective nature of the designed candidates.

Multi-Subunit SARS-CoV-2 Vaccine Design Using Evolutionarily Conserved T- and B- Cell Epitopes

The SARS-CoV-2 pandemic has created a public health crisis worldwide. Although vaccines against the virus are efficiently being rolled out, they are proving to be ineffective against certain emerging SARS-CoV-2 variants. The high degree of sequence similarity between SARS-CoV-2 and other human coronaviruses (HCoV) presents the opportunity for designing vaccines that may offer protection against SARS-CoV-2 and its emerging variants, with cross-protection against other HCoVs. In this study, we performed bioinformatics analyses to identify T and B cell epitopes originating from spike, membrane, nucleocapsid, and envelope protein sequences found to be evolutionarily conserved among seven major HCoVs. Evolutionary conservation of these epitopes indicates that they may have critical roles in viral fitness and are, therefore, unlikely to mutate during viral replication thus making such epitopes attractive candidates for a vaccine. Our designed vaccine construct comprises of twelve T and six B cell epitopes that are conserved among HCoVs. The vaccine is predicted to be soluble in water, stable, have a relatively long half-life, and exhibit low allergenicity and toxicity. Our docking results showed that the vaccine forms stable complex with toll-like receptor 4, while the immune simulations predicted that the vaccine may elicit strong IgG, IgM, and cytotoxic T cell responses. Therefore, from multiple perspectives, our multi-subunit vaccine design shows the potential to elicit a strong immune-protective response against SARS-CoV-2 and its emerging variants while carrying minimal risk for causing adverse effects.

NetCleave: an open-source algorithm for predicting C-terminal antigen processing for MHC-I and MHC-II

Antigens presented on the cell surface have been subjected to multiple biological processes. Among them, C-terminal antigen processing constitutes one of the main bottlenecks of the peptide presentation pathways, as it delimits the peptidome that will be subjected downstream. Here, we present NetCleave, an open-source and retrainable algorithm for the prediction of the C-terminal antigen processing for both MHC-I and MHC-II pathways. NetCleave architecture consists of a neural network trained on 46 different physicochemical descriptors of the cleavage site amino acids. Our results demonstrate that prediction of C-terminal antigen processing achieves high accuracy on MHC-I (AUC of 0.91), while it remains challenging for MHC-II (AUC of 0.66). Moreover, we evaluated the performance of NetCleave and other prediction tools for the evaluation of four independent immunogenicity datasets (H2-Db, H2-Kb, HLA-A*02:01 and HLA-B:07:02). Overall, we demonstrate that NetCleave stands out as one of the best algorithms for the prediction of C-terminal processing, and we provide one of the first evidence that C-terminal processing predictions may help in the discovery of immunogenic peptides.

HBV S antigen evolution in the backdrop of HDV infection affects epitope processing and presentation

INTRODUCTION

HBV can evolve under selection pressure exerted by drugs and/or host immunity, resulting in accumulation of escape mutations that can affect the drug or the immune activity. Hepatitis delta virus (HDV) coinfection is also known to exert selection pressure on HBV, which leads to selective amplification of certain mutations, especially in genes that are required for HDV pathogenesis, such as HBsAg. However, little is known about the function of these mutations on HBV or HDV life cycle. The purpose of this study is to determine mutations selectively amplified in the backdrop of HDV, and how these mutations affect processing of CD4- and CD8-T cell epitopes.

METHODS

HBsAg was successfully amplified from 49/50 HBV mono- and 36/50 coinfected samples. The sequences were used to identify mutations specific to each study group, followed by an in silico analysis to determine the effect of these mutations on (1) proteasomal degradation, (2) MHC-I and MHC-II biding, and (3) processing of T-cell epitopes.

RESULTS

HBV-HDV coinfected sequences exhibited certain unique mutations in HBsAg genes. Some of these mutations affected the generation of proteasomal sites, binding of HBsAg epitopes to MHC-I and -II ligands, and subsequent generation of T- cell epitopes.

CONCLUSION

These observations suggest that HBV selectively amplifies certain mutations in the backdrop of HDV coinfection. Selective amplification of these mutations at certain strategic locations might not only enable HBV to counteract the inhibitory effects of HDV on HBV replication but also facilitate its survival by escaping the immune response.

Exploring the cancer-testis antigen BORIS to design a novel multi-epitope vaccine against breast cancer based on immunoinformatics approaches

Recently, cancer immunotherapy has gained lots of attention to replace the current chemoradiation approaches and multi-epitope cancer vaccines are manifesting as the next generation of cancer immunotherapy. Therefore, in this study, we used multiple immunoinformatics approaches along with other computational approaches to design a novel multi-epitope vaccine against breast cancer. The most immunogenic regions of the BORIS cancer-testis antigen were selected according to the binding affinity to MHC-I and II molecules as well as containing multiple cytotoxic T lymphocyte (CTL) epitopes by multiple immunoinformatics servers. The selected regions were linked together by GPGPG linker. Also, a T helper epitope (PADRE) and the TLR-4/MD-2 agonist (L7/L12 ribosomal protein from mycobacterium) were incorporated by A(EAAAK)3A linker to form the final vaccine construct. Then, its physicochemical properties, cleavage sites, TAP transport efficiency, B cell epitopes, IFN-γ inducing epitopes and population coverage were predicted. The final vaccine construct was reverse translated, codon-optimized and inserted into pcDNA3.1 to form the DNA vaccine. The final vaccine construct was a stable, immunogenic and non-allergenic protein that contained numerous CTL epitopes, IFN-γ inducing epitopes and several linear and conformational B cell epitopes. Also, the final vaccine construct formed stable and significant interactions with TLR-4/MD-2 complex according to molecular docking and dynamics simulations. Moreover, its world population coverage for HLA-I and HLA-II were about 93% and 96%, respectively. Taking together, these preliminary results can be used as an appropriate platform for further experimental investigations.Communicated by Ramaswamy H. Sarma.

Hemopressin as a breakthrough for the cannabinoid field

Hemopressin (PVNFKFLSH in rats, and PVNFKLLSH in humans and mice), a fragment derived from the α-chain of hemoglobin, was the first peptide described to have type 1 cannabinoid receptor activity. While hemopressin was shown to have inverse agonist/antagonistic activity, extended forms of hemopressin (i.e. RVD-hemopressin, also called pepcan-12) exhibit type 1 and type 2 cannabinoid receptor agonistic/allosteric activity, and recent studies suggest that they can activate intracellular mitochondrial cannabinoid receptors. Therefore, hemopressin and hemopressin-related peptides could have location-specific and biased pharmacological action, which would increase the possibilities for fine-tunning and broadening cannabinoid receptor signal transduction. Consistent with this, hemopressins were shown to play a role in a number of physiological processes including antinociceptive and anti-inflammatory activity, regulation of food intake, learning and memory. The shortest active hemopressin fragment, NFKF, delays the first seizure induced by pilocarpine, and prevents neurodegeneration in an experimental model of autoimmune encephalomyelitis. These functions of hemopressins could be due to engagement of both cannabinoid and non-cannabinoid receptor systems. Self-assembled nanofibrils of hemopressin have pH-sensitive switchable surface-active properties, and show potential as inflammation and cancer targeted drug-delivery systems. Upon disruption of the self-assembled hemopressin nanofibril emulsion, the intrinsic analgesic and anti-inflammatory properties of hemopressin could help bolster the therapeutic effect of anti-inflammatory or anti-cancer formulations. In this article, we briefly review the molecular and behavioral pharmacological properties of hemopressins, and summarize studies on the intricate and unique mode of generation and binding of these peptides to cannabinoid receptors. Thus, the review provides a window into the current status of hemopressins in expanding the repertoire of signaling and activity by the endocannabinoid system, in addition to their new potential for pharmaceutic formulations.

Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion

Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools.

Identification of CD8+ T cell epitopes through proteasome cleavage site predictions

Background

We previously introduced PCPS (Proteasome Cleavage Prediction Server), a web-based tool to predict proteasome cleavage sites using n-grams. Here, we evaluated the ability of PCPS immunoproteasome cleavage model to discriminate CD8⁺ T cell epitopes.

Results

We first assembled an epitope dataset consisting of 844 unique virus-specific CD8⁺ T cell epitopes and their source proteins. We then analyzed cleavage predictions by PCPS immunoproteasome cleavage model on this dataset and compared them with those provided by a related method implemented by NetChop web server. PCPS was clearly superior to NetChop in term of sensitivity (0.89 vs. 0.79) but somewhat inferior with regard to specificity (0.55 vs. 0.60). Judging by the Mathew’s Correlation Coefficient, PCPS predictions were overall superior to those provided by NetChop (0.46 vs. 0.39). We next analyzed the power of C-terminal cleavage predictions provided by the same PCPS model to discriminate CD8⁺ T cell epitopes, finding that they could be discriminated from random peptides with an accuracy of 0.74. Following these results, we tuned the PCPS web server to predict CD8⁺ T cell epitopes and predicted the entire SARS-CoV-2 epitope space.

Conclusions

We report an improved version of PCPS named iPCPS for predicting proteasome cleavage sites and peptides with CD8⁺ T cell epitope features. iPCPS is available for free public use at https://imed.med.ucm.es/Tools/pcps/.

Sub-genomic analysis of Chikungunya virus E2 mutations in Pakistani isolates potentially modulating B-cell & T-Cell immune response

Background & Objectives:

The Chikungunya virus (CHIKV) transmitted to the humans through Aedes species of the mosquitoes. In December 2016, a severe outbreak reported from Pakistan. However, there is no vaccine or anti-viral treatment currently available so host immune response against CHIKV gained significant interest. Therefore, this study was conducted to identify the mutations in CHIKV E2 region of currently circulating Pakistani strains & determine their potential immunogenicity in Pakistani population.

Methods:

It was a cross sectional study in which a total of 60 CHIKV PCR positive samples were collected from Molecular Department of Pathology, Dow University of Health Sciences (DUHS), Karachi during November 2017 to February 2018. CHIKV E2 gene was amplified by PCR & sequenced. Sequences were analyzed by using bioinformatic tools followed by epitope prediction in E2 sequences by In-silico immunoinformatic approach.

Results:

Several single nucleotide variations (SNVs) were identified in Pakistani isolates with six novel mutations in E2 sequences. Immunoinformatic analyses showed more proteasomal sites, CTL & B-Cell epitopes in Pakistani strains with respect to S27 prototype with 69.4% population coverage against these epitopes in Pakistan. The study also identified key mutations responsible for generation of unique epitopes and HLA restriction in Pakistani isolates. The strain specific mutations revealed the current outbreak was caused by ESCA.IOL lineage of CHIKV.

Conclusion:

The evolution of E2 protein in Pakistani strains has increased its immunogenicity in comparison to ancestral s27 strain. The identification of most immunogenic and conserved epitopes with high population coverage has high potential to be used in vaccine development against these local strains.

Comprehensive analysis of cutaneous and uveal melanoma liver metastases

BACKGROUND

The profound disparity in response to immune checkpoint blockade (ICB) by cutaneous melanoma (CM) and uveal melanoma (UM) patients is not well understood. Therefore, we characterized metastases of CM and UM from the same metastatic site (liver), in order to dissect the potential underlying mechanism in differential response on ICB.

METHODS

Tumor liver samples from CM (n=38) and UM (n=28) patients were analyzed at the genomic (whole exome sequencing), transcriptional (RNA sequencing) and protein (immunohistochemistry and GeoMx Digital Spatial Profiling) level.

RESULTS

Comparison of CM and UM metastases from the same metastatic site revealed that, although originating from the same melanocyte lineage, CM and UM differed in somatic mutation profile, copy number profile, tumor mutational burden (TMB) and consequently predicted neoantigens. A higher melanin content and higher expression of the melanoma differentiation antigen MelanA was observed in liver metastases of UM patients. No difference in B2M and human leukocyte antigen-DR (HLA-DR) expression was observed. A higher expression of programmed cell death ligand 1 (PD-L1) was found in CM compared with UM liver metastases, although the majority of CM and UM liver metastases lacked PD-L1 expression. There was no difference in the extent of immune infiltration observed between CM and UM metastases, with the exception of a higher expression of CD163 (p<0.0001) in CM liver samples. While the extent of immune infiltration was similar for CM and UM metastases, the ratio of exhausted CD8 T cells to cytotoxic T cells, to total CD8 T cells and to Th1 cells, was significantly higher in UM metastases.

CONCLUSIONS

While TMB was different between CM and UM metastases, tumor immune infiltration was similar. The greater dependency on PD-L1 as an immune checkpoint in CM and the identification of higher exhaustion ratios in UM may both serve as explanations for the difference in response to ICB. Consequently, in order to improve current treatment for metastatic UM, reversal of T cell exhaustion beyond programmed cell death 1 blockade should be considered.

POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction

Whole genome/exome sequencing data for tumors are now abundant, and many tumor antigens, especially mutant antigens (neoantigens), have been identified for cancer immunotherapy. However, only a small fraction of the peptides from these antigens induce cytotoxic T cell responses. Therefore, efficient methods to identify these antigenic peptides are crucial. The current models of major histocompatibility complex (MHC) binding and antigenic prediction are still inaccurate. In this study, 360 9-mer peptides with verified immunological activity were selected to construct a prediction of tumor neoantigen (POTN) model, an immunogenic prediction model specifically for the human leukocyte antigen-A2 allele. Based on the physicochemical properties of amino acids, such as the residue propensity, hydrophobicity, and organic solvent/water, we found that the predictive capability of POTN is superior to that of the prediction programs SYPEITHI, IEDB, and NetMHCpan 4.0. We used POTN to screen peptides for the cancer-testis antigen located on the X chromosome, and we identified several peptides that may trigger immunogenicity. We synthesized and measured the binding affinity and immunogenicity of these peptides and found that the accuracy of POTN is higher than that of NetMHCpan 4.0. Identifying the properties related to the T cell response or immunogenicity paves the way to understanding the MHC/peptide/T cell receptor complex. In conclusion, POTN is an efficient prediction model for screening high-affinity immunogenic peptides from tumor antigens, and thus provides useful information for developing cancer immunotherapy.

Graph-theoretical formulation of the generalized epitope-based vaccine design problem

Epitope-based vaccines have revolutionized vaccine research in the last decades. Due to their complex nature, bioinformatics plays a pivotal role in their development. However, existing algorithms address only specific parts of the design process or are unable to provide formal guarantees on the quality of the solution. We present a unifying formalism of the general epitope vaccine design problem that tackles all phases of the design process simultaneously and combines all prevalent design principles. We then demonstrate how to formulate the developed formalism as an integer linear program, which guarantees optimality of the designs. This makes it possible to explore new regions of the vaccine design space, analyze the trade-offs between the design phases, and balance the many requirements of vaccines.

A Novel Epitope Quality-Based Immune Escape Mechanism Reveals Patient's Suitability for Immune Checkpoint Inhibition

Background

Immune checkpoint inhibition, especially the blockade of PD-1 and PD-L1, has become one of the most thriving therapeutic approaches in modern oncology. Immune evasion caused by altered tumor epitope processing (so-called processing escapes) may be one way to explain immune checkpoint inhibition therapy failure. In the present study, we aim to demonstrate the effects of processing escapes on immunotherapy outcome in NSCLC patients.

Patients and Methods

Whole exome sequencing data of 400 NSCLC patients (AdC and SCC) were extracted from the TCGA database. The ICB cohort was composed of primary tumor probes from 48 NSCLC patients treated with nivolumab. Mutations were identified by targeted amplicon-based sequencing including hotspots and whole exomes of 22 genes. The effect of mutations on proteasomal processing was evaluated by deep learning methods previously trained on 1260 known MHC-I ligands. Cox regression modelling was used to determine the influence on overall survival.

Results

In the TCGA cohort, processing escapes were associated with decreased overall survival (p= 0.0140). In the ICB cohort, patients showing processing escapes in combination with high levels of PD-L1 (n=8/48) also showed significantly decreased overall survival, independently of mutational load or PD-L1 status.

Conclusion

The concept of altered epitope processing may help to understand immunotherapy failure. Especially when combined with PD-L1 status, this method can be used as a biomarker to identify patients not suitable for immunotherapy.

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.

INeo-Epp: A Novel T-Cell HLA Class-I Immunogenicity or Neoantigenic Epitope Prediction Method Based on Sequence-Related Amino Acid Features

In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g., proteasomal cleavage, transporter associated with antigen processing (TAP), and major histocompatibility complex (MHC) combination. To date, however, the mechanism for the immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties, and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including the amino acid physicochemical property of the epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score, and frequency score for an immunogenic peptide. Subsequently, a random forest classifier for T-cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC = 0.81, external validation data set AUC = 0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called "neoantigens." Based on mutation information and sequence-related amino acid characteristics, a prediction model of a neoantigen was established as well (the optimal AUC = 0.78). Further, an easy-to-use web-based tool "INeo-Epp" was developed for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

DeepLigand: accurate prediction of MHC class I ligands using peptide embedding

Motivation

The computational modeling of peptide display by class I major histocompatibility complexes (MHCs) is essential for peptide-based therapeutics design. Existing computational methods for peptide-display focus on modeling the peptide-MHC-binding affinity. However, such models are not able to characterize the sequence features for the other cellular processes in the peptide display pathway that determines MHC ligand selection.

Results

We introduce a semi-supervised model, DeepLigand that outperforms the state-of-the-art models in MHC Class I ligand prediction. DeepLigand combines a peptide language model and peptide binding affinity prediction to score MHC class I peptide presentation. The peptide language model characterizes sequence features that correspond to secondary factors in MHC ligand selection other than binding affinity. The peptide embedding is learned by pre-training on natural ligands, and can discriminate between ligands and non-ligands in the absence of binding affinity prediction. Although conventional affinity-based models fail to classify peptides with moderate affinities, DeepLigand discriminates ligands from non-ligands with consistently high accuracy.

Availability and implementation

We make DeepLigand available at https://github.com/gifford-lab/DeepLigand.

Supplementary information

Supplementary data are available at Bioinformatics online.

Aggregation Propensities of Herpes Simplex Virus-1 Proteins and Derived Peptides: An In Silico and In Vitro Analysis

Recurrent infections of neurotropic herpes simplex virus-1 (HSV-1) have been implicated in etiology and pathology of Alzheimer's disease (AD). Although protein and peptide aggregation events are at the center of the AD pathophysiology, except a single study where a peptide derived from glycoprotein B of HSV-1 was reported to form β-amyloid-like aggregates, similar investigations with the entire proteome of HSV-1 have not been attempted. In the current study, 70 HSV-1 proteins were screened using bioinformatics tools to identify aggregation-prone candidates. Thereafter, the 20S proteasome cleavage sites within the sequence of the selected proteins were determined using Pcleavage and NetChop algorithms, thereby mimicking a cellular proteasomal activity providing short peptides. Here, we report the biochemical characterization of a 28-residue-long peptide (HSV-1 gK_208-235) derived from glycoprotein K of HSV-1. The peptide showed high aggregation propensity and homology to the C-terminus of Aβ_1-42 peptide. The aggregates of gK_208-235 peptide were characterized by the Congo red and Thioflavin T assays and Fourier transform infrared (FTIR) spectroscopy, and their spheroid oligomeric structure was established by atomic force microscopy (AFM). Furthermore, the aggregates demonstrated dose-dependent cytotoxicity to primary mouse splenocytes. The current findings hypothesize a mechanism by which HSV-1 may contribute to AD, which may be pursued further in the future.

Analysis of predicted proteasomal cleavages in the methyltransferase domain from JEV

The methyltransferase (MTase, a 265 amino acid residues long region at the N-terminal end of the viral nonfunctional supermolecule NS5 domain) is key for viral replication in Japanese Encephalitis Virus (JEV). Sequence to structure to functional information with adequate knowledge on MTase from JEV is currently limited. Therefore, it is of interest to document a report on the comprehensive analysis of predicted proteasomal cleavage data in the methyltransferase domain from JEV. This data is relevant in the design and development of vaccine and other therapeutic candidates for further consideration.

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.

Characterizing MHC-I Genotype Predictive Power for Oncogenic Mutation Probability in Cancer Patients

MHC class I proteins present intracellular peptides on the cell's surface, enabling the immune system to recognize tumor-specific neoantigens of early neoplastic cells and eliminate them before the tumor develops further. However, variability in peptide-MHC-I affinity results in variable presentation of oncogenic peptides, leading to variable likelihood of immune evasion across both individuals and mutations. Since the major determinant of peptide-MHC-I affinity in patients is individual MHC-I genotype, we developed a residue-centric presentation score taking both mutated residues and MHC-I genotype into account and hypothesized that high scores (which correspond to poor presentation) would correlate to high mutation frequencies within tumors. We applied our scoring system to 9176 tumor samples from TCGA across 1018 recurrent mutations and found that, indeed, presentation scores predicted mutation probability. These findings open the door to more personalized treatment plans based on simple genotyping. Here, we outline the computational tools and statistical methods used to arrive at this conclusion.

PCPS: A Web Server to Predict Proteasomal Cleavage Sites

The proteasome complex is mainly responsible for proteolytic degradation of cytosolic proteins, generating the C-terminus of MHC I-restricted peptide ligands and CD8 T cell epitopes. Therefore, prediction of proteasomal cleavage sites is relevant for anticipating CD8 T-cell epitopes. There are two different proteasomes, the constitutive proteasome, expressed in all types of cells, and the immunoproteasome, constitutively expressed in dendritic cells. Although both proteasome forms generate peptides for presentation by MHC I molecules, the immunoproteasome is the main form involved in providing peptide fragments for priming CD8 T cells. On the contrary, the proteasome provides peptides for presentation by MHC I molecules that can be targeted by already primed CD8 T cells. Proteasome cleavage prediction server (PCPS) is a server for predicting cleavage sites generated by both the constitutive proteasome and the immunoproteasome. Here, we illustrate the usage of PCPS to predict proteasome and immunoproteasome cleavage sites and compare the results with those provided by NetChop, a related tool available online. PCPS is implemented for free public use available online at http://imed.med.ucm.es/Tools/pcps/ .

Personalized Neo-Epitope Vaccines for Cancer Treatment

After more than a century of efforts to establish cancer immunotherapy in clinical practice, the advent of checkpoint inhibition (CPI) therapy was a critical breakthrough toward this direction (Hodi et al. in Cell Rep 13(2):412-424, 2010; Wolchok et al. in N Engl J Med 369(2):122-133, 2013; Herbst et al. in Nature 515(7528):563-567, 2014; Tumeh et al. in Nature 515(7528):568-571, 2014). Further, CPIs shifted the focus from long studied shared tumor-associated antigens to mutated ones. As cancer is caused by mutations in somatic cells, the concept to utilize these correlates of 'foreignness' to enable recognition and lysis of the cancer cell by T cell immunity seems an obvious thing to do.

Peptide-based vaccine successfully induces protective immunity against canine visceral leishmaniasis

Dogs are the main reservoir of zoonotic visceral leishmaniasis. Vaccination is a promising approach to help control leishmaniasis and to interrupt transmission of the Leishmania parasite. The promastigote surface antigen (PSA) is a highly immunogenic component of Leishmania excretory/secretory products. A vaccine based on three peptides derived from the carboxy-terminal part of Leishmania amazonensis PSA and conserved among Leishmania species, formulated with QA-21 as adjuvant, was tested on naive Beagle dogs in a preclinical trial. Four months after the full course of vaccination, dogs were experimentally infected with Leishmania infantum promastigotes. Immunization of dogs with peptide-based vaccine conferred immunity against experimental infection with L. infantum. Evidence for macrophage nitric oxide production and anti-leishmanial activity associated with IFN-γ production by lymphocytes was only found in the vaccinated group. An increase in specific IgG2 antibodies was also measured in vaccinated dogs from 2 months after immunization. Additionally, after challenge with L. infantum, the parasite burden was significantly lower in vaccinated dogs than in the control group. These data strongly suggest that this peptide-based vaccine candidate generated cross-protection against zoonotic leishmaniasis by inducing a Th1-type immune response associated with production of specific IgG2 antibodies. This preclinical trial including a peptide-based vaccine against leishmaniasis clearly demonstrates effective protection in a natural host. This approach deserves further investigation to enhance the immunogenicity of the peptides and to consider the possible engineering of a vaccine targeting several Leishmania species.

Leishmaniasis, caused by the protozoan parasite Leishmania, can present in different forms depending on the infecting species. Visceral leishmaniasis is associated with migration of the parasite, in this case Leishmania infantum, to various organs and can infect both humans and canids. Here Rachel Bras-Gonçalves and colleagues test a Leishmania vaccine for dogs as they are the main reservoir for this zoonotic disease. The vaccine is based on the abundant immunogenic component of Leishmania excretory/secretory product, promastigote surface antigen (PSA); specifically, three peptides from the carboxyl-terminal of PSA, which is conserved in Leishmania species. Uninfected Beagle dogs were immunized with QA-21 as an adjuvant, and no local or systemic adverse reactions were observed. Four months later after three doses of the vaccine, dogs were infected with L. infantum promastigotes. Vaccination provided immunity with reduced parasite burden and this was associated with macrophage anti-leishmanial activity, increased IFN-y and nitric oxide production and increased Leishmania-specific IgG2 antibodies.

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.