The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

MONET: a database for prediction of neoantigens derived from microsatellite loci

Background

Microsatellite instability (MSI) secondary to mismatch repair (MMR) deficiency is characterized by insertions and deletions (indels) in short DNA sequences across the genome. These indels can generate neoantigens, which are ideal targets for precision immune interception. However, current neoantigen databases lack information on neoantigens arising from coding microsatellites. To address this gap, we introduce The MicrOsatellite Neoantigen Discovery Tool (MONET).

Method

MONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions.

Results

MONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. <25%) using a set of experimentally validated neoAgs.

Conclusion

MONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.

Comprehensive profiling of cancer neoantigens from aberrant RNA splicing

BACKGROUND

Cancer neoantigens arise from protein-altering somatic mutations in tumor and rank among the most promising next-generation immuno-oncology agents when used in combination with immune checkpoint inhibitors. We previously developed a computational framework, REAL-neo, for identification, quality control, and prioritization of both class-I and class-II human leucocyte antigen (HLA)-presented neoantigens resulting from somatic single-nucleotide mutations, small insertions and deletions, and gene fusions. In this study, we developed a new module, SPLICE-neo, to identify neoantigens from aberrant RNA transcripts from two distinct sources: (1) DNA mutations within splice sites and (2) de novo RNA aberrant splicings.

METHODS

First, SPLICE-neo was used to profile all DNA splice-site mutations in 11,892 tumors from The Cancer Genome Atlas (TCGA) and identified 11 profiles of splicing donor or acceptor site gains or losses. Transcript isoforms resulting from the top seven most frequent profiles were computed using novel logic models. Second, SPLICE-neo identified de novo RNA splicing events using RNA sequencing reads mapped to novel exon junctions from either single, double, or multiple exon-skipping events. The aberrant transcripts from both sources were then ranked based on isoform expression levels and z-scores assuming that individual aberrant splicing events are rare. Finally, top-ranked novel isoforms were translated into protein, and the resulting neoepitopes were evaluated for neoantigen potential using REAL-neo. The top splicing neoantigen candidates binding to HLA-A*02:01 were validated using in vitro T2 binding assays.

RESULTS

We identified abundant splicing neoantigens in four representative TCGA cancers: BRCA, LUAD, LUSC, and LIHC. In addition to their substantial contribution to neoantigen load, several splicing neoantigens were potent tumor antigens with stronger bindings to HLA compared with the positive control of antigens from influenza virus.

CONCLUSIONS

SPLICE-neo is the first tool to comprehensively identify and prioritize splicing neoantigens from both DNA splice-site mutations and de novo RNA aberrant splicings. There are two major advances of SPLICE-neo. First, we developed novel logic models that assemble and prioritize full-length aberrant transcripts from DNA splice-site mutations. Second, SPLICE-neo can identify exon-skipping events involving more than two exons, which account for a quarter to one-third of all skipping events.

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.

In Silico Analysis of a Candidate Multi-epitope Peptide Vaccine Against Human Brucellosis

Brucellosis is one of the neglected endemic zoonoses in the world. Vaccination appears to be a promising health strategy to prevent it. This study used advanced computational techniques to develop a potent multi-epitope vaccine for human brucellosis. Seven epitopes from four main brucella species that infect humans were selected. They had significant potential to induce cellular and humoral responses. They showed high antigenic ability without the allergenic characteristic. In order to improve its immunogenicity, suitable adjuvants were also added to the structure of the vaccine. The physicochemical and immunological properties of the vaccine were evaluated. Then its two and three-dimensional structure was predicted. The vaccine was docked with toll-like receptor4 to assess its ability to stimulate innate immune responses. For successful expression of the vaccine protein in Escherichia coli, in silico cloning, codon optimization, and mRNA stability were evaluated. The immune simulation was performed to reveal the immune response profile of the vaccine after injection. The designed vaccine showed the high ability to induce immune response, especially cellular responses to human brucellosis. It showed the appropriate physicochemical properties, a high-quality structure, and a high potential for expression in a prokaryotic system.

ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model

Peptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this field. We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding affinity. It introduces a degenerate encoding approach to enhance well-established panspecific methods and integrates transfer learning and semi-supervised learning methods into the cutting-edge deep learning framework ConvNeXt. Comprehensive benchmark results demonstrate that ConvNeXt-MHC outperforms state-of-the-art methods in terms of accuracy. We expect that ConvNeXt-MHC will help us foster new discoveries in the field of immunoinformatics in the distant future. We constructed a user-friendly website at http://www.combio-lezhang.online/predict/, where users can access our data and application.

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.

Transfer learning improves pMHC kinetic stability and immunogenicity predictions

The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.

Evaluating the genetic diversity of the Plasmodium vivax siap2 locus: A promising candidate for an effective malaria vaccine?

Malaria is the deadliest parasitic disease in the world. Traditional control measures have become less effective; hence, there is a need to explore alternative strategies, such as antimalarial vaccines. However, designing an anti-Plasmodium vivax vaccine is considered a challenge due to the complex parasite biology and the antigens' high genetic diversity. Recently, the sporozoite invasion-associated protein 2 (SIAP2) has been suggested as a potential antigen to be considered in vaccine design due to its significance during hepatocyte invasion. However, its use may be limited by the incomplete understanding of gene/protein diversity. Here, the genetic diversity of pvsiap2 using P. vivax DNA samples from Colombia was assessed. Through PCR amplification and sequencing, we compared the Colombian sequences with available worldwide sequences, revealing that pvsiap2 displays low genetic diversity. Molecular evolutionary analyses showed that pvsiap2 appears to be influenced by directional selection. Moreover, the haplotypes found differ by a few mutational steps and several of them were shared between different geographical areas. On the other hand, several conserved regions within PvSIAP2 were predicted as potential B-cell or T-cell epitopes. Considering these characteristics and its role in hepatocyte invasion, the PvSIAP2 protein emerges as a promising antigen to be considered in a multi-antigen-multi-stage (multivalent) fully effective vaccine against P. vivax malaria.

Forum on immune digital twins: a meeting report

Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.

Stimulating T cell responses against patient-derived breast cancer cells with neoantigen peptide-loaded peripheral blood mononuclear cells

Breast cancer stands as a formidable global health challenge for women. While neoantigens exhibit efficacy in activating T cells specific to cancer and instigating anti-tumor immune responses, the accuracy of neoantigen prediction remains suboptimal. In this study, we identified neoantigens from the patient-derived breast cancer cells, PC-B-142CA and PC-B-148CA cells, utilizing whole-genome and RNA sequencing. The pVAC-Seq pipeline was employed, with minor modification incorporating criteria (1) binding affinity of mutant (MT) peptide with HLA (IC50 MT) ≤ 500 nm in 3 of 5 algorithms and (2) IC50 wild type (WT)/MT > 1. Sequencing results unveiled 2513 and 3490 somatic mutations, and 646 and 652 non-synonymous mutations in PC-B-142CA and PC-B-148CA, respectively. We selected the top 3 neoantigens to perform molecular dynamic simulation and synthesized 9-12 amino acid neoantigen peptides, which were then pulsed onto healthy donor peripheral blood mononuclear cells (PBMCs). Results demonstrated that T cells activated by ADGRL1E274K, PARP1E619K, and SEC14L2R43Q peptides identified from PC-B-142CA exhibited significantly increased production of interferon-gamma (IFN-γ), while PARP1E619K and SEC14L2R43Q peptides induced the expression of CD107a on T cells. The % tumor cell lysis was notably enhanced by T cells activated with MT peptides across all three healthy donors. Moreover, ALKBH6V83M and GAAI823T peptides from PC-B-148CA remarkably stimulated IFN-γ- and CD107a-positive T cells, displaying high cell-killing activity against target cancer cells. In summary, our findings underscore the successful identification of neoantigens with anti-tumor T cell functions and highlight the potential of personalized neoantigens as a promising avenue for breast cancer treatment.

HLAEquity: Examining biases in pan-allele peptide-HLA binding predictors

Peptide-HLA (pHLA) binding prediction is essential in screening peptide candidates for personalized peptide vaccines. Machine learning (ML) pHLA binding prediction tools are trained on vast amounts of data and are effective in screening peptide candidates. Most ML models report the ability to generalize to HLA alleles unseen during training ("pan-allele" models). However, the use of datasets with imbalanced allele content raises concerns about biased model performance. First, we examine the data bias of two ML-based pan-allele pHLA binding predictors. We find that the pHLA datasets overrepresent alleles from geographic populations of high-income countries. Second, we show that the identified data bias is perpetuated within ML models, leading to algorithmic bias and subpar performance for alleles expressed in low-income geographic populations. We draw attention to the potential therapeutic consequences of this bias, and we challenge the use of the term "pan-allele" to describe models trained with currently available public datasets.

Historical perspective and future directions: computational science in immuno-oncology

Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.

Comparison of the somatic genomic landscape between central- and peripheral-type non-small cell lung cancer

OBJECTIVE

Lung cancer is classified into central and peripheral types based on the anatomic location. The present study aimed to explore the distinct patterns of genomic alterations between central- and peripheral-type non-small cell lung cancers (NSCLCs) with negative driver genes and identify potential driver genes and biomarkers to improve therapy strategies for NSCLC.

METHODS

Whole-exome sequencing (WES) was performed with 182 tumor/control pairs of samples from 145 Chinese NSCLC patients without EGFR, ALK, or ROS1 alterations. Significantly mutated genes (SMGs) and somatic copy number alterations (SCNAs) were identified. Subsequently, tumor mutation burden (TMB), weighted genome integrity index (wGII), copy number alteration (CNA) burden, Shannon diversity index (SDI), intratumor heterogeneity (ITH), neoantigen load (NAL), and clonal variations were evaluated in central- and peripheral-type NSCLCs. Furthermore, mutational signature analysis and survival analysis were performed.

RESULTS

TP53 was the most frequently mutated gene in NSCLC and more frequently mutated in central-type NSCLC. Higher wGII, ITH, and SDI were found in central-type lung adenocarcinoma (LUAD) than in peripheral-type LUAD. The NAL of central-type lung squamous cell carcinoma (LUSC) with stage III/IV was significantly higher than that of peripheral-type LUSC. Mutational signature analysis revealed that SBS10b, SBS24, and ID7 were significantly different in central- and peripheral-type NSCLCs. Furthermore, central-type NSCLC was found to evolve at a higher level with fewer clones and more subclones, particularly in central-type LUSC. Survival analysis revealed that TMB, CNA burden, NAL, subclonal driver mutations, and subclonal mutations were negatively related to the overall survival (OS) and the progression-free survival (PFS) of central-type LUAD.

CONCLUSIONS

Central-type NSCLC tended to evolve at a higher level and might suggest a favorable response to immunotherapy. Our study also identified several potential driver genes and promising biomarkers for the prognosis and prediction of chemotherapy responses in NSCLC.

Genomic profiling of subcutaneous patient-derived xenografts reveals immune constraints on tumor evolution in childhood solid cancer

Subcutaneous patient-derived xenografts (PDXs) are an important tool for childhood cancer research. Here, we describe a resource of 68 early passage PDXs established from 65 pediatric solid tumor patients. Through genomic profiling of paired PDXs and patient tumors (PTs), we observe low mutational similarity in about 30% of the PT/PDX pairs. Clonal analysis in these pairs show an aggressive PT minor subclone seeds the major clone in the PDX. We show evidence that this subclone is more immunogenic and is likely suppressed by immune responses in the PT. These results suggest interplay between intratumoral heterogeneity and antitumor immunity may underlie the genetic disparity between PTs and PDXs. We further show that PDXs generally recapitulate PTs in copy number and transcriptomic profiles. Finally, we report a gene fusion LRPAP1-PDGFRA. In summary, we report a childhood cancer PDX resource and our study highlights the role of immune constraints on tumor evolution.

Prioritization of infectious epitopes for translational investigation in type 1 diabetes etiology

Molecular mimicry is one mechanism by which infectious agents are thought to trigger islet autoimmunity in type 1 diabetes. With a growing number of reported infectious agents and islet antigens, strategies to prioritize the study of infectious agents are critically needed to expedite translational research into the etiology of type 1 diabetes. In this work, we developed an in-silico pipeline for assessing molecular mimicry in type 1 diabetes etiology based on sequence homology, empirical binding affinity to specific MHC molecules, and empirical potential for T-cell immunogenicity. We then assess whether potential molecular mimics were conserved across other pathogens known to infect humans. Overall, we identified 61 potentially high-impact molecular mimics showing sequence homology, strong empirical binding affinity, and empirical immunogenicity linked with specific MHC molecules. We further found that peptide sequences from 32 of these potential molecular mimics were conserved across several human pathogens. These findings facilitate translational evaluation of molecular mimicry in type 1 diabetes etiology by providing a curated and prioritized list of peptides from infectious agents for etiopathologic investigation. These results may also provide evidence for generation of infectious and HLA-specific preclinical models and inform future screening and preventative efforts in genetically susceptible populations.

Lymphatic endothelial transcription factor Tbx1 promotes an immunosuppressive microenvironment to facilitate post-myocardial infarction repair

The heart is an autoimmune-prone organ. It is crucial for the heart to keep injury-induced autoimmunity in check to avoid autoimmune-mediated inflammatory disease. However, little is known about how injury-induced autoimmunity is constrained in hearts. Here, we reveal an unknown intramyocardial immunosuppressive program driven by Tbx1, a DiGeorge syndrome disease gene that encodes a T-box transcription factor (TF). We found induced profound lymphangiogenic and immunomodulatory gene expression changes in lymphatic endothelial cells (LECs) after myocardial infarction (MI). The activated LECs penetrated the infarcted area and functioned as intramyocardial immune hubs to increase the numbers of tolerogenic dendritic cells (tDCs) and regulatory T (Treg) cells through the chemokine Ccl21 and integrin Icam1, thereby inhibiting the expansion of autoreactive CD8+ T cells and promoting reparative macrophage expansion to facilitate post-MI repair. Mimicking its timing and implementation may be an additional approach to treating autoimmunity-mediated cardiac diseases.

Using a novel structure/function approach to select diverse swine major histocompatibility complex 1 alleles to predict epitopes for vaccine development

MOTIVATION

Swine leukocyte antigens (SLAs) (i.e. swine major histocompatibility complex proteins) conduct a fundamental role in swine immunity. To generate a protective vaccine across an outbred species, such as pigs, it is critical that epitopes that bind to diverse SLA alleles are used in the vaccine development process. We introduced a new strategy for epitope prediction.

RESULTS

We employed molecular dynamics simulation to identify key amino acids for interactions with epitopes. We developed an algorithm wherein each SLA-1 is compared to a crystalized reference allele with unique weighting for non-conserved amino acids based on R group and position. We then performed homology modeling and electrostatic contact mapping to visualize how relatively small changes in sequences impacted the charge distribution in the binding site. We selected eight diverse SLA-1 alleles and performed homology modeling followed, by protein-peptide docking and binding affinity analyses, to identify porcine reproductive and respiratory syndrome virus matrix protein epitopes that bind with high affinity to these alleles. We also performed docking analysis on the epitopes identified as strong binders using NetMHCpan 4.1. Epitopes predicted to bind to our eight SLA-1 alleles had equivalent or higher energetic interactions than those predicted to bind to the NetMHCpan 4.1 allele repertoire. This approach of selecting diverse SLA-1 alleles, followed by homology modeling, and docking simulations, can be used as a novel strategy for epitope prediction that complements other available tools and is especially useful when available tools do not offer a prediction for SLAs/major histocompatibility complex.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in the online Supplementary Material.

ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction

Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data. In particular, since data for 100+ protease types are available and this number continues to grow, it becomes impractical to publish predictors for new protease types, and instead it might be better to provide a computational platform that helps users to quickly and efficiently build predictors that address their specific needs. To this end, we conceptualized, developed, tested and released a versatile bioinformatics platform, ProsperousPlus, that empowers users, even those with no programming or little bioinformatics background, to build fast and accurate predictors of substrate cleavage sites. ProsperousPlus facilitates the use of the rapidly accumulating substrate cleavage data to train, empirically assess and deploy predictive models for user-selected substrate types. Benchmarking tests on test datasets show that our platform produces predictors that on average exceed the predictive performance of current state-of-the-art approaches. ProsperousPlus is available as a webserver and a stand-alone software package at http://prosperousplus.unimelb-biotools.cloud.edu.au/.

Neoantigen identification: Technological advances and challenges

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

Next-Generation TB Vaccines: Progress, Challenges, and Prospects

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a prevalent global infectious disease and a leading cause of mortality worldwide. Currently, the only available vaccine for TB prevention is Bacillus Calmette-Guérin (BCG). However, BCG demonstrates limited efficacy, particularly in adults. Efforts to develop effective TB vaccines have been ongoing for nearly a century. In this review, we have examined the current obstacles in TB vaccine research and emphasized the significance of understanding the interaction mechanism between MTB and hosts in order to provide new avenues for research and establish a solid foundation for the development of novel vaccines. We have also assessed various TB vaccine candidates, including inactivated vaccines, attenuated live vaccines, subunit vaccines, viral vector vaccines, DNA vaccines, and the emerging mRNA vaccines as well as virus-like particle (VLP)-based vaccines, which are currently in preclinical stages or clinical trials. Furthermore, we have discussed the challenges and opportunities associated with developing different types of TB vaccines and outlined future directions for TB vaccine research, aiming to expedite the development of effective vaccines. This comprehensive review offers a summary of the progress made in the field of novel TB vaccines.

Usefulness of Docking and Molecular Dynamics in Selecting Tumor Neoantigens to Design Personalized Cancer Vaccines: A Proof of Concept

Personalized cancer vaccines based on neoantigens are a new and promising treatment for cancer; however, there are still multiple unresolved challenges to using this type of immunotherapy. Among these, the effective identification of immunogenic neoantigens stands out, since the in silico tools used generate a significant portion of false positives. Inclusion of molecular simulation techniques can refine the results these tools produce. In this work, we explored docking and molecular dynamics to study the association between the stability of peptide-HLA complexes and their immunogenicity, using as a proof of concept two HLA-A2-restricted neoantigens that were already evaluated in vitro. The results obtained were in accordance with the in vitro immunogenicity, since the immunogenic neoantigen ASTN1 remained bound at both ends to the HLA-A2 molecule. Additionally, molecular dynamic simulation suggests that position 1 of the peptide has a more relevant role in stabilizing the N-terminus than previously proposed. Likewise, the mutations may have a "delocalized" effect on the peptide-HLA interaction, which means that the mutated amino acid influences the intensity of the interactions of distant amino acids of the peptide with the HLA. These findings allow us to propose the inclusion of molecular simulation techniques to improve the identification of neoantigens for cancer vaccines.

A pilot study of lymphodepletion intensity for peripheral blood mononuclear cell-derived neoantigen-specific CD8 + T cell therapy in patients with advanced solid tumors

Currently, the optimal lymphodepletion intensity for peripheral blood mononuclear cell-derived neoantigen-specific CD8 + T cell (Neo-T) therapy has yet to be determined. We report a single-arm, open-label and non-randomized phase 1 study (NCT02959905) of Neo-T therapy with lymphodepletion at various dose intensity in patients with locally advanced or metastatic solid tumors that are refractory to standard therapies. The primary end point is safety and the secondary end points are disease control rate (DCR), progression-free survival (PFS), overall survival (OS). Results show that the treatment is well tolerated with lymphopenia being the most common adverse event in the highest-intensity lymphodepletion groups. Neo-T infusion-related adverse events are only grade 1-2 in the no lymphodepletion group. The median PFS is 7.1 months (95% CI:3.7-9.8), the median OS is 16.8 months (95% CI: 11.9-31.7), and the DCR is 66.7% (6/9) among all groups. Three patients achieve partial response, two of them are in the no lymphodepletion group. In the group without lymphodepletion pretreatment, one patient refractory to prior anti-PD1 therapy shows partial response to Neo-T therapy. Neoantigen specific TCRs are examined in two patients and show delayed expansion after lymphodepletion treatment. In summary, Neo-T therapy without lymphodepletion could be a safe and promising regimen for advanced solid tumors.

CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks

The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on separate feature extraction from the peptide and MHC sequences and ignore their pairwise binding information. This paper develops a capsule neural network-based method to efficiently capture the peptide-MHC complex features to predict the peptide-MHC class I binding. Various evaluations confirmed our method outperformance over the alternative methods, while it can provide accurate prediction over less available data. Moreover, for providing precise insights into the results, we explored the essential features that contributed to the prediction. Since the simulation results demonstrated consistency with the experimental studies, we concluded that our method can be utilized for the accurate, rapid, and interpretable peptide-MHC binding prediction to assist biological therapies.

Computational prediction of MHC anchor locations guides neoantigen identification and prioritization

Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes

CD4⁺ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4⁺ T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4⁺ T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

Designing neoantigen cancer vaccines, trials, and outcomes

Neoantigen vaccines are based on epitopes of antigenic parts of mutant proteins expressed in cancer cells. These highly immunogenic antigens may trigger the immune system to combat cancer cells. Improvements in sequencing technology and computational tools have resulted in several clinical trials of neoantigen vaccines on cancer patients. In this review, we have looked into the design of the vaccines which are undergoing several clinical trials. We have discussed the criteria, processes, and challenges associated with the design of neoantigens. We searched different databases to track the ongoing clinical trials and their reported outcomes. We observed, in several trials, the vaccines boost the immune system to combat the cancer cells while maintaining a reasonable margin of safety. Detection of neoantigens has led to the development of several databases. Adjuvants also play a catalytic role in improving the efficacy of the vaccine. Through this review, we can conclude that the efficacy of vaccines can make it a potential treatment across different types of cancers.

Counting is almost all you need

The immune memory repertoire encodes the history of present and past infections and immunological attributes of the individual. As such, multiple methods were proposed to use T-cell receptor (TCR) repertoires to detect disease history. We here show that the counting method outperforms two leading algorithms. We then show that the counting can be further improved using a novel attention model to weigh the different TCRs. The attention model is based on the projection of TCRs using a Variational AutoEncoder (VAE). Both counting and attention algorithms predict better than current leading algorithms whether the host had CMV and its HLA alleles. As an intermediate solution between the complex attention model and the very simple counting model, we propose a new Graph Convolutional Network approach that obtains the accuracy of the attention model and the simplicity of the counting model. The code for the models used in the paper is provided at: https://github.com/louzounlab/CountingIsAlmostAllYouNeed.

Challenges in neoantigen-directed therapeutics

A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.

A Sample Guideline for Reverse Vaccinology Approach for the Development of Subunit Vaccine Using Varicella Zoster as a Model Disease

For the development of multi-peptide vaccine, identification of antigenic epitopes is crucial. If it is done using wet lab techniques, the identification process can be time-consuming, laborious, and cost-intensive. In silico tools, on the other hand, enable researchers to predict potential epitopes with little to no cost for further in vivo and in vitro testing. The rapid identification process using in silico tools helps in responding to health emergencies faster. Developing an efficient and high coverage vaccine is one of the ways to reduce morbidity and mortality rates of the diseases and protect the affected populations. In this chapter, we introduce the necessary tools and methodology for the identification and characterization of antigenic epitopes to design a multi-epitope vaccine using varicella-zoster virus as an example vector model.

Targeting the EGFR in cancer cells by fusion protein consisting of arazyme and third loop of TGF-alpha: an in silico study

The anticancer effects of arazyme, a bacterial metalloprotease, have been revealed in previous studies. Because of the overexpression of epidermal growth factor receptor (EGFR) in tumor cells, targeting this receptor is one of the approaches to cancer therapy. In the present study, we designed fusion protein by using a non-mitogenic binding sequence of TGFα, arazyme, and a suitable linker. The I-TASSER and Robetta web servers were employed to predict the territory structures of the Arazyme-linker-TGFαL3, and TGFαL3-linker-Arazyme. Then, models were validated by using PROCHECK, ERRAT, ProSA, and MolProbity web servers. After docking to EGFR, Arazyme-linker-TGFαL3 showed a higher binding affinity and was selected to be optimized through 100 ns Molecular dynamic (MD) simulation. Next, the stability of ligand-bound receptor was examined utilizing MD simulation for 100 ns. Furthermore, the binding free energy calculation and free energy decomposition were carried out employing MM-PBSA and MM-GBSA methods, respectively. The root mean square deviation and fluctuation (RMSD, RMSF), the radius of gyration, H-bond, and binding free energy analysis revealed the stability of the complex during simulation time. Finally, linear and conformational epitopes of B cells, as well as MHC class I and MHC class II were predicted by using different web servers. Meanwhile, the potential B cell and T cell epitopes were identified throughout arazyme protein sequence. Collectively, this study suggests a novel chimera protein candidate prevent cancer cells potentially by inducing an immune response and inhibiting cell proliferation. Communicated by Ramaswamy H. Sarma.

Epitope identification for p53R273C mutant

BACKGROUND

With the rise of immunotherapy based on cancer neoantigen, identification of neoepitopes has become an urgent problem to be solved. The TP53 R273C mutation is one of the hotspot mutations of TP53, however, the immunogenicity of this mutation is not yet clear. The aim of this study is to identify potential epitopes for p53R273C mutant.

METHODS

In this study, bioinformatic methods, peptide exchange assay, and peptide-immunized human leukocyte antigen (HLA) transgenic mouse model were used to explore the immunogenicity of this mutation.

RESULTS

Peptides with higher affinity to common HLA-A alleles (A*11:01, A*02:01) were discovered by computational prediction. All the 8-11 mer peptides contain the mutation site were synthesized and soluble peptides were used in the peptide exchange assay. However, the exchange efficiencies of these predicted peptides to HLAs were lower. Fortunately, other peptides with higher exchange efficiency were discovered. Then, the immunogenicity of these peptides was validated with the HLA-A2 transgenic mice model.

CONCLUSION

We identified three potential neoepitopes of p53^R273C for HLA-A*02:01, one potential neoepitope for HLA-A*11:01 and no neoepitope for HLA-A*24:02.

Neoantigen-specific TCR-T cell-based immunotherapy for acute myeloid leukemia

Neoantigens derived from non-synonymous somatic mutations are restricted to malignant cells and are thus considered ideal targets for T cell receptor (TCR)-based immunotherapy. Adoptive transfer of T cells bearing neoantigen-specific TCRs exhibits the ability to preferentially target tumor cells while remaining harmless to normal cells. High-avidity TCRs specific for neoantigens expressed on AML cells have been identified in vitro and verified using xenograft mouse models. Preclinical studies of these neoantigen-specific TCR-T cells are underway and offer great promise as safe and effective therapies. Additionally, TCR-based immunotherapies targeting tumor-associated antigens are used in early-phase clinical trials for the treatment of AML and show encouraging anti-leukemic effects. These clinical experiences support the application of TCR-T cells that are specifically designed to recognize neoantigens. In this review, we will provide a detailed profile of verified neoantigens in AML, describe the strategies to identify neoantigen-specific TCRs, and discuss the potential of neoantigen-specific T-cell-based immunotherapy in AML.

Shared 6mer Peptides of Human and Omicron (21K and 21L) at SARS-CoV-2 Mutation Sites

We investigated the short sequences involving Omicron 21K and Omicron 21L variants to reveal any possible molecular mimicry-associated autoimmunity risks and changes in those. We first identified common 6mers of the viral and human protein sequences present for both the mutant (Omicron) and nonmutant (SARS-CoV-2) versions of the same viral sequence and then predicted the binding affinities of those sequences to the HLA supertype representatives. We evaluated change in the potential autoimmunity risk, through comparative assessment of the nonmutant and mutant viral sequences and their similar human peptides with common 6mers and affinities to the same HLA allele. This change is the lost and the new, or de novo, autoimmunity risk, associated with the mutations in the Omicron 21K and Omicron 21L variants. Accordingly, e.g., the affinity of virus-similar sequences of the Ig heavy chain junction regions shifted from the HLA-B*15:01 to the HLA-A*01:01 allele at the mutant sequences. Additionally, peptides of different human proteins sharing 6mers with SARS-CoV-2 proteins at the mutation sites of interest and with affinities to the HLA-B*07:02 allele, such as the respective SARS-CoV-2 sequences, were lost. Among all, any possible molecular mimicry-associated novel risk appeared to be prominent in HLA-A*24:02 and HLA-B*27:05 serotypes upon infection with Omicron 21L. Associated disease, pathway, and tissue expression data supported possible new risks for the HLA-B*27:05 and HLA-A*01:01 serotypes, while the risks for the HLA-B*07:02 serotypes could have been lost or diminished, and those for the HLA-A*03:01 serotypes could have been retained, for the individuals infected with Omicron variants under study. These are likely to affect the complications related to cross-reactions influencing the relevant HLA serotypes upon infection with Omicron 21K and Omicron 21L.

Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction

Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immunogenicity among cancer cells and efficient tools to predict the immunogenicity of neoepitopes are still urgently needed. Here, we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope feature prediction using raw sequencing data. Neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions and gene fusions, are all supported. Importantly, a convolutional neural network (CNN)-based model was trained to predict the immunogenicity of neoepitopes and this model showed an improved performance compared to the currently available tools in immunogenicity prediction using independent datasets. We anticipate that the Seq2Neo pipeline could become a useful tool in the prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is open-source software under an academic free license (AFL) v3.0 and is freely available at Github.

Computational Structural and Functional Analyses of ORF10 in Novel Coronavirus SARS-CoV-2 Variants to Understand Evolutionary Dynamics

Introduction:

In an effort to combat SARS-CoV-2 through multi-subunit vaccine design, during studies using whole genome and immunome, ORF10, located at the 3′ end of the genome, displayed unique features. It showed no homology to any known protein in other organisms, including SARS-CoV. It was observed that its nucleotide sequence is 100% identical in the SARS-CoV-2 genomes sourced worldwide, even in the recent-most VoCs and VoIs of B.1.1.529 (Omicron), B.1.617 (Delta), B.1.1.7 (Alpha), B.1.351 (Beta), and P.1 (Gamma) lineages, implicating its constant nature throughout the evolution of deadly variants.

Aim:

The structure and function of SARS-CoV-2 ORF10 and the role it may play in the viral evolution is yet to be understood clearly. The aim of this study is to predict its structure, function, and understand evolutionary dynamics on the basis of mutations and likely heightened immune responses in the immunopathogenesis of this deadly virus.

Methods:

Sequence analysis, ab-initio structure modeling and an understanding of the impact of likely substitutions in key regions of protein was carried out. Analyses of viral T cell epitopes and primary anchor residue mutations was done to understand the role it may play in the evolution as a molecule with likely enhanced immune response and consequent immunopathogenesis.

Results:

Few amino acid substitution mutations are observed, most probably due to the ribosomal frameshifting, and these mutations may not be detrimental to its functioning. As ORF10 is observed to be an expressed protein, ab-initio structure modeling shows that it comprises mainly an α-helical region and maybe an ER-targeted membrane mini-protein. Analyzing the whole proteome, it is observed that ORF10 presents amongst the highest number of likely promiscuous and immunogenic CTL epitopes, specifically 11 out of 30 promiscuous ones and 9 out of these 11, immunogenic CTL epitopes. Reactive T cells to these epitopes have been uncovered in independent studies. Majority of these epitopes are located on the α-helix region of its structure, and the substitution mutations of primary anchor residues in these epitopes do not affect immunogenicity. Its conserved nucleotide sequence throughout the evolution and diversification of virus into several variants is a puzzle yet to be solved.

Conclusions:

On the basis of its sequence, structure, and epitope mapping, it is concluded that it may function like those mini-proteins used to boost immune responses in medical applications. Due to the complete nucleotide sequence conservation even a few years after SARS-CoV-2 genome was first sequenced, it poses a unique puzzle to be solved, in view of the evolutionary dynamics of variants emerging in the populations worldwide.

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

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

Association of PTPRT Mutations with Cancer Metastasis in Multiple Cancer Types

Metastasis is one of the characteristics of advanced cancer and the primary cause of cancer-related deaths from cancer, but the mechanism underlying metastasis is unclear, and there is a lack of metastasis markers. PTPRT is a protein-coding gene involved in both signal transduction and cellular adhesion. It is also known as a tumor suppressor gene that inhibits cell malignant proliferation by inhibiting the STAT3 pathway. Recent studies have reported that PTPRT is involved in the early metastatic seeding of colorectal cancer; however, the correlation between PTPRT and metastasis in other types of cancer has not been revealed. A combined analysis using a dataset from the genomics evidence neoplasia information exchange (GENIE) and cBioPortal revealed that PTPRT mutation is associated with poor prognosis in pan-cancer and non-small-cell lung cancer. The mutations of PTPRT or “gene modules” containing PTPRT are significantly enriched in patients with metastatic cancer in multiple cancers, suggesting that the PTPRT mutations serve as potential biomarkers of cancer metastasis.

3pHLA-score improves structure-based peptide-HLA binding affinity prediction

Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta's ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protocol outperforms the standard training protocol and leads to an increase from 0.82 to 0.99 of the area under the precision-recall curve. 3pHLA-score outperforms widely used scoring functions (AutoDock4, Vina, Dope, Vinardo, FoldX, GradDock) in a structural virtual screening task. Overall, this work brings structure-based methods one step closer to epitope discovery pipelines and could help advance the development of cancer and viral vaccines.

In silico SARS-CoV-2 vaccine development for Omicron strain using reverse vaccinology

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic began in 2019 but it remains as a serious threat today. To reduce and prevent spread of the virus, multiple vaccines have been developed. Despite the efforts in developing vaccines, Omicron strain of the virus has recently been designated as a variant of concern (VOC) by the World Health Organization (WHO).

OBJECTIVE

To develop a vaccine candidate against Omicron strain (B.1.1.529, BA.1) of the SARS-CoV-19.

METHODS

We applied reverse vaccinology methods for BA.1 and BA.2 as the vaccine target and a control, respectively. First, we predicted MHC I, MHC II and B cell epitopes based on their viral genome sequences. Second, after estimation of antigenicity, allergenicity and toxicity, a vaccine construct was assembled and tested for physicochemical properties and solubility. Third, AlphaFold2, RaptorX and RoseTTAfold servers were used to predict secondary structures and 3D structures of the vaccine construct. Fourth, molecular docking analysis was performed to test binding of our construct with angiotensin converting enzyme 2 (ACE2). Lastly, we compared mutation profiles on the epitopes between BA.1, BA.2, and wild type to estimate the efficacy of the vaccine.

RESULTS

We collected a total of 10 MHC I, 9 MHC II and 5 B cell epitopes for the final vaccine construct for Omicron strain. All epitopes were predicted to be antigenic, non-allergenic and non-toxic. The construct was estimated to have proper stability and solubility. The best modelled tertiary structures were selected for molecular docking analysis with ACE2 receptor.

CONCLUSIONS

These results suggest the potential efficacy of our newly developed vaccine construct as a novel vaccine candidate against Omicron strain of the coronavirus.

Durable complete response to neoantigen-loaded dendritic-cell vaccine following anti-PD-1 therapy in metastatic gastric cancer

Neoantigens are ideal targets for dendritic cell (DC) vaccines. So far, only a few neoantigen-based DC vaccines have been investigated in clinical trials. Here, we reported a case of a patient with metastatic gastric cancer who received personalized neoantigen-loaded monocyte-derived dendritic cell (Neo-MoDC) vaccines followed by combination therapy of the Neo-MoDC and immune checkpoint inhibitor (ICI). The patient developed T cell responses against neoantigens after receiving the Neo-MoDC vaccine alone. The following combination therapy triggered a stronger immune response and mediated complete regression of all tumors for over 25 months till October, 2021. Peripheral blood mononuclear cells recognized seven of the eight vaccine neoantigens. And the frequency of neoantigen-specific T cell clones increased obviously after vaccination. Overall, this report describing a complete tumor regression in a gastric cancer patient mediated by Neo-MoDC vaccine in combination with ICI, and suggesting a promising treatment for patients with metastatic gastric cancer.

Durable complete response to neoantigen-loaded dendritic-cell vaccine following anti-PD-1 therapy in metastatic gastric cancer

Neoantigens are ideal targets for dendritic cell (DC) vaccines. So far, only a few neoantigen-based DC vaccines have been investigated in clinical trials. Here, we reported a case of a patient with metastatic gastric cancer who received personalized neoantigen-loaded monocyte-derived dendritic cell (Neo-MoDC) vaccines followed by combination therapy of the Neo-MoDC and immune checkpoint inhibitor (ICI). The patient developed T cell responses against neoantigens after receiving the Neo-MoDC vaccine alone. The following combination therapy triggered a stronger immune response and mediated complete regression of all tumors for over 25 months till October, 2021. Peripheral blood mononuclear cells recognized seven of the eight vaccine neoantigens. And the frequency of neoantigen-specific T cell clones increased obviously after vaccination. Overall, this report describing a complete tumor regression in a gastric cancer patient mediated by Neo-MoDC vaccine in combination with ICI, and suggesting a promising treatment for patients with metastatic gastric cancer.

Durable complete response to neoantigen-loaded dendritic-cell vaccine following anti-PD-1 therapy in metastatic gastric cancer

Neoantigens are ideal targets for dendritic cell (DC) vaccines. So far, only a few neoantigen-based DC vaccines have been investigated in clinical trials. Here, we reported a case of a patient with metastatic gastric cancer who received personalized neoantigen-loaded monocyte-derived dendritic cell (Neo-MoDC) vaccines followed by combination therapy of the Neo-MoDC and immune checkpoint inhibitor (ICI). The patient developed T cell responses against neoantigens after receiving the Neo-MoDC vaccine alone. The following combination therapy triggered a stronger immune response and mediated complete regression of all tumors for over 25 months till October, 2021. Peripheral blood mononuclear cells recognized seven of the eight vaccine neoantigens. And the frequency of neoantigen-specific T cell clones increased obviously after vaccination. Overall, this report describing a complete tumor regression in a gastric cancer patient mediated by Neo-MoDC vaccine in combination with ICI, and suggesting a promising treatment for patients with metastatic gastric cancer.

Durable complete response to neoantigen-loaded dendritic-cell vaccine following anti-PD-1 therapy in metastatic gastric cancer

Neoantigens are ideal targets for dendritic cell (DC) vaccines. So far, only a few neoantigen-based DC vaccines have been investigated in clinical trials. Here, we reported a case of a patient with metastatic gastric cancer who received personalized neoantigen-loaded monocyte-derived dendritic cell (Neo-MoDC) vaccines followed by combination therapy of the Neo-MoDC and immune checkpoint inhibitor (ICI). The patient developed T cell responses against neoantigens after receiving the Neo-MoDC vaccine alone. The following combination therapy triggered a stronger immune response and mediated complete regression of all tumors for over 25 months till October, 2021. Peripheral blood mononuclear cells recognized seven of the eight vaccine neoantigens. And the frequency of neoantigen-specific T cell clones increased obviously after vaccination. Overall, this report describing a complete tumor regression in a gastric cancer patient mediated by Neo-MoDC vaccine in combination with ICI, and suggesting a promising treatment for patients with metastatic gastric cancer.

Durable complete response to neoantigen-loaded dendritic-cell vaccine following anti-PD-1 therapy in metastatic gastric cancer

Neoantigens are ideal targets for dendritic cell (DC) vaccines. So far, only a few neoantigen-based DC vaccines have been investigated in clinical trials. Here, we reported a case of a patient with metastatic gastric cancer who received personalized neoantigen-loaded monocyte-derived dendritic cell (Neo-MoDC) vaccines followed by combination therapy of the Neo-MoDC and immune checkpoint inhibitor (ICI). The patient developed T cell responses against neoantigens after receiving the Neo-MoDC vaccine alone. The following combination therapy triggered a stronger immune response and mediated complete regression of all tumors for over 25 months till October, 2021. Peripheral blood mononuclear cells recognized seven of the eight vaccine neoantigens. And the frequency of neoantigen-specific T cell clones increased obviously after vaccination. Overall, this report describing a complete tumor regression in a gastric cancer patient mediated by Neo-MoDC vaccine in combination with ICI, and suggesting a promising treatment for patients with metastatic gastric cancer.

Durable complete response to neoantigen-loaded dendritic-cell vaccine following anti-PD-1 therapy in metastatic gastric cancer

Neoantigens are ideal targets for dendritic cell (DC) vaccines. So far, only a few neoantigen-based DC vaccines have been investigated in clinical trials. Here, we reported a case of a patient with metastatic gastric cancer who received personalized neoantigen-loaded monocyte-derived dendritic cell (Neo-MoDC) vaccines followed by combination therapy of the Neo-MoDC and immune checkpoint inhibitor (ICI). The patient developed T cell responses against neoantigens after receiving the Neo-MoDC vaccine alone. The following combination therapy triggered a stronger immune response and mediated complete regression of all tumors for over 25 months till October, 2021. Peripheral blood mononuclear cells recognized seven of the eight vaccine neoantigens. And the frequency of neoantigen-specific T cell clones increased obviously after vaccination. Overall, this report describing a complete tumor regression in a gastric cancer patient mediated by Neo-MoDC vaccine in combination with ICI, and suggesting a promising treatment for patients with metastatic gastric cancer.

Durable complete response to neoantigen-loaded dendritic-cell vaccine following anti-PD-1 therapy in metastatic gastric cancer

Neoantigens are ideal targets for dendritic cell (DC) vaccines. So far, only a few neoantigen-based DC vaccines have been investigated in clinical trials. Here, we reported a case of a patient with metastatic gastric cancer who received personalized neoantigen-loaded monocyte-derived dendritic cell (Neo-MoDC) vaccines followed by combination therapy of the Neo-MoDC and immune checkpoint inhibitor (ICI). The patient developed T cell responses against neoantigens after receiving the Neo-MoDC vaccine alone. The following combination therapy triggered a stronger immune response and mediated complete regression of all tumors for over 25 months till October, 2021. Peripheral blood mononuclear cells recognized seven of the eight vaccine neoantigens. And the frequency of neoantigen-specific T cell clones increased obviously after vaccination. Overall, this report describing a complete tumor regression in a gastric cancer patient mediated by Neo-MoDC vaccine in combination with ICI, and suggesting a promising treatment for patients with metastatic gastric cancer.