Prediction of epitopes using neural network based methods

Leveraging artificial intelligence in vaccine development: A narrative review

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.

Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies

Therapeutic cancer vaccines have been considered in recent decades as important immunotherapeutic strategies capable of leading to tumor regression. In the development of these vaccines, the identification of neoepitopes plays a critical role, and different computational methods have been proposed and employed to direct and accelerate this process. In this context, this review identified and systematically analyzed the most recent studies published in the literature on the computational prediction of epitopes for the development of therapeutic vaccines, outlining critical steps, along with the associated program's strengths and limitations. A scoping review was conducted following the PRISMA extension (PRISMA-ScR). Searches were performed in databases (Scopus, PubMed, Web of Science, Science Direct) using the keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, and tumor. Forty-nine articles published from 2012 to 2024 were synthesized and analyzed. Most of the identified studies focus on the prediction of epitopes with an affinity for MHC I molecules in solid tumors, such as lung carcinoma. Predicting epitopes with class II MHC affinity has been relatively underexplored. Besides neoepitope prediction from high-throughput sequencing data, additional steps were identified, such as the prioritization of neoepitopes and validation. Mutect2 is the most used tool for variant calling, while NetMHCpan is favored for neoepitope prediction. Artificial/convolutional neural networks are the preferred methods for neoepitope prediction. For prioritizing immunogenic epitopes, the random forest algorithm is the most used for classification. The performance values related to the computational models for the prediction and prioritization of neoepitopes are high; however, a large part of the studies still use microbiome databases for training. The in vitro/in vivo validations of the predicted neoepitopes were verified in 55% of the analyzed studies. Clinical trials that led to successful tumor remission were identified, highlighting that this immunotherapeutic approach can benefit these patients. Integrating high-throughput sequencing, sophisticated bioinformatics tools, and rigorous validation methods through in vitro/in vivo assays as well as clinical trials, the tumor neoepitope-based vaccine approach holds promise for developing personalized therapeutic vaccines that target specific tumor cancers.

Construction by artificial intelligence and immunovalidation of hypoallergenic mite allergen Der f 36 vaccine

Background

The house dust mite (HDM) is widely recognized as the most prevalent allergen in allergic diseases. Allergen-specific immunotherapy (AIT) has been successfully implemented in clinical treatment for HDM. Hypoallergenic B-cell epitope-based vaccine designed by artificial intelligence (AI) represents a significant progression of recombinant hypoallergenic allergen derivatives.

Method

The three-dimensional protein structure of Der f 36 was constructed using Alphafold2. AI-based tools were employed to predict B-cell epitopes, which were subsequently verified through IgE-reaction testing. Hypoallergenic Der f 36 was then synthesized, expressed, and purified. The reduced allergenicity was assessed by enzyme-linked immunosorbent assay (ELISA), immunoblotting, and basophil activation test. T-cell response to hypoallergenic Der f 36 and Der f 36 was evaluated based on cytokine expression in the peripheral blood mononuclear cells (PBMCs) of patients. The immunogenicity was evaluated and compared through rabbit immunization with hypoallergenic Der f 36 and Der f 36, respectively. The inhibitory effect of the blocking IgG antibody on the specific IgE-binding activity and basophil activation of Der f 36 allergen was also examined.

Results

The final selected non-allergic B-cell epitopes were 25-48, 57-67, 107-112, 142-151, and 176-184. Hypoallergenic Der f 36 showed significant reduction in IgE-binding activity. The competitive inhibition of IgE-binding to Der f 36 was investigated using the hypoallergenic Der f 36, and only 20% inhibition could be achieved, which is greatly reduced when compared with inhibition by Der f 36 (98%). The hypoallergenic Der f 36 exhibited a low basophil-stimulating ratio similar to that of the negative control, and it could induce an increasing level of IFN-γ but not Th2 cytokines IL-5 and IL-13 in PBMCs. The vaccine-specific rabbit blocking IgG antibodies could inhibit the patients' IgE binding and basophil stimulation activity of Derf 36.

Conclusion

This study represents the first application of an AI strategy to facilitate the development of a B-cell epitope-based hypoallergenic Der f 36 vaccine, which may become a promising immunotherapy for HDM-allergic patients due to its reduced allergenicity and its high immunogenicity in inducing blocking of IgG.

Characterizing and forecasting neoantigens-resulting from MUC mutations in COAD

BACKGROUND

The treatment for colon adenocarcinoma (COAD) faces challenges in terms of immunotherapy effectiveness due to multiple factors. Because of the high tumor specificity and immunogenicity, neoantigen has been considered a pivotal target for cancer immunotherapy. Therefore, this study aims to identify and predict the potential tumor antigens of MUC somatic mutations (MUCmut) in COAD.

METHODS

Three databases of TCGA, TIMER2.0, and cBioPortal were used for a detailed evaluation of the association between MUCmut and multi-factors like tumor mutation burden (TMB), microsatellite instability (MSI), prognosis, and the tumor microenvironment within the context of total 2242 COAD patients. Next, TSNAdb and the differential agretopicity index (DAI) were utilized to predict high-confidence neopeptides for MUCmut based on 531 COAD patients' genomic information. DAI was calculated by subtraction of its predicted HLA binding affinity of the MUCmut peptide from the corresponding wild-type peptide.

RESULTS

The top six mutation frequencies (14 to 2.9%) were from MUC16, MUC17, MUC5B, MUC2, MUC4 and MUC6. COAD patients with MUC16 and MUC4 mutations had longer DFS and PFS. However, patients with MUC13 and MUC20 mutations had shorter OS. Patients with the mutation of MUC16, MUC5B, MUC2, MUC4, and MUC6 exhibited higher TMB and MSI. Moreover, these mutations from the MUC family were associated with the infiltration of diverse lymphocyte cells and the expression of immune checkpoint genes. Through TSNAdb 1.0/NetMHCpan v2.8, 452 single nucleotide variants (SNVs) of MUCmut peptides were identified. Moreover, through TSNAdb2.0/NetMHCpan v4.0, 57 SNVs, 1 Q-frame shift (TS), and 157 short insertions/deletions (INDELs) of MUCmut were identified. Finally, 10 high-confidence neopeptides of MUCmut were predicted by DAI.

CONCLUSIONS

Together, our findings establish the immunogenicity and therapeutic potential of mutant MUC family-derived neoantigens. Through combining the tools of TSNAdb and DAI, a group of novel MUCmut neoantigens were identified as potential targets for immunotherapy.

Computational and Experimental Evaluation of the Immune Response of Neoantigens for Personalized Vaccine Design

In the last few years, the importance of neoantigens in the development of personalized antitumor vaccines has increased remarkably. In order to study whether bioinformatic tools are effective in detecting neoantigens that generate an immune response, DNA samples from patients with cutaneous melanoma in different stages were obtained, resulting in a total of 6048 potential neoantigens gathered. Thereafter, the immunological responses generated by some of those neoantigens ex vivo were tested, using a vaccine designed by a new optimization approach and encapsulated in nanoparticles. Our bioinformatic analysis indicated that no differences were found between the number of neoantigens and that of non-mutated sequences detected as potential binders by IEDB tools. However, those tools were able to highlight neoantigens over non-mutated peptides in HLA-II recognition (p-value 0.03). However, neither HLA-I binding affinity (p-value 0.08) nor Class I immunogenicity values (p-value 0.96) indicated significant differences for the latter parameters. Subsequently, the new vaccine, using aggregative functions and combinatorial optimization, was designed. The six best neoantigens were selected and formulated into two nanoparticles, with which the immune response ex vivo was evaluated, demonstrating a specific activation of the immune response. This study reinforces the use of bioinformatic tools in vaccine development, as their usefulness is proven both in silico and ex vivo.

Advanced immunotherapies for glioblastoma: tumor neoantigen vaccines in combination with immunomodulators

Glial-origin brain tumors, including glioblastomas (GBM), have one of the worst prognoses due to their rapid and fatal progression. From an oncological point of view, advances in complete surgical resection fail to eliminate the entire tumor and the remaining cells allow a rapid recurrence, which does not respond to traditional therapeutic treatments. Here, we have reviewed new immunotherapy strategies in association with the knowledge of the immune micro-environment. To understand the best lines for the future, we address the advances in the design of neoantigen vaccines and possible new immune modulators. Recently, the efficacy and availability of vaccine development with different formulations, especially liposome plus mRNA vaccines, has been observed. We believe that the application of new strategies used with mRNA vaccines in combination with personalized medicine (guided by different omic's strategies) could give good results in glioma therapy. In addition, a large part of the possible advances in new immunotherapy strategies focused on GBM may be key improving current therapies of immune checkpoint inhibitors (ICI), given the fact that this type of tumor has been highly refractory to ICI.

T-cell epitope-based vaccine prediction against Aspergillus fumigatus: a harmful causative agent of aspergillosis

BACKGROUND

Among the most common causes of invasive aspergillosis and acute bronchopulmonary aspergillosis is Aspergillus fumigatus. Transmission with A. fumigatus produces aggressive aspergillosis in allogeneic haematopoietic stem cell transplant recipients, HIV patients, and cancer patients. Asthmatics and cystic fibrosis patients are allergic to A. fumigatus. MHC class-II binding epitopes can initiate immunogenic responses in patients. In this study, we deployed immunoinformatic study to reveal epitopes from fungal proteins.

RESULTS

In modern research, we found multiple epitopes ITLKLLHRYSYKLAG, KLVLRAFPNHFRGDS, RYSYKLAGVNQVDVV, GKSFELNQAARAVTQ, and LHRYSYKLAGVNQVD from crucial proteins of A. fumigatus 5,8-linoleate diol synthase (ACO55067.2) and ChainB-chitinase A1 (2XVN_B). RYSYKLAGVNQVDVV, GKSFELNQAARAVTQ, and LHRYSYKLAGVNQVD epitopes interact with HLA-DRB01_0101, while ITLKLLHRYSYKLAG and KLVLRAFPNHFRGDS epitopes interact with HLA-DRB01_1501. Molecular docking analysis reveals atomic contact energy (ACE) value for these five epitopes shown below -5 Kcal/mol in docked state.

CONCLUSIONS

The invasive aspergillosis and acute bronchopulmonary aspergillosis are caused by harmful fungal pathogen Aspergillus fumigatus. Our modern immunoinformatic research shows ITLKLLHRYSYKLAG, KLVLRAFPNHFRGDS, RYSYKLAGVNQVDVV, GKSFELNQAARAVTQ, and LHRYSYKLAGVNQVD epitopes could bind to MHC-II HLA allelic determinants and can initiate immunogenic response in patients affected by Aspergillus fumigatus.

Case Report: Pathological Complete Response in a Lung Metastasis of Phyllodes Tumor Patient Following Treatment Containing Peptide Neoantigen Nano-Vaccine

Some of the mutant peptides produced by gene mutation transcription and translation have the ability to induce specific T cells, which are called new antigens. Neoantigen-based peptide, DNA, RNA, and dendritic cell vaccines have been used in the clinic. In this paper, we describe a lung metastasis of a phyllodes tumor patient demonstrating pathological complete response following treatment containing personalized multi-epitope peptide neoantigen nano-vaccine. Based on whole-exome sequencing (WES), RNA sequencing, and new antigen prediction, several mutated peptide fragments were predicted to bind to the patient’s human leukocyte antigen (HLA) allotypes, including ten peptides with high predicted binding affinity for six genes. The pulmonary metastases remained stable after the four cycles of anti-PD1 and anlotinib. After the addition of the multi-epitope peptide neoantigen nano-vaccine, the tumor began to collapse and contracture developed, accompanied by a decrease of tumor markers to normal, and complete pathological remission was achieved. With the use of the vaccination, recombinant human granulocyte-macrophage colony-stimulating factor (rhGM-CSF) was used every time, and low-dose cyclophosphamide was injected every 3 weeks to improve efficacy. Peripheral blood immune monitoring demonstrated immune reactivity against a series of peptides, with the most robust post-vaccine T-cell response detected against the HLA-DRB1*0901-restricted SLC44A5 V54F peptide.

Searching Epitope-Based Vaccines Using Bioinformatics Studies

Epitope-based vaccines is one of the most recent methodologies applied in bioinformatics studies. This strategy consists of identifying regions of the protein (peptides or epitopes) which show antigen properties capable of stimulating the immune system against proteins from virus, bacteria, fungi, etc. This chapter describes a general procedure to identify epitopes to be used as epitope vaccine using bioinformatics methods including primary protein sequence analyses, epitope predictor, docking, and molecular dynamics simulations for the selection of T- and B-cell epitopes.

DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning

MOTIVATION

The mutations of cancers can encode the seeds of their own destruction, in the form of T-cell recognizable immunogenic peptides, also known as neoantigens. It is computationally challenging, however, to accurately prioritize the potential neoantigen candidates according to their ability of activating the T-cell immunoresponse, especially when the somatic mutations are abundant. Although a few neoantigen prioritization methods have been proposed to address this issue, advanced machine learning model that is specifically designed to tackle this problem is still lacking. Moreover, none of the existing methods considers the original DNA loci of the neoantigens in the perspective of 3D genome which may provide key information for inferring neoantigens' immunogenicity.

RESULTS

In this study, we discovered that DNA loci of the immunopositive and immunonegative MHC-I neoantigens have distinct spatial distribution patterns across the genome. We therefore used the 3D genome information along with an ensemble pMHC-I coding strategy, and developed a group feature selection-based deep sparse neural network model (DNN-GFS) that is optimized for neoantigen prioritization. DNN-GFS demonstrated increased neoantigen prioritization power comparing to existing sequence-based approaches. We also developed a webserver named deepAntigen (http://yishi.sjtu.edu.cn/deepAntigen) that implements the DNN-GFS as well as other machine learning methods. We believe that this work provides a new perspective toward more accurate neoantigen prediction which eventually contribute to personalized cancer immunotherapy.

AVAILABILITY AND IMPLEMENTATION

Data and implementation are available on webserver: http://yishi.sjtu.edu.cn/deepAntigen.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Personal neoantigen vaccines induce persistent memory T cell responses and epitope spreading in patients with melanoma

Personal neoantigen vaccines have been envisioned as an effective approach to induce, amplify and diversify antitumor T cell responses. To define the long-term effects of such a vaccine, we evaluated the clinical outcome and circulating immune responses of eight patients with surgically resected stage IIIB/C or IVM1a/b melanoma, at a median of almost 4 years after treatment with NeoVax, a long-peptide vaccine targeting up to 20 personal neoantigens per patient ( NCT01970358 ). All patients were alive and six were without evidence of active disease. We observed long-term persistence of neoantigen-specific T cell responses following vaccination, with ex vivo detection of neoantigen-specific T cells exhibiting a memory phenotype. We also found diversification of neoantigen-specific T cell clones over time, with emergence of multiple T cell receptor clonotypes exhibiting distinct functional avidities. Furthermore, we detected evidence of tumor infiltration by neoantigen-specific T cell clones after vaccination and epitope spreading, suggesting on-target vaccine-induced tumor cell killing. Personal neoantigen peptide vaccines thus induce T cell responses that persist over years and broaden the spectrum of tumor-specific cytotoxicity in patients with melanoma.

Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification

High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in these large datasets, in particular the identification of therapeutic antibodies (Abs) or those associated with disease exposure and protection. Here, we describe our efforts to use artificial intelligence (AI)-based image-analyses for prospective classification of Abs based solely on sequence information. We hypothesized that Abs recognizing the same part of an antigen share a limited set of features at the binding interface, and that the binding site regions of these Abs share share common structure and physicochemical property patterns that can serve as a "fingerprint" to recognize uncharacterized Abs. We combined large-scale sequence-based protein-structure predictions to generate ensembles of 3-D Ab models, reduced the Ab binding interface to a 2-D image (fingerprint), used pre-trained convolutional neural networks to extract features, and trained deep neural networks (DNNs) to classify Abs. We evaluated this approach using Ab sequences derived from human HIV and Ebola viral infections to differentiate between two Abs, Abs belonging to specific B-cell family lineages, and Abs with different epitope preferences. In addition, we explored a different type of DNN method to detect one class of Abs from a larger pool of Abs. Testing on Ab sets that had been kept aside during model training, we achieved average prediction accuracies ranging from 71-96% depending on the complexity of the classification task. The high level of accuracies reached during these classification tests suggests that the DNN models were able to learn a series of structural patterns shared by Abs belonging to the same class. The developed methodology provides a means to apply AI-based image recognition techniques to analyze high-throughput B-cell sequencing datasets (repertoires) for Ab classification.

Predicting Immunogenicity Risk in Biopharmaceuticals

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

Prediction and identification of CD4+ T cell epitope for the protective antigens of Mycobacterium tuberculosis

CD4+T cell epitopes plays a key role in anti-tuberculosis (TB) immunity, CD4+T cell epitopes suitable for the domestic population are lacking. Therefore, we predicted and identified novel CD4+T cell epitopes.The bioinformatics software, namely, DNAStar (DNASTAR of the United States), SYFPEITHI (INTERFACTORS INSTITUT Für ZELL Biologie of Germany), RANKPEP, and NetMHC IIpan (National Cancer Institute, United States of America), were used to comprehensively predict the CD4+T cell immune epitope of Mycobacterium TB, and the predicted epitope polypeptide was synthesized by the standard Fmoc scheme. The proliferation of PBMC and CD4+T cells stimulated by peptides was preliminarily detected by the CCK8 method. Then, the candidate polypeptides screened out by the CCK8 method were verified again by the BrdU assay, and flow cytometry was performed to analyze further the extent of their stimulation on the proliferation of CD4+T cells. The changes in the secreted cytokines IFN-γ, TNF-α, IL-2, and IL-10 before and after the candidate polypeptide stimulation of CD4+T lymphocytes were detected by ELISA. The preliminary humoral immunity test was conducted by indirect ELISA to evaluate the serological diagnostic value of the CD4+T cell epitope polypeptide.In this study, 5 novel candidate CD4+T cell epitope polypeptides with the amino acid sequences of LQGQWRGAAGTAAQA, PVTLAETGSTLLYPL, AAAWGGSGSEAYQGV, QFVYAGAMSGLLDPS, and KAALTRTASNMNAAA and others that have not been reported in the research were predicted. For convenience, the 5 candidates were successively named as P39, P50, P40, P185, and P62. P39, P62, and the mixed peptide P39+P62 could effectively induce the proliferation of CD4+T cells and increase the secretion of IFN-γ, TNF-α, and IL-2 from the CD4+T cells, while reducing the content of IL-10. The serological test showed that the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of P39 were 75%, 67.71%, and 0.844, respectively. The sensitivity, specificity, and AUC of P62 were 91.66%, 46.87%, and 0.649, respectively. The sensitivity of the mixed peptide P39+P62 was 95.83%, the specificity was 97.91%, and the AUC was 0.793.The P39 and P62 polypeptides were predicted and identified as potential CD4+T cell immune epitope polypeptides of M. TB. The polypeptide had better diagnosis effect, which provided potential candidate epitope polypeptides for the development of TB-specific diagnosis reagents and novel TB epitope vaccines.

In silico prediction of tumor antigens derived from functional missense mutations of the cancer gene census

Antigen-specific immune responses against peptides derived from missense gene mutations have been identified in multiple cancers. The application of personalized peptide vaccines based on the tumor mutation repertoire of each cancer patient is a near-term clinical reality. These peptides can be identified for pre-validation by leveraging the results of massive gene sequencing efforts in cancer. In this study, we utilized NetMHC 3.2 to predict nanomolar peptide binding affinity to 57 human HLA-A and B alleles. All peptides were derived from 5,685 missense mutations in 312 genes annotated as functionally relevant in the Cancer Genome Project. Of the 26,672,189 potential 8-11 mer peptide-HLA pairs evaluated, 0.4% (127,800) display binding affinities < 50 nM, predicting high affinity interactions. These peptides can be segregated into two groups based on the binding affinity to HLA proteins relative to germline-encoded sequences: peptides for which both the mutant and wild-type forms are high affinity binders, and peptides for which only the mutant form is a high affinity binder. Current evidence directs the attention to mutations that increase HLA binding affinity, as compared with cognate wild-type peptide sequences, as these potentially are more relevant for vaccine development from a clinical perspective. Our analysis generated a database including all predicted HLA binding peptides and the corresponding change in binding affinity as a result of point mutations. Our study constitutes a broad foundation for the development of personalized peptide vaccines that hone-in on functionally relevant targets in multiple cancers in individuals with diverse HLA haplotypes.

SARS-CoV-2 Vaccine Development: Current Status

In the midst of the severe acute respiratory syndrome coronavirus 2 pandemic and its attendant morbidity and mortality, safe and efficacious vaccines are needed that induce protective and long-lived immune responses. More than 120 vaccine candidates worldwide are in various preclinical and phase 1 to 3 clinical trials that include inactivated, live-attenuated, viral-vectored replicating and nonreplicating, protein- and peptide-based, and nucleic acid approaches. Vaccines will be necessary both for individual protection and for the safe development of population-level herd immunity. Public-private partnership collaborative efforts, such as the Accelerating COVID-19 Therapeutic Interventions and Vaccines mechanism, are key to rapidly identifying safe and effective vaccine candidates as quickly and efficiently as possible. In this article, we review the major vaccine approaches being taken and issues that must be resolved in the quest for vaccines to prevent coronavirus disease 2019. For this study, we scanned the PubMed database from 1963 to 2020 for all publications using the following search terms in various combinations: SARS, MERS, COVID-19, SARS-CoV-2, vaccine, clinical trial, coronavirus, pandemic, and vaccine development. We also did a Web search for these same terms. In addition, we examined the World Health Organization, Centers for Disease Control and Prevention, and other public health authority websites. We excluded abstracts and all articles that were not written in English.

The combination of artificial intelligence and systems biology for intelligent vaccine design

INTRODUCTION

A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development.

AREAS COVERED

This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases.

EXPERT OPINION

Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.

Nanomedicine and Onco-Immunotherapy: From the Bench to Bedside to Biomarkers

The broad relationship between the immune system and cancer is opening a new hallmark to explore for nanomedicine. Here, all the common and synergy points between both areas are reviewed and described, and the recent approaches which show the progress from the bench to the beside to biomarkers developed in nanomedicine and onco-immunotherapy.

Neoantigen-Specific Adoptive Cell Therapies for Cancer: Making T-Cell Products More Personal

Mutation-derived neoantigens are taking central stage as a determinant in eliciting effective antitumor immune responses following adoptive T-cell therapies. These mutations are patient-specific, and their targeting calls for highly personalized pipelines. The promising clinical outcomes of tumor-infiltrating lymphocyte (TIL) therapy have spurred interest in generating T-cell infusion products that have been selectively enriched in neoantigen (or autologous tumor) reactivity. The implementation of an isolation step, prior to T-cell in vitro expansion and reinfusion, may provide a way to improve the overall response rates achieved to date by adoptive T-cell therapies in metastatic cancer patients. Here we provide an overview of the main technologies [i.e., peptide major histocompatibility complex (pMHC) multimers, cytokine capture, and activation markers] to enrich infiltrating or circulating T-cells in predefined neoantigen specificities (or tumor reactivity). The unique technical and regulatory challenges faced by such highly specialized and patient-specific manufacturing T-cell platforms are also discussed.

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence

The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has resulted in long-term survival in advanced cancer patient subgroups. However, the majority of patients do not benefit from single-agent ICI and therefore new combination strategies are eagerly necessitated. In addition to conventional chemotherapy, kinase inhibitors as well as tumor-specific vaccinations are extensively investigated in combination with ICI to augment therapy responses. An unprecedented clinical outcome with chimeric antigen receptor (CAR-)T cell therapy has led to the approval for relapsed/refractory diffuse large B cell lymphoma and B cell acute lymphoblastic leukemia whereas response rates in solid tumors are unsatisfactory. Immune-checkpoints negatively impact CAR-T cell therapy in hematologic and solid malignancies and as a consequence provide a therapeutic target to overcome resistance. Established biomarkers such as programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB) help to select patients who will benefit most from ICI, however, biomarker negativity does not exclude responses. Investigating alterations in the antigen presenting pathway as well as radiomics have the potential to determine tumor immunogenicity and response to ICI. Within this review we summarize the literature about specific combination partners for ICI and the applicability of artificial intelligence to predict ICI therapy responses.

A pipeline for identification and validation of tumor-specific antigens in a mouse model of metastatic breast cancer

Cancer immunotherapy continues to make headway as a treatment for advanced stage tumors, revealing an urgent need to understand the fundamentals of anti-tumor immune responses. Noteworthy is a scarcity of data pertaining to the breadth and specificity of tumor-specific T cell responses in metastatic breast cancer. Autochthonous transgenic models of breast cancer display spontaneous metastasis in the FVB/NJ mouse strain, yet a lack of knowledge regarding tumor-bound MHC/peptide immune epitopes in this mouse model limits the characterization of tumor-specific T cell responses, and the mechanisms that regulate T cell responses in the metastatic setting. We recently generated the NetH2pan prediction tool for murine class I MHC ligands by building an FVB/NJ H-2q ligand database and combining it with public information from six other murine MHC alleles. Here, we deployed NetH2pan in combination with an advanced proteomics workflow to identify immunogenic T cell epitopes in the MMTV-PyMT transgenic model for metastatic breast cancer. Five unique MHC I/PyMT epitopes were identified. These tumor-specific epitopes were confirmed to be presented by the class I MHC of primary MMTV-PyMT tumors and their T cell immunogenicity was validated. Vaccination using a DNA construct encoding a truncated PyMT protein generated CD8 + T cell responses to these MHC class I/peptide complexes and prevented tumor development. In sum, we have established an MHC-ligand discovery pipeline in FVB/NJ mice, identified and tracked H-2D^q/PyMT neoantigen-specific T cells, and developed a vaccine that prevents tumor development in this metastatic model of breast cancer.

The Heterogeneity Between Lynch-Associated and Sporadic MMR Deficiency in Colorectal Cancers

Background

Previous studies demonstrated that prognosis of germline deficiency in mismatch repair protein (dMMR) was different from that of sporadic dMMR. The underlying mechanism has not been studied.

Methods

From a prospectively maintained database, we collected dMMR colorectal cancer (CRC) patients identified by postoperative immunohistochemistry screening. According to genetic test, patients were grouped as Lynch-associated or sporadic dMMR. We compared the clinical-pathological features, prognosis, and immunoreactive differences between the two groups. By whole-exome sequencing and neoantigen detection pipeline, mutational frequencies and neoantigen burdens were also compared. All statistical tests were two-sided.

Results

Sixty-seven sporadic dMMR and 85 Lynch-associated CRC patients were included in the study. Sporadic dMMR patients were older (P < .001) and their tumors were poorly differentiated (P = .03). The survival was better in the Lynch-associated group (P = .001). After adjustment, the difference still remained statistically significant (hazard ratio = 0.29, 95% confidence interval = 0.09 to 0.95, P = .04). The scores of Crohn's-like reaction (CRO; P < .001), immunoreactions in the invasive margin (IM; P = .01), tumor stroma (TS; P = .009), and cancer nest (CN; P = .02) of the Lynch-associated group were statistically significantly higher. The numbers of CD3+, CD8+, Foxp3+ tumor-infiltrating lymphocytes (TILs) in IM; CD3+, CD4+ TILs in TS; and CD3+, CD4+, CD8+ TILs in CN were statistically significantly higher in Lynch-associated dMMR patients. Based on the 16 patients who under went whole-exome sequencing, there were also more somatic mutations and neoantigen burdens in the Lynch-associated group compared with the sporadic dMMR group (439/pt vs 68/pt, P = .006; 628/pt vs 97/pt, P = .009).

Conclusions

There are heterogeneities in dMMR CRCs. Lynch-associated dMMR patients present with more somatic mutations and neoantigens compared with sporadic dMMR, which probably results in stronger immunoreactions and survival improvement.

Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial

Neoantigens, which are derived from tumour-specific protein-coding mutations, are exempt from central tolerance, can generate robust immune responses1,2 and can function as bona fide antigens that facilitate tumour rejection3. Here we demonstrate that a strategy that uses multi-epitope, personalized neoantigen vaccination, which has previously been tested in patients with high-risk melanoma4-6, is feasible for tumours such as glioblastoma, which typically have a relatively low mutation load1,7 and an immunologically 'cold' tumour microenvironment8. We used personalized neoantigen-targeting vaccines to immunize patients newly diagnosed with glioblastoma following surgical resection and conventional radiotherapy in a phase I/Ib study. Patients who did not receive dexamethasone-a highly potent corticosteroid that is frequently prescribed to treat cerebral oedema in patients with glioblastoma-generated circulating polyfunctional neoantigen-specific CD4+ and CD8+ T cell responses that were enriched in a memory phenotype and showed an increase in the number of tumour-infiltrating T cells. Using single-cell T cell receptor analysis, we provide evidence that neoantigen-specific T cells from the peripheral blood can migrate into an intracranial glioblastoma tumour. Neoantigen-targeting vaccines thus have the potential to favourably alter the immune milieu of glioblastoma.

A cloning and expression system to probe T-cell receptor specificity and assess functional avidity to neoantigens

Recent studies have highlighted the promise of targeting tumor neoantigens to generate potent antitumor immune responses and provide strong motivation for improving our understanding of antigen-T-cell receptor (TCR) interactions. Advances in single-cell sequencing technologies have opened the door for detailed investigation of the TCR repertoire, providing paired information from TCRα and TCRβ, which together determine specificity. However, a need remains for efficient methods to assess the specificity of discovered TCRs. We developed a streamlined approach for matching TCR sequences with cognate antigen through on-demand cloning and expression of TCRs and screening against candidate antigens. Here, we first demonstrate the system's capacity to identify viral-antigen-specific TCRs and compare the functional avidity of TCRs specific for a given antigen target. We then apply this system to identify neoantigen-specific TCR sequences from patients with melanoma treated with personalized neoantigen vaccines and characterize functional avidity of neoantigen-specific TCRs. Furthermore, we use a neoantigen-prediction pipeline to show that an insertion-deletion mutation in a putative chronic lymphocytic leukemia (CLL) driver gives rise to an immunogenic neoantigen mut-MGA, and use this approach to identify the mut-MGA-specific TCR sequence. This approach provides a means to identify and express TCRs, and then rapidly assess antigen specificity and functional avidity of a reconstructed TCR, which can be applied for monitoring antigen-specific T-cell responses, and potentially for guiding the design of effective T-cell-based immunotherapies.

General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept

The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes. Several attempts have been made to use molecular docking for such analyses, but pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome these limitations, we describe the use of an incremental meta-docking approach for structural prediction of pMHC complexes. Previous methods applied in this context used specific constraints to reduce the complexity of this prediction problem, at the expense of generality. Our strategy makes no assumption and can potentially be used to predict binding modes for any pMHC complex. Our method has been tested in a re-docking experiment, reproducing the binding modes of 25 pMHC complexes whose crystal structures are available. This study is a proof of concept that incremental docking strategies can lead to general geometry prediction of pMHC complexes, with potential applications for immunotherapy against cancer or infectious diseases.

PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity

Predicting peptide binding affinity with human leukocyte antigen (HLA) is a crucial step in developing powerful antitumor vaccine for cancer immunotherapy. Currently available methods work quite well in predicting peptide binding affinity with HLA alleles such as HLA-A*0201, HLA-A*0101, and HLA-B*0702 in terms of sensitivity and specificity. However, quite a few types of HLA alleles that are present in the majority of human populations including HLA-A*0202, HLA-A*0203, HLA-A*6802, HLA-B*5101, HLA-B*5301, HLA-B*5401, and HLA-B*5701 still cannot be predicted with satisfactory accuracy using currently available methods. Furthermore, currently the most popularly used methods for predicting peptide binding affinity are inefficient in identifying neoantigens from a large quantity of whole genome and transcriptome sequencing data. Here we present a Position Specific Scoring Matrix (PSSM)-based software called PSSMHCpan to accurately and efficiently predict peptide binding affinity with a broad coverage of HLA class I alleles. We evaluated the performance of PSSMHCpan by analyzing 10-fold cross-validation on a training database containing 87 HLA alleles and obtained an average area under receiver operating characteristic curve (AUC) of 0.94 and accuracy (ACC) of 0.85. In an independent dataset (Peptide Database of Cancer Immunity) evaluation, PSSMHCpan is substantially better than the popularly used NetMHC-4.0, NetMHCpan-3.0, PickPocket, Nebula, and SMM with a sensitivity of 0.90, as compared to 0.74, 0.81, 0.77, 0.24, and 0.79. In addition, PSSMHCpan is more than 197 times faster than NetMHC-4.0, NetMHCpan-3.0, PickPocket, sNebula, and SMM when predicting neoantigens from 661 263 peptides from a breast tumor sample. Finally, we built a neoantigen prediction pipeline and identified 117 017 neoantigens from 467 cancer samples of various cancers from TCGA. PSSMHCpan is superior to the currently available methods in predicting peptide binding affinity with a broad coverage of HLA class I alleles.

Cancer vaccine: learning lessons from immune checkpoint inhibitors

Cancer vaccines have been exclusively studied all through the past decades, and have made exceptional achievements in cancer treatment. Few cancer vaccines have been approved by the US Food and Drug Administration (FDA), for instance, Provenge, which was approved for the treatment of prostate carcinoma in 2012. Moreover, more recently, T-VEC got approval for the treatment of melanoma. While, the overall therapeutic effects of cancer vaccines have been taken into consideration as below expectations, low antigenicity of targeting antigen and tumor heterogeneity are the two key limiting barriers encountered by the cancer vaccines. Nonetheless, recent developments in cancer immune-therapies together with associated technologies, for instance the unparalleled achievements bagged by immune checkpoint inhibitor based therapies and neo-antigen identification tools, envisage potential improvements in cancer vaccines in respect to the treatments of malignancies. This review brings forth measures for the purpose of refining therapeutic cancer vaccines by learning lessons from the success of PD-1 inhibitor based immune-therapies.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

Analyzing the effect of peptide-HLA-binding ability on the immunogenicity of potential CD8+ and CD4+ T cell epitopes in a large dataset

Immunogenicity is a key factor that influences whether a peptide presented by major histocompatibility complex (MHC) can be a T cell epitope. However, peptide immunization experiments have shown that approximately half of MHC class I-binding peptides cannot elicit a T cell response, indicating the importance of analyzing the variables affecting the immunogenicity of MHC-binding peptides. In this study, we hierarchically investigated the contribution of the binding stability and affinity of peptide-MHC complexes to immunogenicity based on the available quantitative data. We found that the immunogenicity of peptides presented by human leukocyte antigen (HLA) class I molecules was still predictable using the experimental binding affinity, although approximately one-third of the peptides with a binding affinity stronger than 500 nM were non-immunogenic, whereas the immunogenicity of HLA-II-presented peptides was predicted well using the experimental affinity and even the predicted affinity. The positive correlation between the binding affinity and stability was only observed in peptide-HLA-I complexes with a binding affinity stronger than 500 nM, which suggested that the stability alone could not be used for the prediction of immunogenicity. A characterization and comparison of the 'holes' in the CD8+ and CD4+ T cell repertoire provided an explanation for the observed differences between the immunogenicity of peptides presented by HLA class I and II molecules. We also provided the optimal affinity threshold for the potential CD4+ and CD8+ T cell epitopes. Our results provide important insights into the cellular immune response and the accurate prediction of T cell epitopes.

The PROSECCO server for chemical shift predictions in ordered and disordered proteins

The chemical shifts measured in solution-state and solid-state nuclear magnetic resonance (NMR) are powerful probes of the structure and dynamics of protein molecules. The exploitation of chemical shifts requires methods to correlate these data with the protein structures and sequences. We present here an approach to calculate accurate chemical shifts in both ordered and disordered proteins using exclusively the information contained in their sequences. Our sequence-based approach, protein sequences and chemical shift correlations (PROSECCO), achieves the accuracy of the most advanced structure-based methods in the characterization of chemical shifts of folded proteins and improves the state of the art in the study of disordered proteins. Our analyses revealed fundamental insights on the structural information carried by NMR chemical shifts of structured and unstructured protein states.

The neoepitope landscape in pediatric cancers

BACKGROUND

Neoepitopes derived from tumor-specific somatic mutations are promising targets for immunotherapy in childhood cancers. However, the potential for such therapies in targeting these epitopes remains uncertain due to a lack of knowledge of the neoepitope landscape in childhood cancer. Studies to date have focused primarily on missense mutations without exploring gene fusions, which are a major class of oncogenic drivers in pediatric cancer.

METHODS

We developed an analytical workflow for identification of putative neoepitopes based on somatic missense mutations and gene fusions using whole-genome sequencing data. Transcriptome sequencing data were incorporated to interrogate the expression status of the neoepitopes.

RESULTS

We present the neoepitope landscape of somatic alterations including missense mutations and oncogenic gene fusions identified in 540 childhood cancer genomes and transcriptomes representing 23 cancer subtypes. We found that 88% of leukemias, 78% of central nervous system tumors, and 90% of solid tumors had at least one predicted neoepitope. Mutation hotspots in KRAS and histone H3 genes encode potential epitopes in multiple patients. Additionally, the ETV6-RUNX1 fusion was found to encode putative neoepitopes in a high proportion (69.6%) of the pediatric leukemia harboring this fusion.

CONCLUSIONS

Our study presents a comprehensive repertoire of potential neoepitopes in childhood cancers, and will facilitate the development of immunotherapeutic approaches designed to exploit them. The source code of the workflow is available at GitHub ( https://github.com/zhanglabstjude/neoepitope ).

Neoepitopes as cancer immunotherapy targets: key challenges and opportunities

Over the last half century, it has become well established that cancers can elicit a host immune response that can target them with high specificity. Only within the last decade, with the advances in high-throughput gene sequencing and bioinformatics approaches, are we now on the forefront of harnessing the host's immune system to treat cancer. Recently, some strides have been taken toward understanding effective tumor-specific MHC I restricted epitopes or neoepitopes. However, many fundamental questions still remain to be addressed before this therapy can live up to its full clinical potential. In this review, we discuss the major hurdles that lie ahead and the work being done to address them.

An immunogenic personal neoantigen vaccine for patients with melanoma

Effective anti-tumour immunity in humans has been associated with the presence of T cells directed at cancer neoantigens, a class of HLA-bound peptides that arise from tumour-specific mutations. They are highly immunogenic because they are not present in normal tissues and hence bypass central thymic tolerance. Although neoantigens were long-envisioned as optimal targets for an anti-tumour immune response, their systematic discovery and evaluation only became feasible with the recent availability of massively parallel sequencing for detection of all coding mutations within tumours, and of machine learning approaches to reliably predict those mutated peptides with high-affinity binding of autologous human leukocyte antigen (HLA) molecules. We hypothesized that vaccination with neoantigens can both expand pre-existing neoantigen-specific T-cell populations and induce a broader repertoire of new T-cell specificities in cancer patients, tipping the intra-tumoural balance in favour of enhanced tumour control. Here we demonstrate the feasibility, safety, and immunogenicity of a vaccine that targets up to 20 predicted personal tumour neoantigens. Vaccine-induced polyfunctional CD4+ and CD8+ T cells targeted 58 (60%) and 15 (16%) of the 97 unique neoantigens used across patients, respectively. These T cells discriminated mutated from wild-type antigens, and in some cases directly recognized autologous tumour. Of six vaccinated patients, four had no recurrence at 25 months after vaccination, while two with recurrent disease were subsequently treated with anti-PD-1 (anti-programmed cell death-1) therapy and experienced complete tumour regression, with expansion of the repertoire of neoantigen-specific T cells. These data provide a strong rationale for further development of this approach, alone and in combination with checkpoint blockade or other immunotherapies.

Sequence-Based Genotyping of Expressed Swine Leukocyte Antigen Class I Alleles by Next-Generation Sequencing Reveal Novel Swine Leukocyte Antigen Class I Haplotypes and Alleles in Belgian, Danish, and Kenyan Fattening Pigs and Göttingen Minipigs

The need for typing of the swine leukocyte antigen (SLA) is increasing with the expanded use of pigs as models for human diseases and organ-transplantation experiments, their use in infection studies, and for design of veterinary vaccines. Knowledge of SLA sequences is furthermore a prerequisite for the prediction of epitope binding in pigs. The low number of known SLA class I alleles and the limited knowledge of their prevalence in different pig breeds emphasizes the need for efficient SLA typing methods. This study utilizes an SLA class I-typing method based on next-generation sequencing of barcoded PCR amplicons. The amplicons were generated with universal primers and predicted to resolve 68-88% of all known SLA class I alleles dependent on amplicon size. We analyzed the SLA profiles of 72 pigs from four different pig populations; Göttingen minipigs and Belgian, Kenyan, and Danish fattening pigs. We identified 67 alleles, nine previously described haplotypes and 15 novel haplotypes. The highest variation in SLA class I profiles was observed in the Danish pigs and the lowest among the Göttingen minipig population, which also have the highest percentage of homozygote individuals. Highlighting the fact that there are still numerous unknown SLA class I alleles to be discovered, a total of 12 novel SLA class I alleles were identified. Overall, we present new information about known and novel alleles and haplotypes and their prevalence in the tested pig populations.

Antigen Discovery and Therapeutic Targeting in Hematologic Malignancies

Historically, immune-based therapies have played a leading role in the treatment of hematologic malignancies, with the efficacy of stem cell transplantation largely attributable to donor immunity against malignant cells. As new and more targeted immunotherapies have developed, their role in the treatment of hematologic malignancies is evolving and expanding. Herein, we discuss approaches for antigen discovery and review known and novel tumor antigens in hematologic malignancies. We further explore the role of established and investigational immunotherapies in hematologic malignancies, with a focus on personalization of treatment modalities such as cancer vaccines and adoptive cell therapy. Finally, we identify areas of active investigation and development. Immunotherapy is at an exciting crossroads for the treatment of hematologic malignancies, with further investigation aimed at producing effective, targeted immune therapies that maximize antitumor effects while minimizing toxicity.

In silico and cell-based analyses reveal strong divergence between prediction and observation of T-cell-recognized tumor antigen T-cell epitopes

Tumor exomes provide comprehensive information on mutated, overexpressed genes and aberrant splicing, which can be exploited for personalized cancer immunotherapy. Of particular interest are mutated tumor antigen T-cell epitopes, because neoepitope-specific T cells often are tumoricidal. However, identifying tumor-specific T-cell epitopes is a major challenge. A widely used strategy relies on initial prediction of human leukocyte antigen-binding peptides by in silico algorithms, but the predictive power of this approach is unclear. Here, we used the human tumor antigen NY-ESO-1 (ESO) and the human leukocyte antigen variant HLA-A*0201 (A2) as a model and predicted in silico the 41 highest-affinity, A2-binding 8-11-mer peptides and assessed their binding, kinetic complex stability, and immunogenicity in A2-transgenic mice and on peripheral blood mononuclear cells from ESO-vaccinated melanoma patients. We found that 19 of the peptides strongly bound to A2, 10 of which formed stable A2-peptide complexes and induced CD8⁺ T cells in A2-transgenic mice. However, only 5 of the peptides induced cognate T cells in humans; these peptides exhibited strong binding and complex stability and contained multiple large hydrophobic and aromatic amino acids. These results were not predicted by in silico algorithms and provide new clues to improving T-cell epitope identification. In conclusion, our findings indicate that only a small fraction of in silico-predicted A2-binding ESO peptides are immunogenic in humans, namely those that have high peptide-binding strength and complex stability. This observation highlights the need for improving in silico predictions of peptide immunogenicity.

Rapid tumor regression in an Asian lung cancer patient following personalized neo-epitope peptide vaccination

Personalized immunotherapy targeting tumor-specific mutations represents a highly promising approach to cancer treatment. Here, we describe an Asian lung squamous cell carcinoma patient demonstrating frank disease progression following chemotherapy and EGFR inhibitor treatment. Based on tumor mutational profiling and HLA typing, a saline-based multi-epitope peptide vaccine was designed and administered along with topical imiquimod as an adjuvant. Weekly neo-epitope peptide vaccination was followed by a rapid and dramatic regression of multiple lung tumor nodules, while a much larger liver metastasis remained refractory to treatment. Peripheral blood immune monitoring showed that specific cytotoxic T lymphocytes (CTLs) were induced primarily against peptide targets encompassing the widely shared EGFR L858R mutation, particularly one restricted to HLA-A*3101. Immunological targeting of this driver mutation may be of particular benefit to Asian lung cancer patients due to its relatively high prevalence within this patient population.

Neoantigen-based cancer immunotherapy

Emerging clinical evidence on the role of the antitumor activity of the immune system has generated great interest in immunotherapy in all cancer types. Recent clinical data clearly demonstrated that human tumor cells express antigenic peptides (epitopes) that can be recognized by autologous tumor-specific T cells and that enhancement of such immune reactivity can potentially lead to cancer control and cancer regression in patients with advanced disease. However, in most cases, it is unclear which tumor antigens (Ags) mediated cancer regression. Mounting evidence indicates that numerous endogenous mutated cancer proteins, a hallmark of tumor cells, can be processed into peptides and presented on the surface of tumor cells, leading to their immune recognition in vivo as "non-self" or foreign. Massively parallel sequencing has now overcome the challenge of rapidly identifying the comprehensive mutational spectrum of individual tumors (i.e., the "mutanome") and current technologies, as well as computational tools, have emerged that allow the identification of private epitopes derived from their mutanome and called neoantigens (neoAgs). On this basis, both CD4(+) and CD8(+) neoantigen-specific T cells have been identified in multiple human cancers and shown to be associated with a favorable clinical outcome. Notably, emerging data also indicate that neoantigen recognition represents a major factor in the activity of clinical immunotherapies. In the post-genome era, the mutanome holds promise as a long-awaited 'gold mine' for the discovery of unique cancer cell targets, which are exclusively tumor-specific and unlikely to drive immune tolerance, hence offering the chance for highly promising clinical programs of cancer immunotherapy.

Neoantigens encoded in the cancer genome

Somatic mutations in the genome represent one of the major drivers of malignancy. However, non-synonymous mutations are also a source of mutated peptides that are presented by HLA molecules to induce protective CD4 and CD8 T cell responses. Consistent with this notion, the mutation burden of a tumor is correlated with local immunity as well as outcome of therapy and patient survival. Furthermore, neoantigen-specific T cells appear sufficient to control tumors prophylactically and therapeutically. While the role of neoantigens as a determinant of the foreignness of human cancers is now well established, major questions, including the relative importance of clonal vs subclonal neoantigens, and CD4 vs CD8 T cells, remain unanswered. We expect continued animal studies to address some of the open issues and ongoing clinical trials to establish the utility of therapeutic strategies to enhance neoantigen-specific T cell responses in human cancer.

Expanding specificity of class I restricted CD8+ T cells for viral epitopes following multiple inoculations of swine with a human adenovirus vectored foot-and-mouth disease virus (FMDV) vaccine

The immune response to the highly acute foot-and-mouth disease virus (FMDV) is routinely reported as a measure of serum antibody. However, a critical effector function of immune responses combating viral infection of mammals is the cytotoxic T lymphocyte (CTL) response mediated by virus specific CD8 expressing T cells. This immune mechanism arrests viral spread by killing virus infected cells before new, mature virus can develop. We have previously shown that infection of swine by FMDV results in a measurable CTL response and have correlated CTL killing of virus-infected cells with specific class I major histocompatibility complex (MHC) tetramer staining. We also showed that a modified replication defective human adenovirus 5 vector expressing the FMDV structural proteins (Ad5-FMDV-T vaccine) targets the induction of a CD8⁺ CTL response with a minimal humoral response. In this report, we show that the specificity of the CD8⁺ T cell response to Ad5-FMDV-T varies between cohorts of genetically identical animals. Further, we demonstrate epitope specificity of CD8⁺ T cells expands following multiple immunizations with this vaccine.

Current tools for predicting cancer-specific T cell immunity

Tumor exome and RNA sequencing data provide a systematic and unbiased view on cancer-specific expression, over-expression, and mutations of genes, which can be mined for personalized cancer vaccines and other immunotherapies. Of key interest are tumor-specific mutations, because T cells recognizing neoepitopes have the potential to be highly tumoricidal. Here, we review recent developments and technical advances in identifying MHC class I and class II-restricted tumor antigens, especially neoantigen derived MHC ligands, including in silico predictions, immune-peptidome analysis by mass spectrometry, and MHC ligand validation by biochemical methods on T cells.

In silico and in vivo analysis of Toxoplasma gondii epitopes by correlating survival data with peptide-MHC-I binding affinities

BACKGROUND

Protein antigens comprising peptide motifs with high binding affinity to major histocompatibility complex class I (MHC-I) molecules are expected to induce a stronger cytotoxic T-lymphocyte response and thus provide better protection against infection with microorganisms where cytotoxic T-cells are the main effector arm of the immune system.

METHODS

Data on cyst formation and survival were extracted from past studies on the DNA immunization of mice with plasmids coding for Toxoplasma gondii antigens. From in silico analyses of the vaccine antigens, the correlation was tested between the predicted affinity for MHC-I molecules of the vaccine peptides and the survival of immunized mice after challenge with T. gondii. ELISPOT analysis was used for the experimental testing of peptide immunogenicity.

RESULTS

Predictions for the Db MHC-I molecule produced a strong, negative correlation between survival and the dissociation constant of vaccine-derived peptides. The in silico analyses of nine T. gondii antigens identified peptides with a predicted dissociation constant in the interval from 10nM to 40μM. ELISPOT assays with splenocytes from T. gondii-infected mice further supported the importance of the peptide affinity for MHC-I.

CONCLUSIONS

In silico analysis clearly helped the search for protective vaccine antigens. The ELISPOT analysis confirmed that the predicted T-cell epitopes were immunogenic by their ability to release interferon gamma in spleen cells.

A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin

BACKGROUND

Understanding the interactions between antibodies and the linear epitopes that they recognize is an important task in the study of immunological diseases. We present a novel computational method for the design of linear epitopes of specified binding affinity to Intravenous Immunoglobulin (IVIg).

RESULTS

We show that the method, called Pythia-design can accurately design peptides with both high-binding affinity and low binding affinity to IVIg. To show this, we experimentally constructed and tested the computationally constructed designs. We further show experimentally that these designed peptides are more accurate that those produced by a recent method for the same task. Pythia-design is based on combining random walks with an ensemble of probabilistic support vector machines (SVM) classifiers, and we show that it produces a diverse set of designed peptides, an important property to develop robust sets of candidates for construction. We show that by combining Pythia-design and the method of (PloS ONE 6(8):23616, 2011), we are able to produce an even more accurate collection of designed peptides. Analysis of the experimental validation of Pythia-design peptides indicates that binding of IVIg is favored by epitopes that contain trypthophan and cysteine.

CONCLUSIONS

Our method, Pythia-design, is able to generate a diverse set of binding and non-binding peptides, and its designs have been experimentally shown to be accurate.

pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens

Cancer immunotherapy has gained significant momentum from recent clinical successes of checkpoint blockade inhibition. Massively parallel sequence analysis suggests a connection between mutational load and response to this class of therapy. Methods to identify which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell immunity are needed to improve predictions of checkpoint therapy response and to identify targets for vaccines and adoptive T cell therapies. Here, we present a flexible, streamlined computational workflow for identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available at https://github.com/griffithlab/pVAC-Seq.