SYFPEITHI: database for MHC ligands and peptide motifs

Impact of Cysteine Residues on MHC Binding Predictions and Recognition by Tumor-Reactive T Cells

The availability of MHC-binding prediction tools has been useful in guiding studies aimed at identifying candidate target Ags to generate reactive T cells and to characterize viral and tumor-reactive T cells. Nevertheless, prediction algorithms appear to function poorly for epitopes containing cysteine (Cys) residues, which can oxidize and form disulfide bonds with other Cys residues under oxidizing conditions, thus potentially interfering with their ability to bind to MHC molecules. Analysis of the results of HLA-A*02:01 class I binding assays carried out in the presence and absence of the reducing agent 2-ME indicated that the predicted affinity for 25% of Cys-containing epitopes was underestimated by a factor of 3 or more. Additional analyses were undertaken to evaluate the responses of human CD8+ tumor-reactive T cells against 10 Cys-containing HLA class I-restricted minimal determinants containing substitutions of α-aminobutyric acid (AABA), a cysteine analogue containing a methyl group in place of the sulfhydryl group present in Cys, for the native Cys residues. Substitutions of AABA for Cys at putative MHC anchor positions often significantly enhanced T cell recognition, whereas substitutions at non-MHC anchor positions were neutral, except for one epitope where this modification abolished T cell recognition. These findings demonstrate the need to evaluate MHC binding and T cell recognition of Cys-containing peptides under conditions that prevent Cys oxidation, and to adjust current prediction binding algorithms for HLA-A*02:01 and potentially additional class I alleles to more accurately rank peptides containing Cys anchor residues.

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.

Identification of immunogenic T-cell peptides of Mycobacterium tuberculosis PE_PGRS33 protein

The development of a more efficient vaccine is needed to improve tuberculosis control. One of the current approaches is to identify immunogenic T-cell peptides that can elicit a protective and specific immune response. These peptides come from immunogenic proteins of the pathogen. The PE_PGRS33 protein of Mycobacterium tuberculosis has been proved immunogenic. However, little is known about immunogenic T-cell peptides of PE_PGRS33 and their interactions with MHC-II molecules. Therefore, we used the SYFPHEITHI database to determine the immunogenic PE_PGRS33 T-cell peptides. Next, we built homology models by using MOE v2018.1 software in order to obtain information about the specific interactions between the peptides and I-A^k. The AlgPred server was employed to look for allergenic sites in PE_PGRS33. We developed a sequence alignment between PE_PGRS33 and all the human proteins by using BLAST. Three peptides were commercially synthesized, and their activity was evaluated in vitro by the stimulation of PBMC from household contacts of TB patients. Our in silico results showed five immunogenic T-cell peptides. BLAST analysis showed low homology of PE_PGRS33 with human proteins and AlgPred did not reveal allergenic sites in PE_PGRS33. The three peptides triggered the activation of CD4⁺ T cells from the households contacts, showed by the production of IFN-γ. We identified three immunogenic peptides of PE_PGRS33 that demonstrated activity in vitro which allows to deepen into the immune response towards mycobacterial antigens, moving forward to the identification of new vaccine candidates.

Immunoinformatics: Predicting Peptide-MHC Binding

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.

Cancer Antigen Discovery Is Enabled by RNA Sequencing of Highly Purified Malignant and Nonmalignant Cells

PURPOSE

Broadly expressed, highly differentiated tumor-associated antigens (TAA) can elicit antitumor immunity. However, vaccines targeting TAAs have demonstrated disappointing clinical results, reflecting poor antigen selection and/or immunosuppressive mechanisms.

EXPERIMENTAL DESIGN

Here, a panel of widely expressed, novel colorectal TAAs were identified by performing RNA sequencing of highly purified colorectal tumor cells in comparison with patient-matched colonic epithelial cells; tumor cell purification was essential to reveal these genes. Candidate TAA protein expression was confirmed by IHC, and preexisting T-cell immunogenicity toward these antigens tested.

RESULTS

The most promising candidate for further development is DNAJB7 [DnaJ heat shock protein family (Hsp40) member B7], identified here as a novel cancer-testis antigen. It is expressed in many tumors and is strongly immunogenic in patients with cancers originating from a variety of sites. DNAJB7-specific T cells were capable of killing colorectal tumor lines in vitro, and the IFNγ+ response was markedly magnified by control of immunosuppression with cyclophosphamide in patients with cancer.

CONCLUSIONS

This study highlights how prior methods that sequence whole tumor fractions (i.e., inclusive of alive/dead stromal cells) for antigen identification may have limitations. Through tumor cell purification and sequencing, novel candidate TAAs have been identified for future immunotherapeutic targeting.

Towards new horizons: characterization, classification and implications of the tumour antigenic repertoire

Immune-checkpoint inhibition provides an unmatched level of durable clinical efficacy in various malignancies. Such therapies promote the activation of antigen-specific T cells, although the precise targets of these T cells remain unknown. Exploiting these targets holds great potential to amplify responses to treatment, such as by combining immune-checkpoint inhibition with therapeutic vaccination or other antigen-directed treatments. In this scenario, the pivotal hurdle remains the definition of valid HLA-restricted tumour antigens, which requires several levels of evidence before targets can be established with sufficient confidence. Suitable antigens might include tumour-specific antigens with alternative or wild-type sequences, tumour-associated antigens and cryptic antigens that exceed exome boundaries. Comprehensive antigen classification is required to enable future clinical development and the definition of innovative treatment strategies. Furthermore, clinical development remains challenging with regard to drug manufacturing and regulation, as well as treatment feasibility. Despite these challenges, treatments based on diligently curated antigens combined with a suitable therapeutic platform have the potential to enable optimal antitumour efficacy in patients, either as monotherapies or in combination with other established immunotherapies. In this Review, we summarize the current state-of-the-art approaches for the identification of candidate tumour antigens and provide a structured terminology based on their underlying characteristics.

Immune-checkpoint inhibition has transformed the treatment of patients with advanced-stage cancers. Nonetheless, the specific antigens targeted by T cells that are activated or reactivated by these agents remain largely unknown. In this Review, the authors describe the characterization and classification of tumour antigens including descriptions of the most appropriate detection methods, and discuss potential regulatory issues regarding the use of tumour antigen-based therapeutics.

Immune-checkpoint inhibition has profoundly changed the paradigm for the care of several malignancies. Although these therapies activate antigen-specific T cells, the precise mechanisms of action and their specific targets remain largely unknown.

Anticancer immunotherapies encompass two fundamentally different therapeutic principles based on knowledge of their therapeutic targets, that either have been characterized (antigen-aware) or have remained elusive (antigen-unaware).

HLA-presented tumour antigens of potential therapeutic relevance can comprise alternative or wild-type amino acid sequences and can be subdivided into different categories based on their mechanisms of formation.

The available methods for the detection of HLA-presented antigens come with intrinsic challenges and limitations and, therefore, warrant multiple lines of evidence of robust tumour specificity before being considered for clinical use.

Knowledge obtained using various antigen-detection strategies can be combined with different therapeutic platforms to create individualized therapies that hold great promise, including when combined with already established immunotherapies.

Tailoring immunotherapies while taking into account the substantial heterogeneity of malignancies as well as that of HLA loci not only requires innovative science, but also demands innovative approaches to trial design and drug regulation.

Modulation of Natural HLA-B*27:05 Ligandome by Ankylosing Spondylitis-associated Endoplasmic Reticulum Aminopeptidase 2 (ERAP2)

The HLA-B*27:05 allele and the endoplasmic reticulum-resident aminopeptidases are strongly associated with AS, a chronic inflammatory spondyloarthropathy. This study examined the effect of ERAP2 in the generation of the natural HLA-B*27:05 ligandome in live cells. Complexes of HLA-B*27:05-bound peptide pools were isolated from human ERAP2-edited cell clones, and the peptides were identified using high-throughput mass spectrometry analyses. The relative abundance of a thousand ligands was established by quantitative tandem mass spectrometry and bioinformatics analysis. The residue frequencies at different peptide position, identified in the presence or absence of ERAP2, determined structural features of ligands and their interactions with specific pockets of the antigen-binding site of the HLA-B*27:05 molecule. Sequence alignment of ligands identified with species of bacteria associated with HLA-B*27-dependent reactive arthritis was performed. In the absence of ERAP2, peptides with N-terminal basic residues and minority canonical P2 residues are enriched in the natural ligandome. Further, alterations of residue frequencies and hydrophobicity profile at P3, P7, and PΩ positions were detected. In addition, several ERAP2-dependent cellular peptides were highly similar to protein sequences of arthritogenic bacteria, including one human HLA-B*27:05 ligand fully conserved in a protein from Campylobacter jejuni These findings highlight the pathogenic role of this aminopeptidase in the triggering of AS autoimmune disease.

Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system

T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2D^b and H-2K^b), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.

Computational prediction tools are used to screen peptides to identify potential T cell epitope candidates. These tools, developed using machine learning methods, save time and resources in many immunological studies including vaccine discovery and cancer neoantigen identification. In addition to the already existing methods several epitope prediction tools are being developed these days but they lack a comprehensive and uniform evaluation to see which method performs best. In this study we did a comprehensive evaluation of publicly accessible MHC I restricted T cell epitope prediction tools using a recently published dataset of Vaccinia virus epitopes identified in the context of H-2D^b and H-2K^b. We found that methods based on artificial neural network architecture and trained on both MHC binding and ligand elution data showed very high performance (NetMHCPan-4.0 and MHCFlurry). This benchmark analysis will help immunologists to choose the right prediction method for their desired work and will also serve as a framework for tool developers to evaluate new prediction methods.

Evaluation of a tandem Chlamydia psittaci Pgp3 multiepitope peptide vaccine against a pulmonary chlamydial challenge in mice

Chlamydia psittaci is the pathogen of psittacosis, and it has emerged as a significant public health threat. Because most infections are easily overlooked, a vaccine is recognized as the best solution to control the spread of C. psittaci. Our previous study showed that Pgp3 protein is efficacious as a subunit vaccine while not the best candidate due to the negative effects. Thus, in this study, we tested the ability of a tandem epitope vaccine candidate designated SP based on Pgp3-dominant epitopes to induce protective immunity against pulmonary chlamydial infection. BALB/c mice were intraperitoneally inoculated with multiepitope peptide antigens followed by intranasal infection with C. psittaci. We found that the multiepitope peptide antigens induced strong humoral and cellular immune responses with high Th1-related (IFN-γ and IL-2) and proinflammatory (IL-6) cytokine levels. Meanwhile, the pathogen burden and inflammatory infiltration were significantly reduced in lungs of SP-immunized mice after chlamydial challenge. In addition, the IFN-γ and IL-6 secretion levels in the infected lungs were substantially reduced. Overall, our findings demonstrate that the peptide vaccine SP plays a significant role with good immunogenicity and protective efficacy against C. psittaci lung infection in BALB/c mice, providing important insights towards understanding the potential of peptide vaccines as new vaccine antigens for inducing protective immunity against chlamydial infection.

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

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

T Cell Epitope Predictions

Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.

Immunization against ROS1 by DNA Electroporation Impairs K-Ras-Driven Lung Adenocarcinomas ​

Non-small cell lung cancer (NSCLC) is still the leading cause of cancer death worldwide. Despite the introduction of tyrosine kinase inhibitors and immunotherapeutic approaches, there is still an urgent need for novel strategies to improve patient survival. ROS1, a tyrosine kinase receptor endowed with oncoantigen features, is activated by chromosomal rearrangement or overexpression in NSCLC and in several tumor histotypes. In this work, we have exploited transgenic mice harboring the activated K-Ras oncogene (K-Ras^G12D) that spontaneously develop metastatic NSCLC as a preclinical model to test the efficacy of ROS1 immune targeting. Indeed, qPCR and immunohistochemical analyses revealed ROS1 overexpression in the autochthonous primary tumors and extrathoracic metastases developed by K-Ras^G12D mice and in a derived transplantable cell line. As proof of concept, we have evaluated the effects of the intramuscular electroporation (electrovaccination) of plasmids coding for mouse- and human-ROS1 on the progression of these NSCLC models. A significant increase in survival was observed in ROS1-electrovaccinated mice challenged with the transplantable cell line. It is worth noting that tumors were completely rejected, and immune memory was achieved, albeit only in a few mice. Most importantly, ROS1 electrovaccination was also found to be effective in slowing the development of autochthonous NSCLC in K-Ras^G12D mice.

Radiation-induced small extracellular vesicles as "carriages" promote tumor antigen release and trigger antitumor immunity

Rationale: Accumulating evidence supports the importance of radiation therapy in the induction of antitumor immunity. Small extracellular vesicles (sEVs) play essential roles in tumor antigen loading and delivery. However, the role of sEVs in radiation-induced antitumor immunity remains unclear. It is therefore important to determine the role and regulatory mechanisms of sEVs in radiation-induced immunity.

Methods: Tumor cells were irradiated (8 Gy), and sEVs were purified via ultracentrifugation. Primary tumor and experimental lung metastasis models were established in mice to evaluate antitumor immunity triggered by immunization with sEVs. Proteomic and bioinformatic analyses were performed to identify altered cargos in sEVs induced by radiation. Peptides derived from up-regulated proteins in sEVs were designed and synthesized as vaccines according to major histocompatibility complex (MHC) I binding and immunogenicity.

Results: Here, we demonstrated that sEVs derived from irradiated tumor cells could trigger antitumor immunity against primary tumor and experimental lung metastasis by enhancing CD8⁺ and CD4⁺ T cell infiltration. Radiation may also enrich sEVs with tumor antigens and heat-shock proteins. Furthermore, CUB domain-containing protein 1 (CDCP1) derived from radiation-induced sEVs was identified as a novel tumor-associated antigen and developed as a peptide vaccine that may generate antitumor immune responses.

Conclusions: Our results demonstrate that the use of sEVs secreted by irradiated tumor cells constitutes an efficient approach for tumor antigen delivery and presentation and highlight the role of sEVs in radiation-triggered antitumor immunity.

Cancer testis antigen Cyclin A1 harbors several HLA-A*02:01-restricted T cell epitopes, which are presented and recognized in vivo

Cyclin A1 is a promising antigen for T cell therapy being selectively expressed in high-grade ovarian cancer (OC) and acute myeloid leukemia (AML) stem cells. For adoptive T cell therapy, a single epitope has to be selected, with high affinity to MHC class I and adequate processing and presentation by malignant cells to trigger full activation of specific T cells. In silico prediction with three algorithms indicated 13 peptides of Cyclin A1 9 to 11 amino acids of length to have high affinity to HLA-A*02:01. Ten of them proved to be affine in an HLA stabilization assay using TAP-deficient T2 cells. Their immunogenicity was assessed by repetitive stimulation of CD8⁺ T cells from two healthy donors with single-peptide-pulsed dendritic cells or monocytes. Intracellular cytokine staining quantified the enrichment of peptide-specific functional T cells. Seven peptides were immunogenic, three of them against both donors. Specific cell lines were cloned and used in killing assays to demonstrate recognition of endogenous Cyclin A1 in the HLA-A*02:01-positive AML cell line THP-1. Immunopeptidome analysis based on direct isolation of HLA-presented peptides by mass spectrometry of primary AML and OC samples identified four naturally presented epitopes of Cyclin A1. The immunopeptidome of HeLa cells transfected with Cyclin A1 and HLA-A*02:01 revealed six Cyclin A1-derived HLA ligands. Epitope p410–420 showed high affinity to HLA-A*02:01 and immunogenicity in both donors. It proved to be naturally presented on primary AML blast and provoked spontaneous functional response of T cells from treatment naïve OC and, therefore, warrants further development for clinical application.

Immunogenicity and Immune Silence in Human Cancer

Despite recent advances in cancer immunotherapy, the process of immunoediting early in tumorigenesis remains obscure. Here, we employ a mathematical model that utilizes the Cancer Genome Atlas (TCGA) data to elucidate the contribution of individual mutations and HLA alleles to the immunoediting process. We find that common cancer mutations including BRAF-V600E and KRAS-G12D are predicted to bind none of the common HLA alleles, and are thus “immunogenically silent” in the human population. We identify regions of proteins that are not presented by HLA at a population scale, coinciding with frequently mutated hotspots in cancer, and other protein regions broadly presented across the population in which few mutations occur. We also find that 9/29 common HLA alleles contribute disproportionately to the immunoediting of early oncogenic mutations. These data provide insights into immune evasion of common driver mutations and a molecular basis for the association of particular HLA genotypes with cancer susceptibility.

Bioinformatics analysis of HPV-68 E6 and E7 oncoproteins for designing a therapeutic epitope vaccine against HPV infection

The incidence and mortality of cervical cancer, which mainly results from the infection of human papillomavirus (HPV) is significantly increasing in Xinjiang. According to the previous research, the incidence of HPV-68 in cervical cancer patients in Xinjiang is significantly higher than in other parts of China. HPV E6 and E7 oncoproteins play a crucial role in cervical cancer, and can be used as ideal targets for therapeutic vaccines. Therefore, we analyzed and identified the possible T-cell and B-cell dominant epitopes and various aspects of HPV-68 E6 and E7 oncoproteins, including the physicochemical properties, secondary and tertiary structures using a bioinformatic approach, which provided a basis for designing an effective HPV infection therapeutic vaccine. The results showed that E6 oncoproteins was an unstable and hydrophilic protein, while E7 oncoproteins was unstable and hydrophilic protein. The secondary structure of the E6 oncoproteins consisted of 45.57% alpha helixes, 14.56% extended strands, 4.43% beta turns and 35.44% random coils. The secondary structure of E7 oncoproteins consisted of 35.45% alpha helixes, 17.27% extended strands, 0.91% beta turns and 46.36% random coils. Moreover, our results identified 5 dominant T-cell epitopes and 6 dominant B-cell epitopes in the E6 oncoproteins structure and 5 dominant T-cell epitopes and 3 dominant B-cell epitopes in E7 oncoproteins. In conclusion, this study provides comprehensive biological information about the HPV-68 E6 and E7 oncoproteins, which will lay a theoretical foundation for multi-epitope vaccines against HPV infection.

Identification of HCMV-derived T cell epitopes in seropositive individuals through viral deletion models

In healthy individuals, immune control of persistent human cytomegalovirus (HCMV) infection is effectively mediated by virus-specific CD4+ and CD8+ T cells. However, identifying the repertoire of T cell specificities for HCMV is hampered by the immense protein coding capacity of this betaherpesvirus. Here, we present a novel approach that employs HCMV deletion mutant viruses lacking HLA class I immunoevasins and allows direct identification of naturally presented HCMV-derived HLA ligands by mass spectrometry. We identified 368 unique HCMV-derived HLA class I ligands representing an unexpectedly broad panel of 123 HCMV antigens. Functional characterization revealed memory T cell responses in seropositive individuals for a substantial proportion (28%) of these novel peptides. Multiple HCMV-directed specificities in the memory T cell pool of single individuals indicate that physiologic anti-HCMV T cell responses are directed against a broad range of antigens. Thus, the unbiased identification of naturally presented viral epitopes enabled a comprehensive and systematic assessment of the physiological repertoire of anti-HCMV T cell specificities in seropositive individuals.

In silico design of a T-cell epitope vaccine candidate for parasitic helminth infection

Trichuris trichiura is a parasite that infects 500 million people worldwide, leading to colitis, growth retardation and Trichuris dysentery syndrome. There are no licensed vaccines available to prevent Trichuris infection and current treatments are of limited efficacy. Trichuris infections are linked to poverty, reducing children's educational performance and the economic productivity of adults. We employed a systematic, multi-stage process to identify a candidate vaccine against trichuriasis based on the incorporation of selected T-cell epitopes into virus-like particles. We conducted a systematic review to identify the most appropriate in silico prediction tools to predict histocompatibility complex class II (MHC-II) molecule T-cell epitopes. These tools were used to identify candidate MHC-II epitopes from predicted ORFs in the Trichuris genome, selected using inclusion and exclusion criteria. Selected epitopes were incorporated into Hepatitis B core antigen virus-like particles (VLPs). Bone marrow-derived dendritic cells and bone marrow-derived macrophages responded in vitro to VLPs irrespective of whether the VLP also included T-cell epitopes. The VLPs were internalized and co-localized in the antigen presenting cell lysosomes. Upon challenge infection, mice vaccinated with the VLPs+T-cell epitopes showed a significantly reduced worm burden, and mounted Trichuris-specific IgM and IgG2c antibody responses. The protection of mice by VLPs+T-cell epitopes was characterised by the production of mesenteric lymph node (MLN)-derived Th2 cytokines and goblet cell hyperplasia. Collectively our data establishes that a combination of in silico genome-based CD4+ T-cell epitope prediction, combined with VLP delivery, offers a promising pipeline for the development of an effective, safe and affordable helminth vaccine.

Identification of Antigenic Targets

The ideal cancer target antigen (Ag) is expressed at high copy numbers on neoplastic cells, absent on normal tissues, and contributes to the survival of cancer cells. Despite significant investments in the identification of cell surface Ags, there is a paucity of targets that meet such ideal cancer target criteria. Recent clinical trials in patients with cancer treated with immune checkpoint inhibitors (ICIs) indicate that cluster of differentiation (CD)8⁺ T cells, by means of their T cell receptors (TCRs) recognizing intracellular targets presented as peptides in the context of human leukocyte antigen (peptide-human leukocyte antigen complex; pHLA) molecules on tumor cells, can mediate deep and long-lasting antitumor responses in patients with solid tumors. Therefore, pHLA-target Ags may represent the long sought-after, ideal targets for solid tumor targeting by high-potency oncology compounds.

Tumor-Specific T Cell Activation in Malignant Brain Tumors

Due to their delicate locations as well as aggressive and infiltrative behavior, malignant brain tumors remain a therapeutic challenge. Harnessing the efficacy and specificity of the T-cell response to counteract malignant brain tumor progression and recurrence, represents an attractive treatment option. With the tremendous advances in the current era of immunotherapy, ongoing studies aim to determine the best treatment strategies for mounting a tumor-specific immune response against malignant brain tumors. However, immunosuppression in the local tumor environment, molecular and cellular heterogeneity as well as a lack of suitable targets for tumor-specific vaccination impede the successful implementation of immunotherapeutic treatment strategies in neuro-oncology. In this review, we therefore discuss the role of T cell exhaustion, the genetic and antigenic landscape, potential pitfalls and ongoing efforts to overcome the individual challenges in order to elicit a tumor-specific T cell response.

A large peptidome dataset improves HLA class I epitope prediction across most of the human population

Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.

Peptide Presentation to T Cells: Solving the Immunogenic Puzzle: Systems Immunology Profiling of Antigen Presentation for Prediction of CD8+ T Cell Immunogenicity

The vertebrate immune system uses an impressive arsenal of mechanisms to combat harmful cellular states such as infection. One way is via cells delivering real-time snapshots of their protein content to the cell surface in the form of short peptides. Specialized immune cells (T cells) sample these peptides and assess whether they are foreign, warranting an action such as destruction of the infected cell. The delivery of peptides to the cell surface is termed antigen processing and presentation, and decades of research have provided unprecedented understanding of this process. However, predicting the capacity for a given peptide to be immunogenic-to elicit a T cell response-has remained both enigmatic and a long sought-after goal. In the era of big data, a point is being approached where the steps of antigen processing and presentation can be quantified and assessed against peptide immunogenicity in order to build predictive models. This review presents new findings in this area and contemplates challenges ahead.

Identification of two HLA-A*0201 immunogenic epitopes of lactate dehydrogenase C (LDHC): potential novel targets for cancer immunotherapy

Lactate dehydrogenase C (LDHC) is an archetypical cancer testis antigen with limited expression in adult tissues and re-expression in tumors. This restricted expression pattern together with the important role of LDHC in cancer metabolism renders LDHC a potential target for immunotherapy. This study is the first to investigate the immunogenicity of LDHC using T cells from healthy individuals. LDHC-specific T cell responses were induced by in vitro stimulation with synthetic peptides, or by priming with autologous peptide-pulsed dendritic cells. We evaluated T cell activation by IFN-γ ELISpot and determined cytolytic activity of HLA-A*0201-restricted T cells in breast cancer cell co-cultures. In vitro T cell stimulation induced IFN-γ secretion in response to numerous LDHC-derived peptides. Analysis of HLA-A*0201 responses revealed a significant T cell activation after stimulation with peptide pools 2 (PP2) and 8 (PP8). The PP2- and PP8-specific T cells displayed cytolytic activity against breast cancer cells with endogenous LDHC expression within a HLA-A*0201 context. We identified peptides LDHC⁴¹⁻⁵⁵ and LDHC^288−303 from PP2 and PP8 to elicit a functional cellular immune response. More specifically, we found an increase in IFN-γ secretion by CD8 + T cells and cancer-cell-killing of HLA-A*0201/LDHC positive breast cancer cells by LDHC⁴¹⁻⁵⁵- and LDHC^288−303-induced T cells, albeit with a possible antigen recognition threshold. The majority of induced T cells displayed an effector memory phenotype. To conclude, our findings support the rationale to assess LDHC as a targetable cancer testis antigen for immunotherapy, and in particular the HLA-A*0201 restricted LDHC^41–55 and LDHC^288–303 peptides within LDHC.

Immunoinformatics and Epitope Prediction

With advancements in sequencing technologies, vast amount of experimental data has accumulated. Due to rapid progress in the development of bioinformatics tools and the accumulation of data, immunoinformatics or computational immunology emerged as a special branch of bioinformatics which utilizes bioinformatics approaches for understanding and interpreting immunological data. One extensively studied aspect of applied immunology involves using available databases and tools for prediction of B- and T-cell epitopes. B and T cells comprise two arms of adaptive immunity.This chapter first reviews the methodology we used for computational identification of B- and T-cell epitopes against enterotoxigenic Escherichia coli (ETEC). Then we discuss other databases of epitopes and analysis tools for T-cell and B-cell epitope prediction and vaccine design. The predicted peptides were analyzed for conservation and population coverage. HLA distribution analysis for predicted epitopes identified efficient MHC binders. Epitopes were further tested using computational docking studies to bind in MHC-I molecule cleft. The predicted epitopes were conserved and covered more than 80% of the world population.

Bioinformatics Tools and Resources for Cancer Immunotherapy Study

In recent years, cancer immunotherapy has emerged as a highly promising approach to treat patients with cancer, as the patient's own immune system is harnessed to attack cancer cells. However, the application of these approaches is still limited to a minority of patients with cancer and it is difficult to predict which patients will derive the greatest clinical benefit.One of the challenges faced by the biomedical community in the search of more effective biomarkers is the fact that translational research efforts involve collecting and accessing data at many different levels: from the type of material examined (e.g., cell line, animal models, clinical samples) to multiple data type (e.g., pharmacodynamic markers, genetic sequencing data) to the scale of a study (e.g., small preclinical study, moderate retrospective study on stored specimen sets, clinical trials with large cohorts).This chapter reviews several publicly available bioinformatics tools and data resources for high throughput molecular analyses applied to a range of data types, including those generated from microarray, whole-exome sequencing (WES), RNA-seq, DNA copy number, and DNA methylation assays, that are extensively used for integrative multidimensional data analysis and visualization.

An Immunoinformatics Approach in Design of Synthetic Peptide Vaccine Against Influenza Virus

Peptide-based vaccines are an appealing strategy which involves usage of short synthetic peptides to engineer a highly targeted immune response. These short synthetic peptides contain potential T- and B-cell epitopes. Experimental approaches in identifying these epitopes are time-consuming and expensive; hence immunoinformatics approach came into picture. Immuninformatics approach involves epitope prediction tools, molecular docking, and population coverage analysis in design of desired immunogenic peptides. In order to overcome the antigenic variation of viruses, conserved regions are targeted to find the potential epitopes. The present chapter demonstrates the use of immunoinformatics approach to select potential peptide containing multiple T- (CD8+ and CD4+) and B-cell epitopes from Avian H3N2 M1 Protein. Further, molecular docking (to analyse HLA-peptide interaction) and population coverage analysis have been used to verify the potential of peptide to be presented by polymorphic HLA molecules. In silico approach of epitope prediction has proven to be successful methodology in screening the putative epitopes among numerous possible vaccine targets in a given protein.

Computational investigation of peptide binding stabilities of HLA-B*27 and HLA-B*44 alleles

Major Histocompatibility Complex (MHC) is a cell surface glycoprotein that binds to foreign antigens and presents them to T lymphocyte cells on the surface of Antigen Presenting Cells (APCs) for appropriate immune recognition. Recently, studies focusing on peptide-based vaccine design have allowed a better understanding of peptide immunogenicity mechanisms, which is defined as the ability of a peptide to stimulate CTL-mediated immune response. Peptide immunogenicity is also known to be related to the stability of peptide-loaded MHC (pMHC) complex. In this study, ENCoM server was used for structure-based estimation of the impact of single point mutations on pMHC complex stabilities. For this purpose, two human MHC molecules from the HLA-B*27 group (HLA-B*27:05 and HLA-B*27:09) in complex with four different peptides (GRFAAAIAK, RRKWRRWHL, RRRWRRLTV and IRAAPPPLF) and three HLA-B*44 molecules (HLA-B*44:02, HLA-B*44:03 and HLA-B*44:05) in complex with two different peptides (EEYLQAFTY and EEYLKAWTF) were analyzed. We found that the stability of pMHC complexes is dependent on both peptide sequence and MHC allele. Furthermore, we demonstrate that allele-specific peptide-binding preferences can be accurately revealed using structure-based computational methods predicting the effect of mutations on protein stability.

A Non-interventional Clinical Trial Assessing Immune Responses After Radiofrequency Ablation of Liver Metastases From Colorectal Cancer

Background: Radiofrequency ablation (RFA) is an established treatment option for malignancies located in the liver. RFA-induced irreversible coagulation necrosis leads to the release of danger signals and cellular content. Hence, RFA may constitute an endogenous in situ tumor vaccination, stimulating innate and adaptive immune responses, including tumor-antigen specific T cells. This may explain a phenomenon termed abscopal effect, namely tumor regression in untreated lesions evidenced after distant thermal ablation or irradiation. In this study, we therefore assessed systemic and local immune responses in individual patients treated with RFA.

Methods: For this prospective clinical trial, patients with liver metastasis from colorectal carcinoma (mCRC) receiving RFA and undergoing metachronous liver surgery for another lesion were recruited (n = 9) during a 5-year period. Tumor and non-malignant liver tissue samples from six patients were investigated by whole transcriptome sequencing and tandem-mass spectrometry, characterizing naturally presented HLA ligands. Tumor antigen-derived HLA-restricted peptides were selected by different predefined approaches. Further, candidate HLA ligands were manually curated. Peripheral blood mononuclear cells were stimulated in vitro with epitope candidate peptides, and functional T cell responses were assessed by intracellular cytokine staining. Immunohistochemical markers were additionally investigated in surgically resected mCRC from patients treated with (n = 9) or without RFA (n = 7).

Results: In all six investigated patients, either induced immune responses and/or pre-existing T cell immunity against the selected targets were observed. Multi-cytokine responses were inter alia directed against known tumor antigens such as cyclin D1 but also against a (predicted) mutation contained in ERBB3. Immunohistochemistry did not show a relevant influx of immune cells into distant malignant lesions after RFA treatment (n = 9) as compared to the surgery only mCRC group (n = 7).

Conclusions: Using an individualized approach for target selection, RFA induced and/or boosted T cell responses specific for individual tumor antigens were more frequently detectable as compared to previously published observations with well-characterized tumor antigens. However, the witnessed modest RFA-induced immunological effects alone may not be sufficient for the rejection of established tumors. Therefore, these findings warrant further clinical investigation including the assessment of RFA combination therapies e.g., with immune stimulatory agents, cancer vaccination, and/or immune checkpoint inhibitors.

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

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

HLA-F Allele-Specific Peptide Restriction Represents an Exceptional Proteomic Footprint

Peptide-dependent engagement between human leucocyte antigens class I (HLA-I) molecules and their cognate receptors has been extensively analyzed. HLA-F belongs to the non-classical HLA-Ib molecules with marginal polymorphic nature and tissue restricted distribution. The three common allelic variants HLA-F*01:01/01:03/01:04 are distinguished by polymorphism outside the peptide binding pockets (residue 50, α1 or residue 251, α3) and are therefore not considered relevant for attention. However, peptide selection and presentation undergoes a most elaborated extraction from the whole available proteome. It is known that HLA-F confers a beneficial effect on disease outcome during HIV-1 infections. The interaction with the NK cell receptor initiates an antiviral downstream immune response and lead to delayed disease progression. During the time of HIV infection, HLA-F expression is upregulated, while its interaction with KIR3DS1 is diminished. The non-polymorphic nature of HLA-F facilitates the conclusion that understanding HLA-F peptide selection and presentation is essential to a comprehensive understanding of this dynamic immune response. Utilizing soluble HLA technology we recovered stable pHLA-F*01:01, 01:03 and 01:04 complexes from K562 cells and analyzed the peptides presented. Utilizing a sophisticated LC-MS-method, we analyzed the complete K562 proteome and matched the peptides presented by the respective HLA-F subtypes with detected proteins. All peptides featured a length of 8 to 24 amino acids and are not N-terminally anchored; the C-terminus is preferably anchored by Lys. To comprehend the alteration of the pHLA-F surface we structurally compared HLA-F variants bound to selected peptides. The peptides were selected from the same cellular content; however, no overlap between the proteomic source of F*01:01, 01:03 or 01:04 selected peptides could be observed. Recognizing the balance between HLA-F expression, HLA-F polymorphism and peptide selection will support to understand the role of HLA-F in viral pathogenesis.

PromPDD, a web-based tool for the prediction, deciphering and design of promiscuous peptides that bind to HLA class I molecules

Promiscuous peptides that can be presented by multiple human leukocyte antigens (HLAs) have great potential for the development of vaccines with wide population coverage. However, the current available methods for the prediction of peptides that bind to major histocompatibility complex (MHC) are mainly aimed at the rapid or mass screening of potential T cell epitopes from pathogen antigens or proteomics. The current approaches do not allow deciphering the contribution of the residue at each peptide position to the promiscuous binding ability of the peptide or obtaining guidelines for the design of promiscuous peptides. In this study, we re-evaluated and characterized four matrix-based prediction models that have been extensively used for the prediction of HLA-binding peptides and found that the prediction models generated based on the average relative binding (ARB) matrix shared a consistent and conservative threshold for all well-studied HLA class I alleles. Evaluations performed using datasets of HLA supertype-specific peptides with various cross-binding abilities and peptide mutant analogues indicated that the ARB-based binding matrices could be used to decipher and design promiscuous peptides that bind to multiple HLA molecules. A web-based tool called PromPDD was developed using ARB matrix-based models, and this tool enables the prediction, deciphering and design of promiscuous peptides that bind to multiple HLA molecules within or across HLA supertypes in a simpler and more direct manner. Furthermore, we expanded the application of PromPDD to HLA class I alleles with limited experimentally verified data by generating pan-specific matrices using a derived modular method, and 2641 HLA molecules encoded by HLA-A and HLA-B genes are available in PromPDD. PromPDD, which is freely available at http://www.immunoinformatics.net/PromPDD/, is the first tool for the deciphering and design of promiscuous peptides that bind to HLA class I molecules.

Peptides Derived of Kunitz-Type Serine Protease Inhibitor as Potential Vaccine Against Experimental Schistosomiasis

Schistosomiasis is a significant public health problem in sub-Saharan Africa, China, Southeast Asia, and regions of South and Central America affecting about 189 million people. Kunitz-type serine protease inhibitors have been identified as important players in the interaction of other flatworm parasites with their mammalian hosts. They are involved in host blood coagulation, fibrinolysis, inflammation, and ion channel blocking, all of them critical biological processes, which make them interesting targets to develop a vaccine. Here, we evaluate the protective efficacy of chemically synthesized T- and B-cell peptide epitopes derived from a kunitz protein from Schistosoma mansoni. Putative kunitz-type protease inhibitor proteins were identified in the S. mansoni genome, and their expression was analyzed by RNA-seq. Gene expression analyses showed that the kunitz protein Smp_147730 (Syn. Smp_311670) was dramatically and significantly up-regulated in schistosomula and adult worms when compared to the invading cercariae. T- and B-cell epitopes were predicted using bioinformatics tools, chemically synthesized, and formulated in the Adjuvant Adaptation (ADAD) vaccination system. BALB/c mice were vaccinated and challenged with S. mansoni cercariae. Kunitz peptides were highly protective in vaccinated BALB/c mice showing significant reductions in recovery of adult females (89-91%) and in the numbers of eggs trapped in the livers (77-81%) and guts (57-77%) of mice. Moreover, liver lesions were significantly reduced in vaccinated mice (64-65%) compared to infected control mice. The vaccination regime was well-tolerated with both peptides. We propose the use of these peptides, alone or in combination, as reliable candidates for vaccination against schistosomiasis.

Tumor-Specific Regulatory T Cells from the Bone Marrow Orchestrate Antitumor Immunity in Breast Cancer

Endogenous antitumor effector T-cell responses and immune-suppressive regulatory T cells (Treg) critically influence the prognosis of patients with cancer, yet many of the mechanisms of how this occurs remain unresolved. On the basis of an analysis of the function, antigen specificity, and distribution of tumor antigen-reactive T cells and Tregs in patients with breast cancer and transgenic mouse tumor models, we showed that tumor-specific Tregs were selectively activated in the bone marrow (BM) and egressed into the peripheral blood. The BM was constantly depleted of tumor-specific Tregs and was instead a site of increased induction and activity of tumor-reactive effector/memory T cells. Treg egress from the BM was associated with activation-induced expression of peripheral homing receptors such as CCR2. Because breast cancer tissues express the CCR2 ligand CCL2, the activation and egress of tumor antigen-specific Tregs in the BM resulted in the accumulation of Tregs in breast tumor tissue. Such immune compartmentalization and redistribution of T-cell subpopulations between the BM and peripheral tissues were achieved by vaccination with adenoviral vector-encoded TRP-2 tumor antigen in a RET transgenic mouse model of spontaneous malignant melanoma. Thus, the BM simultaneously represented a source of tumor-infiltrating Tregs and a site for the induction of endogenous tumor-specific effector T-cell responses, suggesting that both antitumor immunity and local immune suppression are orchestrated in the BM.

In silico/In vivo analysis of high-risk papillomavirus L1 and L2 conserved sequences for development of cross-subtype prophylactic vaccine

Human papillomavirus (HPV) is the most common sexually transmitted infection in the world and the main cause of cervical cancer. Nowadays, the virus-like particles (VLPs) based on L1 proteins have been considered as the best candidate for vaccine development against HPV infections. Two commercial HPV (Gardasil and Cervarix) are available. These HPV VLP vaccines induce genotype-limited protection. The major impediments such as economic barriers especially gaps in financing obstructed the optimal delivery of vaccines in developing countries. Thus, many efforts are underway to develop the next generation of vaccines against other types of high-risk HPV. In this study, we developed DNA constructs (based on L1 and L2 genes) that were potentially immunogenic and highly conserved among the high-risk HPV types. The framework of analysis include (1) B-cell epitope mapping, (2) T-cell epitope mapping (i.e., CD4⁺ and CD8⁺ T cells), (3) allergenicity assessment, (4) tap transport and proteasomal cleavage, (5) population coverage, (6) global and template-based docking, and (7) data collection, analysis, and design of the L1 and L2 DNA constructs. Our data indicated the 8-epitope candidates for helper T-cell and CTL in L1 and L2 sequences. For the L1 and L2 constructs, combination of these peptides in a single universal vaccine could involve all world population by the rate of 95.55% and 96.33%, respectively. In vitro studies showed high expression rates of multiepitope L1 (~57.86%) and L2 (~68.42%) DNA constructs in HEK-293T cells. Moreover, in vivo studies indicated that the combination of L1 and L2 DNA constructs without any adjuvant or delivery system induced effective immune responses, and protected mice against C3 tumor cells (the percentage of tumor-free mice: ~66.67%). Thus, the designed L1 and L2 DNA constructs would represent promising applications for HPV vaccine development.

TCR Fingerprinting and Off-Target Peptide Identification

Adoptive T cell therapy using patient T cells redirected to recognize tumor-specific antigens by expressing genetically engineered high-affinity T-cell receptors (TCRs) has therapeutic potential for melanoma and other solid tumors. Clinical trials implementing genetically modified TCRs in melanoma patients have raised concerns regarding off-target toxicities resulting in lethal destruction of healthy tissue, highlighting the urgency of assessing which off-target peptides can be recognized by a TCR. As a model system we used the clinically efficacious NY-ESO-1-specific TCR C²⁵⁹, which recognizes the peptide epitope SLLMWITQC presented by HLA-A^*02:01. We investigated which amino acids at each position enable a TCR interaction by sequentially replacing every amino acid position outside of anchor positions 2 and 9 with all 19 possible alternative amino acids, resulting in 134 peptides (133 altered peptides plus epitope peptide). Each peptide was individually evaluated using three different in vitro assays: binding of the NY-ESO^c259 TCR to the peptide, peptide-dependent activation of TCR-expressing cells, and killing of peptide-presenting target cells. To represent the TCR recognition kernel, we defined Position Weight Matrices (PWMs) for each assay by assigning normalized measurements to each of the 20 amino acids in each position. To predict potential off-target peptides, we applied a novel algorithm projecting the PWM-defined kernel into the human proteome, scoring NY-ESO^c259 TCR recognition of 336,921 predicted human HLA-A^*02:01 binding 9-mer peptides. Of the 12 peptides with high predicted score, we confirmed 7 (including NY-ESO-1 antigen SLLMWITQC) strongly activate human primary NY-ESO^c259-expressing T cells. These off-target peptides include peptides with up to 7 amino acid changes (of 9 possible), which could not be predicted using the recognition motif as determined by alanine scans. Thus, this replacement scan assay determines the “TCR fingerprint” and, when coupled with the algorithm applied to the database of human 9-mer peptides binding to HLA-A^*02:01, enables the identification of potential off-target antigens and the tissues where they are expressed. This platform enables both screening of multiple TCRs to identify the best candidate for clinical development and identification of TCR-specific cross-reactive peptide recognition and constitutes an improved methodology for the identification of potential off-target peptides presented on MHC class I molecules.

Application of mass spectrometry-based MHC immunopeptidome profiling in neoantigen identification for tumor immunotherapy

One of the challenges for cancer vaccine and adoptive T-cell-based immunotherapy is to identify the major histocompatibility complex (MHC)-associated non-self neoantigens recognized by T cells. T cell epitope in silico prediction algorithms have been widely used for neoantigen prediction; nonetheless, this platform lacks the experimental evidence of directly identification of the presented epitopes on cell surface. Currently, mass spectrometry (MS)-based proteomics is an advanced analytical technology for large-scale peptide sequencing, which has become a powerful tool for directly profiling the immunopeptidome presented by MHC molecules. Integrating with next-generation sequencing, proteogenomic analysis provides the "gold standard" for neoantigen identification at protein level. This method discovers the tumor-specific neoantigens derived from somatic mutations, proteasome splicing, noncoding RNA, and post-translational modified antigens. Herein, we review basis of antigen processing and presentation, tumor antigen classification, existing approaches for neoantigen discovery, quantitative proteomics, epitope prediction programs, and advantages and drawbacks of proteomics workflow for MHC immunopeptidome profiling. Furthermore, we summarize 40 recently published reports addressing the fundamental theory, breakthrough and most advanced updates for the mass spectrometry-based neoantigen discovery for cancer immunotherapy.

Developing neoantigen-targeted T cell-based treatments for solid tumors

Stimulating an immune response against cancer through adoptive transfer of tumor-targeting lymphocytes has shown great promise in hematological malignancies, but clinical efficacy against many common solid epithelial cancers remains low. Targeting 'neoantigens'-the somatic mutations expressed only by tumor cells-might enable tumor destruction without causing undue damage to vital healthy tissues. Major challenges to targeting neoantigens with T cells include heterogeneity and variability in antigen processing and presentation of targets by tumors, and an incomplete understanding of which T cell qualities are essential for clinically effective therapies. Finally, the prospect of targeting somatic tumor mutations to promote T cell destruction of cancer must contend with the biology that not all tumor-expressed 'neoepitopes' actually generate neoantigens that can be functionally recognized and provoke an effective immune response. In this Review, we discuss the promise, progress and challenges for improving neoantigen-targeted T cell-based immunotherapies for cancer.

Computational approaches for characterizing the tumor immune microenvironment

Recent advances in high-throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor-associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor-associated immune system in bulk tissues and at the single-cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.

HLA class I-restricted T cell epitopes isolated and identified from myeloid leukemia cells

Leukemia-associated antigens (LAAs) and HLA-I epitopes published previously have shown promise in inducing leukemia-specific T cell responses. However, the clinical responses are limited, and clinical effectiveness is yet to be achieved. Limitations, among others, being the LAAs themselves, the indirect approach to HLA-I epitope identification by reverse immunology, and the use of single or few LAAs and HLA-I epitopes, which limits the spectrum of inducible tumor-specific T cells. Use of a direct approach to identify naturally processed and presented HLA-I epitopes from LAAs, and higher numbers of antigens for T cell-mediated immunotherapy for leukemia may enhance clinical responses and broaden clinical effectiveness. In a prior study we used immunoaffinity purification of HLA-I peptide complexes from the differentiated myeloid tumor cell lines MUTZ3 and THP1 coupled to high-performance liquid chromatography tandem mass spectrometry (LC-MS/MS). From this we identified in the current study seven new HLA-I epitopes and the corresponding LAAs for myeloid leukemia. In comparison, the myeloid HLA-I epitopes reported here were generally stronger HLA-binders that induce stronger T cell responses than those previously published, and their source LAAs had higher immunogenicity, higher expression levels in myeloid tumors cells compared to normal hemopoietin and other major normal tissues, and more protein interaction partners, and they are targeted by CD8 T cells in CML patients. This study analyses and compares the LAAs and HLA-I epitopes based on various immunotherapeutic targets selection criteria, and highlights new targets for T cell-mediated immunotherapy for leukemia.

T cell recognition of novel shared breast cancer antigens is frequently observed in peripheral blood of breast cancer patients

Advances within cancer immunotherapy have fueled a paradigm shift in cancer treatment, resulting in increasing numbers of cancer types benefitting from novel treatment options. Despite originally being considered an immunologically silent malignancy, recent studies encourage the research of breast cancer immunogenicity to evaluate immunotherapy as a treatment strategy. However, the epitope landscape in breast cancer is minimally described, limiting the options for antigen-specific, targeted strategies. Aromatase, never in mitosis A-related kinase 3 (NEK3), protein inhibitor of activated STAT3 (PIAS3), and prolactin are known as upregulated proteins in breast cancer. In the present study, these four proteins are identified as novel T cell targets in breast cancer. From the four proteins, 147 peptides were determined to bind HLA-A*0201 and -B*0702 using a combined in silico/in vitro affinity screening. T cell recognition of all 147 peptide-HLA-A*0201/-B*0702 combinations was assessed through the use of a novel high-throughput method utilizing DNA barcode labeled multimers. T cell recognition of sequences within all four proteins was demonstrated in peripheral blood of patients, and significantly more T cell responses were detected in patients compared to healthy donors for both HLA-A*0201 and -B*0702. Notably, several of the identified responses were directed toward peptides, with a predicted low or intermediate binding affinity. This demonstrates the importance of including low-affinity binders in the search for epitopes within shared tumor associated antigens (TAAs), as these might be less subject to immune tolerance mechanisms. The study presents four novel TAAs containing multiple possible targets for immunotherapy of breast cancer.

Ligandomes obtained from different HLA-class II-molecules are homologous for N- and C-terminal residues outside the peptide-binding cleft

Human CD4+ T lymphocytes play an important role in inducing potent immune responses. T cells are activated and stimulated by peptides presented in human leucocyte antigen (HLA)-class II molecules. These HLA-class II molecules typically present peptides of between 12 and 20 amino acids in length. The region that interacts with the HLA molecule, designated as the peptide-binding core, is highly conserved in the residues which anchor the peptide to the molecule. In addition, as these peptides are the product of proteolytic cleavages, certain conserved residues may be expected at the N- and C-termini outside the binding core. To study whether similar conserved residues are present in different cell types, potentially harbouring different proteolytic enzymes, the ligandomes of HLA-DRB1*03:01/HLA-DRB > 1 derived from two different cell types (dendritic cells and EBV-transformed B cells) were identified with mass spectrometry and the binding core and N- and C-terminal residues of a total of 16,568 peptides were analysed using the frequencies of the amino acids in the human proteome. Similar binding motifs were found as well as comparable conservations in the N- and C-terminal residues. Furthermore, the terminal conservations of these ligandomes were compared to the N- and C-terminal conservations of the ligandome acquired from dendritic cells homozygous for HLA-DRB1*04:01. Again, comparable conservations were evident with only minor differences. Taken together, these data show that there are conservations in the terminal residues of peptides, presumably the result of the activity of proteases involved in antigen processing.

A transplantable tumor model allowing investigation of NY-BR-1-specific T cell responses in HLA-DRB1*0401 transgenic mice

BACKGROUND

NY-BR-1 has been described as a breast cancer associated differentiation antigen with intrinsic immunogenicity giving rise to endogenous T and B cell responses. The current study presents the first murine tumor model allowing functional investigation of NY-BR-1-specific immune responses in vivo.

METHODS

A NY-BR-1 expressing tumor model was established in DR4tg mice based on heterotopic transplantation of stable transfectant clones derived from the murine H2 compatible breast cancer cell line EO771. Composition and phenotype of tumor infiltrating immune cells were analyzed by qPCR and FACS. MHC I binding affinity of candidate CTL epitopes predicted in silico was determined by FACS using the mutant cell line RMA-S. Frequencies of NY-BR-1 specific CTLs among splenocytes of immunized mice were quantified by FACS with an epitope loaded D^b-dextramer. Functional CTL activity was determined by IFNγ catch or IFNγ ELISpot assays and statistical analysis was done applying the Mann Whitney test. Tumor protection experiments were performed by immunization of DR4tg mice with replication deficient recombinant adenovirus followed by s.c. challenge with NY-BR-1 expressing breast cancer cells.

RESULTS

Our results show spontaneous accumulation of CD8⁺ T cells and F4/80⁺ myeloid cells preferentially in NY-BR-1 expressing tumors. Upon NY-BR-1-specific immunization experiments combined with in silico prediction and in vitro binding assays, the first NY-BR-1-specific H2-D^b-restricted T cell epitope could be identified. Consequently, flow cytometric analysis with fluorochrome conjugated multimers showed enhanced frequencies of CD8⁺ T cells specific for the newly identified epitope in spleens of immunized mice. Moreover, immunization with Ad.NY-BR-1 resulted in partial protection against outgrowth of NY-BR-1 expressing tumors and promoted intratumoral accumulation of macrophages.

CONCLUSION

This study introduces the first H2-D^b-resctricted CD8⁺ T cell epitope-specific for the human breast cancer associated tumor antigen NY-BR-1. Our novel, partially humanized tumor model enables investigation of the interplay between HLA-DR4-restricted T cell responses and CTLs within their joint attack of NY-BR-1 expressing tumors.

Autoimmunity (or Not) in Atopic Dermatitis

Atopic dermatitis (AD), one of the most frequent inflammatory skin diseases worldwide, is believed to result from a disturbed skin barrier as well as aberrant immune reactions against per se harmless allergens. Starting mostly during childhood with a chronic, remitting relapsing course, the disease can persist into adulthood in about one fifth of patients. Immune reactions to self-proteins have been observed in AD patients already in the beginning of the Twentieth century, when human cellular extracts were shown to provoke skin lesions. However, the term “autoimmunity” has never been claimed, since AD is first and foremost an atopic disease. In contrast, this IgE-hallmarked autoreactivity was termed “autoallergy” and is ongoing discussed regarding its impact on the disease. Since severely affected patients tend to develop IgE-hypersensitivity reactions to numerous environmental allergens, the impact of immune responses to self-proteins is difficult to determine. On the other hand: any autoreactivity, irrespective of the magnitude, implicates the potential of driving the chronification of the disease while shaping the immune response. This review article revisits the observations made on autoallergy from an actual point of view and tries to approach the question whether these still point to a contribution to the disease.

Toward in silico Identification of Tumor Neoantigens in Immunotherapy

Cancer immunotherapy includes cancer vaccination, adoptive T cell transfer (ACT) with chimeric antigen receptor (CAR) T cells, and administration of tumor-infiltrating lymphocytes and immune-checkpoint blockade such as anti-CTLA4/anti-PD1 inhibitors that can directly or indirectly target tumor neoantigens and elicit a T cell response. Accurate, rapid, and cost-effective identification of neoantigens, however, is critical for successful immunotherapy. Here, we review computational issues for neoantigen identification by summarizing the various sources of neoantigens and their identification from high-throughput sequencing data. Several opinions are presented to inspire further discussions toward improving neoantigen identification. Continuing efforts are required to improve the sensitivity and specificity of bona fide neoantigens, taking advantage of the development of high-throughput sequencing techniques for effective and personalized cancer immunotherapy.