Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space

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

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

Autophagy degrades immunogenic endogenous retroelements induced by 5-azacytidine in acute myeloid leukemia

The hypomethylating agent 5-azacytidine (AZA) is the first-line treatment for AML patients unfit for intensive chemotherapy. The effect of AZA results in part from T-cell cytotoxic responses against MHC-I-associated peptides (MAPs) deriving from hypermethylated genomic regions such as cancer-testis antigens (CTAs), or endogenous retroelements (EREs). However, evidence supporting higher ERE MAPs presentation after AZA treatment is lacking. Therefore, using proteogenomics, we examined the impact of AZA on the repertoire of MAPs and their source transcripts. AZA-treated AML upregulated both CTA and ERE transcripts, but only CTA MAPs were presented at greater levels. Upregulated ERE transcripts triggered innate immune responses against double-stranded RNAs but were degraded by autophagy, and not processed into MAPs. Autophagy resulted from the formation of protein aggregates caused by AZA-dependent inhibition of DNMT2. Autophagy inhibition had an additive effect with AZA on AML cell proliferation and survival, increased ERE levels, increased pro-inflammatory responses, and generated immunogenic tumor-specific ERE-derived MAPs. Finally, autophagy was associated with a lower abundance of CD8+ T-cell markers in AML patients expressing high levels of EREs. This work demonstrates that AZA-induced EREs are degraded by autophagy and shows that inhibiting autophagy can improve the immune recognition of AML blasts in treated patients.

Development and use of machine learning algorithms in vaccine target selection

Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

Breast cancer immunopeptidomes contain numerous shared tumor antigens

Hormone receptor-positive breast cancer (HR+) is immunologically cold and has not benefited from advances in immunotherapy. In contrast, subsets of triple-negative breast cancer (TNBC) display high leukocytic infiltration and respond to checkpoint blockade. CD8+ T cells, the main effectors of anticancer responses, recognize MHC I-associated peptides (MAPs). Our work aimed to characterize the repertoire of MAPs presented by HR+ and TNBC tumors. Using mass spectrometry, we identified 57,094 unique MAPs in 26 primary breast cancer samples. MAP source genes highly overlapped between both subtypes. We identified 25 tumor-specific antigens (TSAs) mainly deriving from aberrantly expressed regions. TSAs were most frequently identified in TNBC samples and were more shared among The Cancer Genome Atlas (TCGA) database TNBC than HR+ samples. In the TNBC cohort, the predicted number of TSAs positively correlated with leukocytic infiltration and overall survival, supporting their immunogenicity in vivo. We detected 49 tumor-associated antigens (TAAs), some of which derived from cancer-associated fibroblasts. Functional expansion of specific T cell assays confirmed the in vitro immunogenicity of several TSAs and TAAs. Our study identified attractive targets for cancer immunotherapy in both breast cancer subtypes. The higher prevalence of TSAs in TNBC tumors provides a rationale for their responsiveness to checkpoint blockade.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Overview of Nucleocapsid-Targeting Vaccines against COVID-19

The new SARS-CoV-2 coronavirus, which emerged in late 2019, is a highly variable causative agent of COVID-19, a contagious respiratory disease with potentially severe complications. Vaccination is considered the most effective measure to prevent the spread and complications of this infection. Spike (S) protein-based vaccines were very successful in preventing COVID-19 caused by the ancestral SARS-CoV-2 strain; however, their efficacy was significantly reduced when coronavirus variants antigenically different from the original strain emerged in circulation. This is due to the high variability of this major viral antigen caused by escape from the immunity caused by the infection or vaccination with spike-targeting vaccines. The nucleocapsid protein (N) is a much more conserved SARS-CoV-2 antigen than the spike protein and has therefore attracted the attention of scientists as a promising target for broad-spectrum vaccine development. Here, we summarized the current data on various N-based COVID-19 vaccines that have been tested in animal challenge models or clinical trials. Despite the high conservatism of the N protein, escape mutations gradually occurring in the N sequence can affect its protective properties. During the three years of the pandemic, at least 12 mutations have arisen in the N sequence, affecting more than 40 known immunogenic T-cell epitopes, so the antigenicity of the N protein of recent SARS-CoV-2 variants may be altered. This fact should be taken into account as a limitation in the development of cross-reactive vaccines based on N-protein.

A robust deep learning workflow to predict CD8 + T-cell epitopes

BACKGROUND

T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes.

METHODS

We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies.

RESULTS

TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP .

CONCLUSIONS

This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.

A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.

BamQuery: a proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens

MHC-I-associated peptides deriving from non-coding genomic regions and mutations can generate tumor-specific antigens, including neoantigens. Quantifying tumor-specific antigens' RNA expression in malignant and benign tissues is critical for discriminating actionable targets. We present BamQuery, a tool attributing an exhaustive RNA expression to MHC-I-associated peptides of any origin from bulk and single-cell RNA-sequencing data. We show that many cryptic and mutated tumor-specific antigens can derive from multiple discrete genomic regions, abundantly expressed in normal tissues. BamQuery can also be used to predict MHC-I-associated peptides immunogenicity and identify actionable tumor-specific antigens de novo.

Contribution of an Asian-prevalent HLA haplotype to the risk of HBV-related hepatocellular carcinoma

Liver cancer, particularly hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), is more common in Asians than in Caucasians. This is due, at least in part, to regional differences in the prevalence of exogenous factors such as HBV; however, endogenous factors specific to Asia might also play a role. Such endogenous factors include HLA (human leukocyte antigen) genes, which are considered candidates due to their high racial diversity. Here, we performed a pancancer association analysis of 147 alleles of HLA-class I/II genes (HLA-A, B, and C/DRB1, DQA1, DQB1, DPA1, and DPB1) in 31,727 cases of 12 cancer types, including 1684 liver cancer cases and 107,103 controls. HLA alleles comprising a haplotype prevalent in Asia were significantly associated with pancancer risk (e.g., odds ratio [OR] for a DRB1*15:02 allele = 1.12, P = 2.7 × 10-15), and the associations were particularly strong in HBV-related HCC (OR 1.95, P = 2.8 × 10-5). In silico prediction suggested that the DRB1*15:02 molecule encoded by the haplotype does not bind efficiently to HBV-derived peptides. RNA sequencing indicated that HBV-related HCC in carriers of the haplotype shows low infiltration by NK cells. These results indicate that the Asian-prevalent HLA haplotype increases the risk of HBV-related liver cancer risk by attenuating immune activity against HBV infection, and by reducing NK cell infiltration into the tumor.

Regulation of CD19 CAR-T cell activation based on an engineered downstream transcription factor

CAR-T cells present a highly effective therapeutic option for several malignant diseases, based on their ability to recognize the selected tumor surface marker in an MHC-independent manner. This triggers cell activation and cytokine production, resulting in the killing of the cancerous cell presenting markers recognized by the chimeric antigen receptor. CAR-T cells are highly potent serial killers that may cause serious side effects, so their activity needs to be carefully controlled. Here we designed a system to control the proliferation and activation state of CARs based on downstream NFAT transcription factors, whose activity can be regulated via chemically induced heterodimerization systems. Chemical regulators were used to either transiently trigger engineered T cell proliferation or suppress CAR-mediated activation when desired or to enhance activation of CAR-T cells upon engagement of cancer cells, shown also in vivo. Additionally, an efficient sensor to monitor activated CD19 CAR-T cells in vivo was introduced. This implementation in CAR-T cell regulation offers an efficient way for on-demand external control of CAR-T cell activity to improve their safety.

A systems approach evaluating the impact of SARS-CoV-2 variant of concern mutations on CD8+ T cell responses

T cell recognition of SARS-CoV-2 antigens after vaccination and/or natural infection has played a central role in resolving SARS-CoV-2 infections and generating adaptive immune memory. However, the clinical impact of SARS-CoV-2-specific T cell responses is variable and the mechanisms underlying T cell interaction with target antigens are not fully understood. This is especially true given the virus' rapid evolution, which leads to new variants with immune escape capacity. In this study, we used the Omicron variant as a model organism and took a systems approach to evaluate the impact of mutations on CD8+ T cell immunogenicity. We computed an immunogenicity potential score for each SARS-CoV-2 peptide antigen from the ancestral strain and Omicron, capturing both antigen presentation and T cell recognition probabilities. By comparing ancestral vs. Omicron immunogenicity scores, we reveal a divergent and heterogeneous landscape of impact for CD8+ T cell recognition of mutated targets in Omicron variants. While T cell recognition of Omicron peptides is broadly preserved, we observed mutated peptides with deteriorated immunogenicity that may assist breakthrough infection in some individuals. We then combined our scoring scheme with an in silico mutagenesis, to characterise the position- and residue-specific theoretical mutational impact on immunogenicity. While we predict many escape trajectories from the theoretical landscape of substitutions, our study suggests that Omicron mutations in T cell epitopes did not develop under cell-mediated pressure. Our study provides a generalisable platform for fostering a deeper understanding of existing and novel variant impact on antigen-specific vaccine- and/or infection-induced T cell immunity.

Challenges in neoantigen-directed therapeutics

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

Induced pluripotent stem cells display a distinct set of MHC I-associated peptides shared by human cancers

Previous reports showed that mouse vaccination with pluripotent stem cells (PSCs) induces durable anti-tumor immune responses via T cell recognition of some elusive oncofetal epitopes. We characterize the MHC I-associated peptide (MAP) repertoire of human induced PSCs (iPSCs) using proteogenomics. Our analyses reveal a set of 46 pluripotency-associated MAPs (paMAPs) absent from the transcriptome of normal tissues and adult stem cells but expressed in PSCs and multiple adult cancers. These paMAPs derive from coding and allegedly non-coding (48%) transcripts involved in pluripotency maintenance, and their expression in The Cancer Genome Atlas samples correlates with source gene hypomethylation and genomic aberrations common across cancer types. We find that several of these paMAPs were immunogenic. However, paMAP expression in tumors coincides with activation of pathways instrumental in immune evasion (WNT, TGF-β, and CDK4/6). We propose that currently available inhibitors of these pathways could synergize with immune targeting of paMAPs for the treatment of poorly differentiated cancers.

Machine Learning Approaches to TCR Repertoire Analysis

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

Enhanced safety and efficacy of protease-regulated CAR-T cell receptors

Regulatable CAR platforms could circumvent toxicities associated with CAR-T therapy, but existing systems have shortcomings including leakiness and attenuated activity. Here, we present SNIP CARs, a protease-based platform for regulating CAR activity using an FDA-approved small molecule. Design iterations yielded CAR-T cells that manifest full functional capacity with drug and no leaky activity in the absence of drug. In numerous models, SNIP CAR-T cells were more potent than constitutive CAR-T cells and showed diminished T cell exhaustion and greater stemness. In a ROR1-based CAR lethality model, drug cessation following toxicity onset reversed toxicity, thereby credentialing the platform as a safety switch. In the same model, reduced drug dosing opened a therapeutic window that resulted in tumor eradication in the absence of toxicity. SNIP CARs enable remote tuning of CAR activity, which provides solutions to safety and efficacy barriers that are currently limiting progress in using CAR-T cells to treat solid tumors.

Immunopeptidomic analyses of colorectal cancers with and without microsatellite instability

Colorectal cancer is the second leading cause of cancer death worldwide, and the incidence of this disease is expected to increase as global socioeconomic changes occur. Immune checkpoint inhibition therapy is effective in treating a minority of colorectal cancer tumors; however, microsatellite stable tumors do not respond well to this treatment. Emerging cancer immunotherapeutic strategies aim to activate a cytotoxic T cell response against tumor-specific antigens, presented exclusively at the cell surface of cancer cells. These antigens are rare and are most effectively identified with a mass spectrometry-based approach, which allows the direct sampling and sequencing of these peptides. Although the few tumor-specific antigens identified to date are derived from coding regions of the genome, recent findings indicate that a large proportion of tumor-specific antigens originate from allegedly noncoding regions. Here, we employed a novel proteogenomic approach to identify tumor antigens in a collection of colorectal cancer-derived cell lines and biopsy samples consisting of matched tumor and normal adjacent tissue. The generation of personalized cancer databases paired with mass spectrometry analyses permitted the identification of more than 30,000 unique MHC I-associated peptides. We identified 19 tumor-specific antigens in both microsatellite stable and unstable tumors, over two-thirds of which were derived from noncoding regions. Many of these peptides were derived from source genes known to be involved in colorectal cancer progression, suggesting that antigens from these genes could have therapeutic potential in a wide range of tumors. These findings could benefit the development of T cell-based vaccines, in which T cells are primed against these antigens to target and eradicate tumors. Such a vaccine could be used in tandem with existing immune checkpoint inhibition therapies, to bridge the gap in treatment efficacy across subtypes of colorectal cancer with varying prognoses. Data are available via ProteomeXchange with identifier PXD028309.

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens

T cell recognition of a cognate peptide-major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T cell receptors would greatly facilitate identification of vaccine targets for both pathogenic diseases and personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the center of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as severe acute respiratory syndrome coronavirus 2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in the training data of the models seems to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen versus cancer peptides. Overall, we demonstrate that accurate and reliable predictions of immunogenic CD8+ T cell targets remain unsolved; thus, we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.

Identification of neoantigens for individualized therapeutic cancer vaccines

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

Phylogeny, Structure, Functions, and Role of AIRE in the Formation of T-Cell Subsets

It is well known that the most important feature of adaptive immunity is the specificity that provides highly precise recognition of the self, altered-self, and non-self. Due to the high specificity of antigen recognition, the adaptive immune system participates in the maintenance of genetic homeostasis, supports multicellularity, and protects an organism from different pathogens at a qualitatively different level than innate immunity. This seemingly simple property is based on millions of years of evolution that led to the formation of diversification mechanisms of antigen-recognizing receptors and later to the emergence of a system of presentation of the self and non-self antigens. The latter could have a crucial significance because the presentation of nearly complete diversity of auto-antigens in the thymus allows for the "calibration" of the forming repertoires of T-cells for the recognition of self, altered-self, and non-self antigens that are presented on the periphery. The central role in this process belongs to promiscuous gene expression by the thymic epithelial cells that express nearly the whole spectrum of proteins encoded in the genome, meanwhile maintaining their cellular identity. This complex mechanism requires strict control that is executed by several transcription factors. One of the most important of them is AIRE. This noncanonical transcription factor not only regulates the processes of differentiation and expression of peripheral tissue-specific antigens in the thymic medullar epithelial cells but also controls intercellular interactions in the thymus. Besides, it participates in an increase in the diversity and transfer of presented antigens and thus influences the formation of repertoires of maturing thymocytes. Due to these complex effects, AIRE is also called a transcriptional regulator. In this review, we briefly described the history of AIRE discovery, its structure, functions, and role in the formation of antigen-recognizing receptor repertoires, along with other transcription factors. We focused on the phylogenetic prerequisites for the development of modern adaptive immunity and emphasized the importance of the antigen presentation system.

Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.

Negative trade-off between neoantigen repertoire breadth and the specificity of HLA-I molecules shapes antitumor immunity

Human leukocyte antigen class I (HLA-I) genes shape our immune response against pathogens and cancer. Certain HLA-I variants can bind a wider range of peptides than others, a feature that could be favorable against a range of viral diseases. However, the implications of this phenomenon on cancer immune response are unknown. Here we quantified peptide repertoire breadth (or promiscuity) of a representative set of HLA-I alleles and found that patients with cancer who were carrying HLA-I alleles with high peptide-binding promiscuity have significantly worse prognosis after immune checkpoint inhibition. This can be explained by a reduced capacity of the immune system to discriminate tumor neopeptides from self-peptides when patients carry highly promiscuous HLA-I variants, shifting the regulation of tumor-infiltrating T cells from activation to tolerance. In summary, HLA-I peptide-binding specificity shapes neopeptide immunogenicity and the self-immunopeptidome repertoire in an antagonistic manner, and could underlie a negative trade-off between antitumor immunity and genetic susceptibility to viral infections.

Targeted immune epitope prediction to HHLA2 and MAGEB5 protein variants as therapeutic approach to related viral diseases

Background

Targeted immunotherapy is mostly associated with cancer treatment wherein designed molecules engage signaling pathways and mutant proteins critical to the survival of the cell. One of several genetic approaches is the use of in silico methods to develop immune epitopes targeting specific antigenic regions on related mutant proteins. In a recent study we showed a functional association between the gamma retrovirus HERV-H Long Terminal Associating (HHLA1, HHLA2 and HHLA3) proteins and melanoma associated antigen of the B class proteins (MAGEB5), with a resultant decrease in expression of HLA class I and II immune variants. HLA-C and HLA-DRB5 were the main HLA class I and II Immune variants, respectively, that showed expression changes across viral samples of interest. Specific immune variants for HLA-C and HLA-DRB5 were filtered for the top ten based on their relative frequency of counts across the samples.

Results

Protein variants for HHLA1, HHLA2, HHLA3 and MAGEB5 were used to predict antigenic epitope peptides to immune peptide-MHC class I and II binding using artificial neural networks. For IC50 peptide scores (PS) ≥ 0.5 with a transformed binding ability between 0 and 1, the top 5 epitopes identified for all targeted genes HHLA1,2 & 3 and MAGEB5 were qualified as strong or weak binders according to the threshold. Domain analysis using NCBI Conserved Domain Database (CDD) identified HHLA2 with immunoglobulin-like domains (Ig_C1-set) and MAGEB5 with the MAGE Homology Domain (MHD). Linear regression showed a statistical correlation (P < 0.001) for HHLA2 and MAGEB5 predicted epitope peptides to HLA-C but not HLA-DRB5. The prediction model identified HLA-C variant 9 (HLA-C9, BAA08825.1 HLA-B*1511) at 1.1% as the most valuable immune target for clinical considerations. Identification of the 9-mer epitope peptide within the domain showed for HHLA2: YANRTSLFY (PS = 0.5837) and VLAYYLSSSQNTIIN (PS = 0.77) for HLA-C and HLA-DRB5, respectively and for MAGEB5, peptides: FVRLTYLEY (PS = 0.5293) and YPAHYQFLWGPRAYT (PS = 0.62) for HLA-C and HLA-DRB5, respectively.

Conclusion

Specific immune responses to targeted epitope peptides and their prediction models, suggested co-expression and co-evolution for HHLA2 and MAGEB5 in viral related diseases. HHLA2 and MAGEB5 could be considered markers for virus related tumors and targeted therapy for oncogenic diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12865-021-00440-w.

Activating Killer-Cell Immunoglobulin-Like Receptors Are Associated With the Severity of Coronavirus Disease 2019

BACKGROUND

Etiopathogenesis of the clinical variability of the coronavirus disease 2019 (COVID-19) remains mostly unknown. In this study, we investigate the role of killer cell immunoglobulin-like receptor (KIR)/human leukocyte antigen class-I (HLA-I) interactions in the susceptibility and severity of COVID-19.

METHODS

We performed KIR and HLA-I genotyping and natural killer cell (NKc) receptors immunophenotyping in 201 symptomatic patients and 210 noninfected controls.

RESULTS

The NKcs with a distinctive immunophenotype, suggestive of recent activation (KIR2DS4low CD16low CD226low CD56high TIGIThigh NKG2Ahigh), expanded in patients with severe COVID-19. This was associated with a higher frequency of the functional A-telomeric activating KIR2DS4 in severe versus mild and/or moderate patients and controls (83.7%, 55.7% and 36.2%, P < 7.7 × 10-9). In patients with mild and/or moderate infection, HLA-B*15:01 was associated with higher frequencies of activating B-telomeric KIR3DS1 compared with patients with other HLA-B*15 subtypes and noninfected controls (90.9%, 42.9%, and 47.3%; P < .002; Pc = 0.022). This strongly suggests that HLA-B*15:01 specifically presenting severe acute respiratory syndrome coronavirus 2 peptides could form a neoligand interacting with KIR3DS1. Likewise, a putative neoligand for KIR2DS4 could arise from other HLA-I molecules presenting severe acute respiratory syndrome coronavirus 2 peptides expressed on infected an/or activated lung antigen-presenting cells.

CONCLUSIONS

Our results support a crucial role of NKcs in the clinical variability of COVID-19 with specific KIR/ligand interactions associated with disease severity.

The Anti-Proliferative Effect of a Newly-Produced Anti-PSCA-Peptide Antibody by Multiple Bioinformatics Tools, on Prostate Cancer Cells

BACKGROUND

Prostate Stem Cell Antigen (PSCA) is a small cell surface protein, overexpressed in 90% of prostate cancers. Determination of epitopes that elicit an appropriate response to the antibody generation is vital for diagnostic and immunotherapeutic purposes for prostate cancer treatment. Presently, bioinformatics B-cell prediction tools can predict the location of epitopes, which is uncomplicated, faster, and more cost-effective than experimental methods.

OBJECTIVE

We aimed to predict a novel linear peptide for Prostate Stem Cell Antigen (PSCA) protein in order to generate anti-PSCA-peptide (p) antibody and to investigate its effect on prostate cancer cells.

METHODS

In the current study, a novel linear peptide for PSCA was predicted using in silico methods that utilize a set of linear B-cell epitope prediction tools. Polyclonal antibody (anti-PSCA-p antibody "Patent No. 99318") against PSCA peptide was generated. The antibody reactivity was determined by the Enzyme-Linked Immunosorbent Assay (ELISA) and its specificity by immunocytochemistry (ICC), immunohistochemistry (IHC), and Western Blotting (WB) assays. The effect of the anti-PSCA-p antibody on PSCA-expressing prostate cancer cell line was assessed by Methylthiazolyldiphenyl- Tetrazolium bromide (MTT) assay.

RESULTS

New peptide-fragment of PSCA sequence as "N-CVDDSQDYYVGKKN-C" (PSCA-p) was selected and synthesized. The anti-PSCA-p antibody against the PSCA-p showed immunoreactivity with PSCA-p specifically bound to PC-3 cells. Also, the anti-PSCA-p antibody strongly stained the prostate cancer tissues as compared to Benign Prostatic Hyperplasia (BPH) and normal tissues (P < 0.001). As the degree of malignancy increased, the staining intensity was also elevated in prostate cancer tissue (P < 0.001). Interestingly, the anti-PSCA-p antibody showed anti-proliferative effects on PC-3 cells (31%) with no growth inhibition effect on PSCA-negative cells.

CONCLUSION

In this study, we developed a new peptide sequence (PSCA-p) of PSCA. The PSCA-p targeting by anti-PSCA-p antibody inhibited the proliferation of prostate cancer cells, suggesting the potential of PSCA-p immunotherapy for future prostate cancer studies.

Rapid Assessment of T-Cell Receptor Specificity of the Immune Repertoire

Accurate assessment of TCR-antigen specificity at the whole immune repertoire level lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR-peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR-p-MHC systems. Here, we introduce a pairwise energy model, RACER, for rapid assessment of TCR-peptide affinity at the immune repertoire level. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR-peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each specific TCR-p-MHC system. When applied to simulate thymic selection of an MHC-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the large computational challenge of reliably identifying the properties of tumor antigen-specific T-cells at the level of an individual patient's immune repertoire.

Overcoming Immunological Challenges Limiting Capsid-Mediated Gene Therapy With Machine Learning

A key hurdle to making adeno-associated virus (AAV) capsid mediated gene therapy broadly beneficial to all patients is overcoming pre-existing and therapy-induced immune responses to these vectors. Recent advances in high-throughput DNA synthesis, multiplexing and sequencing technologies have accelerated engineering of improved capsid properties such as production yield, packaging efficiency, biodistribution and transduction efficiency. Here we outline how machine learning, advances in viral immunology, and high-throughput measurements can enable engineering of a new generation of de-immunized capsids beyond the antigenic landscape of natural AAVs, towards expanding the therapeutic reach of gene therapy.

AI Aided Design of Epitope-Based Vaccine for the Induction of Cellular Immune Responses Against SARS-CoV-2

The heavy burden imposed by the COVID-19 pandemic on our society triggered the race toward the development of therapies or preventive strategies. Among these, antibodies and vaccines are particularly attractive because of their high specificity, low probability of drug-drug interaction, and potentially long-standing protective effects. While the threat at hand justifies the pace of research, the implementation of therapeutic strategies cannot be exempted from safety considerations. There are several potential adverse events reported after the vaccination or antibody therapy, but two are of utmost importance: antibody-dependent enhancement (ADE) and cytokine storm syndrome (CSS). On the other hand, the depletion or exhaustion of T-cells has been reported to be associated with worse prognosis in COVID-19 patients. This observation suggests a potential role of vaccines eliciting cellular immunity, which might simultaneously limit the risk of ADE and CSS. Such risk was proposed to be associated with FcR-induced activation of proinflammatory macrophages (M1) by Fu et al. (2020) and Iwasaki and Yang (2020). All aspects of the newly developed vaccine (including the route of administration, delivery system, and adjuvant selection) may affect its effectiveness and safety. In this work we use a novel in silico approach (based on AI and bioinformatics methods) developed to support the design of epitope-based vaccines. We evaluated the capabilities of our method for predicting the immunogenicity of epitopes. Next, the results of our approach were compared with other vaccine-design strategies reported in the literature. The risk of immuno-toxicity was also assessed. The analysis of epitope conservation among other Coronaviridae was carried out in order to facilitate the selection of peptides shared across different SARS-CoV-2 strains and which might be conserved in emerging zootic coronavirus strains. Finally, the potential applicability of the selected epitopes for the development of a vaccine eliciting cellular immunity for COVID-19 was discussed, highlighting the benefits and challenges of such an approach.

Atypical acute myeloid leukemia-specific transcripts generate shared and immunogenic MHC class-I-associated epitopes

Acute myeloid leukemia (AML) has not benefited from innovative immunotherapies, mainly because of the lack of actionable immune targets. Using an original proteogenomic approach, we analyzed the major histocompatibility complex class I (MHC class I)-associated immunopeptidome of 19 primary AML samples and identified 58 tumor-specific antigens (TSAs). These TSAs bore no mutations and derived mainly (86%) from supposedly non-coding genomic regions. Two AML-specific aberrations were instrumental in the biogenesis of TSAs, intron retention, and epigenetic changes. Indeed, 48% of TSAs resulted from intron retention and translation, and their RNA expression correlated with mutations of epigenetic modifiers (e.g., DNMT3A). AML TSA-coding transcripts were highly shared among patients and were expressed in both blasts and leukemic stem cells. In AML patients, the predicted number of TSAs correlated with spontaneous expansion of cognate T cell receptor clonotypes, accumulation of activated cytotoxic T cells, immunoediting, and improved survival. These TSAs represent attractive targets for AML immunotherapy.

Proteasome-Generated cis-Spliced Peptides and Their Potential Role in CD8+ T Cell Tolerance

The human immune system relies on the capability of CD8+ T cells to patrol body cells, spot infected cells and eliminate them. This cytotoxic response is supposed to be limited to infected cells to avoid killing of healthy cells. To enable this, CD8+ T cells have T Cell Receptors (TCRs) which should discriminate between self and non-self through the recognition of antigenic peptides bound to Human Leukocyte Antigen class I (HLA-I) complexes-i.e., HLA-I immunopeptidomes-of patrolled cells. The majority of these antigenic peptides are produced by proteasomes through either peptide hydrolysis or peptide splicing. Proteasome-generated cis-spliced peptides derive from a given antigen, are immunogenic and frequently presented by HLA-I complexes. Theoretically, they also have a very large sequence variability, which might impinge upon our model of self/non-self discrimination and central and peripheral CD8+ T cell tolerance. Indeed, a large variety of cis-spliced epitopes might enlarge the pool of viral-human zwitter epitopes, i.e., peptides that may be generated with the exact same sequence from both self (human) and non-self (viral) antigens. Antigenic viral-human zwitter peptides may be recognized by CD8+ thymocytes and T cells, induce clonal deletion or other tolerance processes, thereby restraining CD8+ T cell response against viruses. To test this hypothesis, we computed in silico the theoretical frequency of zwitter non-spliced and cis-spliced epitope candidates derived from human proteome (self) and from the proteomes of a large pool of viruses (non-self). We considered their binding affinity to the representative HLA-A*02:01 complex, self-antigen expression in Medullary Thymic Epithelial cells (mTECs) and the relative frequency of non-spliced and cis-spliced peptides in HLA-I immunopeptidomes. Based on the present knowledge of proteasome-catalyzed peptide splicing and neglecting CD8+ TCR degeneracy, our study suggests that, despite their frequency, the portion of the cis-spliced peptides we investigated could only marginally impinge upon the variety of functional CD8+ cytotoxic T cells (CTLs) involved in anti-viral response.

Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response

Predictive models are becoming more and more commonplace as tools for candidate antigen discovery to meet the challenges of enabling epitope mapping of cohorts with diverse HLA properties. Here we build on the concept of using two key parameters, diversity metric of the HLA profile of individuals within a population and consideration of sequence diversity in the context of an individual's CD8 T-cell immune repertoire to assess the HIV proteome for defined regions of immunogenicity. Using this approach, analysis of HLA adaptation and functional immunogenicity data enabled the identification of regions within the proteome that offer significant conservation, HLA recognition within a population, low prevalence of HLA adaptation and demonstrated immunogenicity. We believe this unique and novel approach to vaccine design as a supplement to vitro functional assays, offers a bespoke pipeline for expedited and rational CD8 T-cell vaccine design for HIV and potentially other pathogens with the potential for both global and local coverage.

Insights into anti-D formation in carriers of RhD variants through studies of 3D intraprotein interactions

BACKGROUND

Many RhD variants associated with anti-D formation (partial D) in carriers exposed to the conventional D antigen carry mutations affecting extracellular loop residues. Surprisingly, some carry mutations affecting transmembrane or intracellular domains, positions not thought likely to have a major impact on D epitopes.

STUDY DESIGN AND METHODS

A wild-type Rh trimer (RhD₁ RhAG₂ ) was modeled by comparative modeling with the human RhCG structure. Taking trimer conformation, residue accessibility, and position relative to the lipid bilayer into account, we redefine the domains of the RhD protein. We generated models for RhD variants carrying one or two amino acid substitutions associated with anti-D formation in published articles (25 variants) or abstracts (12 variants) and for RHD*weak D type 38. We determined the extracellular substitutions and compared the interactions of the variants with those of the standard RhD.

RESULTS

The findings of the three-dimensional (3D) analysis were correlated with anti-D formation for 76% of RhD variants: 15 substitutions associated with anti-D formation concerned extracellular residues, and structural differences in intraprotein interactions relative to standard RhD were observed in the others. We discuss the mechanisms by which D epitopes may be modified in variants in which the extracellular residues are identical to those of standard RhD and provide arguments for the benignity of p.T379M (RHD*DAU0) and p.G278D (RHD*weak D type 38) in transfusion medicine.

CONCLUSION

The study of RhD intraprotein interactions and the precise redefinition of residue accessibility provide insight into the mechanisms through which RhD point mutations may lead to anti-D formation in carriers.

Alterations in the HLA-B*57:01 Immunopeptidome by Flucloxacillin and Immunogenicity of Drug-Haptenated Peptides

Neoantigen formation due to the interaction of drug molecules with human leukocyte antigen (HLA)-peptide complexes can lead to severe hypersensitivity reactions. Flucloxacillin (FLX), a β-lactam antibiotic for narrow-spectrum gram-positive bacterial infections, has been associated with severe immune-mediated drug-induced liver injury caused by an influx of T-lymphocytes targeting liver cells potentially recognizing drug-haptenated peptides in the context of HLA-B*57:01. To identify immunopeptidome changes that could lead to drug-driven immunogenicity, we used mass spectrometry to characterize the proteome and immunopeptidome of B-lymphoblastoid cells solely expressing HLA-B*57:01 as MHC-I molecules. Selected drug-conjugated peptides identified in these cells were synthesized and tested for their immunogenicity in HLA-B*57:01-transgenic mice. T cell responses were evaluated in vitro by immune assays. The immunopeptidome of FLX-treated cells was more diverse than that of untreated cells, enriched with peptides containing carboxy-terminal tryptophan and FLX-haptenated lysine residues on peptides. Selected FLX-modified peptides with drug on P4 and P6 induced drug-specific CD8⁺ T cells in vivo. FLX was also found directly linked to the HLA K146 that could interfere with KIR-3DL or peptide interactions. These studies identify a novel effect of antibiotics to alter anchor residue frequencies in HLA-presented peptides which may impact drug-induced inflammation. Covalent FLX-modified lysines on peptides mapped drug-specific immunogenicity primarily at P4 and P6 suggesting these peptide sites as drivers of off-target adverse reactions mediated by FLX. FLX modifications on HLA-B*57:01-exposed lysines may also impact interactions with KIR or TCR and subsequent NK and T cell function.

Antigen-driven PD-1+ TOX+ BHLHE40+ and PD-1+ TOX+ EOMES+ T lymphocytes regulate juvenile idiopathic arthritis in situ

T lymphocytes accumulate in inflamed tissues of patients with chronic inflammatory diseases (CIDs) and express pro-inflammatory cytokines upon re-stimulation in vitro. Further, a significant genetic linkage to MHC genes suggests that T lymphocytes play an important role in the pathogenesis of CIDs including juvenile idiopathic arthritis (JIA). However, the functions of T lymphocytes in established disease remain elusive. Here we dissect the transcriptional and the clonal heterogeneity of synovial T lymphocytes in JIA patients by single-cell RNA sequencing combined with T cell receptor profiling on the same cells. We identify clonally expanded subpopulations of T lymphocytes expressing genes reflecting recent activation by antigen in situ. A PD-1⁺ TOX⁺ EOMES⁺ population of CD4⁺ T lymphocytes expressed immune regulatory genes and chemoattractant genes for myeloid cells. A PD-1⁺ TOX⁺ BHLHE40⁺ population of CD4⁺ , and a mirror population of CD8⁺ T lymphocytes expressed genes driving inflammation, and genes supporting B lymphocyte activation in situ. This analysis points out that multiple types of T lymphocytes have to be targeted for therapeutic regeneration of tolerance in arthritis.

High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides

MOTIVATION

The binding of T cell receptors (TCRs) to their target peptide MHC (pMHC) ligands initializes the cell-mediated immune response. In autoimmune diseases such as multiple sclerosis, the TCR erroneously recognizes self-peptides as foreign and activates an immune response against healthy cells. Such responses can be triggered by cross-recognition of the autoreactive TCR with foreign peptides. Hence, it would be desirable to identify such foreign-antigen triggers to provide a mechanistic understanding of autoimmune diseases. However, the large sequence space of foreign antigens presents an obstacle in the identification of cross-reactive peptides.

RESULTS

Here, we present an in silico modeling and scoring method which exploits the structural properties of TCR-pMHC complexes to predict the binding of cross-reactive peptides. We analyzed three mouse TCRs and one human TCR isolated from a patient with multiple sclerosis. Cross-reactive peptides for these TCRs were previously identified via yeast display coupled with deep sequencing, providing a robust dataset for evaluating our method. Modeling query peptides in their associated TCR-pMHC crystal structures, our method accurately selected the top binding peptides from sets containing more than a hundred thousand unique peptides.

AVAILABILITY AND IMPLEMENTATION

Analyses were performed using custom Python and R scripts available at https://github.com/tborrman/antigen-predict.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Potential CD8+ T Cell Cross-Reactivity Against SARS-CoV-2 Conferred by Other Coronavirus Strains

While individuals infected with coronavirus disease 2019 (COVID-19) manifested a broad range in susceptibility and severity to the disease, the pre-existing immune memory to related pathogens cross-reactive against SARS-CoV-2 can influence the disease outcome in COVID-19. Here, we investigated the potential extent of T cell cross-reactivity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can be conferred by other coronaviruses and influenza virus, and generated an in silico map of public and private CD8+ T cell epitopes between coronaviruses. We observed 794 predicted SARS-CoV-2 epitopes of which 52% were private and 48% were public. Ninety-nine percent of the public epitopes were shared with SARS-CoV and 5.4% were shared with either one of four common coronaviruses, 229E, HKU1, NL63, and OC43. Moreover, to assess the potential risk of self-reactivity and/or diminished T cell response for peptides identical or highly similar to the host, we identified predicted epitopes with high sequence similarity with human proteome. Lastly, we compared predicted epitopes from coronaviruses with epitopes from influenza virus deposited in IEDB, and found only a small number of peptides with limited potential for cross-reactivity between the two virus families. We believe our comprehensive in silico profile of private and public epitopes across coronaviruses would facilitate design of vaccines, and provide insights into the presence of pre-existing coronavirus-specific memory CD8+ T cells that may influence immune responses against SARS-CoV-2.

Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors

Adaptive immune recognition is mediated by specific interactions between heterodimeric T cell receptors (TCRs) and their cognate peptide-MHC (pMHC) ligands, and the methods to accurately predict TCR:pMHC interaction would have profound clinical, therapeutic and pharmaceutical applications. Herein, we review recent developments in predicting cross-reactivity and antigen specificity of TCR recognition. We discuss current experimental and computational approaches to investigate cross-reactivity and antigen-specificity of TCRs and highlight how integrating kinetic, biophysical and structural features may offer valuable insights in modeling immunogenicity. We further underscore the close inter-relationship of these two interconnected notions and the need to investigate each in the light of the other for a better understanding of T cell responsiveness for the effective clinical applications.

Main Strategies for the Identification of Neoantigens

Genetic instability of tumors leads to the appearance of numerous tumor-specific somatic mutations that could potentially result in the production of mutated peptides that are presented on the cell surface by the MHC molecules. Peptides of this kind are commonly called neoantigens. Their presence on the cell surface specifically distinguishes tumors from healthy tissues. This feature makes neoantigens a promising target for immunotherapy. The rapid evolution of high-throughput genomics and proteomics makes it possible to implement these techniques in clinical practice. In particular, they provide useful tools for the investigation of neoantigens. The most valuable genomic approach to this problem is whole-exome sequencing coupled with RNA-seq. High-throughput mass-spectrometry is another option for direct identification of MHC-bound peptides, which is capable of revealing the entire MHC-bound peptidome. Finally, structure-based predictions could significantly improve the understanding of physicochemical and structural features that affect the immunogenicity of peptides. The development of pipelines combining such tools could improve the accuracy of the peptide selection process and decrease the required time. Here we present a review of the main existing approaches to investigating the neoantigens and suggest a possible ideal pipeline that takes into account all modern trends in the context of neoantigen discovery.

Population genomics of louping ill virus provide new insights into the evolution of tick-borne flaviviruses

The emergence and spread of tick-borne arboviruses pose an increased challenge to human and animal health. In Europe this is demonstrated by the increasingly wide distribution of tick-borne encephalitis virus (TBEV, Flavivirus, Flaviviridae), which has recently been found in the United Kingdom (UK). However, much less is known about other tick-borne flaviviruses (TBFV), such as the closely related louping ill virus (LIV), an animal pathogen which is endemic to the UK and Ireland, but which has been detected in other parts of Europe including Scandinavia and Russia. The emergence and potential spatial overlap of these viruses necessitates improved understanding of LIV genomic diversity, geographic spread and evolutionary history. We sequenced a virus archive composed of 22 LIV isolates which had been sampled throughout the UK over a period of over 80 years. Combining this dataset with published virus sequences, we detected no sign of recombination and found low diversity and limited evidence for positive selection in the LIV genome. Phylogenetic analysis provided evidence of geographic clustering as well as long-distance movement, including movement events that appear recent. However, despite genomic data and an 80-year time span, we found that the data contained insufficient temporal signal to reliably estimate a molecular clock rate for LIV. Additional analyses revealed that this also applied to TBEV, albeit to a lesser extent, pointing to a general problem with phylogenetic dating for TBFV. The 22 LIV genomes generated during this study provide a more reliable LIV phylogeny, improving our knowledge of the evolution of tick-borne flaviviruses. Our inability to estimate a molecular clock rate for both LIV and TBEV suggests that temporal calibration of tick-borne flavivirus evolution should be interpreted with caution and highlight a unique aspect of these viruses which may be explained by their reliance on tick vectors.

Tick-borne pathogens represent a major emerging threat to public health and in recent years have been expanding into new areas. LIV is a neglected virus endemic to the UK and Ireland (though it has been detected in Scandinavia and Russia) which is closely related to the major human pathogen TBEV, but predominantly causes disease in sheep and grouse. The recent detection of TBEV in the UK, which has also emerged elsewhere in Europe, requires more detailed understanding of the spread and sequence diversity of LIV. This could be important for diagnosis and vaccination, but also to improve our understanding of the evolution and emergence of these tick-borne viruses. Here we describe the sequencing of 22 LIV isolates which have been sampled from several host species across the past century. We have utilised this dataset to investigate the evolutionary pressures that LIV is subjected to and have explored the evolution of LIV using phylogenetic analysis. Crucially we were unable to estimate a reliable molecular clock rate for LIV and found that this problem also extends to a larger phylogeny of TBEV sequences. This work highlights a previously unknown caveat of tick-borne flavivirus evolutionary analysis which may be important for understanding the evolution of these important pathogens.

Cross-serotypically conserved epitope recommendations for a universal T cell-based dengue vaccine

Dengue virus (DENV)-associated disease is a growing threat to public health across the globe. Co-circulating as four different serotypes, DENV poses a unique challenge for vaccine design as immunity to one serotype predisposes a person to severe and potentially lethal disease upon infection from other serotypes. Recent experimental studies suggest that an effective vaccine against DENV should elicit a strong T cell response against all serotypes, which could be achieved by directing T cell responses toward cross-serotypically conserved epitopes while avoiding serotype-specific ones. Here, we used experimentally-determined DENV T cell epitopes and patient-derived DENV sequences to assess the cross-serotypic variability of the epitopes. We reveal a distinct near-binary pattern of epitope conservation across serotypes for a large number of DENV epitopes. Based on the conservation profile, we identify a set of 55 epitopes that are highly conserved in at least 3 serotypes. Most of the highly conserved epitopes lie in functionally important regions of DENV non-structural proteins. By considering the global distribution of human leukocyte antigen (HLA) alleles associated with these DENV epitopes, we identify a potentially robust subset of HLA class I and class II restricted epitopes that can serve as targets for a universal T cell-based vaccine against DENV while covering ~99% of the global population.

Failure Mode and Effects Analysis (FMEA) for Immunogenicity of Therapeutic Proteins

Biotherapeutic drugs made by cell-based systems are revolutionizing the practice of medicine. The next generation of biotherapeutics include recombinant proteins, monoclonal antibodies, viral vector expressed proteins, and cell therapies. Immunogenicity associated adverse events is one of the major risks for these biologics. Accurate and precise measurement of the immunogenicity of biologics is a critical component during all phases of drug development. We have utilized the principles of Failure Mode and Effects Analysis (FMEA) in performing assessment of risk of immunogenicity. The multi-dimensional approach involves: i) listing all the potential risks by likelihood of occurrence and severity as part of quality target product profile. ii) ascribing the causes by identifying the risks at each stage of development. iii) predicting the effects. iv) determining the risk mitigation strategy. v) implementing a monitoring process. vi) developing templates for data collection. vii) timely reporting and. viii) life cycle management. FMEA is a continuous process that works throughout the lifecycle of the product or the process and keeps on getting updated with new insights and knowledge.

Methods for sequence and structural analysis of B and T cell receptor repertoires

B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.

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

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

Engineering Strategies to Enhance TCR-Based Adoptive T Cell Therapy

T cell receptor (TCR)-based adoptive T cell therapies (ACT) hold great promise for the treatment of cancer, as TCRs can cover a broad range of target antigens. Here we summarize basic, translational and clinical results that provide insight into the challenges and opportunities of TCR-based ACT. We review the characteristics of target antigens and conventional αβ-TCRs, and provide a summary of published clinical trials with TCR-transgenic T cell therapies. We discuss how synthetic biology and innovative engineering strategies are poised to provide solutions for overcoming current limitations, that include functional avidity, MHC restriction, and most importantly, the tumor microenvironment. We also highlight the impact of precision genome editing on the next iteration of TCR-transgenic T cell therapies, and the discovery of novel immune engineering targets. We are convinced that some of these innovations will enable the field to move TCR gene therapy to the next level.

T-cell receptor affinity in the age of cancer immunotherapy

The strength of the interaction between T-cell receptors (TCRs) and their ligands, peptide/major histocompatibility complex complexes (pMHCs), is one of the most frequently discussed and investigated features of T cells in immuno-oncology today. Although there are many molecules on the surface of T cells that interact with ligands on other cells, the TCR/pMHC is the only receptor-ligand pair that offers antigen specificity and dictates the functional response of the T cell. The strength of the TCR/pMHC interaction, along with the environment in which this interaction takes place, is key to how the T cell will respond. The TCR repertoire of T cells that interact with tumor-associated antigens is vast, although typically of low affinity. Here, we focus on the low-affinity interactions between TCRs from CD8+ T cells and different models used in immuno-oncology.

Widespread and tissue-specific expression of endogenous retroelements in human somatic tissues

BACKGROUND

Endogenous retroelements (EREs) constitute about 42% of the human genome and have been implicated in common human diseases such as autoimmunity and cancer. The dominant paradigm holds that EREs are expressed in embryonic stem cells (ESCs) and germline cells but are repressed in differentiated somatic cells. Despite evidence that some EREs can be expressed at the RNA and protein levels in specific contexts, a system-level evaluation of their expression in human tissues is lacking.

METHODS

Using RNA sequencing data, we analyzed ERE expression in 32 human tissues and cell types, including medullary thymic epithelial cells (mTECs). A tissue specificity index was computed to identify tissue-restricted ERE families. We also analyzed the transcriptome of mTECs in wild-type and autoimmune regulator (AIRE)-deficient mice. Finally, we developed a proteogenomic workflow combining RNA sequencing and mass spectrometry (MS) in order to evaluate whether EREs might be translated and generate MHC I-associated peptides (MAP) in B-lymphoblastoid cell lines (B-LCL) from 16 individuals.

RESULTS

We report that all human tissues express EREs, but the breadth and magnitude of ERE expression are very heterogeneous from one tissue to another. ERE expression was particularly high in two MHC I-deficient tissues (ESCs and testis) and one MHC I-expressing tissue, mTECs. In mutant mice, we report that the exceptional expression of EREs in mTECs was AIRE-independent. MS analyses identified 103 non-redundant ERE-derived MAPs (ereMAPs) in B-LCLs. These ereMAPs preferentially derived from sense translation of intronic EREs. Notably, detailed analyses of their amino acid composition revealed that ERE-derived MAPs presented homology to viral MAPs.

CONCLUSIONS

This study shows that ERE expression in somatic tissues is more pervasive and heterogeneous than anticipated. The high and diversified expression of EREs in mTECs and their ability to generate MAPs suggest that EREs may play an important role in the establishment of self-tolerance. The viral-like properties of ERE-derived MAPs suggest that those not expressed in mTECs can be highly immunogenic.

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.

Long-term survival without graft-versus-host-disease following infusion of allogeneic myeloma-specific Vβ T cell families

Background

Despite chemo-induction therapy and autologous stem cell transplantation (ASCT), the vast majority of patients with Multiple Myeloma (MM) relapse within 7 years and the disease remains incurable. Adoptive Allogeneic T-cell therapy (ATCT) might be curative for MM, however current ATCT protocols often lead to graft versus host disease (GvHD). Transplanting only tumor reactive donor T cells that mediate a graft-versus-myeloma (GvM) but not GvHD may overcome this problem.

Methods

We used an MHC-matched/miHA-disparate B10.D2 → Balb/c bone marrow transplantation (BMT) murine model and MOPC315.BM MM cells to develop an ATCT protocol consisting of total body irradiation, autologous-BMT and infusion of selective, myeloma-reactive lymphocytes of T cell receptor (TCR) Vβ 2, 3 and 8.3 families (MM-auto BMT ATCT).

Results

Pre-stimulation ex vivo of allogeneic T cells by exposure to MOPC315.BM MM cells in the presence of IL-2, anti-CD3 and anti-CD28 resulted in expansion of the myeloma-reactive T cell TCRVβ 2, 3 and 8.3 subfamilies. Their isolation and infusion into MM-bearing mice resulted in a vigorous GvM response without induction GvHD and long-term survival. Repeated infusion of naïve myeloma-reactive T cell TCRVβ 2, 3 and 8.3 subfamilies was also effective.

Conclusions

These data demonstrate that a transplantation protocol involving only selective tumor-reactive donor T cell families is an effective immunotherapy and results in long-term survival in a mouse model of human MM. The results highlight the need to develop similar ATCT strategies for MM patients that result in enhanced survival without symptoms of GvHD.