The cell surface proteome, or surfaceome, is the hub for cells to interact and communicate with the outside world. Many disease-associated changes are hard-wired within the surfaceome, yet approved drugs target less than 50 cell surface proteins. In the past decade, the proteomics community has made significant strides in developing new technologies tailored for studying the surfaceome in all its complexity. In this review, we first dive into the unique characteristics and functions of the surfaceome, emphasizing the necessity for specialized labeling, enrichment, and proteomic approaches. An overview of surfaceomics methods is provided, detailing techniques to measure changes in protein expression and how this leads to novel target discovery. Next, we highlight advances in proximity labeling proteomics (PLP), showcasing how various enzymatic and photoaffinity proximity labeling techniques can map protein-protein interactions and membrane protein complexes on the cell surface. We then review the role of extracellular post-translational modifications, focusing on cell surface glycosylation, proteolytic remodeling, and the secretome. Finally, we discuss methods for identifying tumor-specific peptide MHC complexes and how they have shaped therapeutic development. This emerging field of neo-protein epitopes is constantly evolving, where targets are identified at the proteome level and encompass defined disease-associated PTMs, complexes, and dysregulated cellular and tissue locations. Given the functional importance of the surfaceome for biology and therapy, we view surfaceomics as a critical piece of this quest for neo-epitope target discovery.

Several studies suggest that the heterogeneous spread of SARS-CoV-2 pandemics started on December 2019 could be partially upheld by the prevalence of permissive class I HLA alleles in specific populations. Such HLA alleles are in fact unable to shape an efficient anti-viral immune-response in the hosts or sustain an exaggerated inflammatory T cell mediated response responsible for the COVID-19 disease. We previously reported an ecologic correlation between the risk of COVID-19 spreading across Italy and the germinal expression of permissive HLA-C*01 and -B*44 alleles in specific inter and intraregional populations along the first spreading wave.

Considering that SARS-CoV-2 has undergone multiple adaptative mutations since the beginning of pandemics related to a natural immunization and to the worldwide campaign of anti-SARS-CoV-2 vaccination, we have carried out further analyses to evaluate whether the predictive value of class I HLA-allele gene prevalence and COVID-19 incidence has changed with time along the first four pandemics spreading waves in Italy. To this purpose we carried out an ecologic study followed by a case-control study.

| Our data revealed that the direct correlation of HLA-C*01, and HLA-B*44 gene expression and COVID-19 risk was completely lost just after the first pandemics wave in Italy. On the contrary, the expression of HLA-B*49 allele in specific populations emerged as inversely correlated to the risk of COVID-19 and could be considered as a protective factor. The statistical significance of this correlation was progressively enforced in each subsequent spreading wave until February 2022. The following case-control study in the two Regions of Campania and Calabria in Italy confirmed the protective value of HLA-B*49 allele gene expression (OR = 0.289; p = 0.041), although statistical significance is lost after adjustment by logistic regression model. The analysis also detected multiple class I HLA-alleles whose expression was strongly correlated with COVID-19 risk: HLA-B*08 (ORadj = 3.193; p = 0.015); -B*14:01 (ORadj = 3.596; p = 0.018); -B*15:01 (ORadj = 5.124; p = 0.001); -B*35 (ORadj = 2.972; p = 0.002).

Our study not only identifies specific HLA alleles related to COVID-19 risk but also exemplifies a rapid and inexpensive approach that can be used to identify individuals needing prioritization during vaccination campaigns.

Several barriers for the development of an HCV vaccine still exist, including the genetic diversity of the virus, and the shortage of assessable models for in vitro and in vivo assays. Therefore, in this study, HCV epitope mapping was performed for 59 polyprotein sequences from 7 HCV genotypes. Around 2,880 peptides were considered epitopes for CD8+ T cells. The peptide induction of cytokines from Th1 and/or Th2 axes of the cellular immune response was assessed, indicating a tendency for Th2 axis. In vitro evaluation was performed using peptide microarray and a recombinant HLA-A*02:01 molecule. A total of 615 peptides of high reactivity to HLA-A*02:01 were identified, with predominance of leucine and tryptophan residues, highlighting their importance for TCR-epitope binding and CD8+ T activation. Finally, HCV-derived peptide patterns restricted to HLA-A2*02:01 observed in this study provide important information for the development of a multi-epitope-based pan-genotypic vaccine against the virus.

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

The epidermal growth factor receptor (EGFR) is frequently overexpressed in a variety of human epithelial tumors, and its aberrant activation plays a pivotal role in promoting tumor growth, invasion, and metastasis. The clinically approved passive EGFR-related therapies have numerous limitations. Seven EGFR-ECD epitope peptides (EG1-7) were selected through bioinformatics epitope prediction tools including NetMHCpan-4.1, NetMHCIIpan-3.2, and IEDB Consensus (v2.18 and v2.22) and fused to the translocation domain of diphtheria toxin (DTT). The A549 tumor model was successfully established in a murine mouse model. The vaccine was formulated by combining the adjuvants Alum and CpG and subsequently assessed for its immunogenicity and anti-tumor efficacy. DTT-EG (3;5;6;7) vaccines elicited specific humoral and cellular immune responses and effectively suppressed tumor growth in both prophylactic and therapeutic mouse tumor models. The selected epitopes EG3 (HGAVRFSNNPALCNV145-159), EG5 (KDSLSINATNIKHFK346-360), EG6 (VKEITGFLLIQAWPE398-412), and EG7 (LCYANTINWKKLFGT469-483) were incorporated into vaccines for active immunization, representing a promising strategy for the treatment of tumors with overexpressed epidermal growth factor receptor (EGFR). The vaccine design and fusion method employed in this study demonstrate a viable approach toward the development of cancer vaccines.

Cytotoxic T lymphocytes are key for controlling viral infection. Unravelling CD8+ T cell-mediated immunity to distinct influenza virus strains and subtypes across prominent HLA types is relevant for combating seasonal infections and emerging new variants. Using an immunopeptidomics approach, naturally presented influenza A virus-derived ligands restricted to HLA-A*24:02, HLA-A*68:01, HLA-B*07:02, and HLA-B*51:01 molecules were identified. Functional characterization revealed multifunctional memory CD8+ T cell responses for nine out of sixteen peptides. Peptide presentation kinetics was optimal around 12 h post infection and presentation of immunodominant epitopes shortly after infection was not always persistent. Assessment of immunogenic epitopes revealed that they are highly conserved across the major zoonotic reservoirs and may contain a single substitution in the vicinity of the anchor residues. These findings demonstrate how the identified epitopes promote T cell pools, possibly cross-protective in individuals and can be potential targets for vaccination.

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. Precise identification of conserved T-cell epitopes is critical for designing effective vaccines. Here we introduce a comprehensive computational framework incorporating a machine learning algorithm-MHCvalidator-to enhance mass spectrometry-based immunopeptidomics sensitivity. MHCvalidator identifies unique T-cell epitopes presented by the B7 supertype, including an epitope from a + 1-frameshift in a truncated Spike antigen, supported by ribosome profiling. Analysis of 100,512 COVID-19 patient proteomes shows Spike antigen truncation in 0.85% of cases, revealing frameshifted viral antigens at the population level. Our EpiTrack pipeline tracks global mutations of MHCvalidator-identified CD8 + T-cell epitopes from the BNT162b4 vaccine. While most vaccine epitopes remain globally conserved, an immunodominant A*01-associated epitope mutates in Delta and Omicron variants. This work highlights SARS-CoV-2 antigenic features and emphasizes the importance of continuous adaptation in T-cell vaccine development.

Checkpoint immunotherapy unleashes tumor control by T cells, but it is undermined in non-immunogenic tumors, e.g. with low MHC class I expression and low neoantigen burden, such as neuroblastoma (NB). Endoplasmic reticulum aminopeptidase 1 (ERAP1) is an enzyme that trims peptides before loading on MHC class I molecules. Inhibition of ERAP1 results in the generation of new antigens able of inducing potent anti-tumor immune responses. Here, we identify a novel non-toxic combinatorial strategy based on genetic inhibition of ERAP1 and administration of the HDAC inhibitor (HDACi) entinostat that increase the immunogenicity of NB, making it responsive to PD-1 therapy.

CRISPR/Cas9-mediated gene editing was used to knockout (KO) the ERAP1 gene in 9464D NB cells derived from spontaneous tumors of TH-MYCN transgenic mice. The expression of MHC class I and PD-L1 was evaluated by flow cytometry (FC). The immunopeptidome of these cells was studied by mass spectrometry. Cocultures of splenocytes derived from 9464D bearing mice and tumor cells allowed the assessment of the effect of ERAP1 inhibition on the secretion of inflammatory cytokines and activation and migration of immune cells towards ERAP1 KO cells by FC. Tumor cell killing was evaluated by Caspase 3/7 assay and flow cytometry analysis. The effect of ERAP1 inhibition on the immune content of tumors was analyzed by FC, immunohistochemistry and multiple immunofluorescence.

We found that inhibition of ERAP1 makes 9464D cells more susceptible to immune cell-mediated killing by increasing both the recall and activation of CD4+ and CD8+ T cells and NK cells. Treatment with entinostat induces the expression of MHC class I and PD-L1 molecules in 9464D both in vitro and in vivo. This results in pronounced changes in the immunopeptidome induced by ERAP1 inhibition, but also restrains the growth of ERAP1 KO tumors in vivo by remodelling the tumor-infiltrating T-cell compartment. Interestingly, the absence of ERAP1 in combination with entinostat and PD-1 blockade overcomes resistance to PD-1 immunotherapy and increases host survival.

These findings demonstrate that ERAP1 inhibition combined with HDACi entinostat treatment and PD-1 blockade remodels the immune landscape of a non-immunogenic tumor such as NB, making it responsive to checkpoint immunotherapy.

With the development of novel vaccine strategies, T-cell epitope-based vaccines have become promising prophylactic and therapeutic tools against infectious diseases that cannot be controlled via traditional vaccines. T-cell epitope-based vaccines leverage specific immunogenic peptides to elicit protective T-cell responses against infectious pathogens. Compared to traditional vaccines, they provide superior efficacy and safety, minimizing the risk of adverse side effects. In this review, we summarized and compared the prediction and identification methods of T-cell epitopes. By integrating bioinformatic prediction and experimental validation, efficient and precise screening of T-cell epitopes can be achieved. Importantly, we delved into the development approaches to diverse T-cell epitope-based vaccines, comparing their merits and demerits, as well as discussing the prevalent challenges and perspectives in their applications. This review offers fresh perspectives for the formulation of safe and efficacious epitope-based vaccines for the devastating diseases against which no vaccines are currently available.

Over the past 30 years, immunopeptidomics has grown alongside improvements in mass spectrometry technology, genomics, transcriptomics, T cell receptor sequencing, and immunological assays to identify and characterize the targets of activated T cells. Together, multiple research groups with expertise in immunology, biochemistry, chemistry, and peptide mass spectrometry have come together to enable the isolation and sequence identification of endogenous major histocompatibility complex (MHC)-bound peptides. The idea to apply highly sensitive mass spectrometry techniques to study the landscape of peptide antigens presented by cell surface MHCs was innovative and continues to be successfully used and improved upon to deepen our understanding of how peptide antigens are processed and presented to T cells. Multiple research groups were involved in this bringing immunopeptidomics to the forefront of translational research, and we will highlight the contributions of one of the earliest developers, Professor Donald F. Hunt, and his research group at the University of Virginia. The Hunt laboratory applied cutting edge mass spectroscopy-based immunopeptidomics to study cancer, autoimmunity, transplant rejection, and infectious diseases. Across these diverse research areas, the Hunt laboratory and collaborators would characterize previously unknown MHC peptide-binding motifs and identify immunologically active antigens using ultra sensitive mass spectrometry techniques. Amazingly, many of the MHC-bound peptide antigens discovered in collaborations with the Hunt laboratory were sequenced by mass spectrometry before the completion of the human genome using manual de novo sequencing. In this perspective article, we will chronicle the work of the Hunt laboratory and their many collaborators that would be a major part of the foundation for mass spectrometry-based immunopeptidomics and its application to immunology research.

Personalized cancer immunotherapies such as therapeutic vaccines and adoptive transfer of T cell receptor-transgenic T cells rely on the presentation of tumor-specific peptides by human leukocyte antigen class I molecules to cytotoxic T cells. Such neoepitopes can for example arise from somatic mutations and their identification is crucial for the rational design of new therapeutic interventions. Liquid chromatography mass spectrometry (LC-MS)-based immunopeptidomics is the only method to directly prove actual peptide presentation and we have developed a parameter optimization workflow to tune targeted assays for maximum detection sensitivity on a per peptide basis, termed optiPRM. Optimization of collision energy using optiPRM allows for the improved detection of low abundant peptides that are very hard to detect using standard parameters. Applying this to immunopeptidomics, we detected a neoepitope in a patient-derived xenograft from as little as 2.5 × 106 cells input. Application of the workflow on small patient tumor samples allowed for the detection of five mutation-derived neoepitopes in three patients. One neoepitope was confirmed to be recognized by patient T cells. In conclusion, optiPRM, a targeted MS workflow reaching ultra-high sensitivity by per peptide parameter optimization, makes the identification of actionable neoepitopes possible from sample sizes usually available in the clinic.

Molecules of the Major Histocompatibility Complex (MHC) present short protein fragments on the cell surface, an important step in T cell immune recognition. MHC-I molecules process peptides from intracellular proteins; MHC-II molecules act in antigen-presenting cells and present peptides derived from extracellular proteins. Here we show that the sequence-dependent energy landscapes of MHC-peptide binding encode class-specific nonlinearities (epistasis). MHC-I has a smooth landscape with global epistasis; the binding energy is a simple deformation of an underlying linear trait. This form of epistasis enhances the discrimination between strong-binding peptides. In contrast, MHC-II has a rugged landscape with idiosyncratic epistasis: binding depends on detailed amino acid combinations at multiple positions of the peptide sequence. The form of epistasis affects the learning of energy landscapes from training data. For MHC-I, a low-complexity problem, we derive a simple matrix model of binding energies that outperforms current models trained by machine learning. For MHC-II, higher complexity prevents learning by simple regression methods. Epistasis also affects the energy and fitness effects of mutations in antigen-derived peptides (epitopes). In MHC-I, large-effect mutations occur predominantly in anchor positions of strong-binding epitopes. In MHC-II, large effects depend on the background epitope sequence but are broadly distributed over the epitope, generating a bigger target for escape mutations due to loss of presentation. Together, our analysis shows how an energy landscape of protein-protein binding constrains the target of escape mutations from T cell immunity, linking the complexity of the molecular interactions to the dynamics of adaptive immune response.

Major histocompatibility complex (MHC) plays a vital role in presenting epitopes (short peptides from pathogenic proteins) to T-cell receptors (TCRs) to trigger the subsequent immune responses. Vaccine design targeting MHC generally aims to find epitopes with a high binding affinity for MHC presentation. Nevertheless, to find novel epitopes usually requires high-throughput screening of bulk peptide database, which is time-consuming, labor-intensive, more unaffordable, and very expensive. Excitingly, the past several years have witnessed the great success of artificial intelligence (AI) in various fields, such as natural language processing (NLP, e.g., GPT-4), protein structure prediction and engineering (e.g., AlphaFold2), and so on. Therefore, herein, we propose a deep reinforcement-learning (RL)-based generative algorithm, RLpMIEC, to quantitatively design peptide targeting MHC-I systems. Specifically, RLpMIEC combines the energetic spectrum (namely, the molecular interaction energy component, MIEC) based on the peptide-MHC interaction and the sequence information to generate peptides with strong binding affinity and precise MIEC spectra to accelerate the discovery of candidate peptide vaccines. RLpMIEC performs well in all the generative capability evaluations and can generate peptides with strong binding affinities and precise MIECs and, moreover, with high interpretability, demonstrating its powerful capability in participation for accelerating peptide-based vaccine development.

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

Human leukocyte antigen (HLA) class-I molecules present fragments of the cellular proteome to the T cell receptor (TCR) of cytotoxic T cells to control infectious diseases and cancer. The large number of combinations of HLA class-I allotypes and peptides allows for highly specific and dedicated low-affinity interactions to a diverse array of TCRs and natural killer (NK) cell receptors. Whether the divergent HLA class-I peptide complex is exclusive for interactions with these proteins is unknown. Using genome-wide CRISPR-Cas9 activation and knockout screens, we identified peptide-specific HLA-C∗07 combinations that can interact with the surface molecules CD55 and heparan sulfate. These interactions closely resemble the HLA class-I interaction with the TCR regarding both the affinity range and the specificity of the peptide and HLA allele. These findings indicate that various proteins can specifically bind HLA class-I peptide complexes due to their polymorphic nature, which suggests there are more interactions like the ones we describe here.

Our ability to fight pathogens relies on major histocompatibility complex class I (MHC-I) molecules presenting diverse antigens on the surface of diseased cells. The transporter associated with antigen processing (TAP) transports nearly the entire repertoire of antigenic peptides into the endoplasmic reticulum for MHC-I loading. How TAP transports peptides specific for MHC-I is unclear. In this study, we used cryo-EM to determine a series of structures of human TAP, both in the absence and presence of peptides with various sequences and lengths. The structures revealed that peptides of eight or nine residues in length bind in a similarly extended conformation, despite having little sequence overlap. We also identified two peptide-anchoring pockets on either side of the transmembrane cavity, each engaging one end of a peptide with primarily main chain atoms. Occupation of both pockets results in a global conformational change in TAP, bringing the two halves of the transporter closer together to prime it for isomerization and ATP hydrolysis. Shorter peptides are able to bind to each pocket separately but are not long enough to bridge the cavity to bind to both simultaneously. Mutations that disrupt hydrogen bonds with the N and C termini of peptides almost abolish MHC-I surface expression. Our findings reveal that TAP functions as a molecular caliper that selects peptides according to length rather than sequence, providing antigen diversity for MHC-I presentation.

Lynch syndrome (LS) is an inherited cancer predisposition disorder associated with an elevated risk of developing various solid cancers, but mostly colorectal cancer (CRC). Despite having the same germline pathogenic variant (PV) in one of the mis-match repair genes or the EPCAM gene, Lynch syndrome variant heterozygotes (LSVH) exhibit a remarkable phenotypic variability in the risk of developing cancer. The role of human leukocyte antigen (HLA) in modifying cancer development risk prompted our hypothesis into whether HLA variations act as potential genetic modifiers influencing the age at cancer diagnosis in LSVH. To investigate this, we studied a unique cohort of 426 LSVH carrying the same germline PV in the hMLH1 gene (MLH1:c.1528C > T) in South Africa. We intuitively selected 100 LSVH with the greatest diversity in age at cancer diagnosis (N = 80) and the oldest cancer unaffected LSVH (N = 20) for a high-throughput HLA genotyping of 11 HLA class I and class II loci using the shotgun next-generation sequencing (NGS) technique on the Illumina MiSeq platform. Statistical analyses employed Kaplan-Meier survival analyses with log-rank tests, and Cox proportional hazards using binned HLA data to minimize type I error. Significant associations were observed between young age at cancer diagnosis and HLA-DPB1*04:02 (mean age: 37 y (25-50); hazard ratio (HR) = 3.37; corrected p-value (q) = 0.043) as well as HLA-DPB1 binned alleles (including HLA-DPB1*09:01, HLA-DPB1*10:01, HLA-DPB1*106:01, HLA-DPB1*18:01, HLA-DPB1*20:01, HLA-DPB1*26:01, HLA-DPB1*28:01, HLA-DPB1*296:01, and HLA-DPB1*55:01) (mean age: 37 y (17-63); HR = 2.30, q = 0.045). The involvement of HLA-DPB1 alleles in the age at cancer diagnosis may highlight the potential role of HLA class II in the immune response against cancer development in LSVH. When validated in a larger cohort, these high-risk HLA-DPB1 alleles could be factored into cancer risk prediction models for personalized cancer screening in LSVH.

Cancer cell-killing by CD8+ T cells demands effective tumor antigen presentation by human leukocyte antigen class I (HLA-I) molecules. Screening and designing highly immunogenic neoantigens require quantitative computations to reliably predict HLA-peptide binding affinities. Here, with all-atom molecular dynamics (MD) simulations and free energy perturbation (FEP) methods, we design a collection of antigenic peptide candidates through in silico mutagenesis studies on immunogenic neoantigens, yielding enhanced binding affinities to HLA-B*44:02. In-depth structural dissection shows that introducing positively charged residues such as arginine to position 6 or lysine to position 7 of the candidates triggers conformational shifts in both peptides and the antigen-binding groove of the HLA, following the "induced-fit" mechanism. Enhancement in binding affinities compared to the wild-type was found in three out of five mutated candidates. The HLA pocket, capable of accommodating positively charged residues in positions from 5 to 7, is designated as the "dynamic pocket". Taken together, we showcase an effective structure-based binding affinity optimization framework for antigenic peptides of HLA-B*44:02 and underscore the importance of dynamic nature of the antigen-binding groove in concert with the anchoring motifs. This work provides structural insights for rational design of favorable HLA-peptide bindings and future developments in neoantigen-based therapeutics.

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

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

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

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

Psoriasis is a T-cell mediated autoimmune skin disease. HLA-C*06:02 is the main psoriasis-specific risk gene. Using a Vα3S1/Vβ13S1 T-cell receptor (TCR) from a lesional psoriatic CD8+ T-cell clone we had discovered that, as an underlying pathomechanism, HLA-C*06:02 mediates an autoimmune response against melanocytes in psoriasis, and we had identified an epitope from ADAMTS-like protein 5 (ADAMTSL5) as a melanocyte autoantigen. The conditions activating the psoriatic autoimmune response in genetically predisposed individuals throughout life remain incompletely understood. Here, we aimed to identify environmental antigens that might trigger autoimmunity in psoriasis because of TCR polyspecificity.

We screened databases with the peptide recognition motif of the Vα3S1/Vβ13S1 TCR for environmental proteins containing peptides activating this TCR. We investigated the immunogenicity of these peptides for psoriasis patients and healthy controls by lymphocyte stimulation experiments and peptide-loaded HLA-C*06:02 tetramers.

We identified peptides from wheat, Saccharomyces cerevisiae, microbiota, tobacco, and pathogens that activated both the Vα3S1/Vβ13S1 TCR and CD8+ T cells from psoriasis patients. Using fluorescent HLA-C*06:02 tetramers loaded with ADAMTSL5 or wheat peptides, we find that the same CD8+ T cells may recognize both autoantigen and environmental antigens. A wheat-free diet could alleviate psoriasis in several patients.

Our results show that due to TCR polyspecificity, several environmental antigens corresponding to previously suspected psoriasis risk conditions converge in the reactivity of a pathogenic psoriatic TCR and might thus be able to stimulate the psoriatic autoimmune response against melanocytes. Avoiding the corresponding environmental risk factors could contribute to the management of psoriasis.

Helicobacter pylori (H. pylori) is associated with gastric inflammation and mucosal antibodies against its cytotoxin-associated gene A (CagA) are protective. Vaccine-elicited immunity against H. pylori requires MHC class II expression, indicating that CD4+ T cells are protective. We hypothesized that the HLA-DR genotypes in human populations include protective alleles that more effectively bind immunogenic CagA peptide fragments and susceptible alleles with an impaired capacity to present CagA peptides. We recruited patients (n = 170) admitted for gastroendoscopy procedures and performed high-resolution HLA-DRB1 typing. Serum anti-CagA IgA levels were analyzed by ELISA (23.2% positive) and H. pylori classified as positive or negative in gastric mucosal tissue slides (72.9% positive). Pearson Chi-square analysis revealed that H. pylori infection was significantly increased in DRB1*11:04-positive individuals (p = 0.027). Anti-CagA IgA was significantly decreased in DRB1*11:04 positive individuals (p = 0.041). In contrast, anti-CagA IgA was significantly increased in DRB1*03:01 positive individuals (p = 0.030). For these HLA-DRB1 alleles of interest, we utilized two in silico prediction methods to compare their capacity to present CagA peptides. Both methods predicted increased numbers of peptides for DRB1*03:01 than DRB1*11:04. In addition, both alleles preferred distinctively different CagA 15mer peptide sequences for high affinity binding. These observations suggest that DRB1*11:04 is a susceptible genotype with impaired CagA immunity, whereas DRB1*03:01 is a protective genotype that promotes enhanced CagA immunity.

Listeria monocytogenes (Lm), a foodborne bacterium, can infect people and has a high fatality rate in immunocompromised individuals. Listeriolysin O (LLO), the primary virulence factor of Lm, is critical in regulating the pathogenicity of Lm. This review concludes that LLO may either directly or indirectly activate a number of host cell viral pathophysiology processes, such as apoptosis, pyroptosis, autophagy, necrosis and necroptosis. We describe the invasion of host cells by Lm and the subsequent removal of Lm by CD8 T cells and CD4 T cells upon receipt of the LLO epitopes from major histocompatibility complex class I (MHC-I) and major histocompatibility complex class II (MHC-II). The development of several LLO-based vaccines that make use of the pore-forming capabilities of LLO and the immune response of the host cells is then described. Finally, we conclude by outlining the several natural substances that have been shown to alter the three-dimensional conformation of LLO by binding to particular amino acid residues of LLO, which reduces LLO pathogenicity and may be a possible pharmacological treatment for Lm.

The analysis of antigen-specific T-cell responses has become routine in many laboratories. Functional T-cell assays like enzyme-linked-immuno-spot (ELISPOT), which depend on antigen-specific stimulation, increasingly use peptides to represent the antigen of interest. Besides single peptides, mixtures of peptides (peptide pools) are very frequently applied. Such peptide pools may, for example, represent entire proteins (with overlapping peptides covering a protein sequence) or include noncontiguous peptides such as a collection of T-cell-stimulating peptides. The optimum specification of single peptides or peptide pools for T-cell stimulation assays will depend on the purpose of the test, the target T-cell population, the availability of sample, requirements regarding reproducibility, and, last but not least, the available budget, to mention only the most important factors. Because of the way peptides are produced, they will always contain certain amounts of impurities such as peptides with deletions or truncated peptides, and there may be additional by-products of peptide synthesis. Optimized synthesis protocols as well as purification help reduce impurities that might otherwise cause false-positive assay results. However, specific requirements with respect to purity will vary depending on the purpose of an assay. Finally, storage conditions significantly affect the shelf life of peptides, which is relevant especially for longitudinal studies. The present book chapter addresses all of these aspects in detail. It should provide the researcher with all necessary background knowledge for making the right decisions when it comes to choosing, using, and storing peptides for ELISPOT and other T-cell stimulation assays.

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.

Immunotherapy for the treatment of pancreatic ductal adenocarcinoma (PDAC) has shown limited efficacy. Poor CD8 T-cell infiltration, low neoantigen load and a highly immunosuppressive tumour microenvironment contribute to this lack of response. Here, we aimed to further investigate the immunoregulatory function of focal adhesion kinase (FAK) in PDAC, with specific emphasis on regulation of the type-II interferon response that is critical in promoting T-cell tumour recognition and effective immunosurveillance.

We combined CRISPR, proteogenomics and transcriptomics with mechanistic experiments using a KrasG12Dp53R172H mouse model of pancreatic cancer and validated findings using proteomic analysis of human patient-derived PDAC cell lines and analysis of publicly available human PDAC transcriptomics datasets.

Loss of PDAC cell-intrinsic FAK signalling promotes expression of the immunoproteasome and Major Histocompatibility Complex class-I (MHC-I), resulting in increased antigen diversity and antigen presentation by FAK-/- PDAC cells. Regulation of the immunoproteasome by FAK is a critical determinant of this response, optimising the physicochemical properties of the peptide repertoire for high affinity binding to MHC-I. Expression of these pathways can be further amplified in a STAT1-dependent manner via co-depletion of FAK and STAT3, resulting in extensive infiltration of tumour-reactive CD8 T-cells and further restraint of tumour growth. FAK-dependent regulation of antigen processing and presentation is conserved between mouse and human PDAC, but is lost in cells/tumours with an extreme squamous phenotype.

Therapies aimed at FAK degradation may unlock additional therapeutic benefit for the treatment of PDAC through increasing antigen diversity and promoting antigen presentation.

Immune checkpoint inhibitors (ICI) are cancer therapies that restore anti-tumor immunity; however, only a small percentage of patients have been completely cured by ICI alone. Multiple approaches in combination with other modalities have been used to improve the efficacy of ICI therapy. Among conventional cancer treatments, radiotherapy or DNA damage-based chemotherapy is a promising candidate as a partner of ICI because DNA damage signaling potentially stimulates immune activities turning the tumor's immune environment into hot tumors. Programmed death-ligand 1 (PD-L1) and human leukocyte antigen class I (HLA-I), which are immune ligands, regulate the balance of anti-tumor immunity in the tumor microenvironment. PD-L1 functions as a brake to suppress cytotoxic T cell activity, whereas HLA-I is an immune accelerator that promotes the downstream of the T cell signaling. Accumulating evidence has demonstrated that DNA damage enhances the presentation of HLA-I on the surface of damaged cells. However, it is unclear how signal transduction in DNA-damaged cells upregulates the presentation of HLA-I with antigens. Our recent study uncovered the mechanism underlying DNA damage-induced HLA-I presentation, which requires polypeptide synthesis through a pioneer round of translation. In this review, we summarize the latest overview of how DNA damage stimulates antigen production presented by HLA-I.

Short, synthetic peptides that are displayed by major histocompatibility complex I (MHC I) can stimulate CD8 T cells in vivo to destroy virus-infected or cancer cells. The development of such peptides as vaccines that provide protective immunity, however, is limited by rapid proteolytic degradation. Introduction of unnatural amino acid residues can suppress MHC I antigen proteolysis, but the modified peptides typically display lower affinity for MHC I and/or diminished ability to activate CD8 T cells relative to native antigen. Here, we report a new strategy for modifying MHC I antigens to enhance resistance to proteolysis while preserving MHC I affinity and T cell activation properties. This approach, replacing backbone amide groups with thioamides, was evaluated in two well-characterized antigens presented by HLA-A2, a common human MHC I. For each antigen, singly modified thioamide analogues retained affinity for HLA-A2 and activated T cells specific for the native antigen, as measured via interferon-γ secretion. In each system, we identified a highly potent triply substituted thioamide antigen ("thio-antigen") that displayed substantial resistance to proteolytic cleavage. Collectively, our results suggest that thio-antigens may represent a general and readily accessible source of potent vaccine candidates that resist degradation.

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.

Peptide drugs play an important part in medicine owing to their many therapeutic applications. Of the 80 peptide drugs approved for use in humans, at least five are now off-patent and are consequently being developed as generic alternatives to the originator products. To accelerate access to generic products, the FDA has proposed new regulatory pathways that do not require direct comparisons of generics to originators in clinical trials. The 'Abbreviated New Drug Application' (ANDA) pathway recommends that sponsors provide information on any new impurities in the generic drug, compared with the originator product, because the impurities can have potential to elicit unwanted immune responses owing to the introduction of T-cell epitopes. This review describes how peptide drug impurities can elicit unexpected immunogenicity and describes a framework for performing immunogenicity risk assessment of all types of bioactive peptide products. Although this report primarily focuses on generic peptides and their impurities, the approach might also be of interest for developers of novel peptide drugs who are preparing their products for an initial regulatory review.

Amyloid β (Aβ) oligomers have been linked to Alzheimer's disease (AD) pathogenesis and are the main neurotoxic forms of Aβ. This review focuses on the following: (i) the Aβ(1-42):calmodulin interface as a model for the design of antagonist Aβ peptides and its limitations; (ii) proteolytic degradation as the major source of highly hydrophobic peptides in brain cells; and (iii) brain peptides that have been experimentally demonstrated to bind to Aβ monomers or oligomers, Aβ fibrils, or Aβ plaques. It is highlighted that the hydrophobic amino acid residues of the COOH-terminal segment of Aβ(1-42) play a key role in its interaction with intracellular protein partners linked to its neurotoxicity. The major source of highly hydrophobic endogenous peptides of 8-10 amino acids in neurons is the proteasome activity. Many canonical antigen peptides bound to the major histocompatibility complex class 1 are of this type. These highly hydrophobic peptides bind to Aβ and are likely to be efficient antagonists of the binding of Aβ monomers/oligomers concentrations in the nanomolar range with intracellular proteins. Also, their complexation with Aβ will protect them against endopeptidases, suggesting a putative chaperon-like physiological function for Aβ that has been overlooked until now. Remarkably, the hydrophobic amino acid residues of Aβ responsible for the binding of several neuropeptides partially overlap with those playing a key role in its interaction with intracellular protein partners that mediates its neurotoxicity. Therefore, these latter neuropeptides are also potential candidates to antagonize Aβ peptides binding to target proteins. In conclusion, the analysis performed in this review points out that hydrophobic endogenous brain neuropeptides could be valuable biomarkers to evaluate the risk of the onset of sporadic AD, as well as for the prognosis of AD.

Different strategies based on peptides are available for cancer treatment, in particular to counter-act the progression of tumor growth and disease relapse. In the last decade, in the context of therapeutic strategies against cancer, peptide-based vaccines have been evaluated in different tumor models. The peptides selected for cancer vaccine development can be classified in two main type: tumor-associated antigens (TAAs) and tumor-specific antigens (TSAs), which are captured, internalized, processed and presented by antigen-presenting cells (APCs) to cell-mediated immunity. Peptides loaded onto MHC class I are recognized by a specific TCR of CD8+ T cells, which are activated to exert their cytotoxic activity against tumor cells presenting the same peptide-MHC-I complex. This process is defined as active immunotherapy as the host's immune system is either de novo activated or restimulated to mount an effective, tumor-specific immune reaction that may ultimately lead to tu-mor regression. However, while the preclinical data have frequently shown encouraging results, therapeutic cancer vaccines clinical trials, including those based on peptides have not provided satisfactory data to date. The limited efficacy of peptide-based cancer vaccines is the consequence of several factors, including the identification of specific target tumor antigens, the limited immunogenicity of peptides and the highly immunosuppressive tumor microenvironment (TME). An effective cancer vaccine can be developed only by addressing all such different aspects. The present review describes the state of the art for each of such factors.

Mass spectrometry-based immunopeptidomics is the only unbiased method to identify naturally presented HLA ligands, which is an indispensable prerequisite for characterizing novel tumor antigens for immunotherapeutic approaches. In recent years, improvements based on devices and methodology have been made to optimize sensitivity and throughput in immunopeptidomics. However, developments in ligand isolation, mass spectrometric analysis, and subsequent data processing can have a marked impact on the quality and quantity of immunopeptidomics data.

In this work, we compared the immunopeptidome composition in terms of peptide yields, spectra quality, hydrophobicity, retention time, and immunogenicity of two established immunoprecipitation methods (column-based and 96-well-based) using cell lines as well as primary solid and hematological tumor samples.

Although, we identified comparable overall peptide yields, large proportions of method-exclusive peptides were detected with significantly higher hydrophobicity for the column-based method with potential implications for the identification of immunogenic tumor antigens. We showed that column preparation does not lose hydrophilic peptides in the hydrophilic washing step. In contrast, an additional 50% acetonitrile elution could partially regain lost hydrophobic peptides during 96-well preparation, suggesting a reduction of the bias towards the column-based method but not completely equalizing it.

Together, this work showed how different immunoprecipitation methods and their adaptions can impact the peptide repertoire of immunopeptidomic analysis and therefore the identification of potential tumor-associated antigens.

T cells are main actors of the immune system with an essential role in protection against pathogens and cancer. The molecular key event involved in this absolutely central task is the interaction of membrane-bound specific T cell receptors with peptide-MHC complexes which initiates T cell priming, activation and recall, and thus controls a range of downstream functions. While textbooks teach us that the repertoire of mature T cells is highly diverse, it is clear that this diversity cannot possibly cover all potential foreign peptides that might be encountered during life. TCR cross-reactivity, i.e. the ability of a single TCR to recognise different peptides, offers the best solution to this biological challenge. Reports have shown that indeed, TCR cross-reactivity is surprisingly high. Hence, the T cell dilemma is the following: be as specific as possible to target foreign danger and spare self, while being able to react to a large spectrum of body-threatening situations. This has major consequences for both autoimmune diseases and cancer, and significant implications for the development of T cell-based therapies. In this review, we will present essential experimental evidence of T cell cross-reactivity, implications for two opposite immune conditions, i.e. autoimmunity vs cancer, and how this can be differently exploited for immunotherapy approaches. Finally, we will discuss the tools available for predicting cross-reactivity and how improvements in this field might boost translational approaches.

Since the discovery of Transporter associated with antigen processing-binding protein-related (TAPBPR) over two decades ago, extensive studies have explored its function in the context of the major histocompatibility complex class-I (MHC-I) antigen processing and presentation pathway. As a chaperone and peptide editor, TAPBPR was recently revealed to have overlapping structural features when resolved with peptide-receptive MHC-I molecules compared with the two newly solved tapasin:MHC-I structures. Despite this, the two chaperones seem to have a unique criteria for loading high-affinity peptides on MHC-I molecules. Yet, the mechanism of action of how TAPBPR creates its distinct filter in cargo selection for peptide-receptive MHC-I molecules continues to be a subject of debate.

Lynch syndrome (LS) is the most common inherited cancer syndrome. It is inherited via a monoallelic germline variant in one of the DNA mismatch repair (MMR) genes. LS carriers have a broad 30% to 80% risk of developing various malignancies, and more precise, individual risk estimations would be of high clinical value, allowing tailored cancer prevention and surveillance. Due to MMR deficiency, LS cancers are characterized by the accumulation of frameshift mutations leading to highly immunogenic frameshift peptides (FSPs). Thus, immune surveillance is proposed to inhibit the outgrowth of MMR-deficient cell clones. Recent studies have shown that immunoediting during the evolution of MMR-deficient cancers leads to a counter-selection of highly immunogenic antigens. The immunogenicity of FSPs is dependent on the antigen presentation. One crucial factor determining antigen presentation is the HLA genotype. Hence, a LS carrier's HLA genotype plays an important role in the presentation of FSP antigens to the immune system, and may influence the likelihood of progression from precancerous lesions to cancer. To address the challenge of clarifying this possibility including diverse populations with different HLA types, we have established the INDICATE initiative (Individual cancer risk by HLA type, http://indicate-lynch.org/), an international network aiming at a systematic evaluation of the HLA genotype as a possible cancer risk modifier in LS. Here we summarize the current knowledge on the role of HLA type in cancer risk and outline future research directions to delineate possible association in the scenario of LS with genetically defined risk population and highly immunogenic tumors.

Few neoepitopes detected in tumor biopsies are immunogenic. Tumor-specific T cell responses require both the presentation of an epitope that differs from wildtype and the presence of T cells with neoepitope-cognate receptors. We show that mutations detected in tumor biopsies result in an increased frequency of rare amino acid combinations compared to the human proteome and gastrointestinal microorganisms. Mutations in a large data set of oncogene and tumor suppressor gene products were compared to wildtype, and to the count of corresponding amino acid motifs in the human proteome and gastrointestinal microbiome. Mutant amino acids in T cell exposed positions of potential neoepitopes consistently generated amino acid motifs that are less common in both proteome reference datasets. Approximately 10% of the mutant amino acid motifs are absent from the human proteome. Motif frequency does not change when mutants were positioned in the MHC anchor positions hidden from T cell receptors. Analysis of neoepitopes in GBM and LUSC cases showed less common T cell exposed motifs, and HLA binding preferentially placing mutant amino acids in an anchor position for both MHC I and MHC II. Cross-presentation of mutant exposed neoepitopes by MHC I and MHC II was particularly uncommon. Review of a tumor mutation dataset known to generate T cell responses showed immunogenic epitopes were those with mutant amino acids exposed to the T cell receptor and with exposed pentamer motifs present in the human and microbiome reference databases. The study illustrates a previously unrecognized mechanism of tumor immune evasion, as rare T cell exposed motifs produced by mutation are less likely to have cognate T cells in the T cell repertoire. The complex interactions of HLA genotype, binding positions, and mutation specific changes in T cell exposed motif underscore the necessity of evaluating potential neoepitopes in each individual patient.

The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.