Mass spectrometry-based identification of MHC-bound peptides for immunopeptidomics

Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation

Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.

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

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

Proteogenomic analysis identifies neoantigens and bacterial peptides as immunotherapy targets in colorectal cancer

Considerable progress has recently been made in cancer immunotherapy, including immune checkpoint blockade, cancer vaccine, and adoptive T cell methods. The lack of effective targets is a major cause of the low immunotherapy response rate in colorectal cancer (CRC). Here, we used a proteogenomic strategy comprising immunopeptidomics, whole exome sequencing, and 16 S ribosomal DNA sequencing analyses of 8 patients with CRC to identify neoantigens and bacterial peptides that can serve as antitumor targets. This study directly identified several personalized neoantigens and bacterial immunopeptides. Immunoassays showed that all neoantigens and 5 of 8 bacterial immunopeptides could be recognized by autologous T cells. Additionally, T cell receptor (TCR) αβ sequencing revealed the TCR repertoire of epitope-reactive CD8+ T cells. Functional studies showed that T cell receptor-T (TCR-T) could be activated by epitope pulsed lymphoblastoid cells. Overall, this study comprehensively profiled the CRC immunopeptidome, revealing several neoantigens and bacterial peptides with potential to serve as immunotherapy targets in CRC.

Identifying major histocompatibility complex class II-DR molecules in bovine and swine peripheral blood monocyte-derived macrophages using mAb-L243

Major histocompatibility complex class II (MHC-II) molecules are involved in immune responses against pathogens and vaccine candidates' immunogenicity. Immunopeptidomics for identifying cancer and infection-related antigens and epitopes have benefited from advances in immunopurification methods and mass spectrometry analysis. The mouse anti-MHC-II-DR monoclonal antibody L243 (mAb-L243) has been effective in recognising MHC-II-DR in both human and non-human primates. It has also been shown to cross-react with other animal species, although it has not been tested in livestock. This study used mAb-L243 to identify Staphylococcus aureus and Salmonella enterica serovar Typhimurium peptides binding to cattle and swine macrophage MHC-II-DR molecules using flow cytometry, mass spectrometry and two immunopurification techniques. Antibody cross-reactivity led to identifying expressed MHC-II-DR molecules, together with 10 Staphylococcus aureus peptides in cattle and 13 S. enterica serovar Typhimurium peptides in swine. Such data demonstrates that MHC-II-DR expression and immunocapture approaches using L243 mAb represents a viable strategy for flow cytometry and immunopeptidomics analysis of bovine and swine antigen-presenting cells.

Neoantigen-targeted dendritic cell vaccination in lung cancer patients induces long-lived T cells exhibiting the full differentiation spectrum

Non-small cell lung cancer (NSCLC) is known for high relapse rates despite resection in early stages. Here, we present the results of a phase I clinical trial in which a dendritic cell (DC) vaccine targeting patient-individual neoantigens is evaluated in patients with resected NSCLC. Vaccine manufacturing is feasible in six of 10 enrolled patients. Toxicity is limited to grade 1-2 adverse events. Systemic T cell responses are observed in five out of six vaccinated patients, with T cell responses remaining detectable up to 19 months post vaccination. Single-cell analysis indicates that the responsive T cell population is polyclonal and exhibits the near-entire spectrum of T cell differentiation states, including a naive-like state, but excluding exhausted cell states. Three of six vaccinated patients experience disease recurrence during the follow-up period of 2 years. Collectively, these data support the feasibility, safety, and immunogenicity of this treatment in resected NSCLC.

Antigen-specific immunotherapy via delivery of tolerogenic dendritic cells for multiple sclerosis

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system resulting from loss of immune tolerance. Many disease-modifying therapies for MS have broad immunosuppressive effects on peripheral immune cells, but this can increase risks of infection and attenuate vaccine-elicited immunity. A more targeted approach is to re-establish immune tolerance in an autoantigen-specific manner. This review discusses methods to achieve this, focusing on tolerogenic dendritic cells. Clinical trials in other autoimmune diseases also provide learnings with regards to clinical translation of this approach, including identification of autoantigen(s), selection of appropriate patients and administration route and frequency.

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.

New light on the HLA-DR immunopeptidomic landscape

The set of peptides processed and presented by major histocompatibility complex class II molecules defines the immunopeptidome, and its characterization holds keys to understanding essential properties of the immune system. High-throughput mass spectrometry (MS) techniques enable interrogation of the diversity and complexity of the immunopeptidome at an unprecedented scale. Here, we analyzed a large set of MS immunopeptidomics data from 40 donors, 221 samples, covering 30 unique HLA-DR molecules. We identified likely co-immunoprecipitated HLA-DR irrelevant contaminants using state-of-the-art prediction methods and unveiled novel light on the properties of HLA antigen processing and presentation. The ligandome (HLA binders) was enriched in 15-mer peptides, and the contaminome (nonbinders) in longer peptides. Classification of singletons and nested sets showed that the first were enriched in contaminants. Investigating the source protein location of ligands revealed that only contaminants shared a positional bias. Regarding subcellular localization, nested peptides were found to be predominantly of endolysosomal origin, whereas singletons shared an equal distribution between the cytosolic and endolysosomal origin. According to antigen-processing signatures, no significant differences were observed between the cytosolic and endolysosomal ligands. Further, the sensitivity of MS immunopeptidomics was investigated by analyzing overlap and saturation between biological MS replicas, concluding that at least 5 replicas are needed to identify 80% of the immunopeptidome. Moreover, the overlap in immunopeptidome between donors was found to be very low both in terms of peptides and source proteins, the latter indicating a critical HLA bias in the antigen sampling in the HLA antigen presentation. Finally, the complementarity between MS and in silico approaches for comprehensively sampling the immunopeptidome was demonstrated.

CD8+ T-cell responses towards conserved influenza B virus epitopes across anatomical sites and age

Influenza B viruses (IBVs) cause substantive morbidity and mortality, and yet immunity towards IBVs remains understudied. CD8+ T-cells provide broadly cross-reactive immunity and alleviate disease severity by recognizing conserved epitopes. Despite the IBV burden, only 18 IBV-specific T-cell epitopes restricted by 5 HLAs have been identified currently. A broader array of conserved IBV T-cell epitopes is needed to develop effective cross-reactive T-cell based IBV vaccines. Here we identify 9 highly conserved IBV CD8+ T-cell epitopes restricted to HLA-B*07:02, HLA-B*08:01 and HLA-B*35:01. Memory IBV-specific tetramer+CD8+ T-cells are present within blood and tissues. Frequencies of IBV-specific CD8+ T-cells decline with age, but maintain a central memory phenotype. HLA-B*07:02 and HLA-B*08:01-restricted NP30-38 epitope-specific T-cells have distinct T-cell receptor repertoires. We provide structural basis for the IBV HLA-B*07:02-restricted NS1196-206 (11-mer) and HLA-B*07:02-restricted NP30-38 epitope presentation. Our study increases the number of IBV CD8+ T-cell epitopes, and defines IBV-specific CD8+ T-cells at cellular and molecular levels, across tissues and age.

The physiological interactome of TCR-like antibody therapeutics in human tissues

Selective binding of TCR-like antibodies that target a single tumour-specific peptide antigen presented by human leukocyte antigens (HLA) is the absolute prerequisite for their therapeutic suitability and patient safety. To date, selectivity assessment has been limited to peptide library screening and predictive modeling. We developed an experimental platform to de novo identify interactomes of TCR-like antibodies directly in human tissues using mass spectrometry. As proof of concept, we confirm the target epitope of a MAGE-A4-specific TCR-like antibody. We further determine cross-reactive peptide sequences for ESK1, a TCR-like antibody with known off-target activity, in human liver tissue. We confirm off-target-induced T cell activation and ESK1-mediated liver spheroid killing. Off-target sequences feature an amino acid motif that allows a structural groove-coordination mimicking that of the target peptide, therefore allowing the interaction with the engager molecule. We conclude that our strategy offers an accurate, scalable route for evaluating the non-clinical safety profile of TCR-like antibody therapeutics prior to first-in-human clinical application.

Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy

With significant advances in analytical technologies, research in the field of cancer immunotherapy, such as adoptive T cell therapy, cancer vaccine, and immune checkpoint blockade (ICB), is currently gaining tremendous momentum. Since the efficacy of cancer immunotherapy is recognized only by a minority of patients, more potent tumor-specific antigens (TSAs, also known as neoantigens) and predictive markers for treatment response are of great interest. In cancer immunity, immunopeptides, presented by human leukocyte antigen (HLA) class I, play a role as initiating mediators of immunogenicity. The latest advancement in the interdisciplinary multiomics approach has rapidly enlightened us about the identity of the "dark matter" of cancer and the associated immunopeptides. In this field, mass spectrometry (MS) is a viable option to select because of the naturally processed and actually presented TSA candidates in order to grasp the whole picture of the immunopeptidome. In the past few years the search space has been enlarged by the multiomics approach, the sensitivity of mass spectrometers has been improved, and deep/machine-learning-supported peptide search algorithms have taken immunopeptidomics to the next level. In this review, along with the introduction of key technical advancements in immunopeptidomics, the potential and further directions of immunopeptidomics will be reviewed from the perspective of cancer immunotherapy.

Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome

Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.

Birinapant Reshapes the Tumor Immunopeptidome and Enhances Antigen Presentation

Birinapant, an antagonist of the inhibitor of apoptosis proteins, upregulates MHCs in tumor cells and displays a better tumoricidal effect when used in combination with immune checkpoint inhibitors, indicating that Birinapant may affect the antigen presentation pathway; however, the mechanism remains elusive. Based on high-resolution mass spectrometry and in vitro and in vivo models, we adopted integrated genomics, proteomics, and immunopeptidomics strategies to study the mechanism underlying the regulation of tumor immunity by Birinapant from the perspective of antigen presentation. Firstly, in HT29 and MCF7 cells, Birinapant increased the number and abundance of immunopeptides and source proteins. Secondly, a greater number of cancer/testis antigen peptides with increased abundance and more neoantigens were identified following Birinapant treatment. Moreover, we demonstrate the existence and immunogenicity of a neoantigen derived from insertion/deletion mutation. Thirdly, in HT29 cell-derived xenograft models, Birinapant administration also reshaped the immunopeptidome, and the tumor exhibited better immunogenicity. These data suggest that Birinapant can reshape the tumor immunopeptidome with respect to quality and quantity, which improves the presentation of CTA peptides and neoantigens, thus enhancing the immunogenicity of tumor cells. Such changes may be vital to the effectiveness of combination therapy, which can be further transferred to the clinic or aid in the development of new immunotherapeutic strategies to improve the anti-tumor immune response.

DNAJB1-PRKACA fusion neoantigens elicit rare endogenous T cell responses that potentiate cell therapy for fibrolamellar carcinoma

Fibrolamellar carcinoma (FLC) is a liver tumor with a high mortality burden and few treatment options. A promising therapeutic vulnerability in FLC is its driver mutation, a conserved DNAJB1-PRKACA gene fusion that could be an ideal target neoantigen for immunotherapy. In this study, we aim to define endogenous CD8 T cell responses to this fusion in FLC patients and evaluate fusion-specific T cell receptors (TCRs) for use in cellular immunotherapies. We observe that fusion-specific CD8 T cells are rare and that FLC patient TCR repertoires lack large clusters of related TCR sequences characteristic of potent antigen-specific responses, potentially explaining why endogenous immune responses are insufficient to clear FLC tumors. Nevertheless, we define two functional fusion-specific TCRs, one of which has strong anti-tumor activity in vivo. Together, our results provide insights into the fragmented nature of neoantigen-specific repertoires in humans and indicate routes for clinical development of successful immunotherapies for FLC.

Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides

Circular RNAs (circRNAs) are covalently closed non-coding RNAs lacking the 5' cap and the poly-A tail. Nevertheless, it has been demonstrated that certain circRNAs can undergo active translation. Therefore, aberrantly expressed circRNAs in human cancers could be an unexplored source of tumor-specific antigens, potentially mediating anti-tumor T cell responses. This study presents an immunopeptidomics workflow with a specific focus on generating a circRNA-specific protein fasta reference. The main goal of this workflow is to streamline the process of identifying and validating human leukocyte antigen (HLA) bound peptides potentially originating from circRNAs. We increase the analytical stringency of our workflow by retaining peptides identified independently by two mass spectrometry search engines and/or by applying a group-specific FDR for canonical-derived and circRNA-derived peptides. A subset of circRNA-derived peptides specifically encoded by the region spanning the back-splice junction (BSJ) are validated with targeted MS, and with direct Sanger sequencing of the respective source transcripts. Our workflow identifies 54 unique BSJ-spanning circRNA-derived peptides in the immunopeptidome of melanoma and lung cancer samples. Our approach enlarges the catalog of source proteins that can be explored for immunotherapy.

Characterization of antigen presentation capability for neoantigen-based products using targeted LC-MS/MS method

The generation of an immune response in neoantigen-based products relies on antigen presentation, which is closely analyzed by bioassays for T-cell functions such as tetramer or cytokine release. Mass spectrometry (MS) has the potential to directly assess the antigen-presenting capability of antigen-presenting cells (APCs), offering advantages such as speed, multi-target analysis, robustness, and ease of transferability. However, it has not been used for quality control of these products due to challenges in sensitivity, including the number of cells and peptide diversity. In this study, we describe the development and validation of an improved targeted LC-MS/MS method with high sensitivity for characterizing antigen presentation, which could be applied in the quality control of neoantigen-based products. The parameters for the extraction were carefully optimized by different short peptides. Highly sensitive targeted triple quadrupole mass spectrometry combined with ultra-high performance liquid chromatography (UHPLC) was employed using a selective ion monitoring mode (Multiple Reaction Monitoring, MRM). Besides, we successfully implemented robust quality control peptides to ensure the reliability and consistency of this method, which proved invaluable for different APCs. With reference to the guidelines from ICH Q2 (R2), M10, as well as considering the specific attributes of the product itself, we validated the method for selectivity, specificity, sensitivity, limit of detection (LOD), recovery rate, matrix effect, repeatability, and application in dendritic cells (DCs) associated with neoantigen-based products. The validation process yields satisfactory results. Combining this approach with T cell assays will comprehensively assess cell product quality attributes from physicochemical and biological perspectives.

Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model

Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS2PIP model for tryptic and non-tryptic peptides and implement it in MS2Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.

MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods

Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.

Antigen identification strategies and preclinical evaluation models for advancing tuberculosis vaccine development

In its myriad devastating forms, Tuberculosis (TB) has existed for centuries, and humanity is still affected by it. Mycobacterium tuberculosis (M. tuberculosis), the causative agent of TB, was the foremost killer among infectious agents until the COVID-19 pandemic. One of the key healthcare strategies available to reduce the risk of TB is immunization with bacilli Calmette-Guerin (BCG). Although BCG has been widely used to protect against TB, reports show that BCG confers highly variable efficacy (0-80%) against adult pulmonary TB. Unwavering efforts have been made over the past 20 years to develop and evaluate new TB vaccine candidates. The failure of conventional preclinical animal models to fully recapitulate human response to TB, as also seen for the failure of MVA85A in clinical trials, signifies the need to develop better preclinical models for TB vaccine evaluation. In the present review article, we outline various approaches used to identify protective mycobacterial antigens and recent advancements in preclinical models for assessing the efficacy of candidate TB vaccines.

Instability of the HLA-E peptidome of HIV presents a major barrier to therapeutic targeting

Naturally occurring T cells that recognize microbial peptides via HLA-E, a nonpolymorphic HLA class Ib molecule, could provide the foundation for new universal immunotherapeutics. However, confidence in the biological relevance of putative ligands is crucial, given that the mechanisms by which pathogen-derived peptides can access the HLA-E presentation pathway are poorly understood. We systematically interrogated the HIV proteome using immunopeptidomic and bioinformatic approaches, coupled with biochemical and cellular assays. No HIV HLA-E peptides were identified by tandem mass spectrometry analysis of HIV-infected cells. In addition, all bioinformatically predicted HIV peptide ligands (>80) were characterized by poor complex stability. Furthermore, infected cell elimination assays using an affinity-enhanced T cell receptor bispecific targeted to a previously reported HIV Gag HLA-E epitope demonstrated inconsistent presentation of the peptide, despite normal HLA-E expression on HIV-infected cells. This work highlights the instability of the HIV HLA-E peptidome as a major challenge for drug development.

A Simple and Rapid Protocol for the Isolation of Murine Bone Marrow Suitable for the Differentiation of Dendritic Cells

The generation of bone-marrow-derived dendritic cells is a widely used approach in immunological research to study antigen processing and presentation, as well as T-cell activation responses. However, the initial step of isolating the bone marrow can be time-consuming, especially when larger numbers of precursor cells are required. Here, we assessed whether an accelerated bone marrow isolation method using centrifugation is suitable for the differentiation of FMS-like tyrosine kinase 3 ligand-driven dendritic cells. Compared to the conventional flushing method, the centrifugation-based isolation method resulted in a similar bone marrow cell yield on Day 0, increased cell numbers by Day 8, similar proportions of dendritic cell subsets, and consequently a higher number of type 1 conventional dendritic cells (cDC1) from the culture. Although the primary purpose of this method of optimization was to improve experimental efficiency and increase the output of cDC1s, the protocol is also compatible with the differentiation of other dendritic cell subsets such as cDC2 and plasmacytoid dendritic cells, with an improved output cell count and a consistent phenotype.

Protein degradation by human 20S proteasomes elucidates the interplay between peptide hydrolysis and splicing

If and how proteasomes catalyze not only peptide hydrolysis but also peptide splicing is an open question that has divided the scientific community. The debate has so far been based on immunopeptidomics, in vitro digestions of synthetic polypeptides as well as ex vivo and in vivo experiments, which could only indirectly describe proteasome-catalyzed peptide splicing of full-length proteins. Here we develop a workflow-and cognate software - to analyze proteasome-generated non-spliced and spliced peptides produced from entire proteins and apply it to in vitro digestions of 15 proteins, including well-known intrinsically disordered proteins such as human tau and α-Synuclein. The results confirm that 20S proteasomes produce a sizeable variety of cis-spliced peptides, whereas trans-spliced peptides are a minority. Both peptide hydrolysis and splicing produce peptides with well-defined characteristics, which hint toward an intricate regulation of both catalytic activities. At protein level, both non-spliced and spliced peptides are not randomly localized within protein sequences, but rather concentrated in hotspots of peptide products, in part driven by protein sequence motifs and proteasomal preferences. At sequence level, the different peptide sequence preference of peptide hydrolysis and peptide splicing suggests a competition between the two catalytic activities of 20S proteasomes during protein degradation.

Antigen cross-presentation by dendritic cells: A critical axis in cancer immunotherapy

Dendritic cells (DCs) are professional antigen-presenting cells that play a key role in shaping adaptive immunity. DCs have a unique ability to sample their environment, capture and process exogenous antigens into peptides that are then loaded onto major histocompatibility complex class I molecules for presentation to CD8+ T cells. This process, called cross-presentation, is essential for initiating and regulating CD8+ T cell responses against tumors and intracellular pathogens. In this review, we will discuss the role of DCs in cancer immunity, the molecular mechanisms underlying antigen cross-presentation by DCs, the immunosuppressive factors that limit the efficiency of this process in cancer, and approaches to overcome DC dysfunction and therapeutically promote antitumoral immunity.

Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics

Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure-activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications.

MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes

The major histocompatibility complex (MHC) encodes a range of immune response genes, including the human leukocyte antigens (HLAs) in humans. These molecules bind peptide antigens and present them on the cell surface for T cell recognition. The repertoires of peptides presented by HLA molecules are termed immunopeptidomes. The highly polymorphic nature of the genres that encode the HLA molecules confers allotype-specific differences in the sequences of bound ligands. Allotype-specific ligand preferences are often defined by peptide-binding motifs. Individuals express up to six classical class I HLA allotypes, which likely present peptides displaying different binding motifs. Such complex datasets make the deconvolution of immunopeptidomic data into allotype-specific contributions and further dissection of binding-specificities challenging. Herein, we developed MHCpLogics as an interactive machine learning-based tool for mining peptide-binding sequence motifs and visualization of immunopeptidome data across complex datasets. We showcase the functionalities of MHCpLogics by analyzing both in-house and published mono- and multi-allelic immunopeptidomics data. The visualization modalities of MHCpLogics allow users to inspect clustered sequences down to individual peptide components and to examine broader sequence patterns within multiple immunopeptidome datasets. MHCpLogics can deconvolute large immunopeptidome datasets enabling the interrogation of clusters for the segregation of allotype-specific peptide sequence motifs, identification of sub-peptidome motifs, and the exportation of clustered peptide sequence lists. The tool facilitates rapid inspection of immunopeptidomes as a resource for the immunology and vaccine communities. MHCpLogics is a standalone application available via an executable installation at: https://github.com/PurcellLab/MHCpLogics.

Assessing MAPPs assay as a tool to predict the immunogenicity potential of protein therapeutics

MHC-II-associated peptide proteomics (MAPPs) is a mass spectrometry-based (MS) method to identify naturally presented MHC-II-associated peptides that could elicit CD4+T cell activation. MAPPs assay is considered one of the assays that better characterize the safety of biotherapeutics by driving the selection of the best candidates concerning their immunogenicity risk. However, there is little knowledge about the impact of bead material on the recovery of MHC-II MS-eluted ligands in MAPPs assays. Here, we firstly describe a robust MAPPs protocol by implementing streptavidin magnetic beads for the isolation of these peptides instead of commonly used NHS-activated beads. Moreover, we assessed the impact of the cell medium used for cell cultures on the morphology and recovery of the in vitro-generated APCs, and its potential implications in the amount of MHC-II isolated peptides. We also described an example of a MAPPs assay application to investigate drug-induced immunogenicity of two bispecific antibodies and compared them with monospecific trastuzumab IgG1 control. This work highlighted the importance of MAPPs in the preclinical in vitro strategy to mitigate the immunogenicity risk of biotherapeutics.

Mass Spectrometry-Based Immunopeptidomics of Peptides Presented on Human Leukocyte Antigen Proteins

Human leukocyte antigen (HLA) proteins are a group of glycoproteins that are expressed at the cell surface, where they present peptides to T cells through physical interactions with T-cell receptors (TCRs). Hence, characterizing the set of peptides presented by HLA proteins, referred to hereafter as the immunopeptidome, is fundamental for neoantigen identification, immunotherapy, and vaccine development. As a result, different methods have been used over the years to identify peptides presented by HLA proteins, including competition assays, peptide microarrays, and yeast display systems. Nonetheless, over the last decade, mass spectrometry-based immunopeptidomics (MS-immunopeptidomics) has emerged as the gold-standard method for identifying peptides presented by HLA proteins. MS-immunopeptidomics enables the direct identification of the immunopeptidome in different tissues and cell types in different physiological and pathological states, for example, solid tumors or virally infected cells. Despite its advantages, it is still an experimentally and computationally challenging technique with different aspects that need to be considered before planning an MS-immunopeptidomics experiment, while conducting the experiment and with analyzing and interpreting the results. Hence, we aim in this chapter to provide an overview of this method and discuss different practical considerations at different stages starting from sample collection until data analysis. These points should aid different groups aiming at utilizing MS-immunopeptidomics, as well as, identifying future research directions to improve the method.

IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma

Distinction of non-self from self is the major task of the immune system. Immunopeptidomics studies the peptide repertoire presented by the human leukocyte antigen (HLA) protein, usually on tissues. However, HLA peptides are also bound to plasma soluble HLA (sHLA), but little is known about their origin and potential for biomarker discovery in this readily available biofluid. Currently, immunopeptidomics is hampered by complex workflows and limited sensitivity, typically requiring several mL of plasma. Here, we take advantage of recent improvements in the throughput and sensitivity of mass spectrometry (MS)-based proteomics to develop a highly sensitive, automated, and economical workflow for HLA peptide analysis, termed Immunopeptidomics by Biotinylated Antibodies and Streptavidin (IMBAS). IMBAS-MS quantifies more than 5000 HLA class I peptides from only 200 μl of plasma, in just 30 min. Our technology revealed that the plasma immunopeptidome of healthy donors is remarkably stable throughout the year and strongly correlated between individuals with overlapping HLA types. Immunopeptides originating from diverse tissues, including the brain, are proportionately represented. We conclude that sHLAs are a promising avenue for immunology and potentially for precision oncology.

Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy

Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.

Using long-read CAGE sequencing to profile cryptic-promoter-derived transcripts and their contribution to the immunopeptidome

Recent studies have shown that the noncoding genome can produce unannotated proteins as antigens that induce immune response. One major source of this activity is the aberrant epigenetic reactivation of transposable elements (TEs). In tumors, TEs often provide cryptic or alternate promoters, which can generate transcripts that encode tumor-specific unannotated proteins. Thus, TE-derived transcripts (TE transcripts) have the potential to produce tumor-specific, but recurrent, antigens shared among many tumors. Identification of TE-derived tumor antigens holds the promise to improve cancer immunotherapy approaches; however, current genomics and computational tools are not optimized for their detection. Here we combined CAGE technology with full-length long-read transcriptome sequencing (long-read CAGE, or LRCAGE) and developed a suite of computational tools to significantly improve immunopeptidome detection by incorporating TE and other tumor transcripts into the proteome database. By applying our methods to human lung cancer cell line H1299 data, we show that long-read technology significantly improves mapping of promoters with low mappability scores and that LRCAGE guarantees accurate construction of uncharacterized 5' transcript structure. Augmenting a reference proteome database with newly characterized transcripts enabled us to detect noncanonical antigens from HLA-pulldown LC-MS/MS data. Lastly, we show that epigenetic treatment increased the number of noncanonical antigens, particularly those encoded by TE transcripts, which might expand the pool of targetable antigens for cancers with low mutational burden.

Secreted HLA-Fc fusion profiles immunopeptidome in hypoxic PDAC and cellular senescence

Human leukocyte antigens (HLA) present peptides largely from intracellular proteins on cell surfaces. As these complexes can serve as biomarkers in disease, proper identification of peptides derived from disease-associated antigens and the corresponding presenting HLA is important for the design and execution of therapeutic strategies. Yet, current mass spectrometry methods for immunopeptidomic profiling require large and complex sample inputs, hindering the study of certain disease phenotypes and lowering confidence in peptide and allele identification. Here, we describe a secreted HLA (sHLA)-Fc fusion construct for simple single HLA allele profiling in hypoxic pancreatic ductal adenocarcinoma (PDAC) and cellular senescence. This method streamlines sample preparation, enables temporal control, and provides allele-restricted target identification. Over 30,000 unique HLA-associated peptides were identified across 2 different HLA alleles and 7 cell lines, with ∼9,300 peptides newly discovered. The sHLA-Fc fusion capture technology holds the potential to expedite immunopeptidomics and advance therapeutic interest in HLA-peptide complexes.

Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4+ T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. We present an update to the NetMHCIIpan prediction method, which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.

Development of a semi-automated MHC-associated peptide proteomics (MAPPs) method using streptavidin bead-based immunoaffinity capture and nano LC-MS/MS to support immunogenicity risk assessment in drug development

Major histocompatibility complex (MHC)-Associated Peptide Proteomics (MAPPs) is an ex vivo method used to assess the immunogenicity risk of biotherapeutics. MAPPs can identify potential T-cell epitopes within the biotherapeutic molecule. Using adalimumab treated human monocyte derived dendritic cells (DCs) and a pan anti-HLA-DR antibody (Ab), we systematically automated and optimized biotin/streptavidin (SA)-capture antibody coupling, lysate incubation with capture antibody, as well as the washing and elution steps of a MAPPs method using functionalized magnetic beads and a KingFisher Magnetic Particle processor. Automation of these steps, combined with capturing using biotinylated-Ab/SA magnetic beads rather than covalently bound antibody, improved reproducibility as measured by minimal inter-and intra-day variability, as well as minimal analyst-to-analyst variability. The semi-automated MAPPs workflow improved sensitivity, allowing for a lower number of cells per analysis. The method was assessed using five different biotherapeutics with varying immunogenicity rates ranging from 0.1 to 48% ADA incidence in the clinic. Biotherapeutics with ≥10%immunogenicity incidence consistently presented more peptides (1.8-28 fold) and clusters (10-21 fold) compared to those with <10% immunogenicity incidence. Our semi-automated MAPPs method provided two main advantages over a manual workflow- the robustness and reproducibility affords confidence in the epitopes identified from as few as 5 to 10 donors and the method workflow can be readily adapted to incorporate different capture Abs in addition to anti-HLA-DR. The incorporation of semi-automated MAPPs with biotinylated-Ab/SA bead-based capture in immunogenicity screening strategies allows the generation of more consistent and reliable data, helping to improve immunogenicity prediction capabilities in drug development. MHC associated peptide proteomics (MAPPs), Immunogenicity risk assessment, in vitro/ex vivo, biotherapeutics, Major Histocompatibility Complex Class II (MHC II), LC-MS, Immunoaffinity Capture, streptavidin magnetic beads.

Profound N-glycan remodelling accompanies MHC-II immunopeptide presentation

Immunopeptidomics, the study of peptide antigens presented on the cell surface by the major histocompatibility complex (MHC), offers insights into how our immune system recognises self/non-self in health and disease. We recently discovered that hyper-processed (remodelled) N-glycans are dominant features decorating viral spike immunopeptides presented via MHC-class II (MHC-II) molecules by dendritic cells pulsed with SARS-CoV-2 spike protein, but it remains unknown if endogenous immunopeptides also undergo N-glycan remodelling. Taking a multi-omics approach, we here interrogate published MHC-II immunopeptidomics datasets of cultured monocyte-like (THP-1) and breast cancer-derived (MDA-MB-231) cell lines for overlooked N-glycosylated peptide antigens, which we compare to their source proteins in the cellular glycoproteome using proteomics and N-glycomics data from matching cell lines. Hyper-processed chitobiose core and paucimannosidic N-glycans alongside under-processed oligomannosidic N-glycans were found to prevalently modify MHC-II-bound immunopeptides isolated from both THP-1 and MDA-MB-231, while complex/hybrid-type N-glycans were (near-)absent in the immunopeptidome as supported further by new N-glycomics data generated from isolated MHC-II-bound peptides derived from MDA-MB-231 cells. Contrastingly, the cellular proteomics and N-glycomics data from both cell lines revealed conventional N-glycosylation rich in complex/hybrid-type N-glycans, which, together with the identification of key lysosomal glycosidases, suggest that MHC-II peptide antigen processing is accompanied by extensive N-glycan trimming. N-glycan remodelling appeared particularly dramatic for cell surface-located glycoproteins while less remodelling was observed for lysosomal-resident glycoproteins. Collectively, our findings indicate that both under- and hyper-processed N-glycans are prevalent features of endogenous MHC-II immunopeptides, an observation that demands further investigation to enable a better molecular-level understanding of immune surveillance.

Cryptic MHC-E epitope from influenza elicits a potent cytolytic T cell response

The extent to which unconventional forms of antigen presentation drive T cell immunity is unknown. By convention, CD8 T cells recognize viral peptides, or epitopes, in association with classical major histocompatibility complex (MHC) class I, or MHC-Ia, but immune surveillance can, in some cases, be directed against peptides presented by nonclassical MHC-Ib, in particular the MHC-E proteins (Qa-1 in mice and HLA-E in humans); however, the overall importance of nonclassical responses in antiviral immunity remains unclear. Similarly uncertain is the importance of 'cryptic' viral epitopes, defined as those undetectable by conventional mapping techniques. Here we used an immunopeptidomic approach to search for unconventional epitopes that drive T cell responses in mice infected with influenza virus A/Puerto Rico/8/1934. We identified a nine amino acid epitope, termed M-SL9, that drives a co-immunodominant, cytolytic CD8 T cell response that is unconventional in two major ways: first, it is presented by Qa-1, and second, it has a cryptic origin, mapping to an unannotated alternative reading frame product of the influenza matrix gene segment. Presentation and immunogenicity of M-SL9 are dependent on the second AUG codon of the positive sense matrix RNA segment, suggesting translation initiation by leaky ribosomal scanning. During influenza virus A/Puerto Rico/8/1934 infection, M-SL9-specific T cells exhibit a low level of egress from the lungs and strong differentiation into tissue-resident memory cells. Importantly, we show that M-SL9/Qa-1-specific T cells can be strongly induced by messenger RNA vaccination and that they can mediate antigen-specific cytolysis in vivo. Our results demonstrate that noncanonical translation products can account for an important fraction of the T cell repertoire and add to a growing body of evidence that MHC-E-restricted T cells could have substantial therapeutic value.

The Breast Cancer Proteome and Precision Oncology

The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.

Integrated Immunopeptidomics and Proteomics Study of SARS-CoV-2-Infected Calu-3 Cells Reveals Dynamic Changes in Allele-specific HLA Abundance and Antigen Presentation

We present an integrated immunopeptidomics and proteomics study of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection to comprehensively decipher the changes in host cells in response to viral infection. Immunopeptidomics analysis identified viral antigens presented by host cells through both class I and class II MHC system for recognition by the adaptive immune system. The host proteome changes were characterized by quantitative proteomics and glycoproteomics and from these data, the activation of toll-like receptor 3-interferon pathway was identified. Glycosylation analysis of human leukocyte antigen (HLA) proteins from the elution and flow-through of immunoprecipitation revealed that SARS-CoV-2 infection changed the glycosylation pattern of certain HLA alleles with different HLA alleles, showing distinct dynamic changes in relative abundance. The difference in the glycosylation and abundance of HLA alleles changed the number of strong binding antigens each allele presented, suggesting the impact of SARS-CoV-2 infection on antigen presentation is allele-specific. These results could be further exploited to explain the imbalanced response from innate and adaptive immune system in coronavirus disease 2019 cases, which would be helpful for the development of therapeutics and vaccine for coronavirus disease 2019 and preparation for future pandemic.

Improving the efficacy of peptide vaccines in cancer immunotherapy

Peptide vaccines have shown great potential in cancer immunotherapy by targeting tumor antigens and activating the patient's immune system to mount a specific response against cancer cells. However, the efficacy of peptide vaccines in inducing a sustained immune response and achieving clinical benefit remains a major challenge. In this review, we discuss the current status of peptide vaccines in cancer immunotherapy and strategies to improve their efficacy. We summarize the recent advancements in the development of peptide vaccines in pre-clinical and clinical settings, including the use of novel adjuvants, neoantigens, nano-delivery systems, and combination therapies. We also highlight the importance of personalized cancer vaccines, which consider the unique genetic and immunological profiles of individual patients. We also discuss the strategies to enhance the immunogenicity of peptide vaccines such as multivalent peptides, conjugated peptides, fusion proteins, and self-assembled peptides. Although, peptide vaccines alone are weak immunogens, combining peptide vaccines with other immunotherapeutic approaches and developing novel approaches such as personalized vaccines can be promising methods to significantly enhance their efficacy and improve the clinical outcomes for cancer patients.

An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator

The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments-collectively referred to as antigens-initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS-Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.

Endogenous H3.3K27M derived peptide restricted to HLA-A∗02:01 is insufficient for immune-targeting in diffuse midline glioma

Diffuse midline glioma (DMG) is a childhood brain tumor with an extremely poor prognosis. Chimeric antigen receptor (CAR) T cell therapy has recently demonstrated some success in DMG, but there may a need to target multiple tumor-specific targets to avoid antigen escape. We developed a second-generation CAR targeting an HLA-A∗02:01 restricted histone 3K27M epitope in DMG, the target of previous peptide vaccination and T cell receptor-mimics. These CAR T cells demonstrated specific, titratable, binding to cells pulsed with the H3.3K27M peptide. However, we were unable to observe scFv binding, CAR T cell activation, or cytotoxic function against H3.3K27M+ patient-derived models. Despite using sensitive immunopeptidomics, we could not detect the H3.3K27M26-35-HLA-A∗02:01 peptide on these patient-derived models. Interestingly, other non-mutated peptides from DMG were detected bound to HLA-A∗02:01 and other class I molecules, including a novel HLA-A3-restricted peptide encompassing the K27M mutation and overlapping with the H3 K27M26-35-HLA-A∗02:01 peptide. These results suggest that targeting the H3 K27M26-35 mutation in context of HLA-A∗02:01 may not be a feasible immunotherapy strategy because of its lack of presentation. These findings should inform future investigations and clinical trials in DMG.

Preclinical risk assessment strategy to mitigate the T-cell dependent immunogenicity of protein biotherapeutics: State of the art, challenges and future perspectives

Protein therapeutics hold a prominent role and have brought significant diversity in efficacious medicinal products. Not just monoclonal antibodies and different antibody formats (pegylated antigen-binding fragments, bispecifics, antibody-drug conjugates, single chain variable fragments, nanobodies, dia-, tria- and tetrabodies), but also purified blood products, growth factors, recombinant cytokines, enzyme replacement factors, fusion proteins are all good instances of therapeutic proteins that have been developed in the past decades and approved for their value in oncology, immune-oncology, and autoimmune diseases discovery programs. Although there was an ingrained belief that fully humanized proteins were expected to have limited immunogenicity, adverse effects associated with immune responses to biological therapies raised some concern in biotech companies. Consequently, drug developers are designing strategies to assess potential immune responses to protein therapeutics during both the preclinical and clinical phases of development. Despite the many factors that can contribute to protein immunogenicity, T cell- (thymus-) dependent (Td) immunogenicity seems to play a crucial role in the development of anti-drug antibodies (ADAs) to biologics. A broad range of methodologies to predict and rationally assess Td immune responses to protein drugs has been developed. This review aims to briefly summarize the preclinical immunogenicity risk assessment strategy to mitigate the risk of potential immunogenic candidates coming towards clinical phases, discussing the advantages and limitations of these technologies, and suggesting a rational approach for assessing and mitigating Td immunogenicity.

Ionmob: a Python package for prediction of peptide collisional cross-section values

MOTIVATION

Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section (CCS), a highly reproducible physicochemical property dependent on the ion's mass, charge and conformation in the gas phase. Thus, known peptide ion mobilities can be used to tailor acquisition methods or to refine database search results. The large space of potential peptide sequences, driven also by posttranslational modifications of amino acids, motivates an in silico predictor for peptide CCS. Recent studies explored the general performance of varying machine-learning techniques, however, the workflow engineering part was of secondary importance. For the sake of applicability, such a tool should be generic, data driven, and offer the possibility to be easily adapted to individual workflows for experimental design and data processing.

RESULTS

We created ionmob, a Python-based framework for data preparation, training, and prediction of collisional cross-section values of peptides. It is easily customizable and includes a set of pretrained, ready-to-use models and preprocessing routines for training and inference. Using a set of ≈21 000 unique phosphorylated peptides and ≈17 000 MHC ligand sequences and charge state pairs, we expand upon the space of peptides that can be integrated into CCS prediction. Lastly, we investigate the applicability of in silico predicted CCS to increase confidence in identified peptides by applying methods of re-scoring and demonstrate that predicted CCS values complement existing predictors for that task.

AVAILABILITY AND IMPLEMENTATION

The Python package is available at github: https://github.com/theGreatHerrLebert/ionmob.

What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome?

Ribosome profiling (Ribo-Seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of noncanonical sites of ribosome translation outside the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7000 noncanonical ORFs are translated, which, at first glance, has the potential to expand the number of human protein CDSs by 30%, from ∼19,500 annotated CDSs to over 26,000 annotated CDSs. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of noncanonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome but searching for guidance on how to proceed. Here, we discuss the current state of noncanonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein coding."

The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells

Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.

A Lysine Residue at the C-Terminus of MHC Class I Ligands Correlates with Low C-Terminal Proteasomal Cleavage Probability

The majority of peptides presented by MHC class I result from proteasomal protein turnover. The specialized immunoproteasome, which is induced during inflammation, plays a major role in antigenic peptide generation. However, other cellular proteases can, either alone or together with the proteasome, contribute peptides to MHC class I loading non-canonically. We used an immunopeptidomics workflow combined with prediction software for proteasomal cleavage probabilities to analyze how inflammatory conditions affect the proteasomal processing of immune epitopes presented by A549 cells. The treatment of A549 cells with IFNγ enhanced the proteasomal cleavage probability of MHC class I ligands for both the constitutive proteasome and the immunoproteasome. Furthermore, IFNγ alters the contribution of the different HLA allotypes to the immunopeptidome. When we calculated the HLA allotype-specific proteasomal cleavage probabilities for MHC class I ligands, the peptides presented by HLA-A*30:01 showed characteristics hinting at a reduced C-terminal proteasomal cleavage probability independently of the type of proteasome. This was confirmed by HLA-A*30:01 ligands from the immune epitope database, which also showed this effect. Furthermore, two additional HLA allotypes, namely, HLA-A*03:01 and HLA-A*11:01, presented peptides with a markedly reduced C-terminal proteasomal cleavage probability. The peptides eluted from all three HLA allotypes shared a peptide binding motif with a C-terminal lysine residue, suggesting that this lysine residue impairs proteasome-dependent HLA ligand production and might, in turn, favor peptide generation by other cellular proteases.

The Value of Microbes in Cancer Neoantigen Immunotherapy

Tumor neoantigens are widely used in cancer immunotherapy, and a growing body of research suggests that microbes play an important role in these neoantigen-based immunotherapeutic processes. The human body and its surrounding environment are filled with a large number of microbes that are in long-term interaction with the organism. The microbiota can modulate our immune system, help activate neoantigen-reactive T cells, and play a great role in the process of targeting tumor neoantigens for therapy. Recent studies have revealed the interconnection between microbes and neoantigens, which can cross-react with each other through molecular mimicry, providing theoretical guidance for more relevant studies. The current applications of microbes in immunotherapy against tumor neoantigens are mainly focused on cancer vaccine development and immunotherapy with immune checkpoint inhibitors. This article summarizes the related fields and suggests the importance of microbes in immunotherapy against neoantigens.

A combined immunopeptidomics, proteomics, and cell surface proteomics approach to identify immunotherapy targets for diffuse intrinsic pontine glioma

Introduction

Diffuse intrinsic pontine glioma (DIPG), recently reclassified as a subtype of diffuse midline glioma, is a highly aggressive brainstem tumor affecting children and young adults, with no cure and a median survival of only 9 months. Conventional treatments are ineffective, highlighting the need for alternative therapeutic strategies such as cellular immunotherapy. However, identifying unique and tumor-specific cell surface antigens to target with chimeric antigen receptor (CAR) or T-cell receptor (TCR) therapies is challenging.

Methods

In this study, a multi-omics approach was used to interrogate patient-derived DIPG cell lines and to identify potential targets for immunotherapy.

Results

Through immunopeptidomics, a range of targetable peptide antigens from cancer testis and tumor-associated antigens as well as peptides derived from human endogenous retroviral elements were identified. Proteomics analysis also revealed upregulation of potential drug targets and cell surface proteins such as Cluster of differentiation 27 (CD276) B7 homolog 3 protein (B7H3), Interleukin 13 alpha receptor 2 (IL-13Rα2), Human Epidermal Growth Factor Receptor 3 (HER2), Ephrin Type-A Receptor 2 (EphA2), and Ephrin Type-A Receptor 3 (EphA3).

Discussion

The results of this study provide a valuable resource for the scientific community to accelerate immunotherapeutic approaches for DIPG. Identifying potential targets for CAR and TCR therapies could open up new avenues for treating this devastating disease.

Antiviral CD8+ T-cell immune responses are impaired by cigarette smoke and in COPD

BACKGROUND

Virus infections drive COPD exacerbations and progression. Antiviral immunity centres on the activation of virus-specific CD8+ T-cells by viral epitopes presented on major histocompatibility complex (MHC) class I molecules of infected cells. These epitopes are generated by the immunoproteasome, a specialised intracellular protein degradation machine, which is induced by antiviral cytokines in infected cells.

METHODS

We analysed the effects of cigarette smoke on cytokine- and virus-mediated induction of the immunoproteasome in vitro, ex vivo and in vivo using RNA and Western blot analyses. CD8+ T-cell activation was determined in co-culture assays with cigarette smoke-exposed influenza A virus (IAV)-infected cells. Mass-spectrometry-based analysis of MHC class I-bound peptides uncovered the effects of cigarette smoke on inflammatory antigen presentation in lung cells. IAV-specific CD8+ T-cell numbers were determined in patients' peripheral blood using tetramer technology.

RESULTS

Cigarette smoke impaired the induction of the immunoproteasome by cytokine signalling and viral infection in lung cells in vitro, ex vivo and in vivo. In addition, cigarette smoke altered the peptide repertoire of antigens presented on MHC class I molecules under inflammatory conditions. Importantly, MHC class I-mediated activation of IAV-specific CD8+ T-cells was dampened by cigarette smoke. COPD patients exhibited reduced numbers of circulating IAV-specific CD8+ T-cells compared to healthy controls and asthmatics.

CONCLUSION

Our data indicate that cigarette smoke interferes with MHC class I antigen generation and presentation and thereby contributes to impaired activation of CD8+ T-cells upon virus infection. This adds important mechanistic insight on how cigarette smoke mediates increased susceptibility of smokers and COPD patients to viral infections.

Immunoprecipitation methods impact the peptide repertoire in immunopeptidomics

Introduction

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.

Methods

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.

Results

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.

Discussion

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.

Unlocking the potential of microfluidics in mass spectrometry-based immunopeptidomics for tumor antigen discovery

The identification of tumor-specific antigens (TSAs) is critical for developing effective cancer immunotherapies. Mass spectrometry (MS)-based immunopeptidomics has emerged as a powerful tool for identifying TSAs as physical molecules. However, current immunopeptidomics platforms face challenges in measuring low-abundance TSAs in a precise, sensitive, and reproducible manner from small needle-tissue biopsies (<1 mg). Inspired by recent advances in single-cell proteomics, microfluidics technology offers a promising solution to these limitations by providing improved isolation of human leukocyte antigen (HLA)-associated peptides with higher sensitivity. In this context, we highlight the challenges in sample preparation and the rationale for developing microfluidics technology in immunopeptidomics. Additionally, we provide an overview of promising microfluidic methods, including microchip pillar arrays, valved-based systems, droplet microfluidics, and digital microfluidics, and discuss the latest research on their application in MS-based immunopeptidomics and single-cell proteomics.