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

Systematic discovery of neoepitope-HLA pairs for neoantigens shared among patients and tumor types

The broad application of precision cancer immunotherapies is limited by the number of validated neoepitopes that are common among patients or tumor types. To expand the known repertoire of shared neoantigen-human leukocyte antigen (HLA) complexes, we developed a high-throughput platform that coupled an in vitro peptide-HLA binding assay with engineered cellular models expressing individual HLA alleles in combination with a concatenated transgene harboring 47 common cancer neoantigens. From more than 24,000 possible neoepitope-HLA combinations, biochemical and computational assessment yielded 844 unique candidates, of which 86 were verified after immunoprecipitation mass spectrometry analyses of engineered, monoallelic cell lines. To evaluate the potential for immunogenicity, we identified T cell receptors that recognized select neoepitope-HLA pairs and elicited a response after introduction into human T cells. These cellular systems and our data on therapeutically relevant neoepitopes in their HLA contexts will aid researchers studying antigen processing as well as neoepitope targeting therapies.

The SysteMHC Atlas v2.0, an updated resource for mass spectrometry-based immunopeptidomics

The SysteMHC Atlas v1.0 was the first public repository dedicated to mass spectrometry-based immunopeptidomics. Here we introduce a newly released version of the SysteMHC Atlas v2.0 (https://systemhc.sjtu.edu.cn), a comprehensive collection of 7190 MS files from 303 allotypes. We extended and optimized a computational pipeline that allows the identification of MHC-bound peptides carrying on unexpected post-translational modifications (PTMs), thereby resulting in 471K modified peptides identified over 60 distinct PTM types. In total, we identified approximately 1.0 million and 1.1 million unique peptides for MHC class I and class II immunopeptidomes, respectively, indicating a 6.8-fold increase and a 28-fold increase to those in v1.0. The SysteMHC Atlas v2.0 introduces several new features, including the inclusion of non-UniProt peptides, and the incorporation of several novel computational tools for FDR estimation, binding affinity prediction and motif deconvolution. Additionally, we enhanced the user interface, upgraded website framework, and provided external links to other resources related. Finally, we built and provided various spectral libraries as community resources for data mining and future immunopeptidomic and proteomic analysis. We believe that the SysteMHC Atlas v2.0 is a unique resource to provide key insights to the immunology and proteomics community and will accelerate the development of vaccines and immunotherapies.

Defining the Cell Surface Cysteinome Using Two-Step Enrichment Proteomics

The plasma membrane proteome is a rich resource of functionally important and therapeutically relevant protein targets. Distinguished by high hydrophobicity, heavy glycosylation, disulfide-rich sequences, and low overall abundance, the cell surface proteome remains undersampled in established proteomic pipelines, including our own cysteine chemoproteomics platforms. Here, we paired cell surface glycoprotein capture with cysteine chemoproteomics to establish a two-stage enrichment method that enables chemoproteomic profiling of cell Surface Cysteinome. Our "Cys-Surf" platform captures >2,800 total membrane protein cysteines in 1,046 proteins, including 1,907 residues not previously captured by bulk proteomic analysis. By pairing Cys-Surf with an isotopic chemoproteomic readout, we uncovered 821 total ligandable cysteines, including known and novel sites. Cys-Surf also robustly delineates redox-sensitive cysteines, including cysteines prone to activation-dependent changes to cysteine oxidation state and residues sensitive to addition of exogenous reductants. Exemplifying the capacity of Cys-Surf to delineate functionally important cysteines, we identified a redox sensitive cysteine in the low-density lipoprotein receptor (LDLR) that impacts both the protein localization and uptake of low-density lipoprotein (LDL) particles. Taken together, the Cys-Surf platform, distinguished by its two-stage enrichment paradigm, represents a tailored approach to delineate the functional and therapeutic potential of the plasma membrane cysteinome.

Defining the Cell Surface Cysteinome using Two-step Enrichment Proteomics

The plasma membrane proteome is a rich resource of functional and therapeutically relevant protein targets. Distinguished by high hydrophobicity, heavy glycosylation, disulfide-rich sequences, and low overall abundance, the cell surface proteome remains undersampled in established proteomic pipelines, including our own cysteine chemoproteomics platforms. Here we paired cell surface glycoprotein capture with cysteine chemoproteomics to establish a two-stage enrichment method that enables chemoproteomic profiling of cell Surface Cysteinome. Our "Cys-Surf" platform captures >2,800 total membrane protein cysteines in 1,046 proteins, including 1,907 residues not previously captured by bulk proteomic analysis. By pairing Cys-Surf with an isotopic chemoproteomic readout, we uncovered 821 total ligandable cysteines, including known and novel sites. Cys-Surf also robustly delineates redox-sensitive cysteines, including cysteines prone to activation-dependent changes to cysteine oxidation state and residues sensitive to addition of exogenous reductants. Exemplifying the capacity of Cys-Surf to delineate functionally important cysteines, we identified a redox sensitive cysteine in the low-density lipoprotein receptor (LDLR) that impacts both the protein localization and uptake of LDL particles. Taken together, the Cys-Surf platform, distinguished by its two-stage enrichment paradigm, represents a tailored approach to delineate the functional and therapeutic potential of the plasma membrane cysteinome.

Identification of neoepitope reactive T-cell receptors guided by HLA-A*03:01 and HLA-A*11:01 immunopeptidomics

BACKGROUND

Tumor-specific mutated proteins can create immunogenic non-self, mutation-containing 'neoepitopes' that are attractive targets for adoptive T-cell therapies. To avoid the complexity of defining patient-specific, private neoepitopes, there has been major interest in targeting common shared mutations in driver genes using off-the-shelf T-cell receptors (TCRs) engineered into autologous lymphocytes. However, identifying the precise naturally processed neoepitopes to pursue is a complex and challenging process. One method to definitively demonstrate whether an epitope is presented at the cell surface is to elute peptides bound to a specific major histocompatibility complex (MHC) allele and analyze them by mass spectrometry (MS). These MS data can then be prospectively applied to isolate TCRs specific to the neoepitope.

METHODS

We created mono-allelic cell lines expressing one class I HLA allele and one common mutated oncogene in order to eliminate HLA deconvolution requirements and increase the signal of recovered peptides. MHC-bound peptides on the surface of these cell lines were immunoprecipitated, purified, and analyzed using liquid chromatography-tandem mass spectrometry, producing a list of mutation-containing minimal epitopes. To validate the immunogenicity of these neoepitopes, HLA-transgenic mice were vaccinated using the minimal peptides identified by MS in order to generate neoepitope-reactive TCRs. Specificity of these candidate TCRs was confirmed by peptide titration and recognition of transduced targets.

RESULTS

We identified precise neoepitopes derived from mutated isoforms of KRAS, EGFR, BRAF, and PIK3CA presented by HLA-A*03:01 and/or HLA-A*11:01 across multiple biological replicates. From our MS data, we were able to successfully isolate murine TCRs that specifically recognize four HLA-A*11:01 restricted neoepitopes (KRAS G13D, PIK3CA E545K, EGFR L858R and BRAF V600E) and three HLA-A*03:01 restricted neoepitopes (KRAS G12V, EGFR L858R and BRAF V600E).

CONCLUSIONS

Our data show that an MS approach can be used to demonstrate which shared oncogene-derived neoepitopes are processed and presented by common HLA alleles, and those MS data can rapidly be used to develop TCRs against these common tumor-specific antigens. Although further characterization of these neoepitope-specific murine TCRs is required, ultimately, they have the potential to be used clinically for adoptive cell therapy.

HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery

CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.

High-throughput, targeted MHC class I immunopeptidomics using a functional genetics screening platform

Identification of CD8+ T cell epitopes is critical for the development of immunotherapeutics. Existing methods for major histocompatibility complex class I (MHC class I) ligand discovery are time intensive, specialized and unable to interrogate specific proteins on a large scale. Here, we present EpiScan, which uses surface MHC class I levels as a readout for whether a genetically encoded peptide is an MHC class I ligand. Predetermined starting pools composed of >100,000 peptides can be designed using oligonucleotide synthesis, permitting large-scale MHC class I screening. We exploit this programmability of EpiScan to uncover an unappreciated role for cysteine that increases the number of predicted ligands by 9-21%, reveal affinity hierarchies by analysis of biased anchor peptide libraries and screen viral proteomes for MHC class I ligands. Using these data, we generate and iteratively refine peptide binding predictions to create EpiScan Predictor. EpiScan Predictor performs comparably to other state-of-the-art MHC class I peptide binding prediction algorithms without suffering from underrepresentation of cysteine-containing peptides. Thus, targeted immunopeptidomics using EpiScan will accelerate CD8+ T cell epitope discovery toward the goal of individual-specific immunotherapeutics.

Contemplating immunopeptidomes to better predict them

The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Thiol Modifications in the Extracellular Space—Key Proteins in Inflammation and Viral Infection

Posttranslational modifications (PTMs) allow to control molecular and cellular functions in response to specific signals and changes in the microenvironment of cells. They regulate structure, localization, stability, and function of proteins in a spatial and temporal manner. Among them, specific thiol modifications of cysteine (Cys) residues facilitate rapid signal transduction. In fact, Cys is unique because it contains the highly reactive thiol group that can undergo different reversible and irreversible modifications. Upon inflammation and changes in the cellular microenvironment, many extracellular soluble and membrane proteins undergo thiol modifications, particularly dithiol–disulfide exchange, S-glutathionylation, and S-nitrosylation. Among others, these thiol switches are essential for inflammatory signaling, regulation of gene expression, cytokine release, immunoglobulin function and isoform variation, and antigen presentation. Interestingly, also the redox state of bacterial and viral proteins depends on host cell-mediated redox reactions that are critical for invasion and infection. Here, we highlight mechanistic thiol switches in inflammatory pathways and infections including cholera, diphtheria, hepatitis, human immunodeficiency virus (HIV), influenza, and coronavirus disease 2019 (COVID-19).

A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types

Tumor neoepitopes presented by major histocompatibility complex (MHC) class I are recognized by tumor-infiltrating lymphocytes (TIL) and are targeted by adoptive T-cell therapies. Identifying which mutant neoepitopes from tumor cells are capable of recognition by T cells can assist in the development of tumor-specific, cell-based therapies and can shed light on antitumor responses. Here, we generate a ranking algorithm for class I candidate neoepitopes by using next-generation sequencing data and a dataset of 185 neoepitopes that are recognized by HLA class I-restricted TIL from individuals with metastatic cancer. Random forest model analysis showed that the inclusion of multiple factors impacting epitope presentation and recognition increased output sensitivity and specificity compared to the use of predicted HLA binding alone. The ranking score output provides a set of class I candidate neoantigens that may serve as therapeutic targets and provides a tool to facilitate in vitro and in vivo studies aimed at the development of more effective immunotherapies.

Recent progress on MHC-I epitope prediction in tumor immunotherapy

Tumor immunotherapy has now become one of the most potential therapy for those intractable cancer diseases. The antigens on the cancer cell surfaces are the keys for the immune system to recognize and eliminate them. As reported, the immunogenicity of the tumor antigens could be determined by the binding between the key epitope peptides and MHC molecules. In recent years, the approaches to anticipate the peptides from the candidate epitopes have gradually changed into more efficient methods. Including the improved conventional methods, more diverse methods were coming into view. Here we review the anticipated methods of the tumor associated epitopes that specifically bind with major histocompatibility complex (MHC) class I molecules, and the recent advances and applications of those epitope prediction methods.

Gas Plasma Technology Augments Ovalbumin Immunogenicity and OT-II T Cell Activation Conferring Tumor Protection in Mice

Reactive oxygen species (ROS/RNS) are produced during inflammation and elicit protein modifications, but the immunological consequences are largely unknown. Gas plasma technology capable of generating an unmatched variety of ROS/RNS is deployed to mimic inflammation and study the significance of ROS/RNS modifications using the model protein chicken ovalbumin (Ova vs oxOva). Dynamic light scattering and circular dichroism spectroscopy reveal structural modifications in oxOva compared to Ova. T cells from Ova-specific OT-II but not from C57BL/6 or SKH-1 wild type mice presents enhanced activation after Ova addition. OxOva exacerbates this activation when administered ex vivo or in vivo, along with an increased interferon-gamma production, a known anti-melanoma agent. OxOva vaccination of wild type mice followed by inoculation of syngeneic B16F10 Ova-expressing melanoma cells shows enhanced T cell number and activation, decreased tumor burden, and elevated numbers of antigen-presenting cells when compared to their Ova-vaccinated counterparts. Analysis of oxOva using mass spectrometry identifies three hot spots regions rich in oxidative modifications that are associated with the increased T cell activation. Using Ova as a model protein, the findings suggest an immunomodulating role of multi-ROS/RNS modifications that may spur novel research lines in inflammation research and for vaccination strategies in oncology.

Spliced HLA-bound peptides: a Black Swan event in immunology

Peptides that bind to and are presented on the cell surface by human leucocyte antigen (HLA) molecules play a critical role in adaptive immunity. For a long time it was believed that all the HLA-bound peptides were generated through simple proteolysis of linear sequences of cellular proteins, and therefore are templated in the genome and proteome. However, evidence for untemplated peptide ligands of HLA molecules has accumulated during the last two decades, with a recent global analysis of HLA-bound peptides suggesting that a considerable proportion of HLA-bound peptides are potentially generated through splicing/fusion of discontinuous peptide segments from one or two distinct proteins. In this review, we will evaluate recent discoveries and debates on the contribution of spliced peptides to the HLA class I immunopeptidome, consider biochemical rules for splicing and the potential role of these spliced peptides in immune recognition.