Michal Bassani-Sternberg

Soluble plasma HLA peptidome as a potential source for cancer biomarkers

The HLA molecules are membrane-bound transporters that carry peptides from the cytoplasm to the cell surface for surveillance by circulating T lymphocytes. Although low levels of soluble HLA molecules (sHLA) are normally released into the blood, many types of tumor cells release larger amounts of these sHLA molecules, presumably to counter immune surveillance by T cells. Here we demonstrate that these sHLA molecules are still bound with their authentic peptide repertoires, similar to those of the membranal HLA molecules (mHLA). Therefore, a single immunoaffinity purification of the plasma sHLA molecules, starting with a few milliliters of patients’ blood, allows for identification of very large sHLA peptidomes by mass spectrometry, forming a foundation for development of a simple and universal blood-based cancer diagnosis. The new methodology was validated using plasma and tumor cells of multiple-myeloma and leukemia patients, plasma of healthy controls, and with cultured cancer cells. The analyses identified thousands of sHLA peptides, including some cancer-related peptides, present among the sHLA peptidomes of the cancer patients. Furthermore, because the HLA peptides are the degradation products of the cellular proteins, this sHLA peptidomics approach opens the way for investigation of the patterns of protein synthesis and degradation within the tumor cells.

Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide–HLA Interactions

Ag presentation on HLA molecules plays a central role in infectious diseases and tumor immunology. To date, large-scale identification of (neo-)Ags from DNA sequencing data has mainly relied on predictions. In parallel, mass spectrometry analysis of HLA peptidome is increasingly performed to directly detect peptides presented on HLA molecules. In this study, we use a novel unsupervised approach to assign mass spectrometry–based HLA peptidomics data to their cognate HLA molecules. We show that incorporation of deconvoluted HLA peptidomics data in ligand prediction algorithms can improve their accuracy for HLA alleles with few ligands in existing databases. The results of our computational analysis of large datasets of naturally processed HLA peptides, together with experimental validation and protein structure analysis, further reveal how HLA-binding motifs change with peptide length and predict new cooperative effects between distant residues in HLA-B07:02 ligands.

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity

The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays.

Current tools for predicting cancer-specific T cell immunity

ABSTRACT Tumor exome and RNA sequencing data provide a systematic and unbiased view on cancer-specific expression, over-expression, and mutations of genes, which can be mined for personalized cancer vaccines and other immunotherapies. Of key interest are tumor-specific mutations, because T cells recognizing neoepitopes have the potential to be highly tumoricidal. Here, we review recent developments and technical advances in identifying MHC class I and class II-restricted tumor antigens, especially neoantigen derived MHC ligands, including in silico predictions, immune-peptidome analysis by mass spectrometry, and MHC ligand validation by biochemical methods on T cells.

The SysteMHC Atlas project

Abstract Mass spectrometry (MS)-based immunopeptidomics investigates the repertoire of peptides presented at the cell surface by major histocompatibility complex (MHC) molecules. The broad clinical relevance of MHC-associated peptides, e.g. in precision medicine, provides a strong rationale for the large-scale generation of immunopeptidomic datasets and recent developments in MS-based peptide analysis technologies now support the generation of the required data. Importantly, the availability of diverse immunopeptidomic datasets has resulted in an increasing need to standardize, store and exchange this type of data to enable better collaborations among researchers, to advance the field more efficiently and to establish quality measures required for the meaningful comparison of datasets. Here we present the SysteMHC Atlas (https://systemhcatlas.org), a public database that aims at collecting, organizing, sharing, visualizing and exploring immunopeptidomic data generated by MS. The Atlas includes raw mass spectrometer output files collected from several laboratories around the globe, a catalog of context-specific datasets of MHC class I and class II peptides, standardized MHC allele-specific peptide spectral libraries consisting of consensus spectra calculated from repeat measurements of the same peptide sequence, and links to other proteomics and immunology databases. The SysteMHC Atlas project was created and will be further expanded using a uniform and open computational pipeline that controls the quality of peptide identifications and peptide annotations. Thus, the SysteMHC Atlas disseminates quality controlled immunopeptidomic information to the public domain and serves as a community resource toward the generation of a high-quality comprehensive map of the human immunopeptidome and the support of consistent measurement of immunopeptidomic sample cohorts.

A Case for a Human Immuno-Peptidome Project Consortium

&NA; A multidisciplinary group of researchers gathered at the Hönggerberg Campus at ETH Zurich, Switzerland, for the first meeting on the Human Immuno‐Peptidome Project (https://hupo.org/human‐immuno‐peptidome‐project/). The long‐term goal of this project is to map the entire repertoire of peptides presented by human leukocyte antigen molecules using mass spectrometry technologies, and make its robust analysis accessible to any immunologist. Here we outline the specific challenges identified toward this goal, and within this framework, describe the structure of a multipronged program aimed at addressing these challenges and implementing solutions at a community‐wide level. Pillars of that program are: (1) method and technology development, (2) standardization, (3) effective data sharing, and (4) education. If successful, this community‐driven endeavor might provide a roadmap toward new paradigms in immunology. &NA; A multidisciplinary group of researchers gathered at the Hönggerberg Campus at ETH Zurich, Switzerland, for the first meeting on the Human Immuno‐Peptidome Project (https://hupo.org/human‐immuno‐peptidome‐project/). The long‐term goal of this project is to map the entire repertoire of peptides presented by human leukocyte antigen molecules using mass spectrometry technologies, and make its robust analysis accessible to any immunologist. Here we outline the specific challenges identified toward this goal, and within this framework, describe the structure of a multipronged program aimed at addressing these challenges and implementing solutions at a community‐wide level. Pillars of that program are: (1) method and technology development, (2) standardization, (3) effective data sharing, and (4) education. If successful, this community‐driven endeavor might provide a roadmap toward new paradigms in immunology.

‘Hotspots’ of Antigen Presentation Revealed by Human Leukocyte Antigen Ligandomics for Neoantigen Prioritization

The remarkable clinical efficacy of the immune checkpoint blockade therapies has motivated researchers to discover immunogenic epitopes and exploit them for personalized vaccines. Human leukocyte antigen (HLA)-binding peptides derived from processing and presentation of mutated proteins are one of the leading targets for T-cell recognition of cancer cells. Currently, most studies attempt to identify neoantigens based on predicted affinity to HLA molecules, but the performance of such prediction algorithms is rather poor for rare HLA class I alleles and for HLA class II. Direct identification of neoantigens by mass spectrometry (MS) is becoming feasible; however, it is not yet applicable to most patients and lacks sensitivity. In an attempt to capitalize on existing immunopeptidomics data and extract information that could complement HLA-binding prediction, we first compiled a large HLA class I and class II immunopeptidomics database across dozens of cell types and HLA allotypes and detected hotspots that are subsequences of proteins frequently presented. About 3% of the peptidome was detected in both class I and class II. Based on the gene ontology of their source proteins and the peptide’s length, we propose that their processing may partake by the cellular class II presentation machinery. Our database captures the global nature of the in vivo peptidome averaged over many HLA alleles, and therefore, reflects the propensity of peptides to be presented on HLA complexes, which is complementary to the existing neoantigen prediction features such as binding affinity and stability or RNA abundance. We further introduce two immunopeptidomics MS-based features to guide prioritization of neoantigens: the number of peptides matching a protein in our database and the overlap of the predicted wild-type peptide with other peptides in our database. We show as a proof of concept that our immunopeptidomics MS-based features improved neoantigen prioritization by up to 50%. Overall, our work shows that, in addition to providing huge training data to improve the HLA binding prediction, immunopeptidomics also captures other aspects of the natural in vivo presentation that significantly improve prediction of clinically relevant neoantigens.

Minimal Information About an Immuno-Peptidomics Experiment (MIAIPE)

Minimal information about an immuno‐peptidomics experiment (MIAIPE) is an initiative of the members of the Human Immuno‐Peptidome Project (HIPP), an international program organized by the Human Proteome Organization (HUPO). The aim of the MIAIPE guidelines is to deliver technical guidelines representing the minimal information required to sufficiently support the evaluation and interpretation of immunopeptidomics experiments. The MIAIPE document has been designed to report essential information about sample preparation, mass spectrometric measurement, and associated mass spectrometry (MS)‐related bioinformatics aspects that are unique to immunopeptidomics and may not be covered by the general proteomics MIAPE (minimal information about a proteomics experiment) guidelines.

The C-terminal extension landscape of naturally presented HLA-I ligands

Significance HLA-I molecules play a central role in immune recognition of infected or cancer cells. They bind short intracellular peptides of 9 to 12 amino acids and present them to T cells for immune recognition. For many years, the confinement of HLA-I ligand has been a central dogma in immunology. Combing analysis of mass spectrometry data with novel algorithms, X-ray crystallography, and T cell recognition assays, we show that a substantial fraction of HLA-I molecules bind peptides extending beyond the C terminus of canonical ligands, and that these peptides can be recognized by CD8 T cells. Our ability to accurately predict such epitopes will help studying their role in infectious diseases or cancer immunotherapy. HLA-I molecules play a central role in antigen presentation. They typically bind 9- to 12-mer peptides, and their canonical binding mode involves anchor residues at the second and last positions of their ligands. To investigate potential noncanonical binding modes, we collected in-depth and accurate HLA peptidomics datasets covering 54 HLA-I alleles and developed algorithms to analyze these data. Our results reveal frequent (442 unique peptides) and statistically significant C-terminal extensions for at least eight alleles, including the common HLA-A03:01, HLA-A31:01, and HLA-A68:01. High resolution crystal structure of HLA-A68:01 with such a ligand uncovers structural changes taking place to accommodate C-terminal extensions and helps unraveling sequence and structural properties predictive of the presence of these extensions. Scanning viral proteomes with the C-terminal extension motifs identifies many putative epitopes and we demonstrate direct recognition by human CD8+ T cells of a 10-mer epitope from cytomegalovirus predicted to follow the C-terminal extension binding mode.

The length distribution and multiple specificity of naturally presented HLA-I ligands

HLA-I molecules bind short peptides and present them for recognition by CD8+ T cells. The length of HLA-I ligands typically ranges from 8 to 12 amino acids, but high variability is observed across different alleles. Here we used recent in-depth HLA peptidomics data to analyze the peptide length distribution of 85 different HLA-I alleles. Our results revealed clear clustering of HLA-I alleles with distinct peptide length distributions, which enabled us to study the structural basis of peptide length distributions and predict peptide length distributions from HLA-I sequences. We further took advantage of our collection of curated HLA peptidomics studies to investigate multiple specificity of HLA-I molecules and validated these observations with binding assays. Explicitly modeling peptide length distribution and multiple specificity improved predictions of naturally presented HLA-I ligands, as demonstrated in an independent benchmarking based on 10 newly generated HLA peptidomes (27,882 unique peptides) from meningioma samples.