In Silico Tools for Predicting Novel Epitopes.

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic.

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition.

Background: Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition.

Method: To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy.

MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer.

Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS.

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.

NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction.

Motivation: Allergy is a pathological immune reaction towards innocuous protein antigens. Although only a narrow fraction of plant or animal proteins induce allergy, atopic disorders affect millions of children and adults and cost billions in healthcare systems worldwide. predictors can aid in the development of more innocuous food sources.

Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome.

Human leukocyte antigen (HLA) class II antigen presentation is key for controlling and triggering T cell immune responses. HLA-DQ molecules, which are believed to play a major role in autoimmune diseases, are heterodimers that can be formed as both cis and trans variants depending on whether the α- and β-chains are encoded on the same (cis) or opposite (trans) chromosomes. So far, limited progress has been made for predicting HLA-DQ antigen presentation.

Neoepitope load, T cell signatures and PD-L2 as combined biomarker strategy for response to checkpoint inhibition immunotherapy.

Immune checkpoint inhibition for the treatment of cancer has provided a breakthrough in oncology, and several new checkpoint inhibition pathways are currently being investigated regarding their potential to provide additional clinical benefit. However, only a fraction of patients respond to such treatment modalities, and there is an urgent need to identify biomarkers to rationally select patients that will benefit from treatment. In this study, we explore different tumor associated characteristics for their association with favorable clinical outcome in a diverse cohort of cancer patients treated with checkpoint inhibitors.

The interdependence of machine learning and LC-MS approaches for an unbiased understanding of the cellular immunopeptidome.

Introduction: The comprehensive collection of peptides presented by major histocompatibility complex (MHC) molecules on the cell surface is collectively known as the immunopeptidome. The analysis and interpretation of such data sets holds great promise for furthering our understanding of basic immunology and adaptive immune activation and regulation, and for direct rational discovery of T cell antigens and the design of T-cell-based therapeutics and vaccines. These applications are, however, challenged by the complex nature of immunopeptidome data.

Accurate MHC Motif Deconvolution of Immunopeptidomics Data Reveals a Significant Contribution of DRB3, 4 and 5 to the Total DR Immunopeptidome.

Mass spectrometry (MS) based immunopeptidomics is used in several biomedical applications including neo-epitope discovery in oncology, next-generation vaccine development and protein-drug immunogenicity assessment. Immunopeptidome data are highly complex given the expression of multiple HLA alleles on the cell membrane and presence of co-immunoprecipitated contaminants. The absence of tools that deal with these challenges effectively and guide the analysis and interpretation of this complex type of data is currently a major bottleneck for the large-scale application of this technique.

Improved prediction of HLA antigen presentation hotspots: Applications for immunogenicity risk assessment of therapeutic proteins.

Immunogenicity risk assessment is a critical element in protein drug development. Currently, the risk assessment is most often performed using MHC-associated peptide proteomics (MAPPs) and/or T-cell activation assays. However, this is a highly costly procedure that encompasses limited sensitivity imposed by sample sizes, the MHC repertoire of the tested donor cohort and the experimental procedures applied.

Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction.

Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adverse effects, including allergic reactions and compromised therapeutic efficacy. Production of ADAs is most often associated with activation of CD4 T cell responses resulting from proteolysis of the biotherapeutic and loading of drug-specific peptides into major histocompatibility complex (MHC) class II on professional antigen-presenting cells.

Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data.

Major histocompatibility complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system.

NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions.

The set of peptides presented on a cell's surface by MHC molecules is known as the immunopeptidome. Current mass spectrometry technologies allow for identification of large peptidomes, and studies have proven these data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. Immunopeptidomes are usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in the system under investigation.

Footprints of antigen processing boost MHC class II natural ligand predictions.

Background: Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing.

Methods: We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets.

Corrigendum: An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes.

[This corrects the article on p. 1566 in vol. 8, PMID: 29187854.

Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes.

Recent advances in proteomics and mass-spectrometry have widely expanded the detectable peptide repertoire presented by major histocompatibility complex (MHC) molecules on the cell surface, collectively known as the immunopeptidome. Finely characterizing the immunopeptidome brings about important basic insights into the mechanisms of antigen presentation, but can also reveal promising targets for vaccine development and cancer immunotherapy. This report describes a number of practical and efficient approaches to analyze immunopeptidomics data, discussing the identification of meaningful sequence motifs in various scenarios and considering current limitations.

An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes.

Personalization of cancer immunotherapies such as therapeutic vaccines and adoptive T-cell therapy may benefit from efficient identification and targeting of patient-specific neoepitopes. However, current neoepitope prediction methods based on sequencing and predictions of epitope processing and presentation result in a low rate of validation, suggesting that the determinants of peptide immunogenicity are not well understood. We gathered published data on human neopeptides originating from single amino acid substitutions for which T cell reactivity had been experimentally tested, including both immunogenic and non-immunogenic neopeptides.

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd.

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO).

Experimental and DFT Study on the Compounds [PdCl2L2] (L = 4-methylpyrazole, 4-iodopyrazole).

Theoretical molecular structures of the complexes [PdCl(2)(HmPz)(2)] (1) and [PdCl(2)(HIPz)(2)] (2) (HmPz = 4-methylpyrazole; HIPz = 4-iodopyrazole) were studied using B3LYP/DFT method. The new complex 2 and the complex 1 were synthesized and characterized by elemental analysis and IR spectroscopy. The calculated bond distances and angles showed that both compounds exhibited a slightly distorted square planar coordination environment around the palladium center.

Innate lymphoid cells integrate stromal and immunological signals to enhance antibody production by splenic marginal zone B cells.

Innate lymphoid cells (ILCs) regulate stromal cells, epithelial cells and cells of the immune system, but their effect on B cells remains unclear. Here we identified RORγt(+) ILCs near the marginal zone (MZ), a splenic compartment that contains innate-like B cells highly responsive to circulating T cell-independent (TI) antigens. Splenic ILCs established bidirectional crosstalk with MAdCAM-1(+) marginal reticular cells by providing tumor-necrosis factor (TNF) and lymphotoxin, and they stimulated MZ B cells via B cell-activation factor (BAFF), the ligand of the costimulatory receptor CD40 (CD40L) and the Notch ligand Delta-like 1 (DLL1).

Stromal endothelial cells establish a bidirectional crosstalk with chronic lymphocytic leukemia cells through the TNF-related factors BAFF, APRIL, and CD40L.

Chronic lymphocytic leukemia (CLL) is a clonal B cell disorder of unknown origin. Accessory signals from the microenvironment are critical for the survival, expansion, and progression of malignant B cells. We found that the CLL stroma included microvascular endothelial cells (MVECs) expressing BAFF and APRIL, two TNF family members related to the T cell-associated B cell-stimulating molecule CD40L.

Structural and functional characterization of a novel nonglycosidic type I NKT agonist with immunomodulatory properties.

Activation of type I NKT (iNKT) cells by CD1d-presented agonists is a potent immunotherapeutic tool. α-Galactosylceramide (α-GalCer) is the prototypic agonist, but its excessive potency with simultaneous production of both pro- and anti-inflammatory cytokines hampers its potential therapeutic use. In search for novel agonists, we have analyzed the structure and function of HS44, a synthetic aminocyclitolic ceramide analog designed to avoid unrestrained iNKT cell activation.

B cell-helper neutrophils stimulate the diversification and production of immunoglobulin in the marginal zone of the spleen.

Neutrophils use immunoglobulins to clear antigen, but their role in immunoglobulin production is unknown. Here we identified neutrophils around the marginal zone (MZ) of the spleen, a B cell area specialized in T cell-independent immunoglobulin responses to circulating antigen. Neutrophils colonized peri-MZ areas after postnatal mucosal colonization by microbes and enhanced their B cell-helper function after receiving reprogramming signals, including interleukin 10 (IL-10), from splenic sinusoidal endothelial cells.