HLAncPred: A method for predicting promiscuous non-classical HLA binding sites

In the last two decades, ample of methods have been developed to predict the classical HLA binders in an antigen. In contrast, limited attempts have been made to develop methods for predicting binders for non-classical HLA; due to the scarcity of sufficient experimental data and lack of community interest. Of Note, non-classical HLA plays a crucial immunomodulatory role and regulates various immune responses. Recent studies revealed that non-classical HLA (HLA-E & HLA-G) based immunotherapies have many advantages over classical HLA based-immunotherapy, particularly against COVID-19. In order to facilitate the scientific community, we have developed an artificial intelligence-based method for predicting binders of non-classical HLA alleles (HLA-G and HLA-E). All the models were trained and tested on experimentally validated data obtained from the recent release of IEDB. The machine learning based-models achieved more than 0.98 AUC for HLA-G alleles on validation or independent dataset. Similarly, our models achieved the highest AUC of 0.96 and 0.88 on the validation dataset for HLA-E*01:01, HLA-E*01:03, respectively. We have summarized the models developed in the past for non-classical HLA binders and compared with the models developed in this study. Moreover, we have also predicted the non-classical HLA binders in the spike protein of different variants of virus causing COVID-19 including omicron (B.1.1.529) to facilitate the community. One of the major challenges in the field of immunotherapy is to identify the promiscuous binders or antigenic regions that can bind to a large number of HLA alleles. In order to predict the promiscuous binders for the non-classical HLA alleles, we developed a web server HLAncPred (https://webs.iiitd.edu.in/raghava/hlancpred), and a standalone package.Key PointsNon-classical HLAs play immunomodulatory roles in the immune system.HLA-E restricted T-cell therapy may reduce COVID-19 associated cytokine storm.In silico models developed for predicting binders for HLA-G and HLA-E.Identification of non-classical HLA binders in strains of coronavirusA webserver for predicting promiscuous binders for non-classical HLA allelesAuthor’s BiographyAnjali Dhall is currently working as Ph.D. in Bioinformatics from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Sumeet Patiyal is currently working as Ph.D. in Bioinformatics from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Gajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.

IMMU-31. TUMOR-SPECIFIC ALTERNATIVE SPLICING GENERATES SPATIALLY-CONSERVED HLA-BINDING NEOANTIGEN TARGETS DETECTED THROUGH INTEGRATIVE TRANSCRIPTOMIC AND PROTEOMIC ANALYSES

BACKGROUND While immunotherapy is profoundly efficacious in certain cancers, its success is limited in cancers with lower mutational burden, such as gliomas. Therefore, investigating neoantigens beyond those from somatic mutations can expand the repertoire of immunotherapy targets. Recent studies detected alternative-splicing (AS) events in various cancer types that could potentially translate into tumor-specific proteins. Our study investigates AS within glioma to identify novel MHC-I-presented neoantigen targets through an integrative transcriptomic and proteomic computational pipeline, complemented by an extensive spatiotemporal analysis of the AS candidates. METHODS Bulk RNA-seq of high tumor purity TCGA-GBM/LGG (n=429) were analyzed through a novel systematic pipeline, and tumor-specific splicing junctions (neojunctions) were identified in silico by cross-referencing with bulk RNA-seq of GTEx normal tissue (n=9,166). Two HLA-binding prediction algorithms were subsequently incorporated to predict peptide sequences with high likelihood for HLA-presentation. Investigation of the tumor-wide clonality and temporal stability of the candidates was performed on extensive RNA-seq data from our spatially mapped intratumoral samples and longitudinally collected tumor tissue RNA-seq. Proteomic validation was conducted through mass-spec analysis of the Clinical Proteomic Tumor Analysis Consortium (CPTAC)-GBM repository (n=99). RESULTS Our analysis of TCGA-GBM/LGG bulk RNA-seq identified 249 putative neojunctions that translate into 222 cancer-specific peptide sequences which confer 21,489 tumor-specific n-mers (8-11 amino acids in length). Both prediction algorithms concurrently identified 271 n-mers likely to bind and be presented by HLA*A0101, HLA*A0201, HLA*A0301, HLA*A1101, or HLA*A2402. We confirmed the expression of 15 out of 58 HLA*A0201-binding candidates in HLA*A0201+ patient-derived glioma cell line RNA-seq with a subset of candidates conserved spatially. Analysis of CPTAC-GBM mass-spec data detected 23 tumor-specific peptides with 5 containing detected n-mers highly predicted to be HLA-presented. CONCLUSION Tumor-specific neojunctions identified in our unique integrative pipeline present novel candidate immunotherapy targets for gliomas and offer a new avenue in neoantigen discovery across cancer types.

Unsupervised mining of HLA-I peptidomes reveals new binding motifs and substantial false positives in community database

Modern vaccine designs and studies of human leukocyte antigen (HLA)-mediated immune responses rely heavily on the knowledge of HLA allele-specific binding motifs and computational prediction of HLA-peptide binding affinity. Breakthroughs in HLA peptidomics have considerably expanded the databases of natural HLA ligands and enabled detailed characterizations of HLA-peptide binding specificity. However, cautions must be made when analyzing HLA peptidomics data because identified peptides may be contaminants in mass spectrometry or may weakly bind to the HLA molecules. Here, a hybrid de novo peptide sequencing approach was applied to large-scale mono-allelic HLA peptidomics datasets to uncover new ligands and refine current knowledge of HLA binding motifs. Up to 12-40% of the peptidomics data were low-binding affinity peptides with an arginine or a lysine at the C-terminus and likely to be tryptic peptide contaminants. Thousands of these peptides have been reported in a community database as legitimate ligands and might be erroneously used for training prediction models. Furthermore, unsupervised clustering of identified ligands revealed additional binding motifs for several HLA class I alleles and effectively isolated outliers that were experimentally confirmed to be false positives. Overall, our findings expanded the knowledge of HLA binding specificity and advocated for more rigorous interpretation of HLA peptidomics data that will ensure the high validity of community HLA ligandome databases.

IntroSpect: motif-guided immunopeptidome database building tool to improve the sensitivity of HLA binding peptide identification

Although database search tools originally developed for shotgun proteome have been widely used in immunopeptidomic mass spectrometry identifications, they have been reported to achieve undesirably low sensitivities and/or high false positive rates as a result of the hugely inflated search space caused by the lack of specific enzymic digestions in immunopeptidome. To overcome such a problem, we have developed a motif-guided immunopeptidome database building tool named IntroSpect, which is designed to first learn the peptide motifs from high confidence hits in the initial search and then build a targeted database for refined search. Evaluated on three representative HLA class I datasets, IntroSpect can improve the sensitivity by an average of 80% comparing to conventional searches with unspecific digestions while maintaining a very high accuracy (~96%) as confirmed by synthetic validation experiments. A distinct advantage of IntroSpect is that it does not depend on any external HLA data so that it performs equally well on both well-studied and poorly-studied HLA types, unlike a previously developed method SpectMHC. We have also designed IntroSpect to keep a global FDR that can be conveniently controlled, similar to conventional database search engines. Finally, we demonstrate the practical value of IntroSpect by discovering neoantigens from MS data directly. IntroSpect is freely available at https://github.com/BGI2016/IntroSpect.

Identification of a Human Anti-Alpha-Toxin Monoclonal Antibody Against Staphylococcus aureus Infection

Staphylococcus aureus is a major pathogenic bacterium that causes a variety of clinical infections. The emergence of multi-drug resistant mechanisms requires novel strategies to mitigate S. aureus infection. Alpha-hemolysin (Hla) is a key virulence factor that is believed to play a significant role in the pathogenesis of S. aureus infections. In this study, we screened a naïve human Fab library for identification of monoclonal antibodies targeting Hla by phage display technology. We found that the monoclonal antibody YG1 blocked the Hla-mediated lysis of rabbit red blood cells and inhibited Hla binding to A549 cells in a concentration-dependent manner. YG1 also provided protection against acute peritoneal infection, bacteremia, and pneumonia in murine models. We further characterized its epitope using different Hla variants and found that the amino acids N209 and F210 of Hla were functionally and structurally important for YG1 binding. Overall, these results indicated that targeting Hla with YG1 could serve as a promising protective strategy against S. aureus infection.

Identification and validation of expressed HLA-binding breast cancer neoepitopes for potential use in individualized cancer therapy

BackgroundTherapeutic regimens designed to augment the immunological response of a patient with breast cancer (BC) to tumor tissue are critically informed by tumor mutational burden and the antigenicity of expressed neoepitopes. Herein we describe a neoepitope and cognate neoepitope-reactive T-cell identification and validation program that supports the development of next-generation immunotherapies.MethodsUsing GPS Cancer, NantOmics research, and The Cancer Genome Atlas databases, we developed a novel bioinformatic-based approach which assesses mutational load, neoepitope expression, human leukocyte antigen (HLA)-binding prediction, and in vitro confirmation of T-cell recognition to preferentially identify targetable neoepitopes. This program was validated by application to a BC cell line and confirmed using tumor biopsies from two patients with BC enrolled in the Tumor-Infiltrating Lymphocytes and Genomics (TILGen) study.ResultsThe antigenicity and HLA-A2 restriction of the BC cell line predicted neoepitopes were determined by reactivity of T cells from HLA-A2-expressing healthy donors. For the TILGen subjects, tumor-infiltrating lymphocytes (TILs) recognized the predicted neoepitopes both as peptides and on retroviral expression in HLA-matched Epstein-Barr virus–lymphoblastoid cell line and BC cell line MCF-7 cells; PCR clonotyping revealed the presence of T cells in the periphery with T-cell receptors for the predicted neoepitopes. These high-avidity immune responses were polyclonal, mutation-specific and restricted to either HLA class I or II. Interestingly, we observed the persistence and expansion of polyclonal T-cell responses following neoadjuvant chemotherapy.ConclusionsWe demonstrate our neoepitope prediction program allows for the successful identification of neoepitopes targeted by TILs in patients with BC, providing a means to identify tumor-specific immunogenic targets for individualized treatment, including vaccines or adoptively transferred cellular therapies.

Optimized liquid and gas phase fractionation increase HLA-peptidome coverage for primary cell and tissue samples

Mass spectrometry is the most effective method to directly identify peptides presented on HLA molecules. However, current standard approaches often require many millions of cells for input material to achieve high coverage of the immunopeptidome and are therefore not compatible with the often limited amounts of tissue available from clinical tumor samples. Here, we evaluated microscaled basic reversed-phase fractionation to separate HLA peptide samples off-line followed by ion mobility coupled to LC-MS/MS for analysis. The combination of these two separation methods enabled identification of 20% to 50% more peptides compared to samples analyzed without either prior fractionation or use of ion mobility alone. We demonstrate coverage of HLA immunopeptidomes with up to 8,107 distinct peptides starting with as few as 100 million cells or 150 milligrams of wet weight tumor tissue. This increased sensitivity can improve HLA binding prediction algorithms and enable detection of clinically relevant epitopes such as neoantigens.

Challenges of Neoantigen Targeting in Lynch Syndrome and Constitutional Mismatch Repair Deficiency Syndrome

Lynch syndrome (LS) and constitutional mismatch repair deficiency (CMMRD) are hereditary disorders characterised by a highly increased risk of cancer development. This is due to germline aberrations in the mismatch repair (MMR) genes, which results in a high mutational load in tumours of these patients, including insertions and deletions in genes bearing microsatellites. This generates microsatellite instability and cause reading frameshifts in coding regions that could lead to the generation of neoantigens and opens up avenues for neoantigen targeting immune therapies prophylactically and therapeutically. However, major obstacles need to be overcome, such as the heterogeneity in tumour formation within and between LS and CMMRD patients, which results in considerable variability in the genes targeted by mutations, hence challenging the choice of suitable neoantigens. The machine-learning methods such as NetMHC and MHCflurry that predict neoantigen- human leukocyte antigen (HLA) binding affinity provide little information on other aspects of neoantigen presentation. Immune escape mechanisms that allow MMR-deficient cells to evade surveillance combined with the resistance to immune checkpoint therapy make the neoantigen targeting regimen challenging. Studies to delineate shared neoantigen profiles across patient cohorts, precise HLA binding algorithms, additional therapies to counter immune evasion and evaluation of biomarkers that predict the response of these patients to immune checkpoint therapy are warranted.

Highly conserved, non-human-like, and cross-reactive SARS-CoV-2 T cell epitopes for COVID-19 vaccine design and validation

Natural and vaccine-induced SARS-CoV-2 immunity in humans has been described but correlates of protection are not yet defined. T cells support the SARS-CoV-2 antibody response, clear virus-infected cells, and may be required to block transmission. In this study, we identified peptide epitopes associated with SARS-CoV-2 T-cell immunity. Using immunoinformatic methods, T-cell epitopes from spike, membrane, and envelope were selected for maximal HLA-binding potential, coverage of HLA diversity, coverage of circulating virus, and minimal potential cross-reactivity with self. Direct restimulation of PBMCs collected from SARS-CoV-2 convalescents confirmed 66% of predicted epitopes, whereas only 9% were confirmed in naive individuals. However, following a brief period of epitope-specific T-cell expansion, both cohorts demonstrated robust T-cell responses to 97% of epitopes. HLA-DR3 transgenic mouse immunization with peptides co-formulated with poly-ICLC generated a potent Th1-skewed, epitope-specific memory response, alleviating safety concerns of enhanced respiratory disease associated with Th2 induction. Taken together, these epitopes may be used to improve our understanding of natural and vaccine-induced immunity, and to facilitate the development of T-cell-targeted vaccines that harness pre-existing SARS-CoV-2 immunity.

Empirical Evaluation of the Use of Computational HLA Binding as an Early Filter to the Mass Spectrometry-Based Epitope Discovery Workflow

Immunopeptidomics is used to identify novel epitopes for (therapeutic) vaccination strategies in cancer and infectious disease. Various false discovery rates (FDRs) are applied in the field when converting liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectra to peptides. Subsequently, large efforts have recently been made to rescue peptides of lower confidence. However, it remains unclear what the overall relation is between the FDR threshold and the percentage of obtained HLA-binders. We here directly evaluated the effect of varying FDR thresholds on the resulting immunopeptidomes of HLA-eluates from human cancer cell lines and primary hepatocyte isolates using HLA-binding algorithms. Additional peptides obtained using less stringent FDR-thresholds, although generally derived from poorer spectra, still contained a high amount of HLA-binders and confirmed recently developed tools that tap into this pool of otherwise ignored peptides. Most of these peptides were identified with improved confidence when cell input was increased, supporting the validity and potential of these identifications. Altogether, our data suggest that increasing the FDR threshold for peptide identification in conjunction with data filtering by HLA-binding prediction, is a valid and highly potent method to more efficient exhaustion of immunopeptidome datasets for epitope discovery and reveals the extent of peptides to be rescued by recently developed algorithms.