HLA-I Alleles Research Articles

HLA alleles measured from COVID-19 patient transcriptomes reveal associations with disease prognosis in a New York cohort.

BackgroundThe Human Leukocyte Antigen (HLA) gene locus plays a fundamental role in human immunity, and it is established that certain HLA alleles are disease determinants. Previously, we have identified prevalent HLA class I and class II alleles, including DPA1*02:02, in two small patient cohorts at the COVID-19 pandemic onset.MethodsWe have since analyzed a larger public patient cohort data (n = 126 patients) with controls, associated demographic and clinical data. By combining the predictive power of multiple in silico HLA predictors, we report on HLA-I and HLA-II alleles, along with their associated risk significance.ResultsWe observe HLA-II DPA1*02:02 at a higher frequency in the COVID-19 positive cohort (29%) when compared to the COVID-negative control group (Fisher’s exact test [FET] p = 0.0174). Having this allele, however, does not appear to put this cohort’s patients at an increased risk of hospitalization. Inspection of COVID-19 disease severity outcomes, including admission to intensive care, reveal nominally significant risk associations with A*11:01 (FET p = 0.0078) and C*04:01 (FET p = 0.0087). The association with severe disease outcome is especially evident for patients with C*04:01, where disease prognosis measured by mechanical ventilation-free days was statistically significant after multiple hypothesis correction (Bonferroni p = 0.0323). While prevalence of some of these alleles falls below statistical significance after Bonferroni correction, COVID-19 patients with HLA-I C*04:01 tend to fare worse overall. This HLA allele may hold potential clinical value.

SO-26 In-silico Lynch syndrome-related neoantigens prediction for a dendritic cell-based cancer prevention vaccine

Lynch syndrome (LS), caused by germline mutations in DNA mismatch-repair genes (MLH1, MSH2, MSH6, PMS2) predisposes patients to colorectal and endometrial cancer (CRC, EC), among other tumors. Although CCR prevention methods are effective, no preventive strategies exist for the majority of LS-related tumors. Ex-vivo generated and tumor-antigen-loaded dendritic cell (DC) vaccines are one of the recently featured antitumoral immunotherapies. However, their full therapeutic potential would likely be as a preventive approach in high-risk cancer patients. LS is a paradigmatic model for its limited and predictive mutational spectrum in repetitive DNA sequences termed microsatellites (MS). This study aims to identify the main frame-shift derived neopeptides (FSDN) that are shared among cancers from LS patients with the purpose of developing a DC-based neoepitope vaccine. A systematic search of coding MS and FSDN in LS tumors published in literature and in public databases was performed (Seltar Database; The Cancer Genome Atlas, TCGA), with application of epitope prediction pipelines and priorization of FSDN with the greatest coverage on the most frequent HLA-I and HLA-II haplotypes (pVACbind; pVACtools v2.0.1). 531 frame-shift peptide sequences derived from -1 (m1) and -2 (m2) nucleotide deletions from 269 coding microsatellites (cMS) of at least 8 bases were retrieved from SelTarbase and TCGA. Data in FASTA format was computed through the pVACbind epitope prediction pipeline with a labile HLA-binding affinity threshold (IC50 = < 5000 nM) to consider immunoediting events. We prioritized 98 epitopes, 53 HLA-I and 45 HLA-II restricted, from an original predicted number of 42828 HLA-I and 8350 HLA-II-restricted (m1 and m2) epitopes with a median coverage of 9,4 HLA-I alleles and 8,7 HLA-II alleles per epitope. Our predicted neoepitope set has an optimal coverage among LS patients in terms of HLA alleles, associated cancers and prevalence. These results are key to first perform ex vivo functional analysis to determine neoepitopes immunogenicity to later design a preventive antitumoral vaccine for LS.

Abstract S03-02: Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T-cell epitopes

Abstract The ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Consistent with observations for the closely related SARS-CoV, early reports identify a protective role for both humoral and cell-mediated immunity for SARS-CoV-2. In this study, we leveraged our bioinformatics binding prediction tools for human leukocyte antigen (HLA)-I and HLA-II alleles that cover nearly the entire population and were developed using mass spectrometry-based profiling of 74 individual HLA-I and 83 individual HLA-II alleles. We applied these binding predictors, initially developed to predict tumor neoantigen presentation, to identify T-cell epitopes from SARS-CoV-2 proteins. To determine the ability of our tools to identify viral T-cell epitopes, we validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then applied our HLA-I and HLA-II predictors to 13 open reading frames (ORFs) of SARS-CoV-2 and identified 11,897 HLA-I and 8,046 HLA-II candidate peptides that were highly ranked for binding. From our SARS-CoV-2 predicted peptide-HLA-I allele pairs, 374 pairs identically matched previously reported pairs in the ViPR database, originating from other coronaviruses with homologous sequences. Of these pairs, 333 (89 %) had a positive HLA-binding assay result, reinforcing the validity of our predictions. Furthermore, we assayed a subset of epitopes with highly predicted binding scores for their ability to be recognized by specific CD8+ T cell in human donor PBMCs. These epitopes were chosen from four structural proteins (S, N, M, E) and one nonstructural protein (ORF1ab) from SARS-CoV-2, and epitopes from all five proteins were found to be immunogenic. Finally, it was important to address the expression of SARS-CoV-2 proteins within cells since their subsequent processing is necessary for MHC presentation and the generation of specific epitopes. We utilized publicly available proteomic data to infer the relative expression of SARS-CoV-2 proteins from infected cell lines and determined that the different proteins vary significantly in their expression levels, with the nucleocapsid being the most highly expressed viral protein across these studies. Our predictions identify few epitopes from each SARS-CoV-2 protein, which are predicted to bind multiple HLA-I or HLA-II alleles, potentially covering over 99% of the USA, European, and Asian populations. Finally, using our bioinformatic platform, we identify multiple putative epitopes that are potential targets for CD4+ and CD8+ T cells whose predicted HLA binding properties cover nearly the entire population. We further propose that when considering the protein expression levels of these epitopes and their ability to elicit a T-cell response, these epitopes may be effective when included in vaccines against SARS-CoV-2 to induce broad cellular immunity. Citation Format: Asaf Poran, Dewi Harjanto, Matt Malloy, Christina M. Arieta, Daniel A. Rothenberg, Divya Lenkala, Marit M. van Buuren, Terri A. Addona, Michael S. Rooney, Lakshmi Srinivasan, Richard B. Gaynor. Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T-cell epitopes [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr S03-02.

Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

BackgroundThe ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Early reports identify protective roles for both humoral and cell-mediated immunity for SARS-CoV-2.MethodsWe leveraged our bioinformatics binding prediction tools for human leukocyte antigen (HLA)-I and HLA-II alleles that were developed using mass spectrometry-based profiling of individual HLA-I and HLA-II alleles to predict peptide binding to diverse allele sets. We applied these binding predictors to viral genomes from the Coronaviridae family and specifically focused on T cell epitopes from SARS-CoV-2 proteins. We assayed a subset of these epitopes in a T cell induction assay for their ability to elicit CD8+ T cell responses.ResultsWe first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then utilized our HLA-I and HLA-II predictors to identify 11,897 HLA-I and 8046 HLA-II candidate peptides which were highly ranked for binding across 13 open reading frames (ORFs) of SARS-CoV-2. These peptides are predicted to provide over 99% allele coverage for the US, European, and Asian populations. From our SARS-CoV-2-predicted peptide-HLA-I allele pairs, 374 pairs identically matched what was previously reported in the ViPR database, originating from other coronaviruses with identical sequences. Of these pairs, 333 (89%) had a positive HLA binding assay result, reinforcing the validity of our predictions. We then demonstrated that a subset of these highly predicted epitopes were immunogenic based on their recognition by specific CD8+ T cells in healthy human donor peripheral blood mononuclear cells (PBMCs). Finally, we characterized the expression of SARS-CoV-2 proteins in virally infected cells to prioritize those which could be potential targets for T cell immunity.ConclusionsUsing our bioinformatics platform, we identify multiple putative epitopes that are potential targets for CD4+ and CD8+ T cells, whose HLA binding properties cover nearly the entire population. We also confirm that our binding predictors can predict epitopes eliciting CD8+ T cell responses from multiple SARS-CoV-2 proteins. Protein expression and population HLA allele coverage, combined with the ability to identify T cell epitopes, should be considered in SARS-CoV-2 vaccine design strategies and immune monitoring.

Bioinformatics analyses of immunogenic T-cell epitopes of LeIF and PpSP15 proteins from Leishmania major and sand fly saliva used as model antigens for the design of a multi-epitope vaccine to control leishmaniasis

Leishmaniasis is caused by protozoan parasites belonging to 20 Leishmania species. This infectious disease is transmitted by bites of infected phlebotomine sandflies, and is widespread in 97 countries throughout the world. No preventive or effective vaccine has been developed yet. In this study, diverse computational methods were integrated to calculate evolutionary divergence, immunogenicity, IFN-γ production, epitope conservancy, and population coverage of protein fusion models of LeIF-SP15 namely SaLeish. Immunogenicity of LeIF of Leishmania species and SP15 of sandfly saliva has not been investigated in-silico in fusion form. A complete set of 9-mer MHC class I and 15-mer MHC class II peptides were identified with a high affinity for the antigenic epitopes of SaLeish inducing specific responses of CD8+ and CD4+ T cells from BALB/c and human. Our preferred approach was determining truncated fragment of SaLeish rather than a whole length bearing the capacity to trigger specific immune response. Phylogenetic analysis showed that LeIF protein is under balancing selection and is conserved between different Leishmania species. Selected SaLeish model contained 19 and 35 antigenic peptides for MHC class I and II, respectively, with strong binding affinity to both highly frequent HLA-I and HLA-II alleles. Analysis of class I CTL epitopes showed that promiscuous peptides of KSLKADIRK, MSCIPHCKY, LQAGVIVAV, and YQYYGFVAM have greater affinity to interact with HLA-A*01:01, HLA-A*02 (03, 06), HLA-A*30:02, HLA-B*40:01, and HLA-B*52:01 molecules. Population coverage with a range of 78–85% confirmed SaLeish-Model4 as an appropriate vaccine candidate among Persian, South Asia, Europe, and North America population. Also, predicted antigenic epitopes of AKPEIRTFSNVLIKY, TRVQDDLRKLQAGVI, and VALFSATMPEEVLEL corresponding to MHC class II were found to provide strong ability to produce IFNγ toward TH(1)-biased-DTH responses.Findings of the current investigation warrant the future experimental assessment of promising SaLeish prophylaxis vaccine that is capable to enhance both innate and specific cellular immune responses.

An in silico-in vitro Pipeline Identifying an HLA-A*02:01+ KRAS G12V+ Spliced Epitope Candidate for a Broad Tumor-Immune Response in Cancer Patients.

Targeting CD8+ T cells to recurrent tumor-specific mutations can profoundly contribute to cancer treatment. Some of these mutations are potential tumor antigens although they can be displayed by non-spliced epitopes only in a few patients, because of the low affinity of the mutated non-spliced peptides for the predominant HLA class I alleles. Here, we describe a pipeline that uses the large sequence variety of proteasome-generated spliced peptides and identifies spliced epitope candidates, which carry the mutations and bind the predominant HLA-I alleles with high affinity. They could be used in adoptive T cell therapy and other anti-cancer immunotherapies for large cohorts of cancer patients. As a proof of principle, the application of this pipeline led to the identification of a KRAS G12V mutation-carrying spliced epitope candidate, which is produced by proteasomes, transported by TAPs and efficiently presented by the most prevalent HLA class I molecules, HLA-A*02:01 complexes.

Identification of HLA-I restricted epitopes in six vaccine candidates of Leishmania tropica using immunoinformatics and molecular dynamics simulation approaches

In spite of numerous studies on vaccination for various species of Leishmania, research on the development of an effective vaccine for L. tropica is very scarce. In silico epitope prediction is a new way to survey the best vaccine candidates. Here, we predicted the best epitopes of six L. tropica antigens with vaccine capability against this pathogen, using highly frequent HLA-I alleles. Based on the frequent HLA alleles, the protein sequences were screened individually using four different MHC prediction applications, namely SYFPEITHI, ProPredI, BIMAS, and IEDB. Several in silico assays including clustering, human similarity exclusion, epitope conservancy prediction, investigating in experimental records, immunogenicity prediction, and prediction of population coverage were performed to narrow the results and to find the best epitopes. The selected epitopes and their restricted HLA-I alleles were docked and the final epitopes with the lowest binding energy (the highest binding affinity) were chosen. Finally, the stability and the binding properties of the best epitope-HLA-I combinations were analyzed using molecular dynamics simulation studies. We found ten potential peptides with strong binding affinity to highly frequent HLA-I alleles that can be further evaluated as vaccine targets against L. tropica.

Abstract 3383: EPIC: MHC-I epitope prediction integrating mass spectrometry derived motifs and tissue-specific expression profiles

Abstract Background: Accurate prediction of epitopes presented by human leukocyte antigen (HLA) is crucial for personalized cancer immunotherapies targeting T cell epitopes. Mass spectrometry (MS)profiling of eluted HLA ligands, which provides unbiased, high-throughput measurements of HLA associated peptides resulting from in vivo cellular processing, can be a highly valuable training set to build predictive models of HLA binding. In addition, gene expression profiles measured by RNA-seq data in a specific cell type could significantly improve the positive predictive value (PPV) of epitope presentation prediction. Although large amount of high-quality mass spectrometry data of HLA-bound peptides is being generated in the last few years, few of them provide matching RNA-seq data, which makes incorporating gene expression into epitope prediction difficult. Here, we aim to develop a publicly available prediction tool incorporating both sources of information, and demonstrate its superior performance over existing methods. Methods: We obtained public HLA peptidome datasets with matching RNA-seq data of twelve cell lines derived from multiple tissues. We used these MS HLA ligand data to build Position Score Specific Matrixes (PSSMs) for five HLA-I alleles across these cell lines. We then used logistic regression to model the relationship among PSSM score, gene expression, peptide length distribution and whether the peptide could be presented in each of the twelve cell lines, and compared the feature weights among them. Results: We found that the feature weights across different HLA-I alleles and cell lines were close to each other, suggesting that there is a universal relationship between PSSM score and gene expression across different cell lines that could be applied to epitope presentation prediction for multiple alleles in diverse tissues. When we replaced the cell-line-specific weights with universal weights summarized from all the cell lines, the logistic regression model’s predicted power for each cell line only dropped slightly and still substantially outperformed predictions based on PSSM scores alone. Based on such a finding, we applied the universal feature weights to more than 180,000 unique HLA ligands collected from public HLA peptidomics datasets, and presented an Epitope Presentation Integrated prediCtion (EPIC) model for 66 HLA alleles. EPIC was substantially better than other popular methods, including MixMHCpred, NetMHCpan (v4.0), and MHCflurry, when evaluated on independent HLA eluted ligand datasets, with an average 0.1%PPV of 53.58%, compared to 40.50%, 40.20%, 29.81%, and 25.57% achieved by MixMHCpred, NetMHCpan (EL), NetMHCpan (BA), and MHCflurry, respectively. Conclusion: By integrating MS and expression data, EPIC is superior to currently available methods in predicting epitope presentation for the 66 common HLA alleles that our models were built on. Citation Format: Weipeng Hu, Si Qiu, Youping Li, Geng Liu, Xiuqing Zhang, Leo J Lee. EPIC: MHC-I epitope prediction integrating mass spectrometry derived motifs and tissue-specific expression profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3383.

Immune Correlates of Disease Progression in Linked HIV-1 Infection.

Genetic and immunologic analyses of epidemiologically-linked HIV transmission enable insights into the impact of immune responses on clinical outcomes. Human vaccine trials and animal studies of HIV-1 infection have suggested immune correlates of protection; however, their role in natural infection in terms of protection from disease progression is mostly unknown. Four HIV-1+ Cameroonian individuals, three of them epidemiologically-linked in a polygamous heterosexual relationship and one incidence-matched case, were studied over 15 years for heterologous and cross-neutralizing antibody responses, antibody binding, IgA/IgG levels, antibody-dependent cellular cytotoxicity (ADCC) against cells expressing wild-type or CD4-bound Env, viral evolution, Env epitopes, and host factors including HLA-I alleles. Despite viral infection with related strains, the members of the transmission cluster experienced contrasting clinical outcomes including cases of rapid progression and long-term non-progression in the absence of strongly protective HLA-I or CCR5Δ32 alleles. Slower progression and higher CD4/CD8 ratios were associated with enhanced IgG antibody binding to native Env and stronger V1V2 antibody binding responses in the presence of viruses with residue K169 in V2. ADCC against cells expressing Env in the CD4-bound conformation in combination with low Env-specific IgA/IgG ratios correlated with better clinical outcome. This data set highlights for the first time that V1V2-directed antibody responses and ADCC against cells expressing open, CD4-exposed Env, in the presence of low plasma IgA/IgG ratios, can correlate with clinical outcome in natural infection. These parameters are comparable to the major correlates of protection, identified post-hoc in the RV144 vaccine trial; thus, they may also modulate the rate of clinical progression once infected. The findings illustrate the potential of immune correlate analysis in natural infection to guide vaccine development.

Proteome-wide analysis of CD8+ T cell responses to EBV lytic infection

Epstein-Barr virus (EBV) is one of 9 Human herpesviruses that can develop lifelong persistence. These viruses provide an antigenically complex challenge that induce strong CD8+T cell immunity during primary infection and continue to shape this immunity through recurrent lytic reactivation. Here is described the first lytic proteome-wide analysis of CD8+T cell responses to EBV and the first to compare primary vs memory CD8+T cell responses to any human herpesvirus. Primary CD8+T cells were mitogenically expanded directly from the blood of infectious mononucleosis (IM) patients. Comparatively, memory CD8+T cells required pre-enrichment using autologous dendritic cells loaded with a lytically-infected EBV cell lysate and FACS selection based upon the activation marker 4-1BB. Enriched cells were then expanded in vitro as for IM cells. Preparations from 7 IM patients and 7 healthy carriers were screened against each of the 70 EBV lytic cycle proteins in combination with the donors’ HLA-I alleles. Multiple reactivities were identified across the full lytic cycle with 146 responses identified amongst the 7 IM patients and 96 amongst the 7 healthy carriers. However the distribution of responses varied between the 2 cohorts with primary responses targeting IE and a small group of E proteins whereas memory responses targeted all phases but with some prominent responses against L proteins. This infers that responses in primary infection therefore appear to be shaped by presentation on the infected cell surface prior to the activity of viral evasins. However long-term carriage appears to re-shape the virus-specific response.

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 aa, but variability is observed across different HLA-I alleles. In this study we collected recent in-depth HLA peptidomics data, including 12 newly generated HLA peptidomes (31,896 unique peptides) from human meningioma samples, to analyze the peptide length distribution and multiple specificity across 84 different HLA-I alleles. We observed a 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 identified multiple specificity in several 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 the new human meningioma samples.

P069UTR SNPS rs111686073 and rs73410010 independently modify expression of tapasin in blacks

Aim The transmembrane glycoprotein tapasin (TAPBP) mediates the binding of MHC-I and TAP1/2 of the peptide loading complex in the endoplasmic reticulum and aids in optimal peptide loading. TAPBP absence in mice alters both MHC-I expression and T cell development, and virally mediated inhibition of TAPBP gene transcription impairs antigen presentation in CMV infection. Owing to its importance in antigen presentation, we hypothesized that TAPBP expression may also affect HIV pathogenesis, and gene variants indicating TAPBP expression could then serve as markers for HIV outcome. We sought to identify single nucleotide polymorphisms (SNPs) affecting TAPBP expression and ascertain the mechanism. Methods A scan of the TAPBP gene identified two variants, rs111686073 (G/C) and rs73410010 (A/G), found only in blacks that significantly and independently correlated with TAPBP expression via qPCR in three South African cohorts (FRESH, SK and CAPRISA002). Luciferase expression constructs containing the UTRs of TAPBP were generated and examined in HeLa cells. For rs111686073, electrophoretic mobility shift assays (EMSAs) were performed to determine if the transcription factors predicted to bind by Alibaba2 mediated the change in TAPBP expression. Results Both rs111686073 (p = 0.0003–0.0006) and rs73410010 (p Conclusions The SNPs rs111686073 and rs73410010 are independently and significantly associated with TAPBP expression, and rs111686073G results in increased expression over the C variant, likely due to binding affinity of AP-2a. The lower expression of TAPBP may alter TAPBP-dependent HLA-B expression and antigen presentation, and affect CD8+ T cell development. Tapasin expression and tapasin-dependence of HLA-I alleles will be considered in future studies of HIV pathogenesis.

Understanding the CD8 T-cell response in natural HIV control.

HIV-infected individuals who maintain control of virus without antiretroviral therapy (ART) are called HIV controllers. The immune responses of these individuals suppress HIV viral replication to low levels or, in the case of elite controllers, to undetectable levels. Although some research indicates a role for inferior virulence of the infecting viral strain in natural control, perhaps by way of defective Nef protein function, we find that the majority of research in HIV controllers highlights CD8 T cells as the main suppressor of viral replication. The most convincing evidence for this argument lies in the strong correlation between certain HLA-I alleles, especially B*57, and HIV control status, a finding that has been replicated by many groups. However, natural control can also occur in individuals lacking these specific HLA alleles, and our understanding of what constitutes an effective CD8 T-cell response remains an incomplete picture. Recent research has broadened our understanding of natural HIV control by illustrating the interactions between different immune cells, including innate immune effectors and antigen-presenting cells. For many years, the immune responses of the natural HIV controllers have been studied for clues on how to achieve functional cure in the rest of the HIV-infected population. The goal of a future functional cure to HIV is one where HIV-infected individuals’ immune responses are able to suppress virus long-term without requiring ART. This review highlights recent advances in our understanding of how HIV controllers’ natural immune responses are able to suppress virus.

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

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.

Abstract PL02-02: Immunopeptidomics: Accelerating the development of personalized cancer immunotherapy

Abstract Cancer immunotherapy amplifies the inherent capacity of cytotoxic T cells to eliminate tumor cells by recognizing human leukocyte antigen-binding peptides (HLAp) presented specifically on tumors but not on normal cells. Recent data show that recognition of mutated neoantigens plays a key role. Currently, mass spectrometry (MS) is the only unbiased methodology to comprehensively uncover the repertoire of HLAp presented in vivo. Recently, we have developed an in-depth MS-based immunopeptidomics approach combined with exome sequencing analysis to directly identify neoantigens from human melanoma tumors (Bassani-Sternberg et al., Nat Commun 2016). This approach is not feasible for substantial number of patients for whom not enough tissue material is available. So far, the discovery of neoantigens relies mainly on prediction-based interrogation of the "mutanome." In a proof-of-concept study we showed that incorporation of deconvoluted HLAp data in ligand prediction algorithms can improve their accuracy (Bassani-Sternberg and Gfeller, J Immunol 2016). In addition, we assembled a comprehensive HLAp database in term of number of peptides and diversity of HLA-I molecules. We showed that by taking advantage of co-occurring HLA-I alleles, we can rapidly and accurately identify HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our novel and scalable approach uncovers new motifs for several alleles that up to now had no known ligands. HLA-ligand predictors trained on such data substantially improve neoantigen predictions in four melanoma and two lung cancer patients, indicating that unbiased HLAp data are ideal for in silico identification of neoantigens (Bassani-Sternberg et al., PLOS Comp Biol 2017). Furthermore, our immunopeptidomics database revealed hotspots that are sub-sequences of proteins frequently presented. We 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% (Mueller et al., submitted). 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. Citation Format: Michal Bassani-Sternberg, Chloe Chong, Fabio Marino, HuiSong Pak, David Gfeller, Markus Müller, George Coukos. Immunopeptidomics: Accelerating the development of personalized cancer immunotherapy [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr PL02-02.

P147 A new approach for accurate, fully-phased HLA class I sequencing using PacBio technology

Aim To test the accuracy and sensitivity of a workflow that combines PacBio sequencing and a novel bioinformatics pipeline for multiplexed fully-phased sequencing of full-length HLA-A, -B and -C alleles. This approach involved a new barcoding system and was compared against the LAA approach, commonly used for PacBio-based HLA typing. Methods Full-length HLA-A, -B and -C loci were PCR-amplified from 50 samples (IHWG reference panel) using in-house barcoded primers. PCR amplicons were pooled together and then ligated to SMRTbell regular adapters (LibraryPrep1), or pooled by sample (A+B+C) and then each pool independently ligated to a different barcoded SMRTbell adapter (LibraryPrep2). PacBio sequencing data (RSII system) was analyzed using the MDPSeq tool. Data from LibrarypPrep2 was also analyzed using the LAA tool (Pacific Biosciences). Results The sequence of 113 unique alleles was obtained that included 7 with incomplete reference sequences and 14 novel alleles. The high accuracy of the approach was demonstrated by a complete match between alleles sequences obtained from different samples, by a complete match with the available IMGT reference sequences, and by Sanger sequencing confirmation of the mismatches found in the 14 new alleles. Sequencing results from LibraryPrep1 and LibraryPrep2 were identical when data was analyzed with the MDPSeq tool, while LAA approach exhibited an estimated 10% rate of in silico cross-contamination when compared against the MDPSeq tool on data from LibraryPrep2. Conclusions The MDPSeq tool is able to achieve accurate multiplexed sequencing of HLA-I alleles while the LAA tool exhibits a significant rate of in silico cross-contamination identifying homozygotes as heterozygotes and finding more than two alleles for heterozygotes. The MDPSeq tool, which performs simultaneous step-wise phasing and correction of both barcode and target sequence is able to prevent cross-contamination and provide unequivocally accurate results.

Recovery HLA-A2 and Beta2-microglobulin Expression in Tumor Cells Using Viral Vectors

Background: Tumor elimination and the success of cancer immunotherapy depend on the proper expression of HLA class I complex (HLA-I) required for the presentation of tumor-associated peptides to cytotoxic T-lymphocytes. Tumors escape immune attack by losing HLA-I expression, often due to irreversible genetic/chromosomal alterations, including mutations in beta2-microglobulin (B2M) or lack of HLA-A2 allele due to a haplotype loss. The introduction of these genes and re-expression of the missing HLA-I specificity on the tumor cell surface is an attractive strategy to induce tumor rejection by T-lymphocytes. Methods: Using genomic HLA-I typing and gene sequencing we determined HLA-I phenotypes and alterations in different human tumor cell lines previously characterized in our laboratory. We used adeno- and adeno-associated viruses to reconstitute/up-regulate HLA-A2 or/and B2M expression in these cells in vitro. Using flow cytometry and immunocytochemistry we evaluated levels and patterns of HLA-I expression in these cells. Methods: Using genomic HLA-I typing and gene sequencing we determined HLA-I phenotypes and alterations in different human tumor cell lines previously characterized in our laboratory. We used adeno- and adeno-associated viruses to reconstitute/up-regulate HLA-A2 or/and B2M expression in these cells in vitro. Using flow cytometry and immunocytochemistry we evaluated levels and patterns of HLA-I expression in these cells. Conclusion: Using gene therapy, it is possible to recover normal B2M and HLA-A2 gene expression caused by structural “hard” alteration and to induce co-expression of both genes in cells naturally lacking HLA-A2 allele. In addition, we demonstrated that transfected tumor cells ae able to express seven HLA-I alleles. The recovery of the missing HLA-I molecules frequently observed in tumor cells using adeno and adeno-associated viruses can be a useful strategy to circumvent cancer immune escape and increase tumor rejection.

Characterization of the Anti-HLA Class I and II IgG Antibodies in Moroccan IVIg Using Regular Beads and Ibeads in Luminex Multiplex Single Antigen Immunoassay

Therapeutic Immunoglobulin Intravenous (IVIg), approved to treat a wide range of autoimmune and primary immunodeficiency diseases, contain mixture of polyreactive and polyclonal IgG purified from the pooled plasma of thousands of donors. The aim of this study is to characterize the profiles of anti- Human Leukocyte Antigen (HLA) class-I and class-II IgG antibodies in four lots of Moroccan IVIg preparations using Luminex Multiplex Single Antigen Bead Immunoassay and to compare it with the unique high frequency HLA types found in the Moroccan population. Anti-HLA class I IgG profiles were assessed using regular (Labscreen) Beads and iBeads. The regular beads are coated with all conformational and structural variants of HLA-I (HLA heavy chain (HC) with β2-microglobulin (β2m) with or without peptides, β2m-free HC with or without peptides or HC only), quite contrast to iBeads, which contained only native tissue-associated HLA HC with β2m and with or without peptides. The level of antibodies was measured as Mean Fluorescent Intensity (MFI). The reactivity of anti-HLA-I IgG antibodies to different alleles of HLA-I loci differed in their recognition of native HLA-I and other structural variants of the HLA-I. High MFI IgG antibodies in the IVIg corresponded with several high frequency HLA-I alleles (B*0801, B*5001, Cw*0602 and Cw*0702) and HLA-II haplotypes (DQA1*0201-DQB1*0201/DRB1*0301), which accounted for 50% of the total gene frequencies in the Moroccan population. HLA-I reactivity of the IVIg with iBeads confirms that the IgG reacting to normal tissue associated with peptide -associated or -free β2mHC. These findings caution the use of high dose IVIg for the carriers of the high frequency HLA types for it may cause tissue injury. The β2m-free-HC reactivity of IVIg indicates the potential of IVIg to bind to activated T and B cells that express these variants, to suppress antibody production. Such an immunomodulation by IVIg renders benefit for patients with autoimmune diseases and organ transplantation.

Enhanced Recognition of HIV-1 Cryptic Epitopes Restricted by HLA Class I Alleles Associated With a Favorable Clinical Outcome.

Cryptic epitopes (CEs) are peptides derived from the translation of 1 or more of the 5 alternative reading frames (ARFs; 2 sense and 3 antisense) of genes. Here, we compared response rates to HIV-1-specific CE predicted to be restricted by HLA-I alleles associated with protection against disease progression to those without any such association. Peptides (9mer to 11mer) were designed based on HLA-I-binding algorithms for B*27, B*57, or B*5801 (protective alleles) and HLA-B*5301 or B*5501 (nonprotective allele) in all 5 ARFs of the 9 HIV-1 encoded proteins. Peptides with >50% probability of being an epitope (n = 231) were tested for T-cell responses in an IFN-γ enzyme-linked immunosorbent spot (ELISpot) assay. Peripheral blood mononuclear cell samples from HIV-1 seronegative donors (n = 42) and HIV-1 seropositive patients with chronic clade B infections (n = 129) were used. Overall, 16%, 2%, and 2% of chronic HIV infected patients had CE responses by IFN-γ ELISpot in the protective, nonprotective, and seronegative groups, respectively (P = 0.009, Fischer exact test). Twenty novel CE-specific responses were mapped (median magnitude of 95 spot forming cells/10 peripheral blood mononuclear cells), and most were both antisense derived (90%) and represented ARFs of accessory proteins (55%). CE-specific CD8 T cells were multifunctional and proliferated when assessed by intracellular cytokine staining. CE responses were preferentially restricted by the protective HLA-I alleles in HIV-1 infection, suggesting that they may contribute to viral control in this group of patients.

Suppression of blastogenesis and proliferation of activated CD4(+) T cells: intravenous immunoglobulin (IVIg) versus novel anti-human leucocyte antigen (HLA)-E monoclonal antibodies mimicking HLA-I reactivity of IVIg.

Activated CD4(+) T cells undergo blastogenesis and proliferation and they express several surface receptors, including β2-microglobulin-free human leucocyte antigen (HLA) heavy chains (open conformers). Intravenous immunoglobulin (IVIg) suppresses activated T cells, but the mechanism is unclear. IVIg reacts with HLA-Ia/Ib antigens but its reactivity is lost when the anti-HLA-E Ab is adsorbed out. Anti-HLA-E antibodies may bind to the peptides shared by HLA-E and the HLA-I alleles. These shared peptides are cryptic in intact HLA, but exposed in open conformers. The hypothesis that anti-HLA-E monoclonal antibodies (mAbs) that mimic HLA-I reactivity of IVIg may suppress activated T cells by binding to the shared peptides of the open conformers on the T cell surface was tested by examining the relative binding affinity of those mAbs for open conformers coated on regular beads and for intact HLA coated on iBeads, and by comparing the effects on the suppression of phytohaemagglutinin (PHA)-activated T cells of three entities: IVIg, anti-HLA-E mAbs that mimic IVIg [Terasaki Foundation Laboratory (TFL)-006 and (TFL)-007]; and anti-HLA-E antibodies that do not mimic IVIg (TFL-033 and TFL-037). Suppression of blastogenesis and proliferation of those T cells by both IVIg and the anti-HLA-E mAbs was dose-dependent, the dose required with mAbs 50-150-fold lower than with IVIg. TFL-006 and TFL-007 significantly suppressed blastogenesis and proliferation of activated CD4(+) T cells, but neither the non-IVIg-mimicking mAbs nor control antibodies did so. The suppression may be mediated by Fab-binding of TFL-006/TFL-007 to the exposed shared peptides. The mAb binding to the open conformer may signal T cell deactivation because the open conformers have an elongated cytoplasmic tail with phosphorylation sites (tryosine(320)/serine(335)).