HLA Peptidomes Research Articles

Identification of HLA-A*11:01 and A*02:01-Restricted EBV Peptides Using HLA Peptidomics.

Epstein-Barr Virus (EBV) is closely linked to nasopharyngeal carcinoma (NPC), notably prevalent in southern China. Although type II latency of EBV plays a crucial role in the development of NPC, some lytic genes and intermittent reactivation are also critical for viral propagation and tumor progression. Since T cell-mediated immunity is effective in targeted killing of EBV-positive cells, it is important to identify EBV-derived peptides presented by highly prevalent human leukocyte antigen class I (HLA-I) molecules throughout the EBV life cycle. Here, we constructed an EBV-positive NPC cell model to evaluate the presentation of EBV lytic phase peptides on streptavidin-tagged specific HLA-I molecules. Utilizing a mass spectrometry (LC-MS/MS)-based immunopeptidomic approach, we characterized eleven novel EBV peptides as well as two previously identified peptides. Furthermore, we determined these peptides were immunogenic and could stimulate PBMCs from EBV VCA/NA-IgA positive donors in an NPC endemic southern Chinese population. Overall, this work demonstrates that highly prevalent HLA-I-specific EBV peptides can be captured and functionally presented to elicit immune responses in an in vitro model, which provides insight into the epitopes presented during EBV lytic cycle and reactivation. It expands the range of viral targets for potential NPC early diagnosis and treatment.

Protein-Centric Analysis of Personalized Antibody Repertoires Using LC-MS-Based Fab-Profiling on a timsTOF.

Endogenous antibodies, or immunoglobulins (Igs), abundantly present in body fluids, represent some of the most challenging samples to analyze, largely due to the immense variability in their sequences and concentrations. It has been estimated that our body can produce billions of different Ig proteins with different isotypes, making their individual analysis seemingly impossible. However, recent advances in protein-centric proteomics using LC-MS coupled to Orbitrap mass analyzers to profile intact Fab fragments formed by selective cleavage at the IgG-hinge revealed that IgG repertoires may be less diverse, albeit unique for each donor. Serum repertoires seem to be dominated by a few hundred clones that cumulatively make up 50-95% of the total IgG content. Enabling such analyses required careful optimization of the chromatography and mass analysis, as all Fab analytes are highly alike in mass (46-51 kDa) and sequence. To extend the opportunities of this mass-spectrometry-based profiling of antibody repertoires, we here report the optimization and evaluation of an alternative MS platform, namely, the timsTOF, for antibody repertoire profiling. The timsTOF mass analyzer has gained traction in recent years for peptide-centric proteomics and found wide applicability in plasma proteomics, affinity proteomics, and HLA peptidomics, to name a few. However, for protein-centric analysis, this platform has been less explored. Here, we demonstrate that the timsTOF platform can be adapted to perform protein-centric LC-MS-based profiling of antibody repertoires. In a side-by-side comparison of the timsTOF and the Orbitrap we demonstrate that the extracted serum antibody repertoires are alike qualitatively and quantitatively, whereby in particular the sensitivity of the timsTOF platform excels. Future incorporation of advanced top-down capabilities on the timsTOF may make this platform a very valuable alternative for protein-centric proteomics and top-down proteomics and thus also for personalized antibody repertoire profiling.

Workflow enabling deepscale immunopeptidome, proteome, ubiquitylome, phosphoproteome, and acetylome analyses of sample-limited tissues

Serial multi-omic analysis of proteome, phosphoproteome, and acetylome provides insights into changes in protein expression, cell signaling, cross-talk and epigenetic pathways involved in disease pathology and treatment. However, ubiquitylome and HLA peptidome data collection used to understand protein degradation and antigen presentation have not together been serialized, and instead require separate samples for parallel processing using distinct protocols. Here we present MONTE, a highly sensitive multi-omic native tissue enrichment workflow, that enables serial, deep-scale analysis of HLA-I and HLA-II immunopeptidome, ubiquitylome, proteome, phosphoproteome, and acetylome from the same tissue sample. We demonstrate that the depth of coverage and quantitative precision of each ‘ome is not compromised by serialization, and the addition of HLA immunopeptidomics enables the identification of peptides derived from cancer/testis antigens and patient specific neoantigens. We evaluate the technical feasibility of the MONTE workflow using a small cohort of patient lung adenocarcinoma tumors.

HLA-I and HLA-II Peptidomes of SARS-CoV-2: A Review.

The adaptive (T-cell-mediated) immune response is a key player in determining the clinical outcome, in addition to neutralizing antibodies, after SARS-CoV-2 infection, as well as supporting the efficacy of vaccines. T cells recognize viral-derived peptides bound to major histocompatibility complexes (MHCs) so that they initiate cell-mediated immunity against SARS-CoV-2 infection or can support developing a high-affinity antibody response. SARS-CoV-2-derived peptides bound to MHCs are characterized via bioinformatics or mass spectrometry on the whole proteome scale, named immunopeptidomics. They can identify potential vaccine targets or therapeutic approaches for SARS-CoV-2 or else may reveal the heterogeneity of clinical outcomes. SARS-CoV-2 epitopes that are naturally processed and presented on the human leukocyte antigen class I (HLA-I) and class II (HLA-II) were identified for immunopeptidomics. Most of the identified SARS-CoV-2 epitopes were canonical and out-of-frame peptides derived from spike and nucleocapsid proteins, followed by membrane proteins, whereby many of which are not caught by existing vaccines and could elicit effective responses of T cells in vivo. This review addresses the detection of SARS-CoV-2 viral epitopes on HLA-I and HLA-II using bioinformatics prediction and mass spectrometry (HLA peptidomics). Profiling the HLA-I and HLA-II peptidomes of SARS-CoV-2 is also detailed.

Pitch session 1Large-scale immunopeptidome analysis shed new light on the antigen processing and presentation

Antigen processing and presentation are thought to begin with proteolysis in the proteasome, transport through TAP to the ER, trimming by ERAP1/2, loading assisted by ER chaperones, and transport by the MHC of selected peptides according to their affinities. Large-scale peptidome analysis based on LC-MS/ MS and dynamic heavy isotope amino acids labeling of the HLA peptidome and the cellular proteome shed new light on some of the steps in this pathway 1,2 . The relative contribution of the contribution of the proteasome 1 , the effect of the TAP and ERAP1 3 on the HLA peptidome were investigated using the dynamic immunopeptidome analysis. The results of the analysis suggest somewhat different mechanism and limiting factors than the currently accepted. The contribution of the proteasome seems to be lower than expected (1); ERAP1 affects only a subset of the HLA peptidome 3,4 and TAP inhibition does not block presentation of cytoplasmic peptides. Thus, I suggest reconsidering the APP process based on this data.

Soluble HLA peptidome of pleural effusions is a valuable source for tumor antigens

BackgroundSoluble human leucocyte antigen (sHLA) molecules, released into the plasma, carry their original peptide cargo and provide insight into the protein synthesis and degradation schemes of their source cells and...

Combined presentation and immunogenicity analysis reveals a recurrent RAS.Q61K neoantigen in melanoma.

Neoantigens are now recognized drivers of the antitumor immune response. Recurrent neoantigens, shared among groups of patients, have thus become increasingly coveted therapeutic targets. Here, we report on the data-driven identification of a robustly presented, immunogenic neoantigen that is derived from the combination of HLA-A*01:01 and RAS.Q61K. Analysis of large patient cohorts indicated that this combination applies to 3% of patients with melanoma. Using HLA peptidomics, we were able to demonstrate robust endogenous presentation of the neoantigen in 10 tumor samples. We detected specific reactivity to the mutated peptide within tumor-infiltrating lymphocytes (TILs) from 2 unrelated patients, thus confirming its natural immunogenicity. We further investigated the neoantigen-specific clones and their T cell receptors (TCRs) via a combination of TCR sequencing, TCR overexpression, functional assays, and single-cell transcriptomics. Our analysis revealed a diverse repertoire of neoantigen-specific clones with both intra- and interpatient TCR similarities. Moreover, 1 dominant clone proved to cross-react with the highly prevalent RAS.Q61R variant. Transcriptome analysis revealed a high association of TCR clones with specific T cell phenotypes in response to cognate melanoma, with neoantigen-specific cells showing an activated and dysfunctional phenotype. Identification of recurrent neoantigens and their reactive TCRs can promote "off-the-shelf" precision immunotherapies, alleviating limitations of personalized treatments.

Identification of bacteria-derived HLA-bound peptides in melanoma.

A variety of species of bacteria are known to colonize human tumours1-11, proliferate within them and modulate immune function, which ultimately affects the survival of patients with cancer and their responses to treatment12-14. However, it is not known whether antigens derived from intracellular bacteria are presented by the human leukocyte antigen classI and II (HLA-I and HLA-II, respectively) molecules of tumour cells, or whether such antigens elicit a tumour-infiltrating T cell immune response. Here we used 16S rRNA gene sequencing and HLA peptidomics to identify a peptide repertoire derived from intracellular bacteria that was presented on HLA-I and HLA-II molecules in melanoma tumours. Our analysis of 17melanoma metastases (derived from 9patients) revealed 248 and 35unique HLA-I and HLA-II peptides, respectively, that were derived from 41species of bacteria. We identified recurrent bacterial peptides in tumours from different patients, as well as in different tumours from the same patient. Our study reveals that peptides derived from intracellular bacteria can be presented by tumour cells and elicit immune reactivity, and thus provides insight into a mechanism by which bacteria influence activation of the immune system and responses to therapy.

Immunoproteasome expression is associated with better prognosis and response to checkpoint therapies in melanoma

Predicting the outcome of immunotherapy treatment in melanoma patients is challenging. Alterations in genes involved in antigen presentation and the interferon gamma (IFNγ) pathway play an important role in the immune response to tumors. We describe here that the overexpression of PSMB8 and PSMB9, two major components of the immunoproteasome, is predictive of better survival and improved response to immune-checkpoint inhibitors of melanoma patients. We study the mechanism underlying this connection by analyzing the antigenic peptide repertoire of cells that overexpress these subunits using HLA peptidomics. We find a higher response of patient-matched tumor infiltrating lymphocytes against antigens diferentially presented after immunoproteasome overexpression. Importantly, we find that PSMB8 and PSMB9 expression levels are much stronger predictors of melanoma patientsʼ immune response to checkpoint inhibitors than the tumors’ mutational burden. These results suggest that PSMB8 and PSMB9 expression levels can serve as important biomarkers for stratifying melanoma patients for immune-checkpoint treatment.

Abstract 4445: MHC class II antigen identification for cancer immunotherapy by deep learning on tumor HLA peptides

Abstract A key goal in immuno-oncology is the identification of tumor antigens recognized by T cells. Significant progress has been made in predicting MHC class I presentation of tumor-specific antigens (peptides) recognized by CD8 reactive T cells. However, predicting antigens recognized by CD4 T cells that are presented by the MHC class II has proven to be more challenging. Studies have shown binding affinity to be less predictive for MHC class II presentation than for class I. Class II antigen-directed therapeutics require accurate antigen identification from patient samples, which remains elusive today. Methods: We focused initially on antigen presentation by the HLA-DR and generated a dataset of human tumor transcriptomes and HLA-DR immunopeptidomes from resections of B cell lymphomas (N=39). Transcriptomes were obtained by NGS of exome-captured cDNA and immunopeptidomes by immunoprecipitation using the HLA-DR specific Ab L243 and MS/MS. Each sample was typed for HLA-DRB1,3,4,5 using standard methods. Additionally, we obtained published class II mass spectrometry data for two B cell lines, each of which expressed a single common HLA class II allele (HLA-DRB1*15:01 and HLA-DRB5*01:01). RNA sequencing data was not available for either cell line; therefore, we substituted RNA-sequencing data from a different B cell line, B721.221. We combined these data to train a deep learning model of Class II HLA peptide presentation. Our model addressed two key challenges: (1) learning HLA-allele-specific models from tumor and normal data where each sample expressed up to 4 unique HLA-DR alleles and (2) reflecting information about all aspects of HLA presentation, including gene expression, antigen processing and stable binding of peptides to HLA. We evaluated the performance of the model on two independent test datasets. First, we tested the model on HLA peptides from a held-out sample from the lymphoma training dataset. Second, we tested the model on a separate public cell line expressing both HLA-DRB1*15:01 and HLA-DRB5*01:01. Results: An average of 567 training and 203 testing peptides at q<0.01 and >50M unique transcriptome reads were obtained from the lymphoma samples. From cell-lines, 433 peptides for training and 223 peptides for testing were used. The model demonstrated a significant improvement in prediction accuracy, achieving >10%-point gain in area under the ROC curve (ROC AUC) vs a standard binding affinity-based predictor. On the held-out lymphoma test data, it achieved a ROC AUC of .95 vs the binding affinity model ROC AUC .80. On the cell-line test data, it achieved a ROC AUC of .90 vs the binding affinity model ROC AUC of .79. Conclusion: We used a large dataset of transcriptomes and HLA peptidomes to train a deep learning model for HLA class II antigen presentation. The new model significantly outperforms standard methods and advances in silico HLA class II antigen selection for personalized cancer immunotherapy. Citation Format: Tommy Boucher, Matthew Davis, Christine Palmer, Tyler Murphy, Andrew Clark, Fujiko Duke, Aaron Yang, Lauren Young, Karin Jooss, Mojca Skoberne, Josh Francis, Roman Yelensky, James Sun, Jennifer Busby. MHC class II antigen identification for cancer immunotherapy by deep learning on tumor HLA peptides [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 4445.

Identification of Tumor Antigens Among the HLA Peptidomes of Glioblastoma Tumors and Plasma

Glioblastoma multiforme (GBM) is the most aggressive brain tumor with poor prognosis to most patients. Immunotherapy of GBM is a potentially beneficial treatment option, whose optimal implementation may depend on familiarity with tumor specific antigens, presented as HLA peptides by the GBM cells. Further, early detection of GBM, such as by a routine blood test, may improve survival, even with the current treatment modalities. This study includes large-scale analyses of the HLA peptidome (immunopeptidome) of the plasma-soluble HLA molecules (sHLA) of 142 plasma samples, and the membranal HLA of GBM tumors of 10 of these patients' tumor samples. Tumor samples were fresh-frozen immediately after surgery and the plasma samples were collected before, and at multiple visits after surgery. In total, this HLA peptidome analysis involved 52 different HLA allotypes and resulted in the identification of more than 35,000 different HLA peptides. Strong correlations were observed in the signal intensities and in the repertoires of identified peptides between the tumors and plasma-soluble HLA peptidomes of the individual patients, whereas low correlations were observed between these HLA peptidomes and the tumors' proteomes. HLA peptides derived from Cancer/Testis Antigens (CTAs) were selected based on their presence among the HLA peptidomes of the patients and absence of expression of their source genes from any healthy and essential human tissues, except from immune-privileged sites. Additionally, peptides were selected as potential biomarkers if their levels in the plasma-sHLA peptidome were significantly reduced after the removal of tumor mass. The CTAs identified among the analyzed HLA peptidomes provide new opportunities for personalized immunotherapy and for early diagnosis of GBM.

Abstract B086: Exploiting large-scale HLA peptidomics to generate novel immunotherapies: A data-driven approach to true neoantigen prioritization

Abstract Purpose of Study: In this study we demonstrate accurate prediction of the impact of somatic mutations on the HLA presentation landscape achieved by interrogating a large scale database of 1.4 million unique HLA peptide sequences that have been directly identified by mass spectrometry. Background: Peptides presented to the immune system on HLA complexes are valuable targets for immunotherapeutic treatments. Identifying the full complement of peptides derived from a particular protein that are presented on major class I HLA restrictions will provide a vital step toward increasing the speed and viability of many immunotherapeutic strategies. Advances in next-generation sequencing (NGS) and single-cell technologies have enabled the accurate capture of somatic mutations accumulated by a tumor, yet a significant hurdle remains how this information can be utilized for immunotherapeutic benefit. In particular, identifying which somatic mutations produce neoantigens (peptides that contain a somatic mutation and are presented to the immune system in complex with HLA) is crucial to linking genetic changes with immunologic impact. Materials and Methods: Our approach to understanding the targetable human HLA peptidome is based on three key principles: achieving full proteome coverage, maximising individual protein coverage, and focusing on dominant HLA restrictions. By integrating novel cell biology, mass spectrometry, and bioinformatic technologies across over 1,000 individual experiments we have dramatically increased the depth of the HLA ligandome captured and achieved near total coverage of the protein-coding genome. Over 90% of the proteome has been captured for the restriction HLA-A*02:01, dominant in Caucasian populations. Our comprehensive genome coverage has enabled us to probe both directly and indirectly for the presence of neoantigens. Known somatic mutations within immortalized lines were used to generate bespoke reference databases that has led to direct identification of many hundreds of neoantigens. Results: Proteins that were found to contain neoantigens appeared to follow the same pattern of antigen processing and presentation as their unmutated equivalents. We have therefore found our HLA peptide dataset is able to offer significant value in predicting the likelihood of a somatic mutation creating a neoantigen. To test this, somatic mutations reported in 980 cell lines were probed against the database of HLA peptides. On average we find one peptide containing the mutated amino acid for every five somatic mutations reported. By incorporating the HLA background of the cell carrying the mutation, we narrow this prediction to one high-affinity HLA peptide for every fourteen somatic mutations reported. Comparing the peptides predicted in this analysis with those directly identified by mass spectrometry, we are able to show that we can prioritize mutation data by accurately predicting the presence and relative abundance of neoantigens. Our neoantigen prediction process is fully incorporated into a large scale database system, enabling us to seamlessly integrate NGS data from individual tissue and use peptidomic data to rapidly define the targetable landscape of an individual. Conclusions: An integrative approach to HLA peptidomics has delivered a powerful reference database for developing novel immunotherapies. Citation Format: Alex S. Powlesland, Geert P.M. Mommen, Ricardo J. Carreira, Jacob Hurst, Michael J. Cundell, David Lowne, Floriana Capuano, Bent K. Jakobsen. Exploiting large-scale HLA peptidomics to generate novel immunotherapies: A data-driven approach to true neoantigen prioritization [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B086.

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.

Cancer Exome-Based Identification of Tumor Neo-Antigens Using Mass Spectrometry.

Neo-antigens expressed on tumors are targets for development of cancer immunotherapy strategies. Use of prediction algorithms to identify neo-antigens yields a significant number of peptides that must be validated in laborious and time-consuming methods; many prove to be false-positive identifications. The use of HLA peptidomics allows the isolation of the HLA-peptide complexes directly from cells and can be done on fresh tumor, patient-derived xerographs, or cell lines when the tissue sample is limited. This method can be used to identify both HLA class I and HLA class II or any different MHC from different species. Here we describe the steps to create the immune-affinity columns used from the process, the immunoprecipitation procedure, and also the isolation of the peptides that will be analyzed by mass spectrometry.

Identification of Tumor Antigens Among the HLA Peptidomes of Glioblastoma Tumors and Plasma

Glioblastoma multiforme (GBM) is the most aggressive brain tumor with poor prognosis to most patients. Immunotherapy of GBM is a potentially beneficial treatment option, whose optimal implementation may depend on familiarity with tumor specific antigens, presented as HLA peptides by the GBM cells. Furthermore, early detection of GBM, such as by a routine blood test, may improve survival, even with the current treatment modalities. This study includes large-scale analyses of the HLA peptidome (immunopeptidome) of the plasma-soluble HLA molecules (sHLA) of 142 plasma samples, and the membranal HLA of GBM tumors of 10 of these patients' tumor samples. Tumor samples were fresh-frozen immediately after surgery and the plasma samples were collected before, and at multiple visits after surgery. In total, this HLA peptidome analysis involved 52 different HLA allotypes and resulted in the identification of more than 35,000 different HLA peptides. Strong correlations were observed in the signal intensities and in the repertoires of identified peptides between the tumors and plasma-soluble HLA peptidomes of the individual patients, whereas low correlations were observed between these HLA peptidomes and the tumors' proteomes. HLA peptides derived from Cancer/Testis Antigens (CTAs) were selected based on their presence among the HLA peptidomes of the patients and absence of expression of their source genes from any healthy and essential human tissues, except from immune-privileged sites. Additionally, peptides were selected as potential biomarkers if their levels in the plasma-sHLA peptidome were significantly reduced after the removal of tumor mass. The CTAs identified among the analyzed HLA peptidomes provide new opportunities for personalized immunotherapy and for early diagnosis of GBM.

Abstract 5722: Antigen identification for cancer immunotherapy by deep learning on tumor HLA peptides

Abstract A key goal in immuno-oncology is the identification of tumor antigens recognized by CD8 T cells. Immune modulators such as PD-1 inhibitors indirectly promote T-cell attack against tumor antigens, and may be augmented by antigen-directed therapeutic immunization or adoptive cell therapy to increase clinical benefit. Such personalized therapeutics require accurate antigen identification from patient samples, which remains elusive today. Methods: We generated the largest reported dataset of human tumor transcriptomes and HLA class I peptidomes (N=111) from specimens of multiple tumor types. We used these data to train a deep learning model of HLA peptide presentation for antigen prediction. Our model architecture addressed two key challenges: (1) learning HLA-allele-specific models from tumor data where each sample expressed up to 6 unique HLA class I alleles and (2) incorporating information about all aspects of HLA presentation, including gene expression, proteasomal processing and stable binding of peptides to HLA. We evaluated the performance of the model on two independent test datasets. First, we tested the model on HLA presented peptides from five held-out tumor mass spectrometry samples. Then, to establish that accurate prediction of HLA presentation translates to prediction of antigens recognized by T cells in vivo, we compiled from three published studies a dataset of >2,000 mutations in 16 patients, with 23 mutations (i.e. neoantigens) recognized by PD1+ PBMC or TIL CD8 T cells. Recognition of a peptide by TIL or activated peripheral T-cells implies not only tumor presentation of the peptide, but also its ability to prime T cell responses, and thus represents the most stringent test of tumor antigen prediction. Results: The model demonstrated a breakthrough in prediction accuracy. On the mass spectrometry test data, it achieved a >10-fold improvement in positive predictive value (PPV) vs standard HLA binding affinity (~50% PPV at 40% recall for the MS-based model vs ~5% PPV for binding affinity prediction). On the T-cell test dataset, the model ranked T-cell recognized neoantigens on average >4-fold higher than standard prediction (median rank ~7 for the MS-based model vs ~30 for binding affinity). When selecting candidate neoantigens for hypothetical 10-neoantigen personalized immunotherapy, the model prioritized at least 1 T-cell recognized neoantigen in the top 10 for 9/11 patients with neoantigen responses vs 3/11 for binding affinity. For a hypothetical 20-neoantigen immunotherapy, the model correctly selected the majority (16/23, 70%) of recognized neoantigens. Conclusion: We used the largest dataset of tumor transcriptomes and HLA peptidomes reported to-date to train a deep learning model of HLA epitope presentation. The new model significantly outperforms state of the art methods, and has sufficient predictive accuracy for in silico antigen selection for personalized cancer immunotherapy. Citation Format: Brendan Bulik-Sullivan, Jennifer Busby, Matthew Davis, Lauren Young, Tyler Murphy, Andrew Clark, Fujiko Duke, Michele Busby, Adnan Derti, Mojca Skoberne, Karin Jooss, Corinne E. Gustafson, Assunta De Rienzo, William G. Richards, Nhien T. Dao, Hyeong R. Kim, Jamie E. Anderson, Chang-Min Choi, Vincent De Montpreville, Se Jin Jang, Olaf Mercier, Raphael Bueno, Elie Fadel, Joshua Francis, Roman Yelensky. Antigen identification for cancer immunotherapy by deep learning on tumor HLA peptides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5722.

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.

The natural HLA ligandome of glioblastoma stem-like cells: antigen discovery for T cell-based immunotherapy.

Glioblastoma is the most frequent malignant primary brain tumor. In a hierarchical tumor model, glioblastoma stem-like cells (GSC) play a major role in tumor initiation and maintenance as well as in therapy resistance and recurrence. Thus, targeting this cellular subset may be key to effective immunotherapy. Here, we present a mass spectrometry-based analysis of HLA-presented peptidomes of GSC and glioblastoma patient specimens. Based on the analysis of patient samples (n = 9) and GSC (n = 3), we performed comparative HLA peptidome profiling against a dataset of normal human tissues. Using this immunopeptidome-centric approach we could clearly delineate a subset of naturally presented, GSC-associated HLA ligands, which might serve as highly specific targets for T cell-based immunotherapy. In total, we identified 17 antigens represented by 41 different HLA ligands showing natural and exclusive presentation both on GSC and patient samples. Importantly, in vitro immunogenicity and antigen-specific target cell killing assays suggest these peptides to be epitopes of functional CD8+ T cell responses, thus rendering them prime candidates for antigen-specific immunotherapy of glioblastoma.

The Peptide Repertoire of HLA-B27 may include Ligands with Lysine at P2 Anchor Position.

The HLA-B*27 peptidome has drawn significant attention due to the genetic association between some of the HLA-B*27 alleles and the inflammatory rheumatic disease ankylosing spondylitis (AS), for which a comprehensive biological explanation is still lacking. This study aims to expand the known limits of the HLA-B*27 peptidome to facilitate selection and testing of new peptides, possibly involved in the disease. The HLA peptidomes of HeLa and C1R cell lines stably transfected with the AS-associated HLA-B*27:05 allele, the nonassociated HLA-B*27:09 allele, or their cysteine 67 to serine mutants (C67S), are analyzed on a very large scale. In addition, the peptidomes of HLA-B*27:05 and HLA-B*27:05-C67S are analyzed from the spleens of rats transgenic for these alleles. The results indicate that C67S mutation increases the percentage of peptides with glutamine or lysine at their P2 position (P2-Lys), in both HLA-B*27:05 and HLA-B*27:09. Furthermore, a small fraction of HLA-B*27 peptides contains lysine at their second position (P2), in addition to the more commonly found peptides with arginine (P2-Arg) or the less common glutamine (P2-Gln) located at this anchor position. Overall these data indicate that peptides with P2-Lys should be considered as real ligands of HLA-B*27 molecules and taken into account while looking for putative peptides implicated in the AS.

HLA ligandome analysis of primary chronic lymphocytic leukemia (CLL) cells under lenalidomide treatment confirms the suitability of lenalidomide for combination with T-cell-based immunotherapy

ABSTRACT Recent studies suggest that CLL is an immunogenic disease, which might be effectively targeted by antigen-specific T-cell-based immunotherapy. However, CLL is associated with a profound immune defect, which might represent a critical limitation for mounting clinically effective antitumor immune responses. As several studies have demonstrated that lenalidomide can reinforce effector T-cell responses in CLL, the combination of T-cell-based immunotherapy with the immunomodulatory drug lenalidomide represents a promising approach to overcome the immunosuppressive state in CLL. Antigen-specific immunotherapy also requires the robust presentation of tumor-associated HLA-presented antigens on target cells. We thus performed a longitudinal study of the effect of lenalidomide on the HLA ligandome of primary CLL cells in vitro. We showed that lenalidomide exposure does not affect absolute HLA class I and II surface expression levels on primary CLL cells. Importantly, semi-quantitative mass spectrometric analyses of the HLA peptidome of three CLL patient samples found only minor qualitative and quantitative effects of lenalidomide on HLA class I- and II-restricted peptide presentation. Furthermore, we confirmed stable presentation of previously described CLL-associated antigens under lenalidomide treatment. Strikingly, among the few HLA ligands showing significant modulation under lenalidomide treatment, we identified upregulated IKZF-derived peptides, which may represent a direct reflection of the cereblon-mediated effect of lenalidomide on CLL cells. Since we could not observe any relevant influence of lenalidomide on the established CLL-associated antigen targets of anticancer T-cell responses, this study validates the suitability of lenalidomide for the combination with antigen-specific T-cell-based immunotherapies.