Search

Background The immune peptidome of OPSCC has not previously been studied. Cancer-antigen specific vaccination may improve clinical outcome and efficacy of immune checkpoint inhibitors such as PD1/PD-L1 antibodies. Methods Mapping of the OPSCC HLA ligandome was performed by mass spectrometry (MS) based analysis of naturally presented HLA ligands isolated from tumour tissue samples (n = 40) using immunoaffinity purification. The cohort included 22 HPV-positive (primarily HPV-16) and 18 HPV-negative samples. A benign reference dataset comprised of the HLA ligandomes of benign haematological and tissue datasets was used to identify tumour-associated antigens. Results MS analysis led to the identification of naturally HLA-presented peptides in OPSCC tumour tissue. In total, 22,769 peptides from 9485 source proteins were detected on HLA class I. For HLA class II, 15,203 peptides from 4634 source proteins were discovered. By comparative profiling against the benign HLA ligandomic datasets, 29 OPSCC-associated HLA class I ligands covering 11 different HLA allotypes and nine HLA class II ligands were selected to create a peptide warehouse. Conclusion Tumour-associated peptides are HLA-presented on the cell surfaces of OPSCCs. The established warehouse of OPSCC-associated peptides can be used for downstream immunogenicity testing and peptide-based immunotherapy in (semi)personalised strategies.

Personalized multipeptide vaccines are currently being discussed intensively for tumor immunotherapy. In order to identify epitopes-short, immunogenic peptides-suitable for eliciting a tumor-specific immune response, human leukocyte antigen-presented peptides are isolated by immunoaffinity purification from cancer tissue samples and analyzed by liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS). Here, we present MHCquant, a fully automated, portable computational pipeline able to process LC-MS/MS data automatically and generate annotated, false discovery rate-controlled lists of (neo-)epitopes with associated relative quantification information. We could show that MHCquant achieves higher sensitivity than established methods. While obtaining the highest number of unique peptides, the rate of predicted MHC binders remains still comparable to other tools. Reprocessing of the data from a previously published study resulted in the identification of several neoepitopes not detected by previously applied methods. MHCquant integrates tailor-made pipeline components with existing open-source software into a coherent processing workflow. Container-based virtualization permits execution of this workflow without complex software installation, execution on cluster/cloud infrastructures, and full reproducibility of the results. Integration with the data analysis workbench KNIME enables easy mining of large-scale immunopeptidomics data sets. MHCquant is available as open-source software along with accompanying documentation on our website at https://www.openms.de/mhcquant/ .

Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

Background The human leucocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Understanding the benign HLA ligand repertoire is a prerequisite to define safe T-cell-based immunotherapies against cancer. Due to the poor availability of benign tissues, if available, normal tissue adjacent to the tumor has been used as a benign surrogate when defining tumor-associated antigens. However, this comparison has proven to be insufficient and even resulted in lethal outcomes. In order to match the tumor immunopeptidome with an equivalent counterpart, we created the HLA Ligand Atlas, the first extensive collection of paired HLA-I and HLA-II immunopeptidomes from 227 benign human tissue samples. This dataset facilitates a balanced comparison between tumor and benign tissues on HLA ligand level. Methods Human tissue samples were obtained from 16 subjects at autopsy, five thymus samples and two ovary samples originating from living donors. HLA ligands were isolated via immunoaffinity purification and analyzed in over 1200 liquid chromatography mass spectrometry runs. Experimentally and computationally reproducible protocols were employed for data acquisition and processing. Results The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 90,428 HLA-I- and 142,625 HLA-II ligands. The HLA allotypes are representative for the world population. We observe that immunopeptidomes differ considerably between tissues and individuals on source protein and HLA-ligand level. Moreover, we discover 1407 HLA-I ligands from non-canonical genomic regions. Such peptides were previously described in tumors, peripheral blood mononuclear cells (PBMCs), healthy lung tissues and cell lines. In a case study in glioblastoma, we show that potential on-target off-tumor adverse events in immunotherapy can be avoided by comparing tumor immunopeptidomes to the provided multi-tissue reference. Conclusion Given that T-cell-based immunotherapies, such as CAR-T cells, affinity-enhanced T cell transfer, cancer vaccines and immune checkpoint inhibition, have significant side effects, the HLA Ligand Atlas is the first step toward defining tumor-associated targets with an improved safety profile. The resource provides insights into basic and applied immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy and autoimmunity. It is publicly available and can be browsed in an easy-to-use web interface at https://hla-ligand-atlas.org ., Journal for ImmunoTherapy of Cancer, 9 (4), ISSN:2051-1426

Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. Main advantages include greater reproducibility, sensitivity and dynamic range compared to data-dependent acquisition (DDA). However, data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics a multi-functional, automated high-throughput pipeline implemented in Nextflow that allows to easily process proteomics and peptidomics DIA datasets on diverse compute infrastructures. Central components are well-established tools such as the OpenSwathWorkflow for DIA spectral library search and PyProphet for false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and carry out retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from statistical post-processing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is open-source software and available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

Understanding the molecular principles that govern the composition of the mammalian MHC-I immunopeptidome (MHC-Ii) across different primary tissues is fundamentally important to predict how T cell respond in different contextsin vivo. Here, we performed a global analysis of the mammalian MHC-Ii from 29 and 19 primary human and mouse tissues, respectively. First, we observed that different HLA-A, -B and -C allotypes do not contribute evenly to the global composition of the MHC-Ii across multiple human tissues. Second, we found that peptides that are presented in a tissue-dependent and -independent manner share very distinct properties. Third, we discovered that proteins that are evolutionarily hyperconserved represent the primary source of the MHC-Ii at the organism-wide scale. Finally, we uncovered new components of the antigen processing and presentation network that may drive the high level of heterogeneity of the MHC-Ii across different tissues in mammals. This study opens up new avenues toward a system-wide understanding of antigen presentationin vivoand may serve as ground work to understand tissue-dependent T cell responses in autoimmunity, infectious diseases and cancer.

The immunopeptidome, representing the point of contact between somatic and T cells, is key for adaptive immunity. Each presented peptide holds an abundance of information not yet well understood. Up to now, the scientific focus has been the definition of pathogenic or tumor derived epitopes and the deconvolution of HLA peptide motifs of the entire immunopeptidome. Here we go one step further and assess the properties of individual peptides to identify defined HLA allotype-specific and frequently presented peptides. Such allotypic peptides represent a versatile tool to determine HLA allotypes or serve as internal standard for characterization of cancer antigens and differentially processed antigens. Finally, individual tissue- and dignity-specific antigens were defined, and the latter were successfully implemented for molecular tumor testing.Using mass spectrometry based immunopeptidomics a database was generated consisting of ∼900 HLA-typed samples. The identified allotypic peptides enabled a HLA class I allotype determination, which was 95% correct in our in-house dataset and 98% in an external dataset. These abundant peptides were implemented as internal standard for a semi-quantitative investigation of established tumor antigens and antigens processed differentially in malignant and benign tissue. Defined dignity-specific antigens allowed a 87% correct tumor detection across numerous tumor types.In summary, we describe a machine learning approach for mining immunopeptidomic data in order to develop a classification method, allowing to differentiate HLA class I-allotypes of a sample or distinguish between healthy and malignant state of tissues. Furthermore, based on this method, we developed a procedure for the validation of tumor exclusive antigens. Our results support classification of immunopeptidomic data sets using machine learning and highlight their potential utility for biomarker development.

Antileukemia immunity plays an important role in disease control and maintenance of tyrosine kinase inhibitor (TKI)-free remission in chronic myeloid leukemia (CML). Thus, antigen-specific immunotherapy holds promise for strengthening immune control in CML but requires the identification of CML-associated targets. In this study, we used a mass spectrometry–based approach to identify naturally presented HLA class I– and class II–restricted peptides in primary CML samples. Comparative HLA ligandome profiling using a comprehensive dataset of different hematological benign specimens and samples from CML patients in deep molecular remission delineated a panel of novel frequently presented CML-exclusive peptides. These nonmutated target antigens are of particular relevance because our extensive data-mining approach suggests the absence of naturally presented BCR-ABL– and ABL-BCR–derived HLA-restricted peptides and the lack of frequent tumor-exclusive presentation of known cancer/testis and leukemia-associated antigens. Functional characterization revealed spontaneous T-cell responses against the newly identified CML-associated peptides in CML patient samples and their ability to induce multifunctional and cytotoxic antigen-specific T cells de novo in samples from healthy volunteers and CML patients. Thus, these antigens are prime candidates for T-cell–based immunotherapeutic approaches that may prolong TKI-free survival and even mediate cure of CML patients.

Computational mass spectrometry is plagued by a multitude of issues, including a heterogeneous software environment, complex workflows and proprietary tools. OpenMS addresses these challenges by providing robust open-source software for users and an open, well-designed software environment for developers. OpenMS is an open-source software C++ library for LC-MS data management and analyses using modern C++11. It offers an infrastructure for rapid development of mass spectrometry related software. OpenMS is free software available under the three clause BSD license. It comes with a variety of pre-built and ready-to-use tools for high-throughput Proteomics and Metabolomics data analysis (TOPPTools), covering most MS and LC-MS data processing and mining tasks, as well as visualization (TOPPView). OpenMS offers automated analyses for various quantitation protocols, including label-free quantitation, SILAC, iTRAQ, TMT, SRM, SWATH. It provides built-in algorithms for de novo identification and database search, as well as adapters to other tools like X!Tandem, Mascot, OMSSA, SIRIUS. It supports easy integration of OpenMS built tools into workflow engines like KNIME, Galaxy, WS-Pgrade, and TOPPAS. OpenMS supports the Proteomics Standard Initiative (PSI) formats for MS data including mzML, mzXML, mzIdentXML, pepXML. With pyOpenMS, OpenMS offers Python bindings to a large part of the OpenMS API to enable rapid algorithm development.

This work demonstrates a novel strategy that enables the identification of HCMV-derived T cell epitopes by mass spectrometry. It provides a panel of novel T cell epitopes and presents evidence for their involvement in physiologic immune control of HCMV infections., In healthy individuals, immune control of persistent human cytomegalovirus (HCMV) infection is effectively mediated by virus-specific CD4+ and CD8+ T cells. However, identifying the repertoire of T cell specificities for HCMV is hampered by the immense protein coding capacity of this betaherpesvirus. Here, we present a novel approach that employs HCMV deletion mutant viruses lacking HLA class I immunoevasins and allows direct identification of naturally presented HCMV-derived HLA ligands by mass spectrometry. We identified 368 unique HCMV-derived HLA class I ligands representing an unexpectedly broad panel of 123 HCMV antigens. Functional characterization revealed memory T cell responses in seropositive individuals for a substantial proportion (28%) of these novel peptides. Multiple HCMV-directed specificities in the memory T cell pool of single individuals indicate that physiologic anti-HCMV T cell responses are directed against a broad range of antigens. Thus, the unbiased identification of naturally presented viral epitopes enabled a comprehensive and systematic assessment of the physiological repertoire of anti-HCMV T cell specificities in seropositive individuals.

We provide a comprehensive analysis of the antigenic landscape of glioblastoma using a multi-omics approach including ligandome mapping of the Human Leukocyte Antigen (HLA) ligandome, next generation sequencing (NGS) as well as an in-depth characterization of tumor-infiltrating lymphocytes (TIL) using mass cytometry and ultra-deep sequencing of the T-cell receptor (TCR). Tumor-exclusive HLA class I and class II ligands (immune precipitation and LC-MS/MS) of 24 isocitrate dehydrogenase 1 wild type glioblastoma samples and 10 autologous primary glioblastoma cell lines were defined in comparison to an HLA ligandome normal tissue reference database (n > 418). We found 11,496 glioblastoma exclusive HLA class I ligands (2,064 shared with cell lines; 3,754 on ≥ 2 glioblastoma samples). On the source protein level, 239 glioblastoma exclusive proteins were identified; among them 54 were also found in cell lines. For HLA class II ligands the analysis revealed 11,870 glioblastoma exclusive peptides (444 shared with cell lines; 3,420 on ≥ 2 glioblastoma samples) and 278 glioblastoma exclusive proteins; among which 18 were present also in cell lines. Moreover, whole-exome sequencing and whole RNA sequencing of 13 tumor samples was performed with the aim to predict neoantigens. On average 5,662 somatic missense effects were identified per patient (min: 4,258; max: 7,479). Candidate peptides are grouped into (i) in silico predicted neoepitopes, (ii) tumor-exclusivity on HLA, (iii) gene expression (e.g. cancer testis antigens). Top-ranking candidates from each group will be tested with regards to their immunogenicity in an autologous setting (TIL, peripheral blood mononuclear cells, patient derived tumor cells). Finally, the peptide and immunogenicity data is correlated with the immune phenotype of the TIL compartment as well as the TCR repertoire of the sample.

The human leukocyte antigen (HLA) complex regulates the adaptive immune response by showcasing the intracellular and extracellular protein content to the immune system, where T cells are able to distinguish between self and foreign. Therefore, a comprehensive map of the entirety of both HLA class I- and class II-presented peptides from different tissues, is a highly sought after resource 1 , as it enables the investigation of basic immunological questions beyond the exome level. In this work, we describe the HLA Ligand Atlas, a comprehensive collection of matched HLA class I and class II ligandomes from 29 non-malignant tissues and 13 human subjects (208 samples in total), covering 38 HLA class I, and 17 HLA*DRB alleles. Nearly 50% of HLA ligands have not been previously described. The generated data is relevant for basic research in diverse fields such as systems biology, general immunology, and molecular biology. Furthermore, the HLA Ligand Atlas provides essential information for translational applications by supporting the development of effective cancer immunotherapies. The characterization of HLA ligands from benign tissues, in particular, is necessary in informing proteogenomic HLA-dependent target discovery approaches. Thus, this data set provides a basis for novel insights into immune-associated processes in the context of tissue and organ transplantation and represents a valuable tool for researchers exploring autoimmunity. The HLA Ligand Atlas is publicly available as a raw data resource but also in the form of a user-friendly web interface that allows users to quickly formulate complex queries against the data set. Both downloadable data and the query interface are available at www.hla-ligand-atlas.org .

3128 Background: The human leukocyte antigen (HLA) complex regulates the adaptive immune response by showcasing the intracellular and extracellular protein content to the immune system, which is the basis for T cell-dependent tumor rejection. Therefore, a comprehensive map of the entirety of both HLA class I- and class II-presented peptides from various benign tissues is a highly sought after resource, as it enables the definition of tumor-association on the immunologically pivotal level of the HLA ligandome. Methods: Human tissue samples were snap frozen post mortem during autopsy. The study was approved by the local IRB. HLA ligands were immunopurified and characterized using an Orbitrap Fusion Lumos mass spectrometer coupled to an Ultimate 3000 RSLC Nano UHPLC System. Data acquisition was performed as technical triplicates in data-dependent mode, and data were analyzed using the containerized, computational pipeline MHCquant. Results: In this work, we describe the HLA Ligand Atlas, a comprehensive collection of matched HLA class I and class II ligandomes from 29 non-malignant tissues and 13 human subjects (208 samples in total), covering 38 HLA class I, and 17 HLA*DRB alleles and comprising 48,381 HLA class I and 16,146 HLA class II peptides. Nearly 50% of HLA ligands have not been previously described. The HLA Ligand Atlas is publicly available as a raw data resource, but also in the form of a user-friendly web interface that allows users to quickly formulate complex queries against the data set. Both downloadable data and the query interface are available at www.hla-ligand-atlas.org. Conclusions: This data set provides a valuable tool for research in diverse fields such as systems biology, general immunology, autoimmune disease and organ transplantation. Most importantly, the HLA Ligand Atlas provides essential information for translational applications in immuno-oncology. The knowledge of HLA ligands from benign tissues strongly supports the informed design of proteogenomic HLA-dependent target discovery approaches.

Many proteins have small molecule-binding pockets that are not easily detectable in the ligand-free structures. These cryptic sites require a conformational change to become apparent; a cryptic site can therefore be defined as a site that forms a pocket in a holo structure, but not in the apo structure. Because many proteins appear to lack druggable pockets, understanding and accurately identifying cryptic sites could expand the set of drug targets. Previously, cryptic sites were identified experimentally by fragment-based ligand discovery, and computationally by long molecular dynamics simulations and fragment docking. Here, we begin by constructing a set of structurally defined apo-holo pairs with cryptic sites. Next, we comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes. We find that cryptic sites tend to be as conserved in evolution as traditional binding pockets, but are less hydrophobic and more flexible. Relying on this characterization, we use machine learning to predict cryptic sites with relatively high accuracy (for our benchmark, the true positive and false positive rates are 73% and 29%, respectively). We then predict cryptic sites in the entire structurally characterized human proteome (11,201 structures, covering 23% of all residues in the proteome). CryptoSite increases the size of the potentially “druggable” human proteome from ~40% to ~78% of disease-associated proteins. Finally, to demonstrate the utility of our approach in practice, we experimentally validate a cryptic site in protein tyrosine phosphatase 1B using a covalent ligand and NMR spectroscopy. The CryptoSite web server is available at http://salilab.org/cryptosite.

Background In Germany, approximately 30-50% of Oropharyngeal squamous-cell carcinoma (OPSCC) are related to human papillomaviruses (HPV), of which 90% are caused by HPV-16. The analysis of cancer-associated antigens is needed to develop antigen-specific immunotherapy and to determine the level of personalization required for therapeutic cancer vaccines. Methods Human leucocyte antigen (HLA) ligands from fresh frozen OPSCC biopsies were analyzed. HLA molecules were extracted from tumor tissue using immunoaffinity chromatography. HLA-bound peptides were subjected to high performance liquid chromatography (HPLC) and tandem mass spectrometry (MS/MS). Tumor-exclusive antigens in OPSCC were identified by comparative profiling against an in-house benign and malignant database. Additionally, whole exome sequencing of RNA and DNA isolated from the respective cancer biopsies was performed to complement the database. Results To date, HLA ligandomes from 28 OPSCC (15 HPV+; 13 HPV-) patients were analyzed. Compared to the benign samples database, 148 source proteins were tumor-exclusive for HLA class I and 200 source proteins for HLA class II. The mean number of tumor-exclusive proteins per sample was 6.0 (0-14) for HLA class I and 7.7 for HLA class II (0-41) respectively. Among the tumor exclusive proteins for HLA class I, 23 had not been detected in other malignant tumor ligandomes (n = 703 samples) and 75 for HLA class II (n = 433 samples). A principal component analysis of the HLA-ligandomes from HPV+ and HPV- revealed significant differences between the two groups. For HLA-class I, 85 tumor-exclusive proteins were only found in HPV+ and 55 in HPV-, whereas 8 proteins were found in both groups. Among the 200 HLA class II tumor-exclusive proteins, 96 were only found in HPV+ and 95 in HPV- respectively, whereas 9 were shared by HPV+ and HPV-. Conclusions The analysis of HLA-ligandomes from OPSCC revealed tumor-exclusive and newly discovered antigens. We found clear differences between the HLA ligandomes of HPV+ and HPV- patients. These differences need to be taken into account for therapeutic vaccination strategies. The level of personalization needed for such vaccines remains to be determined. Legal entity responsible for the study The authors. Funding University of Ulm, Clinician Scientist Programme. Disclosure S. Laban: Honoraria (institution), Advisory / Consultancy, Travel / Accommodation / Expenses: Merck Sharp & Dohme (MSD); Honoraria (institution), Advisory / Consultancy, Travel / Accommodation / Expenses: Bristol-Myers Squibb; Honoraria (institution), Advisory / Consultancy, Travel / Accommodation / Expenses: AstraZeneca; Honoraria (institution): Merck Serono. P.J. Schuler: Advisory / Consultancy: Bristol-Myers Squibb. T.K. Hoffmann: Honoraria (institution), Advisory / Consultancy: Merck Sharp & Dohme (MSD); Honoraria (institution): Bristol-Myers Squibb; Honoraria (institution): Merck Serono. H. Rammensee: Advisory / Consultancy, Leadership role, Shareholder / Stockholder / Stock options, Officer / Board of Directors: Immatics; Advisory / Consultancy, Leadership role, Shareholder / Stockholder / Stock options, Officer / Board of Directors: CureVac; Advisory / Consultancy, Leadership role, Shareholder / Stockholder / Stock options, Officer / Board of Directors: Synimmune. All other authors have declared no conflicts of interest.

In recent years, therapeutic approaches for acute myeloid leukemia (AML) have been improved, however the disease is still characterized by high relapse rates and a poor overall survival mainly in elderly patients aged 60 years and older. The standard therapy for these AML patients involves hypomethylating agents (HMAs) such as decitabine. With this, treatment remission can be achieved in some patients, however effective post-remission therapies are still overdue. Recent data suggests that HMAs induce gene expression of various cancer/testis antigens (CTAs), which could lead to the presentation of cancer/testis antigen-derived peptides on human leukocyte antigen (HLA) molecules. These CTA-derived peptides might serve as prime targets for tailored T cell-based immunotherapy approaches, which could represent an effective post-remission combination therapy. Here we present a mass spectrometry-based study, which longitudinally maps the HLA-presented immunopeptidome and in particular cancer/testis antigens of AML cells under in vitro decitabine treatment. To analyze the impact of decitabine on the presentation of HLA ligands we treated the AML cell lines U937 and MONO-MAC-6 as well as primary AML cells (n = 1) with decitabine for 48 h (t48) and 72 h (t72) in vitro. Upon flow cytometry-based quantification of HLA class I and II surface expression levels, no significant changes of HLA surface molecule levels under decitabine treatment compared to untreated controls were observed. Implementing label-free quantitation mass spectrometry, we then quantitatively assessed HLA class I ligand presentation under decitabine treatment. Only minor effects of decitabine on the whole HLA class I-restricted peptidome were observed: We detected a significant upregulation of 2.6 ± 0.9% of HLA class I ligands (fold change (FC) ≥ 4, p ≤ 0.01) after 48 h of decitabine treatment, whereas 9.6 ± 5.7% of the ligands were altered in their abundance over time without treatment. At t72 similar proportions of decitabine modulation were observed with 4.2 ± 2.7% of up-regulated HLA ligands. A total of 69 HLA class I ligands derived from 31 different CTAs were identified by mass spectrometric analysis, 9 of these ligands were presented exclusively under decitabine treatment. Furthermore, we showed that decitabine exposure caused a significantly increased presentation of 7/69 CTA-derived HLA ligands at least at one time point in the cell lines and the primary AML cells (FC ≥ 4, p ≤ 0.01). From the CTA cyclin A1, two HLA class I-presented peptides were significantly upregulated in U937 cells at t48 (FC 79.0 and 8.2) and t72 (FC 14.1 and 12.4). In primary AML cells, two peptides derived from JARID1B and KIAA0100 were significantly upregulated at either t48 (FC 21.8) or t72 (FC 6.6). In addition, we screened our dataset for HLA ligands derived from previously described decitabine-regulated genes and identified a HLA class I-presented peptide from DAZL, which was significantly upregulated in U937 cells at t72 under decitabine treatment (FC 57.2). Taken together, our results demonstrate a modulatory effect of the hypomethylating agent decitabine on the HLA ligandome of AML cells, enhancing the presentation of CTA-derived peptides on HLA class I molecules. The latter will be further evaluated for their eligibility as targets for tailored peptide-based immunotherapeutic approaches in AML patients undergoing HMA treatment. Disclosures Salih: Several patent applications: Patents & Royalties: e.g. EP3064507A1.

Chronic myeloid leukemia (CML) is characterized by the translocation t(9;22), which leads to the formation of the BCR-ABL fusion transcript. Several approved tyrosine kinase inhibitors (TKIs) target the resulting fusion protein, leading to an improved prognosis of CML patients. Currently, the main treatment goal is the achievement of a deep molecular response (MR), in which TKI therapy can be terminated. Several studies provide evidence that immunological control plays a major role for the course of CML and contributes to the achievement of deep MR in CML patients under TKI treatment (CMLTKI). This implies that reinforcing these immune responses might sustain long-term TKI-free survival or even cure for CML patients. Besides unspecific immunotherapy, such as interferon or immune checkpoint blocking antibodies, a more specific and minor side effect targeting of CML cells might be achieved by antigen-specific immunotherapy approaches. The prerequisite for such strategies is the identification of T-cell targets represented by tumor-associated human leukocyte antigen (HLA)-presented peptides on malignant cells. In this study, we used a mass spectrometry-based approach to identify naturally presented, CML-associated peptides in primary CML samples (HLA class I, n=21, 11,945 peptides, 5,478 source proteins; class II, n=20, 5,991 peptides, 1,302 proteins). Comparative HLA peptidome profiling using a comprehensive dataset of various benign tissues (e.g. blood, bone marrow, spleen, and lung) revealed frequently presented and strictly CML-associated antigens. In detail, the benign tissue dataset comprises hematological benign samples (class I, n=108, 51,233 peptides, 11,437 proteins; class II, n=88, 42,753 peptides, 4,877 proteins) and non-hematological benign tissues (28 tissues, n=166; class I, 128,590 peptides, 16,405 proteins; class II, 143,652 peptides, 13,410 proteins). We identified 50 CML-associated, HLA class I-restricted peptides with HLA allotype adjusted representation frequencies of ≥38% presented on HLA-A*02, -A*03, -A*11, and -B*07. HLA class II comparative profiling delineated 36 peptides exclusively and frequently presented in the HLA peptidome of ≥20% analyzed CML patients. For immunological characterization, we selected 8 HLA class I- and 6 class II-restricted highly CML-associated antigens. These peptides were analyzed in IFNγ ELISPOT assays using PBMCs from CMLTKI patients and healthy volunteers (HVs). Peptide-specific immune recognition was detected for 1/8 (13%) HLA class I peptides in 2/17 (12%) of CMLTKI patients. We hypothesized that this weak immune response might be due to an impaired CD8+ T cell function that reportedly is caused by TKI treatment. Thus, we compared T-cell responses against viral epitopes in IFNγ ELISPOT assays of CMLTKI patients with that of HVs and chronic lymphocytic leukemia (CLL) patients: in line with our hypothesis, we observed significantly reduced IFNγ release of T cells from CMLTKI patients compared to HVs and CLL patients (p To assess the immunogenicity of all HLA class I peptides, we performed in vitro artificial antigen presenting cell-based priming experiments using CD8+ T cells of HVs and CML patients. Effective priming of T cells was observed for 8/8 CML-associated peptides in ≥70% of analyzed HVs with frequencies of 0.1-33.9% (mean 2.2%) of CD8+ peptide-specific T cells. Notably, peptide-specific CD8+ T cells with frequencies of 0.1-2.2% (mean 0.4%) could also be induced in samples of CMLTKI patients that had not displayed preexisting immune responses. For 6/8 peptides, we observed multifunctionality of peptide-specific T cells by IFNγ and TNF production as well as upregulation of the degranulation marker CD107a. Cytotoxicity assays with polyclonal peptide-specific effector T cells confirmed the capacity to induce antigen-specific lysis for 3/4 analyzed peptides. Taken together, we here identified novel, naturally presented, CML-associated antigens and validated them as promising targets for tailored T cell-based immunotherapeutic approaches for CML patients. Disclosures Salih: Several patent applications: Patents & Royalties: e.g. EP3064507A1. Kowalewski:Immatics Biotechnologies GmbH: Employment. Schuster:Immatics Biotechnologies GmbH: Employment. Brümmendorf:Pfizer: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Janssen: Consultancy; Merck: Consultancy; Takeda: Consultancy. Niederwieser:Miltenyi: Speakers Bureau; Novartis: Research Funding.

Display Omitted Binding poses of antivirals to monomers and hexameric bundles of p7 of HCV.Guanidine based ligands bind better than derivatives of adamantanes and iminosugars.Binding of the best candidates is at the site of the loop.Hydrogen bonding and hydrophobic interactions are important. Protein p7 of HCV is a 63 amino acid channel forming membrane protein essential for the progression of viral infection. With this momentousness, p7 emerges as an important target for antiviral therapy. A series of small molecule drugs, such as amantadine, rimantadine, amiloride, hexamethylene amiloride, NN-DNJ and BIT225 have been found to affect the channel activity. These compounds are docked against monomeric and hexameric structures of p7 taken at various time steps from a molecular dynamics simulation of the protein embedded in a hydrated lipid bilayer. The energetics of binding identifies the guanidine based ligands as the most potent ligands. The adamantanes and NN-DNJ show weaker binding energies. The lowest energy poses are those at the site of the loop region for the monomer and hexamer. For the latter, the poses show a tendency of the ligand to face the lumen of the pore. The mode of binding is that of a balance between hydrophobic interactions and hydrogen bond formation with backbone atoms of the protein.

Here we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can either be provided manually by the user (table format), or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit. Annotated mirror spectrum plots can be exported from the USE as editable scalable high quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and integration into other websites (https://github.com/kusterlab/universal_spectrum_explorer).