Mass spectrometric analysis of the HLA class I peptidome of melanoma cell lines as a promising tool for the identification of putative tumor-associated HLA epitopes

Breast cancer immunopeptidomes contain numerous shared tumor antigens

Hormone receptor-positive breast cancer (HR+) is immunologically cold and has not benefited from advances in immunotherapy. In contrast, subsets of triple-negative breast cancer (TNBC) display high leukocytic infiltration and respond to checkpoint blockade. CD8+ T cells, the main effectors of anticancer responses, recognize MHC I-associated peptides (MAPs). Our work aimed to characterize the repertoire of MAPs presented by HR+ and TNBC tumors. Using mass spectrometry, we identified 57,094 unique MAPs in 26 primary breast cancer samples. MAP source genes highly overlapped between both subtypes. We identified 25 tumor-specific antigens (TSAs) mainly deriving from aberrantly expressed regions. TSAs were most frequently identified in TNBC samples and were more shared among The Cancer Genome Atlas (TCGA) database TNBC than HR+ samples. In the TNBC cohort, the predicted number of TSAs positively correlated with leukocytic infiltration and overall survival, supporting their immunogenicity in vivo. We detected 49 tumor-associated antigens (TAAs), some of which derived from cancer-associated fibroblasts. Functional expansion of specific T cell assays confirmed the in vitro immunogenicity of several TSAs and TAAs. Our study identified attractive targets for cancer immunotherapy in both breast cancer subtypes. The higher prevalence of TSAs in TNBC tumors provides a rationale for their responsiveness to checkpoint blockade.

TCR-engaging scaffolds selectively expand antigen-specific T-cells with a favorable phenotype for adoptive cell therapy

BACKGROUND

Adoptive cell therapy (ACT) has shown promising results for the treatment of cancer and viral infections. Successful ACT relies on ex vivo expansion of large numbers of desired T-cells with strong cytotoxic capacity and in vivo persistence, which constitutes the greatest challenge to current ACT strategies. Here, in this study, we present a novel technology for ex vivo expansion of antigen-specific T-cells; artificial antigen-presenting scaffolds (Ag-scaffolds) consisting of a dextran-polysaccharide backbone, decorated with combinations of peptide-Major Histocompatibility Complex (pMHC), cytokines and co-stimulatory molecules, enabling coordinated stimulation of antigen-specific T-cells.

METHODS

The capacity of Ag-scaffolds to expand antigen-specific T-cells was explored in ex vivo cultures with peripheral blood mononuclear cells from healthy donors and patients with metastatic melanoma. The resulting T-cell products were assessed for phenotypic and functional characteristics.

RESULTS

We identified an optimal Ag-scaffold for expansion of T-cells for ACT, carrying pMHC and interleukin-2 (IL-2) and IL-21, with which we efficiently expanded both virus-specific and tumor-specific CD8+ T cells from peripheral blood of healthy donors and patients, respectively. The resulting T-cell products were characterized by a high frequency of antigen-specific cells with high self-renewal capacity, low exhaustion, a multifunctional cytokine profile upon antigen-challenge and superior tumor killing capacity. This demonstrates that the coordinated stimuli provided by an optimized stoichiometry of TCR engaging (pMHC) and stimulatory (cytokine) moieties is essential to obtain desired T-cell characteristics. To generate an 'off-the-shelf' multitargeting Ag-scaffold product of relevance to patients with metastatic melanoma, we identified the 30 most frequently recognized shared HLA-A0201-restricted melanoma epitopes in a cohort of 87 patients. By combining these in an Ag-scaffold product, we were able to expand tumor-specific T-cells from 60-70% of patients with melanoma, yielding a multitargeted T-cell product with up to 25% specific and phenotypically and functionally improved T cells.

CONCLUSIONS

Taken together, the Ag-scaffold represents a promising new technology for selective expansion of antigen-specific CD8+ T cells directly from blood, yielding a highly specific and functionally enhanced T-cell product for ACT.

Targeted delivery of tumor necrosis factor in combination with CCNU induces a T cell-dependent regression of glioblastoma

Glioblastoma is the most aggressive primary brain tumor with an unmet need for more effective therapies. Here, we investigated combination therapies based on L19TNF, an antibody-cytokine fusion protein based on tumor necrosis factor that selectively localizes to cancer neovasculature. Using immunocompetent orthotopic glioma mouse models, we identified strong anti-glioma activity of L19TNF in combination with the alkylating agent CCNU, which cured the majority of tumor-bearing mice, whereas monotherapies only had limited efficacy. In situ and ex vivo immunophenotypic and molecular profiling in the mouse models revealed that L19TNF and CCNU induced tumor DNA damage and treatment-associated tumor necrosis. In addition, this combination also up-regulated tumor endothelial cell adhesion molecules, promoted the infiltration of immune cells into the tumor, induced immunostimulatory pathways, and decreased immunosuppression pathways. MHC immunopeptidomics demonstrated that L19TNF and CCNU increased antigen presentation on MHC class I molecules. The antitumor activity was T cell dependent and completely abrogated in immunodeficient mouse models. On the basis of these encouraging results, we translated this treatment combination to patients with glioblastoma. The clinical translation is ongoing but already shows objective responses in three of five patients in the first recurrent glioblastoma patient cohort treated with L19TNF in combination with CCNU (NCT04573192).

Soluble HLA peptidome: A new resource for cancer biomarkers

Using circulating molecular biomarkers to screen for cancer and other debilitating disorders in a high-throughput and low-cost fashion is becoming increasingly attractive in medicine. One major limitation of investigating protein biomarkers in body fluids is that only one-fourth of the entire proteome can be routinely detected in these fluids. In contrast, Human Leukocyte Antigen (HLA) presents peptides from the entire proteome on the cell surface. While peptide-HLA complexes are predominantly membrane-bound, a fraction of HLA molecules is released into body fluids which is referred to as soluble HLAs (sHLAs). As such peptides bound by sHLA molecules represent the entire proteome of their cells/tissues of origin and more importantly, recent advances in mass spectrometry-based technologies have allowed for accurate determination of these peptides. In this perspective, we discuss the current understanding of sHLA-peptide complexes in the context of cancer, and their potential as a novel, relatively untapped repertoire for cancer biomarkers. We also review the currently available tools to detect and quantify these circulating biomarkers, and we discuss the challenges and future perspectives of implementing sHLA biomarkers in a clinical setting.

MEK inhibition enhances presentation of targetable MHC-I tumor antigens in mutant melanomas

Combining multiple therapeutic strategies in NRAS/BRAF mutant melanoma-namely MEK/BRAF kinase inhibitors, immune checkpoint inhibitors (ICIs), and targeted immunotherapies-may offer an improved survival benefit by overcoming limitations associated with any individual therapy. Still, optimal combination, order, and timing of administration remains under investigation. Here, we measure how MEK inhibition (MEKi) alters anti-tumor immunity by utilizing quantitative immunopeptidomics to profile changes in the peptide major histocompatibility molecules (pMHC) repertoire. These data reveal a collection of tumor antigens whose presentation levels are selectively augmented following therapy, including several epitopes present at over 1,000 copies per cell. We leveraged the tunable abundance of MEKi-modulated antigens by targeting four epitopes with pMHC-specific T cell engagers and antibody drug conjugates, enhancing cell killing in tumor cells following MEK inhibition. These results highlight drug treatment as a means to enhance immunotherapy efficacy by targeting specific upregulated pMHCs and provide a methodological framework for identifying, quantifying, and therapeutically targeting additional epitopes of interest.

Extending the Mass Spectrometry-Detectable Landscape of MHC Peptides by Use of Restricted Access Material

Mass spectrometry-based immunopeptidomics enables the comprehensive identification of major histocompatibility complex (MHC) peptides from a cell culture as well as from tissue or tumor samples and is applied for the identification of tumor-specific and viral T-cell epitopes. Although mass spectrometry is generally considered an "unbiased" method for MHC peptide identification, the physicochemical properties of MHC peptides can greatly influence their detectability. Here, we demonstrate that highly hydrophobic peptides are lost during sample preparation when C18 solid-phase extraction (SPE) is used for separating MHC peptides from proteins. To overcome this limitation, we established an optimized protocol involving restricted access material (RAM). Compared to C18-SPE, RAM-SPE improved the overall MHC peptide recovery and extended the landscape of mass spectrometry-detectable MHC peptides toward more hydrophobic peptides.

A TCR mimic CAR T cell specific for NDC80 is broadly reactive with solid tumors and hematologic malignancies

Target identification for chimeric antigen receptor (CAR) T-cell therapies remains challenging due to the limited repertoire of tumor-specific surface proteins. Intracellular proteins presented in the context of cell surface HLA provide a wide pool of potential antigens targetable through T-cell receptor mimic antibodies. Mass spectrometry (MS) of HLA ligands from 8 hematologic and nonhematologic cancer cell lines identified a shared, non-immunogenic, HLA-A*02-restricted ligand (ALNEQIARL) derived from the kinetochore-associated NDC80 gene. CAR T cells directed against the ALNEQIARL:HLA-A*02 complex exhibited high sensitivity and specificity for recognition and killing of multiple cancer types, especially those of hematologic origin, and were efficacious in mouse models against a human leukemia and a solid tumor. In contrast, no toxicities toward resting or activated healthy leukocytes as well as hematopoietic stem cells were observed. This shows how MS can inform the design of broadly reactive therapeutic T-cell receptor mimic CAR T-cell therapies that can target multiple cancer types currently not druggable by small molecules, conventional CAR T cells, T cells, or antibodies.

Unsupervised Mining of HLA-I Peptidomes Reveals New Binding Motifs and Potential False Positives in the Community Database

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

Immuno-transcriptomic profiling of extracranial pediatric solid malignancies

We perform an immunogenomics analysis utilizing whole-transcriptome sequencing of 657 pediatric extracranial solid cancer samples representing 14 diagnoses, and additionally utilize transcriptomes of 131 pediatric cancer cell lines and 147 normal tissue samples for comparison. We describe patterns of infiltrating immune cells, T cell receptor (TCR) clonal expansion, and translationally relevant immune checkpoints. We find that tumor-infiltrating lymphocytes and TCR counts vary widely across cancer types and within each diagnosis, and notably are significantly predictive of survival in osteosarcoma patients. We identify potential cancer-specific immunotherapeutic targets for adoptive cell therapies including cell-surface proteins, tumor germline antigens, and lineage-specific transcription factors. Using an orthogonal immunopeptidomics approach, we find several potential immunotherapeutic targets in osteosarcoma and Ewing sarcoma and validated PRAME as a bona fide multi-pediatric cancer target. Importantly, this work provides a critical framework for immune targeting of extracranial solid tumors using parallel immuno-transcriptomic and -peptidomic approaches.

Proteogenomic Analysis Unveils the HLA Class I-Presented Immunopeptidome in Melanoma and EGFR-Mutant Lung Adenocarcinoma

Immune checkpoint inhibitors and adoptive lymphocyte transfer–based therapies have shown great therapeutic potential in cancers with high tumor mutational burden (TMB), such as melanoma, but not in cancers with low TMB, such as mutant epidermal growth factor receptor (EGFR)–driven lung adenocarcinoma. Precision immunotherapy is an unmet need for most cancers, particularly for cancers that respond inadequately to immune checkpoint inhibitors. Here, we employed large-scale MS-based proteogenomic profiling to identify potential immunogenic human leukocyte antigen (HLA) class I-presented peptides in melanoma and EGFR-mutant lung adenocarcinoma. Similar numbers of peptides were identified from both tumor types. Cell line and patient-specific databases (DBs) were constructed using variants identified from whole-exome sequencing. A de novo search algorithm was used to interrogate the HLA class I immunopeptidome MS data. We identified 12 variant peptides and several classes of tumor-associated antigen-derived peptides. We constructed a cancer germ line (CG) antigen DB with 285 antigens. This allowed us to identify 40 class I-presented CG antigen–derived peptides. The class I immunopeptidome comprised more than 1000 post-translationally modified (PTM) peptides representing 58 different PTMs, underscoring the critical role PTMs may play in HLA binding. Finally, leveraging de novo search algorithm and an annotated long noncoding RNA (lncRNA) DB, we developed a novel lncRNA-encoded peptide discovery pipeline to identify 44 lncRNA-derived peptides that are presented by class I. We validated tandem MS spectra of select variant, CG antigen, and lncRNA-derived peptides using synthetic peptides and performed HLA class I-binding assays to demonstrate binding to class I proteins. In summary, we provide direct evidence of HLA class I presentation of a large number of variant and tumor-associated peptides in both low and high TMB cancer. These results can potentially be useful for precision immunotherapies, such as vaccine or adoptive cell therapies in melanoma and EGFR-mutant lung cancers.

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Proteogenomics identified ∼35,000 class I-presented peptides.

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CG antigen and PTM peptides identified in melanoma and lung cancer.

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De novo search identified variant and lncRNA-derived peptides.

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A new strategy to identify class I-presented lncRNA-derived peptides developed.

Spliced Peptides and Cytokine-Driven Changes in the Immunopeptidome of Melanoma

Antigen recognition by CD8⁺ T cells is governed by the pool of peptide antigens presented on the cell surface in the context of HLA class I complexes. Studies have shown not only a high degree of plasticity in the immunopeptidome, but also that a considerable fraction of all presented peptides is generated through proteasome-mediated splicing of noncontiguous regions of proteins to form novel peptide antigens. Here, we used high-resolution mass spectrometry combined with new bioinformatic approaches to characterize the immunopeptidome of melanoma cells in the presence or absence of IFNγ. In total, we identified more than 60,000 peptides from a single patient-derived cell line (LM-MEL-44) and demonstrated that IFNγ induced changes in the peptidome, with an overlap of only approximately 50% between basal and treated cells. Around 6% to 8% of the peptides were identified as cis-spliced peptides, and 2,213 peptides (1,827 linear and 386 cis-spliced peptides) were derived from known melanoma-associated antigens. These peptide antigens were equally distributed between the constitutive- and IFNγ-induced peptidome. We next examined additional HLA-matched patient-derived cell lines to investigate how frequently these peptides were identified and found that a high proportion of both linear and spliced peptides was conserved between individual patient tumors, drawing on data amassing to more than 100,000 peptide sequences. Several of these peptides showed in vitro immunogenicity across multiple patients with melanoma. These observations highlight the breadth and complexity of the repertoire of immunogenic peptides that can be exploited therapeutically and suggest that spliced peptides are a major class of tumor antigens.

Multiplexed relative and absolute quantitative immunopeptidomics reveals MHC I repertoire alterations induced by CDK4/6 inhibition

Peptides bound to class I major histocompatibility complexes (MHC) play a critical role in immune cell recognition and can trigger an antitumor immune response in cancer. Surface MHC levels can be modulated by anticancer agents, altering immunity. However, understanding the peptide repertoire's response to treatment remains challenging and is limited by quantitative mass spectrometry-based strategies lacking normalization controls. We describe an experimental platform that leverages recombinant heavy isotope-coded peptide MHCs (hipMHCs) and multiplex isotope tagging to quantify peptide repertoire alterations using low sample input. HipMHCs improve quantitative accuracy of peptide repertoire changes by normalizing for variation across analyses and enable absolute quantification using internal calibrants to determine copies per cell of MHC antigens, which can inform immunotherapy design. Applying this platform in melanoma cell lines to profile the immunopeptidome response to CDK4/6 inhibition and interferon-γ - known modulators of antigen presentation - uncovers treatment-specific alterations, connecting the intracellular response to extracellular immune presentation.

In-depth mining of the immunopeptidome of an acute myeloid leukemia cell line using complementary ligand enrichment and data acquisition strategies

The identification of T cell epitopes derived from tumour specific antigens remains a significant challenge for the development of peptide-based vaccines and immunotherapies. The use of mass spectrometry-based approaches (immunopeptidomics) can provide powerful new avenues for the identification of such epitopes. In this study we report the use of complementary peptide antigen enrichment methods and a comprehensive mass spectrometric acquisition strategy to provide in-depth immunopeptidome data for the THP-1 cell line, a cell line used widely as a model of human leukaemia. To accomplish this, we combined robust experimental workflows that incorporated ultrafiltration or off-line reversed phase chromatography to enrich peptide ligand as well as a multifaceted data acquisition strategy using an Orbitrap Fusion LC-MS instrument. Using the combined datasets from the two ligand enrichment methods we gained significant depth in immunopeptidome coverage by identifying a total of 41,816 HLA class I peptides from THP-1 cells, including a significant number of peptides derived from different oncogenes or over expressed proteins associated with cancer. The physicochemical properties of the HLA-bound peptides dictated their recovery using the two ligand enrichment approaches and their distribution across the different precursor charge states considered in the data acquisition strategy. The data highlight the complementarity of the two enrichment procedures, and in cases where sample is not limiting, suggest that the combination of both approaches will yield the most comprehensive immunopeptidome information.

Proteogenomics Uncovers a Vast Repertoire of Shared Tumor-Specific Antigens in Ovarian Cancer

High-grade serous ovarian cancer (HGSC), the principal cause of death from gynecologic malignancies in the world, has not significantly benefited from advances in cancer immunotherapy. Although HGSC infiltration by lymphocytes correlates with superior survival, the nature of antigens that can elicit anti-HGSC immune responses is unknown. The goal of this study was to establish the global landscape of HGSC tumor-specific antigens (TSA) using a mass spectrometry pipeline that interrogated all reading frames of all genomic regions. In 23 HGSC tumors, we identified 103 TSAs. Classic TSA discovery approaches focusing only on mutated exonic sequences would have uncovered only three of these TSAs. Other mutated TSAs resulted from out-of-frame exonic translation (n = 2) or from noncoding sequences (n = 7). One group of TSAs (n = 91) derived from aberrantly expressed unmutated genomic sequences, which were not expressed in normal tissues. These aberrantly expressed TSAs (aeTSA) originated primarily from nonexonic sequences, in particular intronic (29%) and intergenic (22%) sequences. Their expression was regulated at the transcriptional level by variations in gene copy number and DNA methylation. Although mutated TSAs were unique to individual tumors, aeTSAs were shared by a large proportion of HGSCs. Taking into account the frequency of aeTSA expression and HLA allele frequencies, we calculated that, in Caucasians, the median number of aeTSAs per tumor would be five. We conclude that, in view of their number and the fact that they are shared by many tumors, aeTSAs may be the most attractive targets for HGSC immunotherapy.

The Diagnostic, Prognostic, and Therapeutic Potential of Adaptive Immune Receptor Repertoire Profiling in Cancer

Lymphocytes play a critical role in antitumor immune responses. They are directly targeted by some therapies, and the composition and spatial organization of intratumor T-cell populations is prognostic in some cancer types. A better understanding of lymphocyte population dynamics over the course of disease and in response to therapy is urgently needed to guide therapy decisions and to develop new therapy targets. Deep sequencing of the repertoire of antigen receptor-encoding genes expressed in a lymphocyte population has become a widely used approach for profiling the population's immune status. Lymphocyte antigen receptor repertoire deep sequencing data can be used to assess the clonal richness and diversity of lymphocyte populations; to track clone members over time, between tissues, and across lymphocyte subsets; to detect clonal expansion; and to detect the recruitment of new clones into a tissue. Repertoire sequencing is thus a critical complement to other methods of lymphocyte and immune profiling in cancer. This review describes the current state of knowledge based on repertoire sequencing studies conducted on human cancer patients, with a focus on studies of the T-cell receptor beta chain locus. The review then outlines important questions left unanswered and suggests future directions for the field.

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

The set of peptides presented on a cell's surface by MHC molecules is known as the immunopeptidome. Current mass spectrometry technologies allow for identification of large peptidomes, and studies have proven these data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. Immunopeptidomes are usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in the system under investigation. Motif deconvolution -the process of associating each ligand to its presenting MHC molecule(s)- is therefore a critical and challenging step in the analysis of MS-eluted MHC ligand data. Here, we describe NNAlign_MA, a computational method designed to address this challenge and fully benefit from large, poly-specific data sets of MS-eluted ligands. NNAlign_MA simultaneously performs the tasks of (1) clustering peptides into individual specificities; (2) automatic annotation of each cluster to an MHC molecule; and (3) training of a prediction model covering all MHCs present in the training set. NNAlign_MA was benchmarked on large and diverse data sets, covering class I and class II data. In all cases, the method was demonstrated to outperform state-of-the-art methods, effectively expanding the coverage of alleles for which accurate predictions can be made, resulting in improved identification of both eluted ligands and T-cell epitopes. Given its high flexibility and ease of use, we expect NNAlign_MA to serve as an effective tool to increase our understanding of the rules of MHC antigen presentation and guide the development of novel T-cell-based therapeutics.

Application of mass spectrometry-based MHC immunopeptidome profiling in neoantigen identification for tumor immunotherapy

One of the challenges for cancer vaccine and adoptive T-cell-based immunotherapy is to identify the major histocompatibility complex (MHC)-associated non-self neoantigens recognized by T cells. T cell epitope in silico prediction algorithms have been widely used for neoantigen prediction; nonetheless, this platform lacks the experimental evidence of directly identification of the presented epitopes on cell surface. Currently, mass spectrometry (MS)-based proteomics is an advanced analytical technology for large-scale peptide sequencing, which has become a powerful tool for directly profiling the immunopeptidome presented by MHC molecules. Integrating with next-generation sequencing, proteogenomic analysis provides the "gold standard" for neoantigen identification at protein level. This method discovers the tumor-specific neoantigens derived from somatic mutations, proteasome splicing, noncoding RNA, and post-translational modified antigens. Herein, we review basis of antigen processing and presentation, tumor antigen classification, existing approaches for neoantigen discovery, quantitative proteomics, epitope prediction programs, and advantages and drawbacks of proteomics workflow for MHC immunopeptidome profiling. Furthermore, we summarize 40 recently published reports addressing the fundamental theory, breakthrough and most advanced updates for the mass spectrometry-based neoantigen discovery for cancer immunotherapy.

Antibody-Based Delivery of Cytokine Payloads to Carbonic Anhydrase IX Leads to Cancer Cures in Immunocompetent Tumor-Bearing Mice

Antibody-cytokine fusion proteins can have the potential to increase the density and activity of subsets of leukocytes within the tumor mass. Here, we describe the design, production, and characterization of four novel antibody-cytokine fusion proteins directed against human carbonic anhydrase IX, a highly validated marker of hypoxia that is overexpressed in clear cell renal cell carcinoma and other malignancies. As immunomodulatory payloads we used TNF, IL2, IFNα2 (corresponding to products that are in clinical use), and IL12 (as this cytokine potently activates T cells and NK cells). Therapy experiments were performed in BALB/c mice, bearing CT26 tumors transfected with human carbonic anhydrase IX, in order to assess the performance of the fusion proteins in an immunocompetent setting. The biopharmaceuticals featuring TNF, IL2, or IL12 as payloads cured all mice in their therapy groups, whereas only a subset of mice was cured by the antibody-based delivery of IFNα2. Although the antibody fusion with TNF mediated a rapid hemorrhagic necrosis of the tumor mass, a slower regression of the neoplastic lesions (which continued after the last injection) was observed with the other fusion proteins, and treated mice acquired protective anticancer immunity. A high proportion of tumor-infiltrating CD8⁺ T cells was specific to the retroviral antigen AH1; however, the LGPGREYRAL peptide derived from human carbonic anhydrase IX was also present on tumor cells. The results described herein provide a rationale for the clinical use of fully human antibody-cytokine fusions specific to carbonic anhydrase IX.

Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome

BACKGROUND

To further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data, which is less biologically relevant than sampling by mass spectrometry, and other tools are limited by incomplete exploration of machine learning approaches. Herein, we assemble publicly available data describing human peptides discovered by sampling the MHC-I immunopeptidome with mass spectrometry and use this database to train random forest classifiers (ForestMHC) to predict presentation by MHC-I.

RESULTS

As measured by precision in the top 1% of predictions, our method outperforms NetMHC and NetMHCpan on test sets, and it outperforms both these methods and MixMHCpred on new data from an ovarian carcinoma cell line. We also find that random forest scores correlate monotonically, but not linearly, with known chemical binding affinities, and an information-based analysis of classifier features shows the importance of anchor positions for our classification. The random-forest approach also outperforms a deep neural network and a convolutional neural network trained on identical data. Finally, we use our large database to confirm that gene expression partially determines peptide presentation.

CONCLUSIONS

ForestMHC is a promising method to identify peptides bound by MHC-I. We have demonstrated the utility of random forest-based approaches in predicting peptide presentation by MHC-I, assembled the largest known database of MS binding data, and mined this database to show the effect of gene expression on peptide presentation. ForestMHC has potential applicability to basic immunology, rational vaccine design, and neoantigen binding prediction for cancer immunotherapy. This method is publicly available for applications and further validation.

Alteration of gut microbiota-associated epitopes in children with autism spectrum disorders

BACKGROUND

Autism spectrum disorder (ASD) affects 1% of children and has no cure. Gastrointestinal (GI) problems are common in children with ASD, and although gut microbiota is known to play an important role in ASD through the gut-brain axis, the specific mechanism is unknown. Recent evidence suggests that gut microbiota may participate in the pathogenesis of ASD through immune- and inflammation-mediated pathways. Here, we identified potentially immunogenic epitopes derived from gut microbiota in stool samples from ASD children with and without GI problems and typically developing (TD) children.

METHODS

Candidate gut microbiota-associated epitopes (MEs) were identified by blast shotgun metagenomic sequencing of fecal samples from 43 ASD children (19 with and 24 without GI involvement) and 31 sex- and age-matched typically developing (TD) children. Potentially immunogenic epitopes were screened against a predictive human Immune Epitope Database. The composition and abundance of candidate MEs were compared between the three groups of children.

RESULTS

MEs identified in ASD children with GI problems were significantly more diverse than those in TD children. ME composition could discriminate between the three groups of children. We identified 34 MEs that were significantly more or less abundant in ASD children than TD children, most (29/34) of the differences in MEs were reduced in ASD and associated with abnormal gut IgA level and altered gut microbiota composition, these MEs were limited effected by clinical factors such as age, gender, and GI problems, of which eleven MEs were pathogenic microorganisms peptides with strong T or B cell response, nine MEs showed high homology to peptides from human self proteins associated with autoimmune disease occurrence, eliciting immune attack against hematopoietic stem cells and inhibition antigen binding. We also found that the abundance of five MEs were increased in ASD, including three human self proteins, gap junction alpha-1 (GJA1), paired box protein Pax-3 (PAX3) and eyes absent homolog 1 isoform 4 (EYA1) which associated with cancer, and a ME with homology to a Listeriolysin O peptide from the pathogenic bacterium Listeria monocytogenes was significantly increased in ASD children compared with TD children.

CONCLUSIONS

Our findings demonstrate the abnormal of MEs composition in the gut of children with ASD, moreover, the abnormality in MEs composition was associated with abnormal gut IgA levels and altered gut microbiota composition, this abnormality also suggests that there may be abnormalities in intestinal immunity in children with ASD; In all, thirty-four MEs identified were potential biomarker of ASD, and alterations in MEs may contribute to abnormalities in gut immunity and/or homeostasis in ASD children. Further study of the MEs identified here may advance our understanding of the pathogenesis of ASD.

Mass spectrometric imaging of cysteine rich proteins in human skin

Looking insight pathological processes, metallothioneins (MTs) are considered to be potential biomarkers for monitoring of a development of various types of diseases, such as cancer. The early identification of the MTs in biological tissues could be important tool for the estimation of appropriate clinical therapy. Therefore, here we investigated the application of matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) together with immunohistochemical analyses (IHC) using MT-1/2 antibody for MT detection in formalin-fixed paraffin-embedded (FFPE) biopsy specimens of human skin. Principal component analyses revealed differences in the peptide/protein profiles separating healthy skin from the carcinoma specimens. Statistically significant ion peaks at m/z 6038, 6300, 6676, and 7026 were more frequently detected in squamous cell carcinoma (SCC), basal cell carcinoma (BCC) and melanoma. Using IHC, we found that MT-1/2 was significantly higher in SCC and melanoma compared to healthy skin. Surprisingly, significantly low levels of MT-1/2 were found in BCC. On one side, the results indicate important role of MTs in melanoma occurrence and progression, as on the second side, there are hidden processes associated with MTs based on differences of the occurrence of the MS peaks, which could be associated with cycling of MTs isoforms.

Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes

A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex (MHC) class I and II binding affinities. Rationally selecting the most appropriate tool has been complicated by the evolving training data and machine learning methods. Despite the recent advances made in generating high-quality MHC-eluted, naturally processed ligandome, the reliability of new predictors on these epitopes has yet to be evaluated. This study reports the latest benchmarking on an extensive set of MHC-binding predictors by using newly available, untested data of both synthetic and naturally processed epitopes. 32 human leukocyte antigen (HLA) class I and 24 HLA class II alleles are included in the blind test set. Artificial neural network (ANN)-based approaches demonstrated better performance than regression-based machine learning and structural modeling. Among the 18 predictors benchmarked, ANN-based mhcflurry and nn_align perform the best for MHC class I 9-mer and class II 15-mer predictions, respectively, on binding/non-binding classification (Area Under Curves = 0.911). NetMHCpan4 also demonstrated comparable predictive power. Our customization of mhcflurry to a pan-HLA predictor has achieved similar accuracy to NetMHCpan. The overall accuracy of these methods are comparable between 9-mer and 10-mer testing data. However, the top methods deliver low correlations between the predicted versus the experimental affinities for strong MHC binders. When used on naturally processed MHC-ligands, tools that have been trained on elution data (NetMHCpan4 and MixMHCpred) shows better accuracy than pure binding affinity predictor. The variability of false prediction rate is considerable among HLA types and datasets. Finally, structure-based predictor of Rosetta FlexPepDock is less optimal compared to the machine learning approaches. With our benchmarking of MHC-binding and MHC-elution predictors using a comprehensive metrics, a unbiased view for establishing best practice of T-cell epitope predictions is presented, facilitating future development of methods in immunogenomics.

Antibody-based Delivery of TNF to the Tumor Neovasculature Potentiates the Therapeutic Activity of a Peptide Anticancer Vaccine

PURPOSE

There is a growing interest in the use of tumor antigens for therapeutic vaccination strategies. Unfortunately, in most cases, the use of peptide vaccines in patients does not mediate shrinkage of solid tumor masses.Experimental Design: Here, we studied the opportunity to boost peptide vaccination with F8-TNF, an antibody fusion protein that selectively delivers TNF to the tumor extracellular matrix. AH1, a model antigen to investigate CD8⁺ T-cell immunity in BALB/c mice, was used as vaccine.

RESULTS

Peptide antigens alone exhibited only a modest tumor growth inhibition. However, anticancer activity could be substantially increased by combination with F8-TNF. Analysis of T cells in tumors and in draining lymph nodes revealed a dramatic expansion of AH1-specific CD8⁺ T cells, which were strongly positive for PD-1, LAG-3, and TIM-3. The synergistic anticancer activity, observed in the combined use of peptide vaccination and F8-TNF, was largely due to the ability of the fusion protein to induce a rapid hemorrhagic necrosis in the tumor mass, thus leaving few residual tumor cells. While the cell surface phenotype of tumor-infiltrating CD8⁺ T cells did not substantially change upon treatment, the proportion of AH1-specific T cells was strongly increased in the combination therapy group, reaching more than 50% of the CD8⁺ T cells within the tumor mass.

CONCLUSIONS

Because both peptide vaccination strategies and tumor-homing TNF fusion proteins are currently being studied in clinical trials, our study provides a rationale for the combination of these 2 regimens for the treatment of patients with cancer.

Targeting Neoantigens for Personalised Immunotherapy

This review discusses the rapidly evolving field of immunotherapy research, focusing on the types of cancer antigens that can be recognised by the immune system and potential methods by which neoantigens can be exploited clinically to successfully target and clear tumour cells. Recent studies suggest that the likelihood of successful immunotherapeutic targeting of cancer will be reliant on immune response to neoantigens. This type of cancer-specific antigen arises from somatic variants that result in alteration of the expressed protein sequence. Massively parallel sequencing techniques now allow the rapid identification of these genomic mutations, and algorithms can be used to predict those that will be processed by the proteasome, bind to the transporter complex and encode peptides that bind strongly to individual MHC molecules. The emerging data from assessment of the immunogenicity of neoantigens suggests that only a minority of mutations will form targetable epitopes and therefore the potential for immunotherapeutic targeting will be greater in cancers with a higher frequency of protein-altering somatic variants. It is evident that neoantigens contribute to the success of some immunotherapeutic interventions and that there is significant scope for specific targeting of these antigens to develop new treatment approaches.

Identifying neoantigens for use in immunotherapy

This review focuses on the types of cancer antigens that can be recognised by the immune system and form due to alterations in the cancer genome, including cancer testis, overexpressed and neoantigens. Specifically, neoantigens can form when cancer cell-specific mutations occur that result in alterations of the protein from ‘self’. This type of antigen can result in an immune response sufficient to clear tumour cells when activated. Furthermore, studies have reported that the likelihood of successful immunotherapeutic targeting of cancer by many different methods was reliant on immune response to neoantigens. The recent resurgence of interest in the immune response to tumour cells, in conjunction with technological advances, has resulted in a large increase in the predicted, identified and functionally confirmed neoantigens. This growth in identified neoantigen sequences has increased the contents of training sets for algorithms, which in turn improves the prediction of which genetic mutations may form neoantigens. Additionally, algorithms predicting how proteins will be processed into peptide epitopes by the proteasome and which peptides bind to the transporter complex are also improving with this research. Now that large screens of all the tumour-specific protein altering mutations are possible, the emerging data from assessment of the immunogenicity of neoantigens suggest that only a minority of variants will form targetable epitopes. The potential for immunotherapeutic targeting of neoantigens will therefore be greater in cancers with a higher frequency of protein altering somatic variants. There is considerable potential in the use of neoantigens to treat patients, either alone or in combination with other immunotherapies and with continued advancements, these potentials will be realised.

The role of proteomics in the age of immunotherapies

The antigenic landscape of the adaptive immune response is determined by the peptides presented by immune cells. In recent years, a number of immune-based cancer therapies have been shown to induce remarkable clinical responses through the activation of the patient's immune system. As a result, there is a need to identify immune biomarkers capable of predicting clinical response. Recent advances in proteomics have led to considerable developments in the more comprehensive profiling of the immune response. "Immunoproteomics" utilises a rapidly increasing collection of technologies in order to identify and quantify antigenic peptides or proteins. This includes gel-based, array-based, mass spectrometry (MS), DNA-based, or computer-based (in silico) approaches. Immunoproteomics is yielding an understanding of disease and disease progression, vaccine candidates, and biomarkers to a depth not before understood. This review gives an overview of the emerging role of proteomics in improving personalisation of immunotherapy treatment.

Predicting Antigen Presentation-What Could We Learn From a Million Peptides?

Antigen presentation lies at the heart of immune recognition of infected or malignant cells. For this reason, important efforts have been made to predict which peptides are more likely to bind and be presented by the human leukocyte antigen (HLA) complex at the surface of cells. These predictions have become even more important with the advent of next-generation sequencing technologies that enable researchers and clinicians to rapidly determine the sequences of pathogens (and their multiple variants) or identify non-synonymous genetic alterations in cancer cells. Here, we review recent advances in predicting HLA binding and antigen presentation in human cells. We argue that the very large amount of high-quality mass spectrometry data of eluted (mainly self) HLA ligands generated in the last few years provides unprecedented opportunities to improve our ability to predict antigen presentation and learn new properties of HLA molecules, as demonstrated in many recent studies of naturally presented HLA-I ligands. Although major challenges still lie on the road toward the ultimate goal of predicting immunogenicity, these experimental and computational developments will facilitate screening of putative epitopes, which may eventually help decipher the rules governing T cell recognition.

In Silico Typing of Classical and Non-classical HLA Alleles from Standard RNA-Seq Reads

Next-Generation Sequencing (NGS) enables the rapid generation of billions of short nucleic acid sequence fragments (i.e., "sequencing reads"). Especially, the adoption of gene expression profiling using whole transcriptome sequencing (i.e., "RNA-Seq") has been rapid. Here, we describe an in silico method, seq2HLA, that takes standard RNA-Seq reads as input and determines a sample's (classical and non-classical) HLA class I and class II types as well as HLA expression. We demonstrate the application of seq2HLA using publicly available RNA-Seq data from the Burkitt's lymphoma cell line DAUDI and the choriocarcinoma cell line JEG-3.

Tools to define the melanoma-associated immunopeptidome

Immunotherapies have been traditionally applied in malignant melanoma, which represent one of the most immunogenic tumours. Recently, immune checkpoint modulation has shown high therapeutic efficacy and may provide long-term survival in a significant proportion of affected patients. T cells are the major players in tumour rejection and recognize tumour cells predominantly in an MHC-dependent way. The immunopeptidome comprises the peptide repertoire presented by MHC class I and II molecules on the surface of the body's cells including tumour cells. To understand characteristics of suitable rejection antigens as well as respective effective T-cell responses, determination of the immunopeptidome is of utmost importance. Suitable rejection antigens need to be further characterized and validated not only to systematically improve current therapeutic approaches, but also to develop individualized treatment options. In this review, we report on current tools to explore the immunopeptidome in human melanoma and discuss current understanding and future developments to specifically detect and select those antigens that may be most relevant and promising for effective tumour rejection.

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity

The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays.

Predicting the differences between cancer and normal cells that are visible to the immune system is of central importance for cancer immunotherapy. Here we introduce a novel computational framework to harness the wealth of data from in-depth HLA peptidomics studies, including ten novel high quality (<1% FDR) datasets generated for this work, to improve predictions of peptides displayed on HLA-I molecules. These high-throughput and unbiased data enable us to refine models of HLA-I binding specificity for many alleles (including some that had no ligand until this study) and improve predictions of neo-antigens from exome sequencing data in melanoma and lung cancer samples. Moreover, the refined description of HLA-I binding specificity reveals cases of allosteric modulation of HLA-I binding specificity at the second amino acid position (P2) of their ligands by residues that are part of the HLA-I binding site but outside of the B pocket.

Sarcoma Eradication by Doxorubicin and Targeted TNF Relies upon CD8+ T-cell Recognition of a Retroviral Antigen

Antibody-cytokine complexes may offer new tools to treat cancer. Here, we show how TNF-linked antibodies, which recognize tumor-selective splice isoforms of fibronectin (F8-TNF), can be exploited to eradicate sarcomas in immunocompetent mice. We treated mice bearing WEHI-164 fibrosarcoma with a combination of F8-TNF and doxorubicin, curing the majority of treated animals (29/37). Notably, cured mice were resistant to rechallenge not only by WEHI-164 cells but also heterologous C51 or CT26 colorectal tumor cells in a CD8⁺ T-cell-dependent process. Mechanistic analyses revealed that each tumor cell line presented AH1, a common endogenous retroviral peptide. Numbers of AH1-specific CD8⁺ T cells exhibiting cytotoxic capacity were increased by F8-TNF plus doxorubicin treatment, arguing that cognate CD8⁺ T cells contributed to tumor eradication. Sequence analysis of T-cell receptors of CD8⁺ T cells revealed the presence of H-2L^d/AH1-specific T cells and an expansion of sequence diversity in treated mice. Overall, our findings provide evidence that retroviral genes contribute to tumoral immunosurveillance in a process that can be generally boosted by F8-TNF and doxorubicin treatment. Cancer Res; 77(13); 3644-54. ©2017 AACR.

Purification of soluble HLA class I complexes from human serum or plasma deliver high quality immuno peptidomes required for biomarker discovery

Soluble human leukocyte antigen class I (sHLA)-peptide complexes have been suggested to play a role in the modulation of immune responses and in immune evasion of cancer cells. The set of peptides eluted from sHLA molecules could serve as biomarker for the monitoring of patients with cancer or other conditions. Here, we describe an improved sHLA peptidomics methodology resulting in the identification of 1816 to 2761 unique peptide sequences from triplicate analyses of serum or plasma taken from three healthy donors. More than 90% of the identified peptides were 8-11mers and 74% of these sequences were predicted to bind to cognate HLA alleles, confirming the quality of the resulting immunopeptidomes. In comparison to the HLA peptidome of cultured cells, the plasma-derived peptides were predicted to have a higher stability in complex with the cognate HLA molecules and mainly derived from proteins of the plasma membrane or from the extracellular space. The sHLA peptidomes can efficiently be characterized by using the new methodology, thus serving as potential source of biomarkers in various pathological conditions.