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One key barrier to improving efficacy of personalized cancer immunotherapies that are dependent on the tumor antigenic landscape remains patient stratification. Although patients with CD3+CD8+ T cell-inflamed tumors typically show better response to immune checkpoint inhibitors, it is still unknown whether the immunopeptidome repertoire presented in highly inflamed and noninflamed tumors is substantially different. We surveyed 61 tumor regions and adjacent nonmalignant lung tissues from 8 patients with lung cancer and performed deep antigen discovery combining immunopeptidomics, genomics, bulk and spatial transcriptomics, and explored the heterogeneous expression and presentation of tumor (neo)antigens. In the present study, we associated diverse immune cell populations with the immunopeptidome and found a relatively higher frequency of predicted neoantigens located within HLA-I presentation hotspots in CD3+CD8+ T cell-excluded tumors. We associated such neoantigens with immune recognition, supporting their involvement in immune editing. This could have implications for the choice of combination therapies tailored to the patient’s mutanome and immune microenvironment.

Heterochromatin loss and genetic instability enhance cancer progression by favoring clonal diversity, yet uncontrolled replicative stress can lead to mitotic catastrophe and inflammatory responses promoting immune rejection. KRAB-containing zinc finger proteins (KZFPs) are epigenetic modulators, which for many control heterochromatin at transposable element (TE)-embedded regulatory sequences. We identified a cluster of 18 KZFPs associated with poor prognosis in diffuse large B cell lymphoma (DLBCL). We found their upregulation to correlate with increased copy number alterations and suppression of immune responses in tumor samples. Upon depleting two that target evolutionarily recent TEs, the primate-specific ZNF587 and ZNF417 paralogs, the proliferation of DLBCL cell lines was drastically impaired and replicative stress abruptly induced with marked alterations of the chromatin landscape and multiplication of DNA replication origins. Furthermore,ZNF587/417knockdown upregulated interferon/inflammatory-related genes through activation of the cGAS-STING DNA sensing pathway, augmented the susceptibility of tumor cells to macrophage-mediated phagocytosis, and modified their immunogenicity through an increased surface expression of HLA-I and reshuffling of their immunopeptidome. ZNF587 and ZNF417 are thus pro-oncogenic factors allowing for higher degrees of genetic instability through attenuation of replicative stress and secondary inflammation, an influence that likely facilitates the clonal expansion, diversification, and immune evasion of cancer cells.

Abstract FigureGraphical abstractIndividualized neoantigen vaccination against mammary carcinomaBackgroundHarnessing the immune system to purposely recognize and destroy tumours represents a significant breakthrough in clinical oncology. Nonsynonymous mutations (neoantigenic peptides) were identified as powerful cancer targets. This knowledge can be exploited for further improvements of active immunotherapies, including cancer vaccines as T cells specific for neoantigens are not attenuated by immune tolerance mechanism and do not harm healthy tissues. The current study aimed at developing an optimized multi-target vaccine using short or long neoantigenic peptides utilizing virus-like particles (VLPs) as an efficient vaccine platform.MethodsHere we identified mutations of murine mammary carcinoma cells by integrating mass spectrometry-based immunopeptidomics and whole exome sequencing. Neoantigenic peptides were synthesized and covalently linked to virus-like nanoparticles using a Cu-free click-chemistry method for easy preparation of vaccines against mouse mammary carcinoma.ResultsAs compared to short peptides, vaccination with long peptides was superior in the generation of neoantigen-specific CD4+ and CD8+ T cells which readily produced IFN-γ and TNF-α. The resulting anti-tumour effect was associated with favourable immune re-polarization in the tumour microenvironment through reduction of myeloid-derived suppressor cells. Vaccination with long neoantigenic peptides also decreased post-surgical tumour recurrence and metastases, and prolonged mouse survival, despite the tumour’s low mutational burden.ConclusionIntegrating mass spectrometry-based immunopeptidomics and whole exome-sequencing is an efficient technique for identifying neoantigenic peptides. A multi-target VLP-based vaccine shows a promising anti-tumour results in an aggressive murine mammary carcinoma cell line. Future clinical application using this strategy is readily feasible and practical, as click-chemistry coupling of personalized synthetic peptides to the nanoparticles can be done at the bedside directly before injection.

Mass spectrometry (MS) is the state-of-the-art methodology for capturing the breadth and depth of the immunopeptidome across human leukocyte antigen (HLA) allotypes and cell types. The majority of studies in the immunopeptidomics field are discovery driven. Hence, data-dependent tandem MS (MS/MS) acquisition (DDA) is widely used, as it generates high-quality references of peptide fingerprints. However, DDA suffers from the stochastic selection of abundant ions that impairs sensitivity and reproducibility. In contrast, in data-independent acquisition (DIA), the systematic fragmentation and acquisition of all fragment ions within given isolation m/z windows yield a comprehensive map for a given sample. However, many DIA approaches commonly require generating comprehensive DDA-based spectrum libraries, which can become impractical for studying noncanonical and personalized neoantigens. Because the amount of HLA peptides eluted from biological samples such as small tissue biopsies is typically not sufficient for acquiring both meaningful DDA data necessary for generating comprehensive spectral libraries and DIA MS measurements, the implementation of DIA in the immunopeptidomics translational research domain has remained limited. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy by matching DIA data against libraries with growing complexity—from sample-specific libraries to libraries combining 2 to 40 different immunopeptidomics samples. Analyzing DIA immunopeptidomics data against a complex multi-HLA spectral library resulted in a two-fold increase in peptide identification compared with sample-specific library and in a three-fold increase compared with DDA measurements, yet with no detrimental effect on the specificity. Furthermore, we demonstrated the implementation of DIA for sensitive personalized neoantigen discovery through the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands. We conclude that a comprehensive multi-HLA library for DIA approach in combination with MS/MS prediction is highly advantageous for clinical immunopeptidomics, especially when low amounts of biological samples are available., Graphical Abstract, Highlights • So far, DIA in the immunopeptidomics translational research has been limited. • DIA immunopeptidomics data were analyzed against a complex multi-HLA spectral library. • This resulted in improved sensitivity with no detrimental effect on the specificity. • We implemented DIA for clinical antigen discovery combined with predicted MS/MS spectra., In Brief The implementation of DIA in the immunopeptidomics translational research domain has remained limited because the amount of HLA peptides eluted from clinical samples is typically not sufficient for acquiring both meaningful DDA data for generating comprehensive spectral libraries and DIA MS measurements. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy with libraries of growing complexity and multi-HLA libraries. In addition, we demonstrated the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands.

A high- affinity MHC I-peptide interaction is considered essential for immunogenicity. However, some neoepitopes with low affinities for MHC have been reported to elicit CD8-dependent tumor rejection in immunization-challenge studies. Here, we ask if a non-binder, tumor-rejection- mediating neoepitope influences the natural immunogenicity of a tumor in vivo, in the absence of artificial immunization. A mutation in tumor MUT1 was edited to its WT counterpart; the mutation was then re-introduced into the WT tumor, recapitulating the mutation in a tumor MUT2. TILs from all three tumors show T cell activation. However, TILs of MUT1 and MUT2 show significantly stronger transcriptional signatures of cytotoxicity and TCR engagement as well as the greater breadth of TCR reactivity than those of WT. Structural modeling of the Kd-neoepitope complex suggests increased hydrophobicity of the neoepitope surface consistent with higher TCR reactivity. These results reveal the immunogenicity in vivo of low affinity or “non-binding” epitopes that do not follow the canonical view of MHC I-peptide recognition.

CD4+ T cell responses are crucial for inducing and maintaining effective anticancer immunity, and the identification of human leukocyte antigen class II (HLA-II) cancer-specific epitopes is key to the development of potent cancer immunotherapies. In many tumor types, and especially in glioblastoma (GBM), HLA-II complexes are hardly ever naturally expressed. Hence, little is known about immunogenic HLA-II epitopes in GBM. With stable expression of the class II major histocompatibility complex transactivator (CIITA) coupled to a detailed and sensitive mass spectrometry–based immunopeptidomics analysis, we here uncovered a remarkable breadth of the HLA-ligandome in HROG02, HROG17, and RA GBM cell lines. The effect of CIITA expression on the induction of the HLA-II presentation machinery was striking in each of the three cell lines, and it was significantly higher compared with interferon gamma (IFNɣ) treatment. In total, we identified 16,123 unique HLA-I peptides and 32,690 unique HLA-II peptides. In order to genuinely define the identified peptides as true HLA ligands, we carefully characterized their association with the different HLA allotypes. In addition, we identified 138 and 279 HLA-I and HLA-II ligands, respectively, most of which are novel in GBM, derived from known GBM-associated tumor antigens that have been used as source proteins for a variety of GBM vaccines. Our data further indicate that CIITA-expressing GBM cells acquired an antigen presenting cell-like phenotype as we found that they directly present external proteins as HLA-II ligands. Not only that CIITA-expressing GBM cells are attractive models for antigen discovery endeavors, but also such engineered cells have great therapeutic potential through massive presentation of a diverse antigenic repertoire., Graphical Abstract, Highlights • CIITA-expressing glioblastoma cells express high levels of HLA-II complexes. • CIITA induces peptide presentation across the HLA-DP, -DQ, and -DR allotypes. • Many clinically relevant glioblastoma-associated tumor antigens were detected. • Such cells may have great therapeutic potential in clinical settings., In Brief CD4+ T cell responses are crucial for inducing and maintaining effective anticancer immunity; however, in glioblastoma and many solid tumors, HLA-II complexes are hardly ever naturally expressed. Hence, little is known about immunogenic HLA-II epitopes in glioblastoma. With stable expression of the class II major histocompatibility complex transactivator (CIITA) coupled to a detailed immunopeptidomics, we uncovered a remarkable breadth of the HLA-ligandome in three glioblastoma cell lines and identified many cancer-associated ligands with implications for the development of cancer immunotherapies.

The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work forin-vivoidentification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles, and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. This data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.

Neoepitopes are the only truly tumor-specific antigens. Although potential neoepitopes can be readily identified using genomics, the neoepitopes that mediate tumor rejection constitute a small minority, and there is little consensus on how to identify them. Here, for the first time to our knowledge, we use a combination of genomics, unbiased discovery mass spectrometry (MS) immunopeptidomics, and targeted MS to directly identify neoepitopes that elicit actual tumor rejection in mice. We report that MS-identified neoepitopes are an astonishingly rich source of tumor rejection-mediating neoepitopes (TRMNs). MS has also demonstrated unambiguously the presentation by MHC I, of confirmed tumor rejection neoepitopes that bind weakly to MHC I; this was done using DCs exogenously loaded with long peptides containing the weakly binding neoepitopes. Such weakly MHC I–binding neoepitopes are routinely excluded from analysis, and our demonstration of their presentation, and their activity in tumor rejection, reveals a broader universe of tumor-rejection neoepitopes than presently imagined. Modeling studies show that a mutation in the active neoepitope alters its conformation such that its T cell receptor–facing surface is substantially altered, increasing its exposed hydrophobicity. No such changes are observed in the inactive neoepitope. These results broaden our understanding of antigen presentation and help prioritize neoepitopes for personalized cancer immunotherapy., Neoepitopes identified by mass spectrometry are a rich source of tumor rejection antigens, including those with a weak binding to MHC I.

The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. These data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.

Despite the promising therapeutic effects of immune checkpoint blockade (ICB), most patients with solid tumors treated with anti-PD-1/PD-L1 monotherapy do not achieve objective responses, with most tumor regressions being partial rather than complete. It is hypothesized that the absence of pre-existing antitumor immunity and/or the presence of additional tumor immune suppressive factors at the tumor microenvironment are responsible for such therapeutic failures. It is therefore clear that in order to fully exploit the potential of PD-1 blockade therapy, antitumor immune response should be amplified, while tumor immune suppression should be further attenuated. Cancer vaccines may prime patients for treatments with ICB by inducing effective anti-tumor immunity, especially in patients lacking tumor-infiltrating T-cells. These “non-inflamed” non-permissive tumors that are resistant to ICB could be rendered sensitive and transformed into “inflamed” tumor by vaccination. In this article we describe a clinical study where we use pancreatic cancer as a model, and we hypothesize that effective vaccination in pancreatic cancer patients, along with interventions that can reprogram important immunosuppressive factors in the tumor microenvironment, can enhance tumor immune recognition, thus enhancing response to PD-1/PD-L1 blockade. We incorporate into the schedule of standard of care (SOC) chemotherapy adjuvant setting a vaccine platform comprised of autologous dendritic cells loaded with personalized neoantigen peptides (PEP-DC) identified through our own proteo-genomics antigen discovery pipeline. Furthermore, we add nivolumab, an antibody against PD-1, to boost and maintain the vaccine's effect. We also demonstrate the feasibility of identifying personalized neoantigens in three pancreatic ductal adenocarcinoma (PDAC) patients, and we describe their optimal incorporation into long peptides for manufacturing into vaccine products. We finally discuss the advantages as well as the scientific and logistic challenges of such an exploratory vaccine clinical trial, and we highlight its novelty.

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.

Comprehensive knowledge of the human leukocyte antigen (HLA) class-I and class-II peptides presented to T-cells is crucial for designing innovative therapeutics against cancer and other diseases. However methodologies for their purification for mass-spectrometry analysis have been a major limitation. We designed a novel high-throughput, reproducible and sensitive method for sequential immuno-affinity purification of HLA-I and -II peptides from up to 96 samples in a plate format, suitable for both cell lines and tissues. Our methodology drastically reduces sample-handling and can be completed within five hours. We challenged our methodology by extracting HLA peptides from multiple replicates of tissues (n = 7) and cell lines (n = 21, 10 8 cells per replicate), which resulted in unprecedented depth, sensitivity and high reproducibility (Pearson correlations up to 0.98 and 0.97 for HLA-I and HLA-II). Because of the method's achieved sensitivity, even single measurements of peptides purified from 10 7 B-cells resulted in the identification of more than 1700 HLA-I and 2200 HLA-II peptides. We demonstrate the feasibility of performing drug-screening by using ovarian cancer cells treated with interferon gamma (IFNγ). Our analysis revealed an augmented presentation of chymotryptic-like and longer ligands associated with IFNγ induced changes of the antigen processing and presentation machinery. This straightforward method is applicable for basic and clinical applications.

Using tandem mass spectroscopy (MS), we identified 3646 unique sequences among peptides eluted from purified Kd and Dd MHC I molecules of the BALB/c fibrosarcoma Meth A. These peptides were cross-referenced with the output of neoepitopes predicted for this tumor by our prediction pipeline CCCP (Cross Consensus Calling Platform). Eleven (11) of the eluted peptides were identified as neoepitopes and eight neoepitopes (of 11) were confirmed by targeted MS. Each neoepitope was used to immunize BALB/c mice (twice, one week apart, using precise neoepitopes along with bone marrow-derived dendritic cells); mice were challenged with Meth A cells one week after the last immunization, and tumor growth was monitored in individual mice. In parallel, immunized mice were tested for CD8+ T cells to the neoepitopes using tetramer staining and interferon g secretion by CD8 cells. Four of the eight neoepitopes elicited rejection of Meth A fibrosarcoma; two of the four neoepitopes elicited highly potent tumor rejection, while the other two elicited statistically significant but weaker tumor rejection. Of the two strong neoepitopes, only one elicited a measurable CD8 response. Both weak neoepitopes elicited measurable CD8 responses. Of the four neoepitopes that did not elicit tumor rejection, only one elicited a measurable CD8 response; this CD8 response was the strongest of all CD8 responses detected. These observations indicate that MS-defined neoepitopes can be a rich source of neoepitopes that can mediate tumor rejection. Further, they highlight the fact that CD8 responses are not a good predictive surrogates for tumor rejection.

Cancer immunotherapy amplifies the inherent capacity of cytotoxic T cells to eliminate tumor cells by recognizing human leukocyte antigen-binding peptides (HLAp) presented specifically on tumors but not on normal cells. Recent data show that recognition of mutated neoantigens plays a key role. Currently, mass spectrometry (MS) is the only unbiased methodology to comprehensively uncover the repertoire of HLAp presented in vivo. Recently, we have developed an in-depth MS-based immunopeptidomics approach combined with exome sequencing analysis to directly identify neoantigens from human melanoma tumors (Bassani-Sternberg et al., Nat Commun 2016). This approach is not feasible for substantial number of patients for whom not enough tissue material is available. So far, the discovery of neoantigens relies mainly on prediction-based interrogation of the "mutanome." In a proof-of-concept study we showed that incorporation of deconvoluted HLAp data in ligand prediction algorithms can improve their accuracy (Bassani-Sternberg and Gfeller, J Immunol 2016). In addition, we assembled a comprehensive HLAp database in term of number of peptides and diversity of HLA-I molecules. We showed that by taking advantage of co-occurring HLA-I alleles, we can rapidly and accurately identify HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our novel and scalable approach uncovers new motifs for several alleles that up to now had no known ligands. HLA-ligand predictors trained on such data substantially improve neoantigen predictions in four melanoma and two lung cancer patients, indicating that unbiased HLAp data are ideal for in silico identification of neoantigens (Bassani-Sternberg et al., PLOS Comp Biol 2017). Furthermore, our immunopeptidomics database revealed hotspots that are sub-sequences of proteins frequently presented. We introduce two immunopeptidomics MS-based features to guide prioritization of neoantigens: the number of peptides matching a protein in our database and the overlap of the predicted wild-type peptide with other peptides in our database. We show as a proof of concept that our immunopeptidomics MS-based features improved neoantigen prioritization by up to 50% (Mueller et al., submitted). Overall, our work shows that, in addition to providing huge training data to improve the HLA binding prediction, immunopeptidomics also captures other aspects of the natural in vivo presentation that significantly improve prediction of clinically relevant neoantigens. Citation Format: Michal Bassani-Sternberg, Chloe Chong, Fabio Marino, HuiSong Pak, David Gfeller, Markus Müller, George Coukos. Immunopeptidomics: Accelerating the development of personalized cancer immunotherapy [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr PL02-02.

Efforts to precisely identify tumor human leukocyte antigen (HLA) bound peptides capable of mediating T cell-based tumor rejection still face important challenges. Recent studies suggest that non-canonical tumor-specific HLA peptides derived from annotated non-coding regions could elicit anti-tumor immune responses. However, sensitive and accurate mass spectrometry (MS)-based proteogenomics approaches are required to robustly identify these non-canonical peptides. We present an MS-based analytical approach that characterizes the non-canonical tumor HLA peptide repertoire, by incorporating whole exome sequencing, bulk and single-cell transcriptomics, ribosome profiling, and two MS/MS search tools in combination. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides, including an immunogenic peptide derived from an open reading frame downstream of the melanoma stem cell marker gene ABCB5. These findings hold great promise for the discovery of previously unknown tumor antigens for cancer immunotherapy., Non-canonical HLA-bound peptides from presumed non-coding regions are potential targets for cancer immunotherapy, but their discovery remains challenging. Here, the authors integrate exome sequencing, transcriptomics, ribosome profiling, and immunopeptidomics to identify tumor-specific non-canonical HLA-bound peptides.