Human Leukocyte Antigen (HLA) Peptides Derived from Tumor Antigens Induced by Inhibition of DNA Methylation for Development of Drug-facilitated Immunotherapy

Targeting transposable elements in cancer: Developments and opportunities

Transposable elements (TEs), comprising nearly 50% of the human genome, have transitioned from being perceived as "genomic junk" to key players in cancer progression. Contemporary research links TE regulatory disruptions with cancer development, underscoring their therapeutic potential. Advances in long-read sequencing, computational analytics, single-cell sequencing, proteomics, and CRISPR-Cas9 technologies have enriched our understanding of TEs' clinical implications, notably their impact on genome architecture, gene regulation, and evolutionary processes. In cancer, TEs, including LINE-1, Alus, and LTRs, demonstrate altered patterns, influencing both tumorigenic and tumor-suppressive mechanisms. TE-derived nucleic acids and tumor antigens play critical roles in tumor immunity, bridging innate and adaptive responses. Given their central role in oncology, TE-targeted therapies, particularly through reverse transcriptase inhibitors and epigenetic modulators, represent a novel avenue in cancer treatment. Combining these TE-focused strategies with existing chemotherapy or immunotherapy regimens could enhance efficacy and offer a new dimension in cancer treatment. This review delves into recent TE detection advancements, explores their multifaceted roles in tumorigenesis and immune regulation, discusses emerging diagnostic and therapeutic approaches centered on TEs, and anticipates future directions in cancer research.

Koina: Democratizing machine learning for proteomics research

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

Using long-read CAGE sequencing to profile cryptic-promoter-derived transcripts and their contribution to the immunopeptidome

Recent studies have shown that the noncoding genome can produce unannotated proteins as antigens that induce immune response. One major source of this activity is the aberrant epigenetic reactivation of transposable elements (TEs). In tumors, TEs often provide cryptic or alternate promoters, which can generate transcripts that encode tumor-specific unannotated proteins. Thus, TE-derived transcripts (TE transcripts) have the potential to produce tumor-specific, but recurrent, antigens shared among many tumors. Identification of TE-derived tumor antigens holds the promise to improve cancer immunotherapy approaches; however, current genomics and computational tools are not optimized for their detection. Here we combined CAGE technology with full-length long-read transcriptome sequencing (long-read CAGE, or LRCAGE) and developed a suite of computational tools to significantly improve immunopeptidome detection by incorporating TE and other tumor transcripts into the proteome database. By applying our methods to human lung cancer cell line H1299 data, we show that long-read technology significantly improves mapping of promoters with low mappability scores and that LRCAGE guarantees accurate construction of uncharacterized 5' transcript structure. Augmenting a reference proteome database with newly characterized transcripts enabled us to detect noncanonical antigens from HLA-pulldown LC-MS/MS data. Lastly, we show that epigenetic treatment increased the number of noncanonical antigens, particularly those encoded by TE transcripts, which might expand the pool of targetable antigens for cancers with low mutational burden.

The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I-Associated Peptides

Tumor antigens can emerge through multiple mechanisms, including translation of noncoding genomic regions. This noncanonical category of tumor antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry (MS) to enable the discovery of noncanonical MHC class I-associated peptides (ncMAP) from noncoding regions. Considering that the emergence of tumor antigens can also involve posttranslational modifications (PTM), we included an open search component in our pipeline. Leveraging the wealth of MS-based immunopeptidomics, we analyzed data from 26 MHC class I immunopeptidomic studies across 11 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant PTMs, using spectral matching and controlled their FDR to 1%. The noncanonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 55%. The data reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive therapeutic targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targets for T-cell therapies or vaccine development.

The biogenesis of the immunopeptidome

The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.

The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.

Engineered tumor-specific T cells using immunostimulatory photothermal nanoparticles

BACKGROUND

Adoptive T cell therapy (ATCT) has been successful in treating hematological malignancies and is currently under investigation for solid-tumor therapy. In contrast to existing chimeric antigen receptor (CAR) T cell and/or antigen-specific T cell approaches, which require known targets, and responsive to the need for targeting a broad repertoire of antigens in solid tumors, we describe the first use of immunostimulatory photothermal nanoparticles to generate tumor-specific T cells.

METHODS

Specifically, we subject whole tumor cells to Prussian blue nanoparticle-based photothermal therapy (PBNP-PTT) before culturing with dendritic cells (DCs), and subsequent stimulation of T cells. This strategy differs from previous approaches using tumor cell lysates because we use nanoparticles to mediate thermal and immunogenic cell death in tumor cells, rendering them enhanced antigen sources.

RESULTS

In proof-of-concept studies using two glioblastoma (GBM) tumor cell lines, we first demonstrated that when PBNP-PTT was administered at a "thermal dose" targeted to induce the immunogenicity of U87 GBM cells, we effectively expanded U87-specific T cells. Further, we found that DCs cultured ex vivo with PBNP-PTT-treated U87 cells enabled 9- to 30-fold expansion of CD4+ and CD8+ T cells. Upon co-culture with target U87 cells, these T cells secreted interferon-ɣ in a tumor-specific and dose-dependent manner (up to 647-fold over controls). Furthermore, T cells manufactured using PBNP-PTT ex vivo expansion elicited specific cytolytic activity against target U87 cells (donor-dependent 32-93% killing at an effector to target cell (E:T) ratio of 20:1) while sparing normal human astrocytes and peripheral blood mononuclear cells from the same donors. In contrast, T cells generated using U87 cell lysates expanded only 6- to 24-fold and killed 2- to 3-fold less U87 target cells at matched E:T ratios compared with T cell products expanded using the PBNP-PTT approach. These results were reproducible even when a different GBM cell line (SNB19) was used, wherein the PBNP-PTT-mediated approach resulted in a 7- to 39-fold expansion of T cells, which elicited 25-66% killing of the SNB19 cells at an E:T ratio of 20:1, depending on the donor.

CONCLUSIONS

These findings provide proof-of-concept data supporting the use of PBNP-PTT to stimulate and expand tumor-specific T cells ex vivo for potential use as an adoptive T cell therapy approach for the treatment of patients with solid tumors.

Precision Neoantigen Discovery Using Large-Scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation

Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anticancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past 2 decades. However, improvement in the accuracy of prediction algorithms is needed for clinical applications like the development of personalized cancer vaccines, the discovery of biomarkers for response to immunotherapies, and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 monoallelic cell lines and created Systematic Human Leukocyte Antigen (HLA) Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale monoallelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA allele to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC diversity in the training data and extend allelic coverage in underprofiled populations. To improve generalizability, SHERPA systematically integrates 128 monoallelic and 384 multiallelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution, and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44-fold improvement of positive predictive value compared with existing tools when evaluated on independent monoallelic datasets and a 1.17-fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.

The impact of immunopeptidomics: From basic research to clinical implementation

The immunopeptidome is the set of peptides presented by the major histocompatibility complex (MHC) molecules, in humans also known as the human leukocyte antigen (HLA), on the surface of cells that mediate T-cell immunosurveillance. The immunopeptidome is a sampling of the cellular proteome and hence it contains information about the health state of cells. The peptide repertoire is influenced by intra- and extra-cellular perturbations - such as in the case of drug exposure, infection, or oncogenic transformation. Immunopeptidomics is the bioanalytical method by which the presented peptides are extracted from biological samples and analyzed by high-performance liquid chromatography coupled to tandem mass spectrometry (MS), resulting in a deep qualitative and quantitative snapshot of the immunopeptidome. In this review, we discuss published immunopeptidomics studies from recent years, grouped into three main domains: i) basic, ii) pre-clinical and iii) clinical research and applications. We review selected fundamental immunopeptidomics studies on the antigen processing and presentation machinery, on HLA restriction and studies that advanced our understanding of various diseases, and how exploration of the antigenic landscape allowed immune targeting at the pre-clinical stage, paving the way to pioneering exploratory clinical trials where immunopeptidomics is directly implemented in the conception of innovative treatments for cancer patients.

Activated T cell therapy targeting glioblastoma cancer stem cells

Naïve T cells become effector T cells following stimulation by antigen-loaded dendritic cells (DCs) and sequential cytokine activation. We aimed to develop procedures to efficiently activate T cells with tumor-associated antigens (TAAs) to glioblastoma (GBM) stem cells. To remove antigen presentation outside of the immunosuppressive tumor milieu, three different glioma stem cell (GSC) specific antigen sources to load DCs were compared in their ability to stimulate lymphocytes. An activated T cell (ATC) protocol including cytokine activation and expansion in culture to target GSCs was generated and optimized for a planned phase I clinical trial. We compared three different antigen-loading methods on DCs to effectively activate T cells, which were GBM patient-derived GSC-lysate, acid-eluate of GSCs and synthetic peptides derived from proteins expressed in GSCs. DCs derived from HLA-A2 positive blood sample were loaded with TAAs. Autologous T cells were activated by co-culturing with loaded DCs. Efficiency and cytotoxicity of ATCs were evaluated by targeting TAA-pulsed DCs or T2 cells, GSCs, or autologous PHA-blasts. Characteristics of ATCs were evaluated by Flow Cytometry and ELISpot assay, which showed increased number of ATCs secreting IFN-γ targeting GSCs as compared with non-activated T cells and unloaded target cells. Neither GSC-lysate nor acid-eluate loading showed enhancement in response of ATCs but the synthetic peptide pool showed significantly increased IFN-γ secretion and increased cytotoxicity towards target cells. These results demonstrate that ATCs activated using a TAA synthetic peptide pool efficiently enhance cytotoxicity specifically to target cells including GSC.

Decitabine increases neoantigen and cancer testis antigen expression to enhance T-cell-mediated toxicity against glioblastoma

BACKGROUND

Glioblastoma (GBM) is the most common and malignant primary brain tumor in adults. Despite maximal treatment, median survival remains dismal at 14-24 months. Immunotherapies, such as checkpoint inhibition, have revolutionized management of some cancers but have little benefit for GBM patients. This is, in part, due to the low mutational and neoantigen burden in this immunogenically "cold" tumor.

METHODS

U87MG and patient-derived cell lines were treated with 5-aza-2'-deoxycytidine (DAC) and underwent whole-exome and transcriptome sequencing. Cell lines were then subjected to cellular assays with neoantigen and cancer testis antigen (CTA) specific T cells.

RESULTS

We demonstrate that DAC increases neoantigen and CTA mRNA expression through DNA hypomethylation. This results in increased neoantigen presentation by MHC class I in tumor cells, leading to increased neoantigen- and CTA-specific T-cell activation and killing of DAC-treated cancer cells. In addition, we show that patients have endogenous cancer-specific T cells in both tumor and blood, which show increased tumor-specific activation in the presence of DAC-treated cells.

CONCLUSIONS

Our work shows that DAC increases GBM immunogenicity and consequent susceptibility to T-cell responses in vitro. Our results support a potential use of DAC as a sensitizing agent for immunotherapy.

Sources of Cancer Neoantigens beyond Single-Nucleotide Variants

The success of checkpoint blockade therapy against cancer has unequivocally shown that cancer cells can be effectively recognized by the immune system and eliminated. However, the identity of the cancer antigens that elicit protective immunity remains to be fully explored. Over the last decade, most of the focus has been on somatic mutations derived from non-synonymous single-nucleotide variants (SNVs) and small insertion/deletion mutations (indels) that accumulate during cancer progression. Mutated peptides can be presented on MHC molecules and give rise to novel antigens or neoantigens, which have been shown to induce potent anti-tumor immune responses. A limitation with SNV-neoantigens is that they are patient-specific and their accurate prediction is critical for the development of effective immunotherapies. In addition, cancer types with low mutation burden may not display sufficient high-quality [SNV/small indels] neoantigens to alone stimulate effective T cell responses. Accumulating evidence suggests the existence of alternative sources of cancer neoantigens, such as gene fusions, alternative splicing variants, post-translational modifications, and transposable elements, which may be attractive novel targets for immunotherapy. In this review, we describe the recent technological advances in the identification of these novel sources of neoantigens, the experimental evidence for their presentation on MHC molecules and their immunogenicity, as well as the current clinical development stage of immunotherapy targeting these neoantigens.

Single-cell RNA-seq-based proteogenomics identifies glioblastoma-specific transposable elements encoding HLA-I-presented peptides

We analyze transposable elements (TEs) in glioblastoma (GBM) patients using a proteogenomic pipeline that combines single-cell transcriptomics, bulk RNA sequencing (RNA-seq) samples from tumors and healthy-tissue cohorts, and immunopeptidomic samples. We thus identify 370 human leukocyte antigen (HLA)-I-bound peptides encoded by TEs differentially expressed in GBM. Some of the peptides are encoded by repeat sequences from intact open reading frames (ORFs) present in up to several hundred TEs from recent long interspersed nuclear element (LINE)-1, long terminal repeat (LTR), and SVA subfamilies. Other HLA-I-bound peptides are encoded by single copies of TEs from old subfamilies that are expressed recurrently in GBM tumors and not expressed, or very infrequently and at low levels, in healthy tissues (including brain). These peptide-coding, GBM-specific, highly recurrent TEs represent potential tumor-specific targets for cancer immunotherapies.

Identification of tumor antigens with immunopeptidomics

The identification of actionable tumor antigens is indispensable for the development of several cancer immunotherapies, including T cell receptor-transduced T cells and patient-specific mRNA or peptide vaccines. Most known tumor antigens have been identified through extensive molecular characterization and are considered canonical if they derive from protein-coding regions of the genome. By eluting human leukocyte antigen-bound peptides from tumors and subjecting these to mass spectrometry analysis, the peptides can be identified by matching the resulting spectra against reference databases. Recently, mass-spectrometry-based immunopeptidomics has enabled the discovery of noncanonical antigens-antigens derived from sequences outside protein-coding regions or generated by noncanonical antigen-processing mechanisms. Coupled with transcriptomics and ribosome profiling, this method enables the identification of thousands of noncanonical peptides, of which a substantial fraction may be detected exclusively in tumors. Spectral matching against the immense noncanonical reference may generate false positives. However, sensitive mass spectrometry, analytical validation and advanced bioinformatics solutions are expected to uncover the full landscape of presented antigens and clinically relevant targets.

Pharmacological strategies for improving the prognosis of glioblastoma

Introduction: Treatments for brain cancer have radically evolved in the past decade due to a better understanding of the interplay between the immune system and tumors of the central nervous system (CNS). However, glioblastoma multiforme (GBM) remains the most common and lethal CNS malignancy affecting adults.Areas covered: The authors review the literature on glioblastoma pharmacologic therapies with a focus on trials of combination chemo-/immunotherapies and drug delivery platforms from 2015 to 2021.Expert opinion: Few therapeutic advances in GBM treatment have been made since the Food and Drug Administration (FDA) approval of the BCNU-eluting wafer, Gliadel, in 1996 and oral temozolomide (TMZ) in 2005. Recent advances in our understanding of GBM have promoted a wide assortment of new therapeutic approaches including combination therapy, immunotherapy, vaccines, and Car T-cell therapy along with developments in drug delivery. Given promising preclinical data, these novel pharmacotherapies for the treatment of GBM are currently being evaluated in various stages of clinical trials.

Overexpression of transposable elements is associated with immune evasion and poor outcome in colorectal cancer

AIM

High immune cell infiltration of the tumour microenvironment is generally associated with a good prognosis in solid cancers. However, a subset of patients with colorectal cancer (CRC) tumours with high immune cell infiltration have a poor outcome. These tumours have a high level of T cell infiltration and are also characterised by increased expression of programmed death-ligand 1 (PD-L1). As these tumours comprise both microsatellite instability and microsatellite stable subtypes, the mechanism underlying this phenotype is unknown.

METHODS

Using RNA-seq data from The Cancer Genome Atlas, we quantified transposable element (TE) expression and developed a TE expression score that is predictive of prognosis and immune infiltration independent of microsatellite instability status and tumour staging in CRC.

RESULTS

Tumours with the highest TE expression score showed increased immune cell infiltration with upregulation of interferon (IFN) signalling pathways and downstream activation of IFN-simulated genes. As expected, cell lines treated with DNA methyltransferase inhibitor mimicked patient tumours with increased TE expression and IFN signalling. However, surprisingly, unlike high TE expressing CRC, there is little evidence for the activation of JAK-STAT signalling and PD-L1 expression in DNA methyltransferase inhibitor-treated cells. Single-cell RNA-seq analysis of CRC samples showed that PD-L1 expression is mainly confined to tumour-associated macrophages and T cells, suggesting that TE mediated IFN signalling is triggering expression of PD-L1 in immune cells rather than in tumour cells.

CONCLUSIONS

Our study uncovers a novel mechanism of TE driven immune evasion and highlights TE expression as an important factor for patient prognosis in CRC.

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.

Recent progress on MHC-I epitope prediction in tumor immunotherapy

Tumor immunotherapy has now become one of the most potential therapy for those intractable cancer diseases. The antigens on the cancer cell surfaces are the keys for the immune system to recognize and eliminate them. As reported, the immunogenicity of the tumor antigens could be determined by the binding between the key epitope peptides and MHC molecules. In recent years, the approaches to anticipate the peptides from the candidate epitopes have gradually changed into more efficient methods. Including the improved conventional methods, more diverse methods were coming into view. Here we review the anticipated methods of the tumor associated epitopes that specifically bind with major histocompatibility complex (MHC) class I molecules, and the recent advances and applications of those epitope prediction methods.

Withdrawn: Precision Neoantigen Discovery Using Large-scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation

This article has been withdrawn by the authors. A publication of the manuscript with the correct figures and tables has been approved and the authors state the conclusions of the manuscript remain unaffected. Specifically, errors are in Figure 6A, Supplementary Figure 10B, Supplementary Figure 10C, and Supplementary Table 5. The details of the errors are as follows: the HLA types for one sample were incorrectly assigned because of a tumor/normal mislabeling from the biobank vendor. Due to the differing HLA types between the tumor and normal sample, the sequence analysis established that the HLA alleles for this patient had been deleted (HLA LOH). The authors conclude that this was an artifact caused by the normal sample mislabeling. The corrected version can be accessed (Pyke, R.M., Mellacheruvu, D., Dea, S., Abbott, C.W., Zhang, S.V., Philips, N.A., Harris, J., Bartha, G., Desai, S., McClory, R., West, J., Snyder, M,P., Chen, R., Boyle, S.M. (2022) Precision Neoantigen Discovery Using Large-Scale Immunopeptidomics and Composite Modeling of MHC Peptide Presentation. Mol. Cell. Proteomics 22, 100506

IFNγ Modulates the Immunopeptidome of Triple Negative Breast Cancer Cells by Enhancing and Diversifying Antigen Processing and Presentation

Peptide vaccination remains a viable approach to induce T-cell mediated killing of tumors. To identify potential T-cell targets for Triple-Negative Breast Cancer (TNBC) vaccination, we examined the effect of the pro-inflammatory cytokine interferon-γ (IFNγ) on the transcriptome, proteome, and immunopeptidome of the TNBC cell line MDA-MB-231. Using high resolution mass spectrometry, we identified a total of 84,131 peptides from 9,647 source proteins presented by human leukocyte antigen (HLA)-I and HLA-II alleles. Treatment with IFNγ resulted in a remarkable remolding of the immunopeptidome, with only a 34% overlap between untreated and treated cells across the HLA-I immunopeptidome, and expression of HLA-II only detected on treated cells. IFNγ increased the overall number, diversity, and abundance of peptides contained within the immunopeptidome, as well increasing the coverage of individual source antigens. The suite of peptides displayed under conditions of IFNγ treatment included many known tumor associated antigens, with the HLA-II repertoire sampling 17 breast cancer associated antigens absent from those sampled by HLA-I molecules. Quantitative analysis of the transcriptome (10,248 transcripts) and proteome (6,783 proteins) of these cells revealed 229 common proteins and transcripts that were differentially expressed. Most of these represented downstream targets of IFNγ signaling including components of the antigen processing machinery such as tapasin and HLA molecules. However, these changes in protein expression did not explain the dramatic modulation of the immunopeptidome following IFNγ treatment. These results demonstrate the high degree of plasticity in the immunopeptidome of TNBC cells following cytokine stimulation and provide evidence that under pro-inflammatory conditions a greater variety of potential HLA-I and HLA-II vaccine targets are unveiled to the immune system. This has important implications for the development of personalized cancer vaccination strategies.

The origin of the expressed retrotransposed gene ACTBL2 and its influence on human melanoma cells' motility and focal adhesion formation

We have recently found that β-actin-like protein 2 (actbl2) forms complexes with gelsolin in human melanoma cells and can polymerize. Phylogenetic and bioinformatic analyses showed that actbl2 has a common origin with two non-muscle actins, which share a separate history from the muscle actins. The actin groups' divergence started at the beginning of vertebrate evolution, and actbl2 actins are characterized by the largest number of non-conserved amino acid substitutions of all actins. We also discovered that ACTBL2 is expressed at a very low level in several melanoma cell lines, but a small subset of cells exhibited a high ACTBL2 expression. We found that clones with knocked-out ACTBL2 (CR-ACTBL2) or overexpressing actbl2 (OE-ACTBL2) differ from control cells in the invasion, focal adhesion formation, and actin polymerization ratio, as well as in the formation of lamellipodia and stress fibers. Thus, we postulate that actbl2 is the seventh actin isoform and is essential for cell motility.

The Peptide Vaccine of the Future

The approach of peptide-based anticancer vaccination has proven the ability to induce cancer-specific immune responses in multiple studies for various cancer entities. However, clinical responses remain so far limited to single patients and broad clinical applicability was not achieved. Therefore, further efforts are required to improve peptide vaccination in order to integrate this low-side-effect therapy into the clinical routine of cancer therapy. To design clinically effective peptide vaccines in the future, different issues have to be addressed and optimized comprising antigen target selection as well as choice of optimal adjuvants and vaccination schedules. Furthermore, the combination of peptide-based vaccines with other immuno- and molecular targeted therapies as well as the development of predictive biomarkers could further improve efficacy. In this review, current approaches in the development of peptide-based vaccines and critical implications for optimal vaccine design are discussed.

Epigenetic modulation combined with PD-1/PD-L1 blockade enhances immunotherapy based on MAGE-A11 antigen-specific CD8+T cells against esophageal carcinoma

Cancer testis antigens (CTAs) are promising targets for T cell-based immunotherapy and studies have shown that certain CT genes are epigenetically depressed in cancer cells through DNA demethylation. Melanoma-associated antigen A11 (MAGE-A11) is a CTA that is frequently expressed in esophageal cancer and is correlated with a poor esophageal cancer prognosis. Consequently, MAGE-A11 is a potential immunotherapy target. In this study, we evaluated MAGE-A11 expression in esophageal cancer cells and found that it was downregulated in several tumor cell lines, which restricted the effect of immunotherapy. Additionally, the specific recognition and lytic potential of cytotoxic T lymphocytes (CTLs) derived from the MAGE-A11 was determined. Specific CTLs could kill esophageal cancer cells expressing MAGE-A11 but rarely lysed MAGE-A11-negative tumor cells. Therefore, induction of MAGE-A11 expression is critical for CTLs recognition and lysis of esophageal cancer cells. Treatment with the DNA methyltransferase inhibitor 5-aza-2'-deoxycytidine increased MAGE-A11 expression in esophageal cancer cells and subsequently enhanced the cytotoxicity of MAGE-A11-specific CD8+T cells against cancer cell lines. Furthermore, we found that PD-L1 expression in esophageal cancer cells affected the antitumor function of CTLs. programmed death-1 (PD-1)/PD-L1 blockade could increase the specific CTL-induced lysis of HLA-A2+/MAGE-A11+ tumor cell lines treated with 5-aza-2'-deoxycytidine. These findings indicate that the treatment of tumor cells with the DNA methyltransferase inhibitor 5-aza-2'-deoxycytidine augments MAGE-A11 expression in esophageal cancer cells. The combination of epigenetic modulation by 5-aza-2'-deoxycytidine and PD-1/PD-L1 blockade may be useful for T cell-based immunotherapy against esophageal cancer.

Research on the Human Proteome Reaches a Major Milestone: >90% of Predicted Human Proteins Now Credibly Detected, According to the HUPO Human Proteome Project

According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020-01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019-01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020-01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.

Identification of Tumor Antigens in the HLA Peptidome of Patient-derived Xenograft Tumors in Mouse

Personalized cancer immunotherapy targeting patient-specific cancer/testis antigens (CTA) and neoantigens may benefit from large-scale tumor human leukocyte antigen (HLA) peptidome (immunopeptidome) analysis, which aims to accurately identify antigens presented by tumor cells. Although significant efforts have been invested in analyzing the HLA peptidomes of fresh tumors, it is often impossible to obtain sufficient volumes of tumor tissues for comprehensive HLA peptidome characterization. This work attempted to overcome some of these obstacles by using patient-derived xenograft tumors (PDX) in mice as the tissue sources for HLA peptidome analysis. PDX tumors provide a proxy for the expansion of the patient tumor by re-grafting them through several passages to immune-compromised mice. The HLA peptidomes of human biopsies were compared with those derived from PDX tumors. Larger HLA peptidomes were obtained from the significantly larger PDX tumors as compared with the patient biopsies. The HLA peptidomes of different PDX tumors derived from the same source tumor biopsy were very reproducible, even following subsequent passages to new naïve mice. Many CTA-derived HLA peptides were discovered, as well as several potential neoantigens/variant sequences. Taken together, the use of PDX tumors for HLA peptidome analysis serves as a highly expandable and stable source of reproducible and authentic peptidomes, opening up new opportunities for defining large HLA peptidomes when only small tumor biopsies are available. This approach provides a large source for tumor antigens identification, potentially useful for personalized immunotherapy.

New directions in chimeric antigen receptor T cell [CAR-T] therapy and related flow cytometry

Chimeric antigen receptor (CAR) T cells provide a promising approach to the treatment of hematologic malignancies and solid tumors. Flow cytometry is a powerful analytical modality, which plays an expanding role in all stages of CAR T therapy, from lymphocyte collection, to CAR T cell manufacturing, to in vivo monitoring of the infused cells and evaluation of their function in the tumor environment. Therefore, a thorough understanding of the new directions is important for designing and implementing CAR T-related flow cytometry assays in the clinical and investigational settings. However, the speed of new discoveries and the multitude of clinical and preclinical trials make it challenging to keep up to date in this complex field. In this review, we summarize the current state of CAR T therapy, highlight the areas of emergent research, discuss applications of flow cytometry in modern cell therapy, and touch upon several considerations particular to CAR detection and assessing the effectiveness of CAR T therapy.

Integrative -omics and HLA-ligandomics analysis to identify novel drug targets for ccRCC immunotherapy

Background

Clear cell renal cell carcinoma (ccRCC) is the dominant subtype of renal cancer. With currently available therapies, cure of advanced and metastatic ccRCC is achieved only in rare cases. Here, we developed a workflow integrating different -omics technologies to identify ccRCC-specific HLA-presented peptides as potential drug targets for ccRCC immunotherapy.

Methods

We analyzed HLA-presented peptides by MS-based ligandomics of 55 ccRCC tumors (cohort 1), paired non-tumor renal tissues, and 158 benign tissues from other organs. Pathways enriched in ccRCC compared to its cell type of origin were identified by transcriptome and gene set enrichment analyses in 51 tumor tissues of the same cohort. To retrieve a list of candidate targets with involvement in ccRCC pathogenesis, ccRCC-specific pathway genes were intersected with the source genes of tumor-exclusive peptides. The candidates were validated in an independent cohort from The Cancer Genome Atlas (TCGA KIRC, n = 452). DNA methylation (TCGA KIRC, n = 273), somatic mutations (TCGA KIRC, n = 392), and gene ontology (GO) and correlations with tumor metabolites (cohort 1, n = 30) and immune-oncological markers (cohort 1, n = 37) were analyzed to characterize regulatory and functional involvements. CD8⁺ T cell priming assays were used to identify immunogenic peptides. The candidate gene EGLN3 was functionally investigated in cell culture.

Results

A total of 34,226 HLA class I- and 19,325 class II-presented peptides were identified in ccRCC tissue, of which 443 class I and 203 class II peptides were ccRCC-specific and presented in ≥ 3 tumors. One hundred eighty-five of the 499 corresponding source genes were involved in pathways activated by ccRCC tumors. After validation in the independent cohort from TCGA, 113 final candidate genes remained. Candidates were involved in extracellular matrix organization, hypoxic signaling, immune processes, and others. Nine of the 12 peptides assessed by immunogenicity analysis were able to activate naïve CD8⁺ T cells, including peptides derived from EGLN3. Functional analysis of EGLN3 revealed possible tumor-promoting functions.

Conclusions

Integration of HLA ligandomics, transcriptomics, genetic, and epigenetic data leads to the identification of novel functionally relevant therapeutic targets for ccRCC immunotherapy. Validation of the identified targets is recommended to expand the treatment landscape of ccRCC.

Tumor-Specific T Cell Activation in Malignant Brain Tumors

Due to their delicate locations as well as aggressive and infiltrative behavior, malignant brain tumors remain a therapeutic challenge. Harnessing the efficacy and specificity of the T-cell response to counteract malignant brain tumor progression and recurrence, represents an attractive treatment option. With the tremendous advances in the current era of immunotherapy, ongoing studies aim to determine the best treatment strategies for mounting a tumor-specific immune response against malignant brain tumors. However, immunosuppression in the local tumor environment, molecular and cellular heterogeneity as well as a lack of suitable targets for tumor-specific vaccination impede the successful implementation of immunotherapeutic treatment strategies in neuro-oncology. In this review, we therefore discuss the role of T cell exhaustion, the genetic and antigenic landscape, potential pitfalls and ongoing efforts to overcome the individual challenges in order to elicit a tumor-specific T cell response.

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.

Transposable element expression in tumors is associated with immune infiltration and increased antigenicity

Profound global loss of DNA methylation is a hallmark of many cancers. One potential consequence of this is the reactivation of transposable elements (TEs) which could stimulate the immune system via cell-intrinsic antiviral responses. Here, we develop REdiscoverTE, a computational method for quantifying genome-wide TE expression in RNA sequencing data. Using The Cancer Genome Atlas database, we observe increased expression of over 400 TE subfamilies, of which 262 appear to result from a proximal loss of DNA methylation. The most recurrent TEs are among the evolutionarily youngest in the genome, predominantly expressed from intergenic loci, and associated with antiviral or DNA damage responses. Treatment of glioblastoma cells with a demethylation agent results in both increased TE expression and de novo presentation of TE-derived peptides on MHC class I molecules. Therapeutic reactivation of tumor-specific TEs may synergize with immunotherapy by inducing inflammation and the display of potentially immunogenic neoantigens.

Treatment with demethylation agents can reactivate transposable elements. Here in glioblastoma, the authors also show that this is accompanied by de novo presentation of TE-derived peptides on MHC class I molecules.

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.

Multi-level Proteomics Identifies CT45 as a Chemosensitivity Mediator and Immunotherapy Target in Ovarian Cancer

Most high-grade serous ovarian cancer (HGSOC) patients develop resistance to platinum-based chemotherapy and recur, but 15% remain disease free over a decade. To discover drivers of long-term survival, we quantitatively analyzed the proteomes of platinum-resistant and -sensitive HGSOC patients from minute amounts of formalin-fixed, paraffin-embedded tumors. This revealed cancer/testis antigen 45 (CT45) as an independent prognostic factor associated with a doubling of disease-free survival in advanced-stage HGSOC. Phospho- and interaction proteomics tied CT45 to DNA damage pathways through direct interaction with the PP4 phosphatase complex. In vitro, CT45 regulated PP4 activity, and its high expression led to increased DNA damage and platinum sensitivity. CT45-derived HLA class I peptides, identified by immunopeptidomics, activate patient-derived cytotoxic T cells and promote tumor cell killing. This study highlights the power of clinical cancer proteomics to identify targets for chemo- and immunotherapy and illuminate their biological roles.

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

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

Proteomics Analysis Reveals That Structural Proteins of the Virion Core and Involved in Gene Expression Are the Main Source for HLA Class II Ligands in Vaccinia Virus-Infected Cells

Protective cellular and humoral immune responses require previous recognition of viral antigenic peptides complexed with human leukocyte antigen (HLA) class II molecules on the surface of the antigen presenting cells. The HLA class II-restricted immune response is important for the control and the clearance of poxvirus infection including vaccinia virus (VACV), the vaccine used in the worldwide eradication of smallpox. In this study, a mass spectrometry analysis was used to identify VACV ligands bound to HLA-DR and -DP class II molecules present on the surface of VACV-infected cells. Twenty-six naturally processed viral ligands among the tens of thousands of cell peptides bound to HLA class II proteins were identified. These viral ligands arose from 19 parental VACV proteins: A4, A5, A18, A35, A38, B5, B13, D1, D5, D7, D12, D13, E3, E8, H5, I2, I3, J2, and K2. The majority of these VACV proteins yielded one HLA ligand and were generated mainly, but not exclusively, by the classical HLA class II antigen processing pathway. Medium-sized and abundant proteins from the virion core and/or involved in the viral gene expression were the major source of VACV ligands bound to HLA-DR and -DP class II molecules. These findings will help to understand the effectiveness of current poxvirus-based vaccines and will be important in the design of new ones.

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.

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

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

Cell cycle-resolved chromatin proteomics reveals the extent of mitotic preservation of the genomic regulatory landscape

Regulation of transcription, replication, and cell division relies on differential protein binding to DNA and chromatin, yet it is unclear which regulatory components remain bound to compacted mitotic chromosomes. By utilizing the buoyant density of DNA–protein complexes after cross-linking, we here develop a mass spectrometry-based approach to quantify the chromatin-associated proteome at separate stages of the cell cycle. While epigenetic modifiers that promote transcription are lost from mitotic chromatin, repressive modifiers generally remain associated. Furthermore, while proteins involved in transcriptional elongation are evicted, most identified transcription factors are retained on mitotic chromatin to varying degrees, including core promoter binding proteins. This predicts conservation of the regulatory landscape on mitotic chromosomes, which we confirm by genome-wide measurements of chromatin accessibility. In summary, this work establishes an approach to study chromatin, provides a comprehensive catalog of chromatin changes during the cell cycle, and reveals the degree to which the genomic regulatory landscape is maintained through mitosis.

Mitosis poses a challenge for transcriptional programs, as it is thought that several proteins lose binding on condensed chromosomes. Here, the authors analyze the chromatin-bound proteome through the cell cycle, revealing retention of most transcription factors and preservation of the regulatory landscape.

Activation of viral defense signaling in cancer

A coordinated action of innate and adaptive immune responses is required to efficiently combat a microbial infection. It has now become clear that cancer therapies also largely benefit when both arms of the immune response are engaged. In this review, we will briefly describe the current knowledge of innate immunity and how this can be utilized to prime tumors for a better response to immune checkpoint inhibitors. Comments on compounds in development and ongoing clinical trials will be provided.

In Silico Analysis of the Minor Histocompatibility Antigen Landscape Based on the 1000 Genomes Project

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is routinely used to treat hematopoietic malignancies. The eradication of residual tumor cells during engraftment is mediated by donor cytotoxic T lymphocytes reactive to alloantigens. In a HLA-matched transplantation context, alloantigens are encoded by various polymorphic genes situated outside the HLA locus, also called minor histocompatibility antigens (MiHAs). Recently, MiHAs have been recognized as promising targets for post-transplantation T-cell immunotherapy as they have several appealing advantages over tumor-associated antigens (TAAs) and neoantigens, i.e., they are more abundant than TAAs, which potentially facilitates multiple targeting; and unlike neoantigens, they are encoded by germline polymorphisms, some of which are common and thus, suitable for off-the-shelf therapy. The genetic sources of MiHAs are nonsynonymous polymorphisms that cause differences between the recipient and donor proteomes and subsequently, the immunopeptidomes. Systematic description of the alloantigen landscape in HLA-matched transplantation is still lacking as previous studies focused only on a few immunogenic and common MiHAs. Here, we perform a thorough in silico analysis of the public genomic data to classify genetic polymorphisms that lead to MiHA formation and estimate the number of potentially available MiHA mismatches. Our findings suggest that a donor/recipient pair is expected to have at least several dozen mismatched strong MHC-binding SNP-associated peptides per HLA allele (116 ± 26 and 65 ± 15 for non-related pairs and siblings respectively in European populations as predicted by two independent algorithms). Over 70% of them are encoded by relatively frequent polymorphisms (minor allele frequency > 0.1) and thus, may be targetable by off-the-shelf therapeutics. We showed that the most appealing targets (probability of mismatch over 20%) reside in the asymmetric allele frequency region, which spans from 0.15 to 0.47 and corresponds to an order of several hundred (213 ± 47) possible targets per HLA allele that can be considered for immunogenicity validation. Overall, these findings demonstrate the significant potential of MiHAs as targets for T-cell immunotherapy and emphasize the need for the systematic discovery of novel MiHAs.

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.

Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry

The entirety of human leukocyte antigen (HLA)-presented peptides is referred to as the HLA ligandome of a cell or tissue, in tumours often termed immunopeptidome. Mapping the tumour immunopeptidome by mass spectrometry (MS) comprehensively views the pathophysiologically relevant antigenic signature of human malignancies. MS is an unbiased approach stringently filtering the candidates to be tested as opposed to epitope prediction algorithms. In the setting of peptide-specific immunotherapies, MS-based strategies significantly diminish the risk of lacking clinical benefit, as they yield highly enriched amounts of truly presented peptides. Early immunopeptidomic efforts were severely limited by technical sensitivity and manual spectra interpretation. The technological progress with development of orbitrap mass analysers and enhanced chromatographic performance led to vast improvements in mass accuracy, sensitivity, resolution, and speed. Concomitantly, bioinformatic tools were developed to process MS data, integrate sequencing results, and deconvolute multi-allelic datasets. This enabled the immense advancement of tumour immunopeptidomics. Studying the HLA-presented peptide repertoire bears high potential for both answering basic scientific questions and translational application. Mapping the tumour HLA ligandome has started to significantly contribute to target identification for the design of peptide-specific cancer immunotherapies in clinical trials and compassionate need treatments. In contrast to prediction algorithms, rare HLA allotypes and HLA class II can be adequately addressed when choosing MS-guided target identification platforms. Herein, we review the identification of tumour HLA ligands focusing on sources, methods, bioinformatic data analysis, translational application, and provide an outlook on future developments.

A meta-analysis of HLA peptidome composition in different hematological entities: entity-specific dividing lines and "pan-leukemia" antigens

Hematological malignancies (HM) are highly amenable targets for immunotherapeutic intervention and may be effectively treated by antigen-specific T-cell based treatment. Recent studies demonstrate that physiologically occurring anti-cancer T-cell responses in certain HM entities target broadly presented non-mutated epitopes. HLA ligands are thus implied as prime targets for broadly applicable and antigen-specific off-the-shelf compounds. With the aim of assessing the presence of common targets shared among different HM which may enable addressing a larger patient collective we conducted a meta-analysis of 83 mass spectrometry-based HLA peptidome datasets (comprising 40,361 unique peptide identifications) across four major HM (19 AML, 16 CML, 35 CLL, and 13 MM/MCL samples) and investigated similarities and differences within the HLA presented antigenic landscape. We found the cancer HLA peptidome datasets to cluster specifically along entity and lineage lines, suggesting that the immunopeptidome directly reflects the differences in the underlying (tumor-)biology. In line with these findings, we only detected a small set of entity-spanning antigens, which were predominantly characterized by low presentation frequencies within the different patient cohorts. These findings suggest that design of T-cell immunotherapies for the treatment of HM should ideally be conducted in an entity-specific fashion.

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

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

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.