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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.

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.

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.

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., Author summary 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 (