mRNA 3'UTR lengthening by alternative polyadenylation attenuates inflammatory responses and correlates with virulence of Influenza A virus

Changes of mRNA 3'UTRs by alternative polyadenylation (APA) have been associated to numerous pathologies, but the mechanisms and consequences often remain enigmatic. By combining transcriptomics, proteomics and recombinant viruses we show that all tested strains of IAV, including A/PR/8/34(H1N1) (PR8) and A/Cal/07/2009 (H1N1) (Cal09), cause APA. We mapped the effect to the highly conserved glycine residue at position 184 (G184) of the viral non-structural protein 1 (NS1). Unbiased mass spectrometry-based analyses indicate that NS1 causes APA by perturbing the function of CPSF4 and that this function is unrelated to virus-induced transcriptional shutoff. Accordingly, IAV strain PR8, expressing an NS1 variant with weak CPSF binding, does not induce host shutoff but only APA. However, recombinant IAV (PR8) expressing NS1(G184R) lacks binding to CPSF4 and thereby also the ability to cause APA. Functionally, the impaired ability to induce APA leads to an increased inflammatory cytokine production and an attenuated phenotype in a mouse infection model. Investigating diverse viral infection models showed that APA induction is a frequent ability of many pathogens. Collectively, we propose that targeting of the CPSF complex, leading to widespread alternative polyadenylation of host transcripts, constitutes a general immunevasion mechanism employed by a variety of pathogenic viruses.

Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

An integrated cellular and molecular model of gastric neuroendocrine cancer evolution highlights therapeutic targets

Gastric neuroendocrine carcinomas (G-NEC) are aggressive malignancies with poorly understood biology and a lack of disease models. Here, we use genome sequencing to characterize the genomic landscapes of human G-NEC and its histologic variants. We identify global and subtype-specific alterations and expose hitherto unappreciated gains of MYC family members in a large part of cases. Genetic engineering and lineage tracing in mice delineate a model of G-NEC evolution, which defines MYC as a critical driver and positions the cancer cell of origin to the neuroendocrine compartment. MYC-driven tumors have pronounced metastatic competence and display defined signaling addictions, as revealed by large-scale genetic and pharmacologic screening of cell lines and organoid resources. We create global maps of G-NEC dependencies, highlight critical vulnerabilities, and validate therapeutic targets, including candidates for clinical drug repurposing. Our study gives comprehensive insights into G-NEC biology.

In vivo interrogation of regulatory genomes reveals extensive quasi-insufficiency in cancer evolution

In contrast to mono- or biallelic loss of tumor-suppressor function, effects of discrete gene dysregulations, as caused by non-coding (epi)genome alterations, are poorly understood. Here, by perturbing the regulatory genome in mice, we uncover pervasive roles of subtle gene expression variation in cancer evolution. Genome-wide screens characterizing 1,450 tumors revealed that such quasi-insufficiency is extensive across entities and displays diverse context dependencies, such as distinct cell-of-origin associations in T-ALL subtypes. We compile catalogs of non-coding regions linked to quasi-insufficiency, show their enrichment with human cancer risk variants, and provide functional insights by engineering regulatory alterations in mice. As such, kilo-/megabase deletions in a Bcl11b-linked non-coding region triggered aggressive malignancies, with allele-specific tumor spectra reflecting gradual gene dysregulations through modular and cell-type-specific enhancer activities. Our study constitutes a first survey toward a systems-level understanding of quasi-insufficiency in cancer and gives multifaceted insights into tumor evolution and the tissue-specific effects of non-coding mutations.

CRISPR somatic genome engineering and cancer modeling in the mouse pancreas and liver

Genetically engineered mouse models (GEMMs) transformed the study of organismal disease phenotypes but are limited by their lengthy generation in embryonic stem cells. Here, we describe methods for rapid and scalable genome engineering in somatic cells of the liver and pancreas through delivery of CRISPR components into living mice. We introduce the spectrum of genetic tools, delineate viral and nonviral CRISPR delivery strategies and describe a series of applications, ranging from gene editing and cancer modeling to chromosome engineering or CRISPR multiplexing and its spatio-temporal control. Beyond experimental design and execution, the protocol describes quantification of genetic and functional editing outcomes, including sequencing approaches, data analysis and interpretation. Compared to traditional knockout mice, somatic GEMMs face an increased risk for mouse-to-mouse variability because of the higher experimental demands of the procedures. The robust protocols described here will help unleash the full potential of somatic genome manipulation. Depending on the delivery method and envisaged application, the protocol takes 3-5 weeks.

HDAC2 Facilitates Pancreatic Cancer Metastasis

The mortality of patients with pancreatic ductal adenocarcinoma (PDAC) is strongly associated with metastasis, a multistep process that is incompletely understood in this disease. Although genetic drivers of PDAC metastasis have not been defined, transcriptional and epigenetic rewiring can contribute to the metastatic process. The epigenetic eraser histone deacetylase 2 (HDAC2) has been connected to less differentiated PDAC, but the function of HDAC2 in PDAC has not been comprehensively evaluated. Using genetically defined models, we show that HDAC2 is a cellular fitness factor that controls cell cycle in vitro and metastasis in vivo, particularly in undifferentiated, mesenchymal PDAC cells. Unbiased expression profiling detected a core set of HDAC2-regulated genes. HDAC2 controlled expression of several prosurvival receptor tyrosine kinases connected to mesenchymal PDAC, including PDGFRα, PDGFRβ, and EGFR. The HDAC2-maintained program disabled the tumor-suppressive arm of the TGFβ pathway, explaining impaired metastasis formation of HDAC2-deficient PDAC. These data identify HDAC2 as a tractable player in the PDAC metastatic cascade. The complexity of the function of epigenetic regulators like HDAC2 implicates that an increased understanding of these proteins is needed for implementation of effective epigenetic therapies.

SIGNIFICANCE

HDAC2 has a context-specific role in undifferentiated PDAC and the capacity to disseminate systemically, implicating HDAC2 as targetable protein to prevent metastasis.

Functional analysis of peripheral and intratumoral neoantigen-specific TCRs identified in a patient with melanoma

Background

Neoantigens derived from somatic mutations correlate with therapeutic responses mediated by treatment with immune checkpoint inhibitors. Neoantigens are therefore highly attractive targets for the development of therapeutic approaches in personalized medicine, although many aspects of their quality and associated immune responses are not yet well understood. In a case study of metastatic malignant melanoma, we aimed to perform an in-depth characterization of neoantigens and respective T-cell responses in the context of immune checkpoint modulation.

Methods

Three neoantigens, which we identified either by immunopeptidomics or in silico prediction, were investigated using binding affinity analyses and structural simulations. We isolated seven T-cell receptors (TCRs) from the patient’s immune repertoire recognizing these antigens. TCRs were compared in vitro by multiparametric analyses including functional avidity, multicytokine secretion, and cross-reactivity screenings. A xenograft mouse model served to study in vivo functionality of selected TCRs. We investigated the patient’s TCR repertoire in blood and different tumor-related tissues over 3 years using TCR beta deep sequencing.

Results

Selected mutated peptide ligands with proven immunogenicity showed similar binding affinities to the human leukocyte antigen complex and comparable disparity to their wild-type counterparts in molecular dynamic simulations. Nevertheless, isolated TCRs recognizing these antigens demonstrated distinct patterns in functionality and frequency. TCRs with lower functional avidity showed at least equal antitumor immune responses in vivo. Moreover, they occurred at high frequencies and particularly demonstrated long-term persistence within tumor tissues, lymph nodes and various blood samples associated with a reduced activation pattern on primary in vitro stimulation.

Conclusions

We performed a so far unique fine characterization of neoantigen-specific T-cell responses revealing defined reactivity patterns of neoantigen-specific TCRs. Our data highlight qualitative differences of these TCRs associated with function and longevity of respective T cells. Such features need to be considered for further optimization of neoantigen targeting including adoptive T-cell therapies using TCR-transgenic T cells.

Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune-oncology. Still, the identification of non-tryptic peptides presents substantial computational challenges. To address these, we synthesized and analyzed >300,000 peptides by multi-modal LC-MS/MS within the ProteomeTools project representing HLA class I & II ligands and products of the proteases AspN and LysN. The resulting data enabled training of a single model using the deep learning framework Prosit, allowing the accurate prediction of fragment ion spectra for tryptic and non-tryptic peptides. Applying Prosit demonstrates that the identification of HLA peptides can be improved up to 7-fold, that 87% of the proposed proteasomally spliced HLA peptides may be incorrect and that dozens of additional immunogenic neo-epitopes can be identified from patient tumors in published data. Together, the provided peptides, spectra and computational tools substantially expand the analytical depth of immunopeptidomics workflows.