3588 Background: The combination of TAS-102 and bevacizumab is standard in the treatment of advanced colorectal cancer (aCRC). Ramucirumab (Ram) is a mAb that blocks VEGFR2. Ram in combination with Tas-102 did not improve OS compared to Tas-102 alone in the ITT population of the RAMTAS trial. Subgroups such as female patients (pts) and pts with left-sided tumors appeared to benefit from the combination (DOI:10.1016/j.annonc.2024.08.2264). Here we aimed at identifying angiogenesis-related, soluble, predictive biomarkers defining a RAMTAS subpopulation benefitting from the addition of Ram to Tas-102. Methods: Serum samples were available from 194 of 428 pts from the RAMTAS study (96 pts randomized to TAS-102, 98 pts to TAS-102 plus Ram). Serum levels of 85 angiogenesis-related proteins obtained prior to treatment with Tas-102 +/- Ram (baseline, BL) and at cycle 2 day 1 prior to the start of the next cycle (treatment, TR) were quantified via multiplex-ELISA by Myriad-RBM. Multivariate Cox regression models were developed within the combination arm using stepwise selection and ridge regularization. Model performance was assessed via C-index and validated using 10 x 5-fold cross-validation (CV) as well as full quality control for multicollinearity and proportional hazard assumptions. To distinguish predictive from general prognostic effects, the identified signatures were applied to the monotherapy control arm. Results: A TR model incorporating just 4 serum proteins demonstrated robust discrimination in the combination arm (Training C-index: 0.712; CV C-index: 0.708 ± 0.063). This model successfully stratified patients into a “Ram benefit” and a “no Ram benefit” group with a corresponding median PFS of 7.0 mo and 1.9 mo, respectively (HR 6.97, p < 0.001). A Baseline (BL) model yielded a C-index of 0.699, also utilizing just 4 serum proteins. Application of both models to the TAS-102 mono arm resulted in poor discrimination (C-index ~0.60) indicating that the signatures are specific predictors for the combination. In a comparative survival analysis, "Ram-benefit" TR model patients achieved a significantly longer PFS with the combination compared to monotherapy (7.00 vs. 2.14 mo; HR 0.558, p=0.0078). Conversely, "no-Ram-benefit" patients derived no significant benefit from the addition of Ram (PFS 1.91 vs. 1.91 mo; HR 0.946, p=0.4204). Conclusions: Oligo-marker serum signatures, when incorporating early on-treatment dynamics after one cycle of Tas-102/Ram, may effectively predict a survival benefit for aCRC-pts receiving TAS-102 in combination with Ram. We propose that a two-stage “Screen & Monitor” (BL+TR) strategy will help to identify primary refractory patients who gain no benefit from the addition of Ram. Such an approach could maximize therapeutic utility while reducing potential toxicity. Clinical trial information: NCT03520946 .

Prognosis in advanced pancreatic ductal adenocarcinoma (aPDAC) is particularly poor, only few patients benefit from treatment, and there are few biomarkers. The PREDICT trial examined whether first-line time-to-treatment failure (TTF1) predicts second-line treatment failure (TTF2) in aPDAC patients but found no association. We hypothesized that the tumor immune microenvironment (TiME) could correlate with the outcome in this trial and assessed whether tissue features were reflected in peripheral blood. PREDICT patients received 5-FU/LV plus nanoliposomal irinotecan as second-line treatment. We stratified patients by shortest vs. longest TTF2 and analyzed 20 treatment-naïve tumor tissues samples via transcriptomics and immunohistochemistry. Peripheral blood mononuclear cells (PBMCs) from 82 patients collected prior to second-line therapy underwent flow cytometry and gene expression profiling. A machine learning pipeline integrated PBMC and clinical data to predict second-line outcome including external validation in 30 patients. Long-TTF2 tumors exhibited an immune-active (“hot”) TiME with cytotoxic CXCR3+CD8+-T-cell infiltration. PBMC analysis showed that these immune features were reflected in peripheral blood after one line of treatment. A novel 7-feature PBMC-based model (“TTF2Pred”) accurately predicted TTF2 and overall survival, outperforming clinical or CA19-9 models and was confirmed in an external validation cohort. Long-TTF2 patients exhibited more circulating CXCR3⁺-T-cells and plasmacytoid dendritic cells. Short-TTF2 patients had more platelet-leukocyte aggregates. An immune-active, treatment-naïve TiME predicts a better second-line outcome, and these characteristics imprinted into PBMCs obtained after one line of chemotherapy. We here first describe a minimally invasive, PBMC-based predictor of second-line outcome as a powerful prognostic tool for triaging patients. ClinicalTrials.gov NCT03468335 (registered March 15, 2018).

755 Background: The phase II NEONAX trial examined PO and A gemcitabine/nab-paclitaxel (G/nP) efficacy for rPDAC pts with a comprehensive biomarker program. This translational study aimed at identifying liquid biomarker for early prediction of G/nP success in each treatment group (PO and A) and for the combined group using machine learning (ML). Methods: Pts from both study arms were stratified into groups based on Short- or Long-DFS ( n=80 pts total; PO n=42 pts; A n=38 pts). Blood plasma from selected G/nP naïve pts was analyzed by multiplexed ELISA (mELISA, 80-Plex ProcartaPlex Human Immune Response) and mass spectrometry (MS; feature list generation) and clinical data were acquired for each patient. For ML pts were divided into training (80%) and validation (20%) datasets. The Weka-based algorithm WrapperSubsetEval (WSE) with 8 different classifiers was used for feature selection. The best performing (highest accuracy, best ROC-AUC, minimal signature) classifier with the corresponding feature panel was selected by a 10x10-fold cross-validation (CV). Respective panels for all features combined as compared to clinical features and Ca19-9 blood levels were tested for performance via CV and bootstrap aggregating for training and validation datasets (10x with replacement). Results: The feature generation process generated 579 features from G/nP-naive pts (mELISA: 80; clinical data: 99; MS: 400). For response prediction of the whole rPDAC group to G/nP (S-/L-DFS), the ML-based flow identified a panel of 8 proteins from MS (SERPINA1, C1QB, KRT1, C4B, UBB, VCAM1, HPD, LYZ) and 3 proteins from mELISA (Galectin3, IL34, CCL4) combined with a logistic regression classifier. The predictive panel with the best performance for determining PO G/nP success was established using a kernel logistic regression classifier and included 2 proteins from mELISA (CXCL2, IL17A), 4 proteins from MS (C3, IGLV3-25, HLA-B, SHBG), and 2 clinical data (WHO grade, Na/Mg blood level ratio). The best performing biomarker for predicting success in A included 2 clinical data (tumor size at staging, hematocrit) and 1 protein from mELISA (IL22) and was established with a random forest classifier. All panels represented minimal feature signatures (rPDAC: 11; PO: 8; A; 3) and showed a high performance with ROC-AUC (training) > 0.90, ROC-AUC (CV) > 0.85, and ROC-AUC (validation) > 0.90. All panels described were superior compared to similar predictive signatures (with corresponding ML classifiers) for all clinical data or Ca19-9 blood levels. Conclusions: We show that minimal liquid biomarker signatures for early prediction of G/nP success in the NEONAX trial based on mELISA, MS and clinical data can be established by ML. The study shows the potential of ML for biomarker panel development and the value of mELISA/MS for generation of feature lists to use in ML. Clinical trial information: NCT02047513 .

4187 Background: The PREDICT trial, a recent phase IIIb/IV study, aimed to address the critical need for improved personalized treatment strategies in advanced pancreatic cancer. This translational investigation examined the predictive value of 1 st line chemotherapy (CTX) response on the efficacy of subsequent 2 nd line treatment by using liquid biomarkers combined with machine learning (ML). Methods: Pts. were stratified into two cohorts based on short or long 2 nd line CTX time to treatment failure (S-/L-TTF2; n = 10 per group, 80/20% quantiles). Treatment-naïve PDAC tissue specimens underwent laser microdissection for tumor cell enrichment, followed by RNA profiling using NanoString™ PanCancer IO360. Selected differentially regulated genes from the omics results were utilized to screen peripheral blood mononuclear cells (PBMCs) from 2 nd line treatment-naïve patients at protein (flow cytometry, FC) or RNA (RT-qPCR) levels. FC data was analyzed using R-based single-cell clustering (x-Shift, FlowSOM, T-REX) to generate HyperGates (HGs), with subsequent ML-based back-gating (HyperFinder algorithm) of the most differential clusters. Additionally, classical ManualGates (MGs) were generated. Feature selection employed the Weka-based WrapperSubsetEval (WSE) algorithm with eight different classifiers (NB, KLR, LR, SMO, IBk, RT, RF, J48). The classifier/subset with optimal performance was utilized for binary classification (S-TTF2 vs. L-TTF2) of training (80%, n = 66) and validation (20%, n = 16) datasets. Results: Transcriptome analysis of L- vs. S-TTF2 tumor tissues revealed increased inflammation (upregulated 18-gene signature), immune cell activation/infiltration (e.g. CD4/CD8 T cells), and immune exhaustion (upregulation of e.g. PDCD1, LAG3, CTLA4, TIGIT) in L-TTF tumors. Further, the favorable Bailey immunogenic subtype was enriched in the L-TTF2 group. Analysis of eight FC panels (19 candidates) revealed 1198 differential clusters with HGs and 881 classical MGs. Feature selection, combining FC data with RT-qPCR results and clinical parameters, identified a best performing signature of 5 HGs and 2 MGs for 7 protein markers (CXCR4, CD8, CD4, CD62P, CD307b, CD45, CD121b). ML using a kernel logistic regression successfully predicted S- and L-TTF2 binary groups prior to 2 nd line CTX with nal-IRI/5-FU/LV (ROC-AUC > 0.90 for training and validation). Conclusions: We identified a favorable tumor immune microenvironment in L-TTF aPDAC patients, characterized by CD8 T cell-inflamed ("hot") tumor tissues prior to 2 nd line CTX. A 7-marker liquid biomarker panel, comprising 7 flow cytometry PBMC population gates, was developed for early prediction of 2 nd line nal-IRI/5-FU/LV CTX success. These findings aim to advance personalized treatment strategies. Clinical trial information: NCT03468335 .

Background Pancreatic ductal adenocarcinoma (PDAC) is mostly refractory to immunotherapy due to immunosuppression in the tumor microenvironment and cancer cell-intrinsic T cell tolerance mechanisms. PDAC is described as a “cold” tumor type with poor infiltration by T cells and factors leading to intratumoral T cell suppression have thus received less attention. Here, we identify a cancer cell-intrinsic mechanism that contributes to a T cell-resistant phenotype and describes potential combinatorial therapy.Methods We used an unbiased screening approach of T cell resistant and sensitive murine KPC (KrasLSL-G12D/+; Trp53fl/fl; Ptf1aCre/+) PDAC cells in a three-dimensional co-culture platform with syngeneic antigen-educated T cells to identify potential cell-intrinsic drivers of T cell suppression in PDAC. Comparative transcriptomic analysis was performed to reveal promising candidates that mediate resistance to T cells. We investigated their contribution by shRNA-mediated knockdown and pharmacological inhibition in murine in vitro and in vivo studies, as well as in patient-derived organoids (PDOs). A combination of transcriptomic analyses, cytometric and immunohistochemistry techniques allowed us to validate the underlying T cell response phenotypes of PDAC cells. The action of TGM2 via interaction with tubulin and the impact of microtubule dynamics and vesicle trafficking were evaluated by protein analyses and live-cell imaging. Correlation analyses via TCGA data complemented the functional studies.Results We identified transglutaminase 2 (TGM2) as a mediator of T cell suppression in PDAC. We report that high levels of TGM2 expression in patients’ tumors correlate with immunosuppressive signatures and poor overall survival. We found that TGM2 regulates vesicle trafficking by modulating microtubule network density and dynamics in pancreatic cancer cells, thus facilitating the secretion of immunosuppressive cytokines, which impair effector T cell functionality. In TGM2-expressing PDOs, pharmacological TGM2 inhibition or treatment with nocodazole increased T cell-mediated apoptosis. Also, pretreatment of TGM2high PDOs with sublethal doses of the spindle poisons paclitaxel or vincristine increased CD8+T cell activation and sensitized PDOs toward T cell-mediated cytotoxicity.Conclusions These findings indicate that targeting microtubular function therapeutically may enhance antitumor T cell responses by impacting activity of immunosuppressive cytokines in the PDAC microenvironment.

Poor treatment responses of pancreatic ductal adenocarcinoma (PDAC) are in large part due to tumor heterogeneity and an immunosuppressive desmoplastic tumor stroma that impacts interactions with cells in the tumor microenvironment (TME). Thus, there is a pressing need for models to probe the contributions of cellular and noncellular crosstalk. Organoids are promising model systems with the potential to generate a plethora of data including phenotypic, transcriptomic and genomic characterization but still require improvements in culture conditions mimicking the TME. Here, we describe an INTERaction with Organoid-in-MatriX ("InterOMaX") model system, that presents a 3D co-culture-based platform for investigating matrix-dependent cellular crosstalk. We describe its potential to uncover new molecular mechanisms of T cell responses to murine KPC (LSL-KrasG12D/+27/Trp53tm1Tyj/J/p48Cre/+) PDAC cells as well as PDAC patient-derived organoids (PDOs). For this, a customizable matrix and homogenously sized organoid-in-matrix positioning of cancer cells were designed based on a standardized agarose microwell chip array system and established for co-culture with T cells and inclusion of stromal cells. We describe the detection and orthogonal analysis of murine and human PDAC cell populations with distinct sensitivity to T cell killing that is corroborated in vivo. By enabling both identification and validation of gene candidates for T cell resistance, this platform sets the stage for better mechanistic understanding of cancer cell-intrinsic resistance phenotypes in PDAC.

Abstract Background We have previously identified an unsuspected role for GJB3 showing that the deficiency of this connexin protein induces aneuploidy in human and murine cells and accelerates cell transformation as well as tumor formation in xenograft models. The molecular mechanisms by which loss of GJB3 leads to aneuploidy and cancer initiation and progression remain unsolved. Methods GJB3 expression levels were determined by RT-qPCR and Western blot. The consequences of GJB3 knockdown on genome instability were assessed by metaphase chromosome counting, multinucleation of cells, by micronuclei formation and by the determination of spindle orientation. Interactions of GJB3 with α-tubulin and F-actin was analyzed by immunoprecipitation and immunocytochemistry. Consequences of GJB3 deficiency on microtubule and actin dynamics were measured by live cell imaging and fluorescence recovery after photobleaching experiments, respectively. Immunohistochemistry was used to determine GJB3 levels on human and murine bladder cancer tissue sections. Bladder cancer in mice was chemically induced by BBN-treatment. Results We find that GJB3 is highly expressed in the ureter and bladder epithelium, but it is downregulated in invasive bladder cancer cell lines and during tumor progression in both human and mouse bladder cancer. Downregulation of GJB3 expression leads to aneuploidy and genomic instability in karyotypically stable urothelial cells and experimental modulation of GJB3 levels alters the migration and invasive capacity of bladder cancer cell lines. Importantly, GJB3 interacts both with α-tubulin and F-actin. The impairment of these interactions alters the dynamics of these cytoskeletal components and leads to defective spindle orientation. Conclusion We conclude that deregulated microtubule and actin dynamics have an impact on proper chromosome separation and tumor cell invasion and migration. Consequently, these observations indicate a possible role for GJB3 in the onset and spreading of bladder cancer and demonstrate a molecular link between enhanced aneuploidy and invasive capacity cancer cells during tumor cell dissemination. Graphical Abstract

Discovered in the late eighties, sEVs are small extracellular nanovesicles (30–150 nm diameter) that gained increasing attention due to their profound roles in cancer, immunology, and therapeutic approaches. They were initially described as cellular waste bins; however, in recent years, sEVs have become known as important mediators of intercellular communication. They are secreted from cells in substantial amounts and exert their influence on recipient cells by signaling through cell surface receptors or transferring cargos, such as proteins, RNAs, miRNAs, or lipids. A key role of sEVs in cancer is immune modulation, as well as pro-invasive signaling and formation of pre-metastatic niches. sEVs are ideal biomarker platforms, and can be engineered as drug carriers or anti-cancer vaccines. Thus, sEVs further provide novel avenues for cancer diagnosis and treatment. This review will focus on the role of sEVs in GI-oncology and delineate their functions in cancer progression, diagnosis, and therapeutic use.

Poor treatment responses of pancreatic ductal adenocarcinoma (PDAC) are in large part due to tumor heterogeneity and a desmoplastic tumor stroma that impacts interactions with immune cells. Thus, there is a pressing need for a comprehensive understanding and models to probe the contributions of cellular and noncellular crosstalk. Organoids are promising model systems with the potential to generate a plethora of data including phenotypic, transcriptomic and genomic characterization but still require improvements in culture conditions mimicking the tumor microenvironment. Here, we describe an INTERaction with Organoid-in-MatriX ("InterOMaX") model system, that presents an advanced 3D co-culture-in-matrix-based imaging-transcriptomic platform for investigating matrix-dependent cellular crosstalk. We describe its potential to uncover new molecular mechanisms of T cell responses to murine KPC (LSL-KrasG12D/+27/Trp53tm1Tyj/J/p48Cre/+) PDAC cells as well as PDAC patient-derived organoids (PDO). For this, a customizable matrix, as well as a homogenously sized organoid-in-matrix positioning of heterogeneous cancer cell mixtures were designed based on a standardized agarose microwell chip array system and established for co-culture with T cells and inclusion of stromal cells. We describe the detection and analysis of murine and human PDAC cell populations with distinct sensitivity to T cell killing, thus setting the stage towards better mechanistic understanding required for therapeutic decision making.

Mesenchymal stromal cells (MSCs) are promising therapeutic candidates in a variety of diseases due to having immunomodulatory and pro-regenerative properties. In recent years, MSC-derived small extracellular vesicles (sEVs) have attracted increasing interest as a possible alternative to conventional cell therapy. However, translational processes of sEVs for clinical applications are still impeded by inconsistencies regarding isolation procedures and culture conditions. We systematically compared different methods for sEV isolation from conditioned media of ex vivo expanded bone marrow-derived MSCs and demonstrated considerable variability of quantity, purity, and characteristics of sEV preparations obtained by these methods. The combination of cross flow filtration with ultracentrifugation for sEV isolation resulted in sEVs with similar properties as compared to isolation by differential centrifugation combined with ultracentrifugation, the latter is still considered as gold standard for sEV isolation. In contrast, sEV isolation by a combination of precipitation with polyethylene glycol and ultracentrifugation as well as cross flow filtration and size exclusion chromatography resulted in sEVs with different characteristics, as shown by surface antigen expression patterns. The MSC culture requires a growth-promoting supplement, such as platelet lysate, which contains sEVs itself. We demonstrated that MSC culture with EV-depleted platelet lysate does not alter MSC characteristics, and conditioned media of such MSC cultures provide sEV preparations enriched for MSC-derived sEVs. The results from the systematic stepwise evaluation of various aspects were combined with culture of MSCs in a hollow fiber bioreactor. This resulted in a strategy using cross flow filtration with subsequent ultracentrifugation for sEV isolation. In conclusion, this workflow provides a semi-automated, efficient, large-scale-applicable, and good manufacturing practice (GMP)-grade approach for the generation of sEVs for clinical use. The use of EV-depleted platelet lysate is an option to further increase the purity of MSC-derived sEVs.

We have recently shown that loss of ORP3 leads to aneuploidy induction and promotes tumor formation. However, the specific mechanisms by which ORP3 contributes to ploidy-control and cancer initiation and progression is still unknown. Here, we report that ORP3 is highly expressed in ureter and bladder epithelium while its expression is downregulated in invasive bladder cancer cell lines and during tumor progression, both in human and in mouse bladder cancer. Moreover, we observed an increase in the incidence of N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN)-induced invasive bladder carcinoma in the tissue-specific Orp3 knockout mice. Experimental data demonstrate that ORP3 protein interacts with γ-tubulin at the centrosomes and with components of actin cytoskeleton. Altering the expression of ORP3 induces aneuploidy and genomic instability in telomerase-immortalized urothelial cells with a stable karyotype and influences the migration and invasive capacity of bladder cancer cell lines. These findings demonstrate a crucial role of ORP3 in ploidy-control and indicate that ORP3 is a bona fide tumor suppressor protein. Of note, the presented data indicate that ORP3 affects both cell invasion and migration as well as genome stability through interactions with cytoskeletal components, providing a molecular link between aneuploidy and cell invasion and migration, two crucial characteristics of metastatic cells.

Trauma is a major cause of death worldwide. The post-traumatic immune response culminates in the release of pro-inflammatory mediators, translating in the infiltration of neutrophils (PMNs) at injury sites. The extent of this inflammation is determined by multiple factors, such as PMN adhesion to the endothelium, transendothelial migration, endothelial barrier integrity as well as PMN swarming, mass infiltration and activation. This process is initiated by secondary lipid mediators, such as leukotriene B4 (LTB4). We here provide evidence that Protein kinase D1 (PRKD1) in endothelial cells is implicated in all these processes. Endothelial PRKD1 is activated by pro-inflammatory stimuli and amplifies PMN-mediated inflammation by upregulation of cytokine and chemokines as well as adhesion molecules, such as ICAM-1, VCAM-1 and E-selectin. This induces enhanced PMN adhesion and trans-migration. PRKD1 activation also destabilizes endothelial VE-cadherin adhesion complexes and thus the endothelial barrier, fostering PMN infiltration. We even describe a yet unrecognized PRKD1-dependant mechanism to induce biosynthesis of the PMN-swarming mediator LTB4 directed via intercellular communication through small extracellular vesicles (sEVs) and enhanced CXCL8 secretion from activated endothelial cells. These endothelial sEVs transfer the LTB4 biosynthesis enzyme LTA4 hydrolase (LTA4H) to prime PMNs, while initiating biosynthesis also requires additional signals, like CXCL8. We further demonstrate the respective LTA4H-positive sEVs in the serum of polytrauma patients, peaking 12 h post injury. Therefore, PRKD1 is a key regulator in the coordinated communication of the endothelium with PMNs and a vital signaling node during post-traumatic inflammation.

Cell migration plays an essential role in wound healing and inflammatory processes inside the human body. Peripheral blood neutrophils, a type of polymorphonuclear leukocyte (PMN), are the first cells to be activated during inflammation and subsequently migrate toward an injured tissue or infection site. This response is dependent on both biochemical signaling and the extracellular environment, one aspect of which includes increased temperature in the tissues surrounding the inflammation site. In our study, we analyzed temperature-dependent neutrophil migration using differentiated HL-60 cells. The migration speed of differentiated HL-60 cells was found to correlate positively with temperature from 30 to 42 °C, with higher temperatures inducing a concomitant increase in cell detachment. The migration persistence time of differentiated HL-60 cells was higher at lower temperatures (30–33 °C), while the migration persistence length stayed constant throughout the temperature range. Coupled with the increased speed observed at high temperatures, this suggests that neutrophils are primed to migrate more effectively at the elevated temperatures characteristic of inflammation. Temperature gradients exist on both cell and tissue scales. Taking this into consideration, we also investigated the ability of differentiated HL-60 cells to sense and react to the presence of temperature gradients, a process known as thermotaxis. Using a two-dimensional temperature gradient chamber with a range of 27–43 °C, we observed a migration bias parallel to the gradient, resulting in both positive and negative thermotaxis. To better mimic the extracellular matrix (ECM) environment in vivo, a three-dimensional collagen temperature gradient chamber was constructed, allowing observation of biased neutrophil-like differentiated HL-60 migration toward the heat source.

Pancreatic ductal adenocarcinomas (PDACs) are tumors with poor prognosis and limited treatment options. Personalized medicine aims at characterizing actionable DNA variants by next-generation sequencing, thereby improving treatment strategies and outcomes. Fine-needle tumor biopsies are currently the gold standard to acquire samples for DNA profiling. However, liquid biopsies have considerable advantages as they are minimally invasive and frequently obtainable and thus may help to monitor tumor evolution over time. However, which liquid analyte works best for this purpose is currently unclear. Our study aims to directly compare tumor-, circulating free (cf-) and extracellular vesicle-derived (ev)DNA by panel sequencing of matching patient material. We evaluated copy number variations (CNVs), single nucleotide variants (SNVs) and insertions and deletions (indels). Our data show that evDNA contains significantly larger DNA fragments up to 5.5 kb, in line with previous observations. Stringent bioinformatic processing revealed a significant advantage of evDNA with respect to cfDNA concerning detection performance for SNVs and a numerical increase for indels. A combination of ev- and cfDNA was clearly superior for SNV detection, as compared to either single analyte, thus potentially improving actionable variant prediction upon further optimization. Finally, calling of CNVs from liquid biopsies still remained challenging and uninformative.

Infection-related diabetes can arise as a result of virus-associated β-cell destruction. Clinical data suggest that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing the coronavirus disease 2019 (COVID-19), impairs glucose homoeostasis, but experimental evidence that SARS-CoV-2 can infect pancreatic tissue has been lacking. In the present study, we show that SARS-CoV-2 infects cells of the human exocrine and endocrine pancreas ex vivo and in vivo. We demonstrate that human β-cells express viral entry proteins, and SARS-CoV-2 infects and replicates in cultured human islets. Infection is associated with morphological, transcriptional and functional changes, including reduced numbers of insulin-secretory granules in β-cells and impaired glucose-stimulated insulin secretion. In COVID-19 full-body postmortem examinations, we detected SARS-CoV-2 nucleocapsid protein in pancreatic exocrine cells, and in cells that stain positive for the β-cell marker NKX6.1 and are in close proximity to the islets of Langerhans in all four patients investigated. Our data identify the human pancreas as a target of SARS-CoV-2 infection and suggest that β-cell infection could contribute to the metabolic dysregulation observed in patients with COVID-19.

Cancer is a complex disease, driven by genetic defects and environmental cues. Systemic dissemination of cancer cells by metastasis is generally associated with poor prognosis and is responsible for more than 90% of cancer deaths. Metastasis is thought to follow a sequence of events, starting with loss of epithelial features, detachment of tumor cells, basement membrane breakdown, migration, intravasation and survival in the circulation. At suitable distant niches, tumor cells reattach, extravasate and establish themselves by proliferating and attracting vascularization to fuel metastatic growth. These processes are facilitated by extensive cross-communication of tumor cells with cells in the primary tumor microenvironment (TME) as well as at distant pre-metastatic niches. A vital part of this communication network are small extracellular vesicles (sEVs, exosomes) with a size of 30–150 nm. Tumor-derived sEVs educate recipient cells with bioactive cargos, such as proteins, and in particular, major nucleic acid classes, to drive tumor growth, cell motility, angiogenesis, immune evasion and formation of pre-metastatic niches. Circulating sEVs are also utilized as biomarker platforms for diagnosis and prognosis. This review discusses how tumor cells facilitate progression through the metastatic cascade by employing sEV-based communication and evaluates their role as biomarkers and vehicles for drug delivery.