Background Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression. Objective To explore the faecal and salivary microbiota as potential diagnostic biomarkers. Methods We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case–control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case–control study (n=76), in the validation phase. Results Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19–9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation. Conclusion Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder with rising incidence. Diseased tissues are heavily vascularized. Surprisingly, the pathogenic impact of the vasculature in IBD and the underlying regulatory mechanisms remain largely unknown. IFN-γ is a major cytokine in IBD pathogenesis, but in the context of the disease, it is almost exclusively its immune-modulatory and epithelial cell-directed functions that have been considered. Recent studies by our group demonstrated that IFN-γ also exerts potent effects on blood vessels. Based on these considerations, we analyzed the vessel-directed pathogenic functions of IFN-γ and found that it drives IBD pathogenesis through vascular barrier disruption. Specifically, we show that inhibition of the IFN-γ response in vessels by endothelial-specific knockout of IFN-γ receptor 2 ameliorates experimentally induced colitis in mice. IFN-γ acts pathogenic by causing a breakdown of the vascular barrier through disruption of the adherens junction protein VE-cadherin. Notably, intestinal vascular barrier dysfunction was also confirmed in human IBD patients, supporting the clinical relevance of our findings. Treatment with imatinib restored VE-cadherin/adherens junctions, inhibited vascular permeability, and significantly reduced colonic inflammation in experimental colitis. Our findings inaugurate the pathogenic impact of IFN-γ-mediated intestinal vessel activation in IBD and open new avenues for vascular-directed treatment of this disease.

Significance: Diabetes mellitus type 1 is an autoimmune disease that results in irreversible destruction of insulin-producing beta cells. Substantial advances have been made in beta cell replacement therapies over the last decades. However, lack of eligible donor organs and the need for chronic immunosuppression to prevent rejection critically limit a widespread application of these strategies. In this paper we present the clinical success of using a bioartificial pancreas for the transplantation of insulin-producing islets without affecting the immune system. In a patient with long-standing type-1 diabetes we could demonstrate persistent graft function and regulated insulin secretion without the need for immune-modulating medication. This strategy opens up avenues for more widespread and safe application of various cell-based therapies.

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice. Why do some people with the same type of cancer die early and some live long? Apart from influences from the environment and personal lifestyle, we believe that differences in the individual tumor genome account for different survival times. Recently, powerful methods have become available to systematically read genomic information of patient samples. The major remaining challenge is how to spot, among the thousands of changes, those few that are relevant for tumor aggressiveness and thereby affecting patient survival. Here, we make use of the fact that genes and proteins in a cell never act alone, but form a network of interactions. Finding the relevant information in big networks of web documents and hyperlinks has been mastered by Google with their PageRank algorithm. Similar to PageRank, we have developed an algorithm that can identify genes that are better indicators for survival than genes found by traditional algorithms. Our method can aid the clinician in deciding if a patient should receive chemotherapy or not. Reliable prediction of survival and response to therapy based on molecular markers bears a great potential to improve and personalize patient therapies in the future.