Transplantation of pancreatic islets is emerging as a successful treatment for type-1 diabetes. Its current stringent restriction to patients with critical metabolic lability is justified by the long-term need for immunosuppression and a persistent shortage of donor organs. We developed an oxygenated chamber system composed of immune-isolating alginate and polymembrane covers that allows for survival and function of islets without immunosuppression. A patient with type-1 diabetes received a transplanted chamber and was followed for 10 mo. Persistent graft function in this chamber system was demonstrated, with regulated insulin secretion and preservation of islet morphology and function without any immunosuppressive therapy. This approach may allow for future widespread application of cell-based therapies.β-cell replacement | immune barrier | oxygenation T he transplantation of isolated islets of Langerhans has evolved into a successful method to restore endogenous insulin secretion and stabilize glycemic control without the risk of hypoglycemia (1, 2). However, due to persistent lack of human donor pancreata and the requirement of chronic immune suppression to prevent graft rejection through allo-and autoimmunity, the indication for islet transplantation is restricted to patients with complete insulin deficiency, critical metabolic lability, and repeated severe hypoglycemia despite optimal diabetes management and compliance (3). Furthermore, progressive loss of islet function over time due to chronic hypoxia and inflammatory processes at the intraportal transplantation site remain additional unresolved challenges in islet transplantation (4, 5).When islets are immune-isolated, the lack of oxygen impairs the survival and long-term function of the cells. Experimental approaches to overcome this impediment have involved the implantation of hypoxia-resistant islets, stimulation and sprouting of vessels, and the use of islets designed to contain an intracellular oxygen carrier as well as local oxygen production by electrochemical processes or photosynthesis (6). However, so far, none of these methods have been capable of guaranteeing an adequate physiological oxygen concentration or to allow, at the same time, an adequate immunoprotective environment. To overcome these major obstacles, we have developed a strategy for islet macroencapsulation that provides sufficient immune isolation and permits endogenously regulated islet graft function. Here we demonstrate a system that allows a controlled oxygen supply to the islet graft by means of an integrated oxygen reservoir that can be refilled regularly and can maintain oxygen pressure. Earlier we demonstrated that a sufficient supply of oxygen for maintaining optimal islet function can simultaneously ensure functional potency and immunoprotective characteristics of the device. After application of this bioartificial pancreas system in allogeneic and xenogeneic preclinical diabetes models (7-9) the method was then applied to allogeneic human islet transplantation in an ind...

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in (partial) fulfillment of the requirements for obtaining the degree Dr. rer. nat. by Victoria Langer. Supporting grants were from the German Research Foundation (DFG) (KFO 257 [subproject 4 to M Stürzl and MJW], FOR 2438 [subproject 2 to EN and M Stürzl], SFB/TRR241 [subproject A06 to M Stürzl and NBL], and BR 5196/2-1 [to NBL]); the Interdisciplinary Center for Clinical Research (IZKF) of the Clinical Center Erlangen (D28 to EN and M Stürzl); the W. Lutz Stiftung (to M Stürzl); and the Forschungsstiftung Medizin am Universitätsklinikum Erlangen (to M Stürzl).

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.