Felix Rückert

Bio: Felix Rückert is an academic researcher from Heidelberg University. The author has contributed to research in topics: Pancreatic cancer & Pancreatitis. The author has an hindex of 22, co-authored 82 publications receiving 4330 citations. Previous affiliations of Felix Rückert include University of Mannheim & Dresden University of Technology.

Inhibition of Hedgehog Signaling Enhances Delivery of Chemotherapy in a Mouse Model of Pancreatic Cancer

Abstract: Pancreatic ductal adenocarcinoma (PDA) is among the most lethal human cancers in part because it is insensitive to many chemotherapeutic drugs. Studying a mouse model of PDA that is refractory to the clinically used drug gemcitabine, we found that the tumors in this model were poorly perfused and poorly vascularized, properties that are shared with human PDA. We tested whether the delivery and efficacy of gemcitabine in the mice could be improved by coadministration of IPI-926, a drug that depletes tumor-associated stromal tissue by inhibition of the Hedgehog cellular signaling pathway. The combination therapy produced a transient increase in intratumoral vascular density and intratumoral concentration of gemcitabine, leading to transient stabilization of disease. Thus, inefficient drug delivery may be an important contributor to chemoresistance in pancreatic cancer.

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

Abstract: 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.

Pathohistological Subtype Predicts Survival in Patients With Intraductal Papillary Mucinous Neoplasm (IPMN) of the Pancreas

Abstract: Objective:To investigate different subtypes of intraductal papillary mucinous neoplasms (IPMNs) of the pancreas and their prognostic value.Background:IPMNs of the pancreas are estimated to have a better prognosis than pancreatic ductal adenocarcinomas (PDACs). In addition to the different growth typ

Delivering nanomedicine to solid tumors

Abstract: Recent advances in nanotechnology have offered new hope for cancer detection, prevention, and treatment. While the enhanced permeability and retention effect has served as a key rationale for using nanoparticles to treat solid tumors, it does not enable uniform delivery of these particles to all regions of tumors in sufficient quantities. This heterogeneous distribution of therapeutics is a result of physiological barriers presented by the abnormal tumor vasculature and interstitial matrix. These barriers are likely to be responsible for the modest survival benefit offered by many FDA-approved nanotherapeutics and must be overcome for the promise of nanomedicine in patients to be realized. Here, we review these barriers to the delivery of cancer therapeutics and summarize strategies that have been developed to overcome these barriers. Finally, we discuss design considerations for optimizing the delivery of nanoparticles to tumors.