Targeting tumor angiogenesis is a potential therapeutic strategy for glioblastoma because of its high vascularization. Tivozanib is an oral pan-VEGF receptor tyrosine kinase inhibitor that hits a central pathway in glioblastoma angiogenesis. We conducted a phase II study to test the effectiveness of tivozanib in patients with recurrent glioblastoma. Ten adult patients were enrolled and treated with tivozanib 1.5 mg daily, 3 weeks on/1 week off in 28-day cycles. Brain MRI and blood biomarkers of angiogenesis were performed at baseline, within 24-72 h of treatment initiation, and monthly thereafter. Dynamic contrast enhanced MRI, dynamic susceptibility contrast MRI, and vessel architecture imaging were used to assess vascular effects. Resting state MRI was used to assess brain connectivity. Best RANO criteria responses were: 1 complete response, 1 partial response, 4 stable diseases, and 4 progressive disease (PD). Two patients were taken off study for toxicity and 8 patients were taken off study for PD. Median progression-free survival was 2.3 months and median overall survival was 8.1 months. Baseline abnormal tumor vascular permeability, blood flow, tissue oxygenation and plasma sVEGFR2 significantly decreased and plasma PlGF and VEGF increased after treatment, suggesting an anti-angiogenic effect of tivozanib. However, there were no clear structural changes in vasculature as vessel caliber and enhancing tumor volume did not significantly change. Despite functional changes in tumor vasculature, tivozanib had limited anti-tumor activity, highlighting the limitations of anti-VEGF monotherapy. Future studies in glioblastoma should leverage the anti-vascular activity of agents targeting VEGF to enhance the activity of other therapies.

BACKGROUND/AIMS: Radixin has recently been shown to correlate with the metastasis of gastric cancer, but the pathogenesis is elusive. Adhesion proteins contribute to the regulation of metastasis, and thus this study sought to investigate the role of radixin in the migration, invasion and adhesion of gastric cancer cells, as well as its interaction with adhesion proteins in vitro. METHODS: Radixin stable knockdown human gastric carcinoma SGC-7901 cells were constructed. Alterations in the migration, invasion and adhesion ability were examined by matrigel-coated plate and transwell assays. The expression pattern of adhesion proteins, including E-cadherin, β-catenin and claudin-1, was determined by quantitative real-time PCR and western blot. Possible involvement of NF-κB/snail pathway was also evaluated. RESULTS: Stable knockdown of radixin significantly suppressed migration and invasion, but enhanced adhesion in SGC-7901 cells. The expression of E-cadherin was manifestly increased in radixin knockdown cells, whereas the expression of β-catenin and claudin-1 was unchanged. The nuclear exclusion of NF-κB followed by conspicuous reduction of snail expression was involved in the regulation of E-cadherin expression. CONCLUSIONS: Radixin knockdown suppresses the metastasis of SGC-7901 cells in vitro by up-regulation of E-cadherin. The NF-κB/snail pathway contributes to the regulation of E-cadherin in response to depletion of radixin.

Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https://ipp.cbica.upenn.edu), a web based platform for remote processing of medical images.