Eribulin mesylate is a synthetic macrocyclic ketone analog of the marine sponge natural product halichondrin B and an inhibitor of microtubule dynamics. Some tubulin-binding drugs are known to have antivascular (antiangiogenesis or vascular-disrupting) activities that can target abnormal tumor vessels. Using dynamic contrast-enhanced MRI analyses, here we show that eribulin induces remodeling of tumor vasculature through a novel antivascular activity in MX-1 and MDA-MB-231 human breast cancer xenograft models. Vascular remodeling associated with improved perfusion was shown by Hoechst 33342 staining and by increased microvessel density together with decreased mean vascular areas and fewer branched vessels in tumor tissues, as determined by immunohistochemical staining for endothelial marker CD31. Quantitative RT-PCR analysis of normal host cells in the stroma of xenograft tumors showed that eribulin altered the expression of mouse (host) genes in angiogenesis signaling pathways controlling endothelial cell–pericyte interactions, and in the epithelial–mesenchymal transition pathway in the context of the tumor microenvironment. Eribulin also decreased hypoxia-associated protein expression of mouse (host) vascular endothelial growth factor by ELISA and human CA9 by immunohistochemical analysis. Prior treatment with eribulin enhanced the anti-tumor activity of capecitabine in the MDA-MB-231 xenograft model. These findings suggest that eribulin-induced remodeling of abnormal tumor vasculature leads to a more functional microenvironment that may reduce the aggressiveness of tumors due to elimination of inner tumor hypoxia. Because abnormal tumor microenvironments enhance both drug resistance and metastasis, the apparent ability of eribulin to reverse these aggressive characteristics may contribute to its clinical benefits.

Improved bifunctional chelating agents (BFC) are required for indium-111 radiolabeling of monoclonal antibodies (mAbs) under mild conditions to yield stable, target-specific agents. 2,2',2"-(10-(2,6-Dioxotetrahydro-2H-pyran-3-yl)-1,4,7,10-tetraazacyclododecane-1,4,7-triyl)triacetic acid (DOTAGA-anhydride) was evaluated for mAb conjugation and labeling with indium-111. The DOTA analogue was synthesized and conjugated to trastuzumab-which targets the HER2/neu receptor-in mild conditions (PBS pH 7.4, 25 °C, 30 min) and gave a mean degree of conjugation of 2.6 macrocycle per antibody. Labeling of this immunoconjugate with indium-111 was performed in 75% yield after 1 h at 37 °C, and the proportion of (111)In-DOTAGA-trastuzumab reached 97% after purification. The affinity of DOTAGA-trastuzumab was 5.5 ± 0.6 nM as evaluated by in vitro saturation assays using HCC1954 breast cancer cell line. SPECT/CT imaging and biodistribution studies were performed in mice bearing breast cancer BT-474 xenografts. BT-474 tumors were clearly visualized on SPECT images at 24, 48, and 72 h postinjection. The tumor uptake of [(111)In-DOTAGA]-trastuzumab reached 65%ID/g 72 h postinjection. These results show that the DOTAGA BFC appears to be a valuable tool for biologics conjugation.

Early imaging or blood biomarkers of tumor response is needed to customize anti-tumor therapy on an individual basis. This study evaluates the sensitivity and relevance of five potential MRI biomarkers. Sixty nude rats were implanted with human glioma cells (U-87 MG) and randomized into three groups: one group received an anti-angiogenic treatment (Sorafenib), a second a cytotoxic drug [1,3-bis(2-chloroethyl)-1-nitrosourea, BCNU (Carmustine)] and a third no treatment. The tumor volume, apparent diffusion coefficient (ADC) of water, blood volume fraction (BVf), microvessel diameter (vessel size index, VSI) and vessel wall integrity (contrast enhancement, CE) were monitored before and during treatment. Sorafenib reduced tumor CE as early as 1 day after treatment onset. By 4 days after treatment onset, tumor BVf was reduced and tumor VSI was increased. By 14 days after treatment onset, ADC was increased and the tumor growth rate was reduced. With BCNU, ADC was increased and the tumor growth rate was reduced 14 days after treatment onset. Thus, the estimated MRI parameters were sensitive to treatment at different times after treatment onset and in a treatment-dependent manner. This study suggests that multiparametric MR monitoring could allow the assessment of new anti-tumor drugs and the optimization of combined therapies.

Purpose:To describe and present some preliminary results for a novel algorithm for segmentation with gray-scale connectedness as a means to separate arteries and veins in magnetic resonance angiography (MRA). Materials and Methods:The proposed algorithm, SeparaSeed, uses the gray-scale degree of connectedness as a tool to find the zone surrounding each vessel, in order to split the original volume into its different vessel components. In contrast to traditional segmentation methods, no gray-scale information is lost in the process. The segmentation is performed in one step, resulting in a partition of the initial volume into a chosen number of regions of interest (ROIs). Finally, visualization is achieved by projecting the 3D vessel trees to 2D using the common maximum intensity projection (MIP). The algorithm was tested in two MRA data sets of the vessels of the pelvis acquired after injection of an intravascular contrast agent and in one data set of the vessels of the neck with gadolinium. Results:In all data sets, a large proportion of the venous signal was removed while preserving that of the arteries, thus improving visualization of the relevant vessels. Conclusion:Separation of arteries and veins is feasible with the proposed algorithm with a moderate amount of interaction.

Prostate cancer is the most common cancer in men over 50 years of age and it has been shown that nuclear magnetic resonance spectra are sensitive enough to distinguish normal and cancer. In this paper, we propose a classification technique of spectra from magnetic resonance spectroscopy. We studied automatic classification with and without quantification of metabolite signals. The dataset is composed of 22 patient datasets with a biopsy-proven cancer, from which we extracted 2464 spectra from the whole prostate and of which 1062 were localised in the peripheral zone. The spectra were manually classed into 3 different categories by a spectroscopist with 4 years experience in clinical spectroscopy of prostate cancer: undetermined, healthy and pathologic. We used different preprocessing methods (module, phase correction only, phase correction and baseline correction) as input for Support Vector Machine and for Multilayer Perceptron, and we compared the results with those from the expert. If we class only healthy and pathologic spectra we reach a total error rate of 4.51%. However, if we class all spectra (undetermined, healthy and pathologic) the total error rate rises to 11.49%. We have shown in this paper that the best results are obtained using the * Corresponding author * * Principal corresponding author Email addresses: sebastien.parfait@u-bourgogne.fr (S. Parfait), johel.miteran@u-bourgogne.fr (J. Mitéran) Preprint submitted to Biomedical Signal Processing and ControlDecember 12, 2011 pre-processed spectra without quantification as input for the classifiers and we confirm that Support Vector Machine are more efficient than Multilayer Perceptron in processing high dimensional data.