Inhibition of histone deacetylase 6 (HDAC6) protects against vincristine-induced peripheral neuropathies and inhibits tumor growth

As cancer is becoming more and more a chronic disease, a large proportion of patients is confronted with devastating side effects of certain anti-cancer drugs. The most common neurological complications are painful peripheral neuropathies. Chemotherapeutics that interfere with microtubules, including plant-derived vinca-alkaloids such as vincristine, can cause these chemotherapy-induced peripheral neuropathies (CIPN). Available treatments focus on symptom alleviation and pain reduction rather than prevention of the neuropathy. The aim of this study was to investigate the potential of specific histone deacetylase 6 (HDAC6) inhibitors as a preventive therapy for CIPN using multiple rodent models for vincristine-induced peripheral neuropathies (VIPN). HDAC6 inhibition increased the level of acetylated α-tubulin in tissues of rodents undergoing vincristine-based chemotherapy, which correlates to a reduced severity of the neurological symptoms, both at the electrophysiological and the behavioral level. Mechanistically, disturbances in axonal transport of mitochondria is considered as an important contributing factor in the pathophysiology of VIPN. As vincristine interferes with the polymerization of microtubules, we investigated whether disturbances in axonal transport could contribute to VIPN. We observed that increasing α-tubulin acetylation through HDAC6 inhibition restores vincristine-induced defects of axonal transport in cultured dorsal root ganglion neurons. Finally, we assured that HDAC6-inhibition offers neuroprotection without interfering with the anti-cancer efficacy of vincristine using a mouse model for acute lymphoblastic leukemia. Taken together, our results emphasize the therapeutic potential of HDAC6 inhibitors with beneficial effects both on vincristine-induced neurotoxicity, as well as on tumor proliferation. ispartof: Neurobiology of Disease vol:111 pages:59-69 ispartof: location:United States status: publishe

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe

An untrained and unsupervised method for MRI brain tumor segmentation

© 2016 IEEE. We present a fully-automated MRI brain tumor segmentation method that does not require any manually annotated training data. The method is independent of the scanner or acquisition protocol and is directly applicable to any individual patient image. An Expectation Maximization-approach is used to estimate intensity models for both normal and tumorous tissue. The segmentation is represented by a level-set that is iteratively updated to label voxels as normal or tumorous, based on which intensity model explains the voxels' intensity the best. The method is compared with the method by Menze et al. [1], which is considered to be a benchmark for unsupervised tumor segmentation. The performance of our method for segmenting the tumor volume is summarized by an average Dice score of 0.87 ± 0.06 on the training data set of the MICCAI BraTS Challenge 2012-2013.Haeck T., Maes F., Suetens P., ''An untrained and unsupervised method for MRI brain tumor segmentation'', Proceedings 13th IEEE international symposium on biomedical imaging - ISBI 2016, pp. 265-268, April 13-16, 2016, Prague, Czech Republic.status: publishe

Feasibility of atlas-based segmentation of the brain in the presence of tumor by a weighted least-squares demons algorithm

Haeck T., Dhollander T., Maes F., Sunaert S., Suetens P., ''Feasibility of atlas-based segmentation of the brain in the presence of tumor by a weighted least-squares demons algorithm'', ISMRM 21st annual meeting & exhibition, April 20-26, 2013, Salt Lake City, Utah, USA.status: publishe

Interpolation of high angular resolution diffusion imaging data by imposing distances on Q-space

Haeck T., Dhollander T., Van Hecke W., Maes F., Sunaert S., Suetens P., ''Interpolation of high angular resolution diffusion imaging data by imposing distances on Q-space'', Proceedings CDMRI’11 - MICCAI 2011 workshop on computational diffusion MRI, September 22, 2011, Toronto, Canada.status: publishe

Synthesis and SAR assessment of novel Tubathian analogs in the pursuit of potent and selective HDAC6 inhibitors

The synthesis of novel isoform-selective HDAC inhibitors is considered to be an important, emerging field in medicinal chemistry. In this paper, the preparation and assessment of thirteen selective HDAC6 inhibitors is disclosed, elaborating on a previously developed thiaheterocyclic Tubathian series. All compounds were evaluated in vitro for their ability to inhibit HDAC6, and a selection of five potent compounds was further screened toward all HDAC isoforms (HDAC1-11). The capability of these Tubathian analogs to inhibit alpha-tubulin deacetylation was assessed as well, and ADME/Tox data were collected. This thorough SAR evaluation revealed that the oxidized, para-substituted hydroxamic acids can be recognized as valuable lead structures in the pursuit of novel potent and selective HDAC6 inhibitors

Optimized preoperative motor cortex mapping in brain tumors using advanced processing of transcranial magnetic stimulation data

BACKGROUND AND OBJECTIVE: Transcranial magnetic stimulation (TMS) is a useful technique to help localize motor function prior to neurosurgical procedures. Adequate modelling of the effect of TMS on the brain is a prerequisite to obtain reliable data. METHODS: Twelve patients were included with perirolandic tumors to undergo TMS-based motor mapping. Several models were developed to analyze the mapping data, from a projection to the nearest brain surface to motor evoked potential (MEP) amplitude informed weighted average of the induced electric fields over a multilayer detailed individual head model. The probability maps were compared with direct cortical stimulation (DCS) data in all patients for the hand and in three for the foot. The gold standard was defined as the results of the DCS sampling (with on average 8 DCS-points per surgery) extrapolated over the exposed cortex (of the tailored craniotomy), and the outcome parameters were based on the similarity of the probability maps with this gold standard. RESULTS: All models accurately gauge the location of the motor cortex, with point-cloud based mapping algorithms having an accuracy of 83-86%, with similarly high specificity. To delineate the whole area of the motor cortex representation, the model based on the weighted average of the induced electric fields calculated with a realistic head model performs best. The optimal single threshold to visualize the field based maps is 40% of the maximal value for the anisotropic model and 50% for the isotropic model, but dynamic thresholding adds information for clinical practice. CONCLUSIONS: The method with which TMS mapping data are analyzed clearly affects the predicted area of the primary motor cortex representation. Realistic electric field based modelling is feasible in clinical practice and improves delineation of the motor cortex representation compared to more simple point-cloud based methods.status: publishe