ProNGF expression has been linked to several types of cancers including breast cancer, and we have previously shown that proNGF stimulates breast cancer invasion in an autocrine manner through membrane receptors sortilin and TrkA. However, little is known regarding TrkA-associated protein partners upon proNGF stimulation. By proteomic analysis and proximity ligation assays, we found that proNGF binding to sortilin induced sequential formation of the functional sortilin/TrkA/EphA2 complex, leading to TrkA-phosphorylation dependent Akt activation and EphA2-dependent Src activation. EphA2 inhibition using siRNA approach abolished proNGF-stimulated clonogenic growth of breast cancer cell lines. Combinatorial targeting of TrkA and EphA2 dramatically reduced colony formation in vitro, primary tumor growth and metastatic dissemination towards the brain in vivo. Finally, proximity ligation assay in breast tumor samples revealed that increased TrkA/EphA2 proximity ligation assay signals were correlated with a decrease of overall survival in patients. All together, these data point out the importance of TrkA/EphA2 functional association in proNGF-induced tumor promoting effects, and provide a rationale to target proNGF/TrkA/EphA2 axis by alternative methods other than the simple use of tyrosine kinase inhibitors in breast cancer.

There is accumulating evidence that TrkA and its ligand Nerve Growth Factor (NGF) are involved in cancer development. Staurosporine derivatives such as K252a and lestaurtinib have been developed to block TrkA kinase signaling, but no clinical trial has fully demonstrated their therapeutic efficacy. Therapeutic failures are likely due to the existence of intrinsic signaling pathways in cancer cells that impede or bypass the effects of TrkA tyrosine kinase inhibitors. To verify this hypothesis, we combined different approaches including mass spectrometry proteomics, co-immunoprecipitation and proximity ligation assays. We found that NGF treatment induced CD44 binding to TrkA at the plasma membrane and subsequent activation of the p115RhoGEF/RhoA/ROCK1 pathway to stimulate breast cancer cell invasion. The NGF-induced CD44 signaling was independent of TrkA kinase activity. Moreover, both TrkA tyrosine kinase inhibition with lestaurtinib and CD44 silencing with siRNA inhibited cell growth in vitro as well as tumor development in mouse xenograft model; combined treatment significantly enhanced the antineoplastic effects of either treatment alone. Altogether, our results demonstrate that NGF-induced tyrosine kinase independent TrkA signaling through CD44 was sufficient to maintain tumor aggressiveness. Our findings provide an alternative mechanism of cancer resistance to lestaurtinib and indicate that dual inhibition of CD44 and TrkA tyrosine kinase activity may represent a novel therapeutic strategy.

Glioblastoma multiform is the most common and aggressive brain tumor with a worse prognostic. Ionizing radiation is a cornerstone in the treatment of glioblastome with chemo-radiation association being the actual standard. As a paradoxal effect, it has been suggested that radiotherapy could have a deleterious effect on local recurrence of cancer. In vivo studies have studied the effect of radiotherapy on biological modification and pathogenous effect of cancer cells. It seems that ionizing radiations with photon could activate oncogenic pathways in glioblastoma cell lines. We realized a review of the literature of photon-enhanced effect on invasion and migration of glioblastoma cells by radiotherapy.

Radiation therapy is a keystone treatment in cancer. Photon radiation has proved its benefits in overall survival in many clinical studies. However, some patients present local recurrences or metastases when cancer cells survive to treatment.Metastasis is a process which includes adhesion of the cell to the extracellular matrix, degradation of the matrix by proteases, cell motility, intravasation in blood or lymphatic vessels, extravasation in distant parenchyma and development of cell colonies.Several studies demonstrated that ionizing radiation might promote migration and invasion of tumor cells by intricate implications in the micro-environment, cell cell junctions, extracellular matrix junctions, proteases secretion, and induction of epithelial mesenchymal transition. This review reports various cellular pathways involved in the photon-enhanced cell invasion process for which potential therapeutic target may be employed for enhancing antitumor effectiveness. Understanding these mechanisms could lead to therapeutic strategies to counter the highly invasive cell lines via specific inhibitors or carbon-ion therapy. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

Le glioblastome multiforme est la tumeur cérébrale primitive la plus fréquente et la plus agressive avec un mauvais pronostic. Les radiations ionisantes sont un traitement clé dans le traitement du glioblastome avec comme standard actuel une association radio-chimiothérapie. Il a été suggéré, comme effet paradoxal, que la radiothérapie pourrait avoir un effet délétère sur la récidive locale des cancers. Des études in vivo ont étudié l’effet de la radiothérapie sur le pouvoir pathogène et sur les modifications biologiques cellulaires. Il semblerait que les radiations ionisantes par photons pourraient activer des signalisations oncogènes dans les lignées cellulaires de glioblastomes. Nous avons réalisé une revue de la littérature sur l’induction des propriétés invasives et de migration des cellules de glioblastomes par la radiothérapie.

In this work, a novel ferrocenyl complex (ansa-FcdiOH) was assessed for brain tumor therapy through stealth lipid nanocapsules (LNCs). Stealth LNCs, prepared according to a one-step process, showed rapid uptake by cancer cells and extended blood circulation time. The ferrocenyl complex was successfully encapsulated into these LNCs measuring 40 nm with a high loading capacity (6.4%). In vitro studies showed a potent anticancer effect of ansa-FcdiOH on 9L cells with a low IC50 value (0.1 mu M) associated with an oxidative stress and a dose-dependent alteration of the cell cycle. Repeated intravenous injections of stealth ansa-FcdiOH LNCs in ectopic glioma bearing rats induced a significant tumor growth inhibition, supported by a reduced number of proliferative cells in tumors compared to control group. Additionally, no liver damage was observed in treated animals. These results indicated that stealth ansa-FcdiOH LNCs might be considered as a potential new approach for cancer chemotherapy.From the Clinical Editor: In this study, a novel ferrocenyl complex was assessed for brain tumor therapy through stealth lipid nanocapsules, demonstrating no liver damage, and superior tumor volume reduction compared to saline and stealth lipid nanocapsules alone in an ectopic glioma model. (C) 2014 Elsevier Inc. All rights reserved.

Cancer stem cells are a subject of increasing interest in oncology. In particular, several data suggest that cancer stem cells are involved in the mechanisms of tumor radioresistance, and may explain the therapeutic failures after radiotherapy. Because of its poor prognosis and high recurrence rate after irradiation, glioblastoma model is often studied in the search for new radiosensitizers. There are several preclinical data suggesting that cancer stem cells could be a potential therapeutic target for improving the biological effectiveness of radiation therapy. Through the example of glioblastoma, we review the main signaling pathways involved in the mechanisms of radiation resistance of cancer stem cells and for which pharmacological targeting could potentially enhance tumor radiosensitivity.

Cancer stem cells are a subject of increasing interest in oncology. In particular, several data suggest that cancer stem cells are involved in the mechanisms of tumor radioresistance, and may explain the therapeutic failures after radiotherapy. Because of its poor prognosis and high recurrence rate after irradiation, glioblastoma model is often studied in the search for new radiosensitizers. There are several preclinical data suggesting that cancer stem cells could be a potential therapeutic target for improving the biological effectiveness of radiation therapy. Through the example of glioblastoma, we review the main signaling pathways involved in the mechanisms of radiation resistance of cancer stem cells and for which pharmacological targeting could potentially enhance tumor radiosensitivity.

This paper deals with minimum time trajectory optimization along a specified path subject to thermal constraints. We point out here that robots are often integrated into complex robotic cells, and the interactions between the robot and its environment are often difficult or even impossible to model. The structure of the optimization problem allows us to decompose the optimization into two levels, the first being based on models and results of the theory of the calculus of variations, the second being based on measurements and derivative free algorithms. This decomposition allows us to optimize the velocity profiles efficiently without any advance knowledge of the interactions between the robot and its environment. We propose here two numerical algorithms for these two levels of the decomposition which show good convergence properties. The resulting optimal velocity profiles are 5—10% faster than classical profiles, and have been executed successfully on a real Stäubli Rx90 manipulator robot.

We propose here to deal with the optimization of velocity profiles of manipulator robots with a minimum time criterion subject to thermal constraints. This paper deals with the real impact of thermal limitations on optimal velocity profiles and the methods to calculate the corresponding optimal trajectories. We first calculate analytically the optimal solution in a simple case in order to verify the validity of the numerical algorithm and also to present a general methodology to calculate optimal trajectories in robotics using results from the theory of calculus of variations and not from the theory of optimal control. We derive then a numerical algorithm based on the discretization of the time law through an interpolation with non uniform cubic splines. This algorithm shows robust and efficient convergence properties and the trajectories thus generated were executed successfully on a Staubli Rtimes90

Scene flow represents the 3-D motion of points in the scene, just as optical flow is related to their 2-D motion in the images. As opposed to classical methods which compute scene flow from optical flow, we propose to compute it by tracking 3-D points and surface elements (surfels) in a multi-camera setup (at least two cameras are needed). Two methods are proposed: in the first one, the translation of each 3-D point is found by matching the neighborhoods of its 2-D projections in each camera between two time steps; in the second one, the full pose of a surfel is recovered by matching the image of its projection with a texture template attached to the surfel, and visibility changes caused by occlusion or rotation of surfels are handled. Both methods detect lost or untrackable points and surfels. They were designed for real-time execution and can be used for fast extraction of scene flow from multi-camera sequences.

Abstract: This paper deals with minimum,time trajectory optimization along a specied path subject to thermal constraints. We point out here that robots are often integrated in complex robotic cells, and the interactions between the robot and its environment are often dicult,or even impossible to modelize. The structure of the optimization problem allows us to decompose the optimization in two levels, the rst one being based on models and results of the theory of the calculus of variations, the second one being based on measurements and derivative free algorithms. This decomposition allows us to optimize the velocity proles ecien tly without knowing in advance the interactions between the robot and is environment. We propose here two numerical algorithms for these two levels of the decomposition which show good convergence properties. The resulting optimal velocity proles are 5 to 10% faster than classical ones, and have been executed on successfully on a real St aubli Rx90 manipulator robot. Key-words: Robotics, Trajectory, numerical optimization, calculus of variations, thermal model, derivative

This paper deals with optimizing the task cycle time of industrial robots integrated in complex robot cells. Trajectory optimizers are usually based on models and can't properly deal with uncertainties due to interactions between the robot and its environment. We propose here a trajectory optimizer with hardware in the loop which can take into account constraints such as maximum authorized temperature and maximum authorized torque. Our approach is based on unconstrained optimization algorithms without derivatives and penalty methods. Experiments on real industrial applications showed good robustness properties of this algorithm even with a high number of parameters and with changes of the robot task.

This paper deals with minimum time trajectory optimization along a specified path subject to thermal constraints. We point out here that robots are often integrated in complex robotic cells, and the interactions between the robot and its environment are often difficult or even impossible to modelize. The structure of the optimization problem allows us to decompose the optimization in two levels, the first one being based on models and results of the theory of the calculus of variations, the second one being based on measurements and derivative free algorithms. This decomposition allows us to optimize the velocity profiles efficiently without knowing in advance the interactions between the robot and is environment. We propose here two numerical algorithms for these two levels of the decomposition which show good convergence properties. The resulting optimal velocity profiles are 5 to 10\% faster than classical ones, and have been executed on successfully on a real Staubli Rx90 manipulator robot.