Cardiac metabolic deregulation induced by the tyrosine kinase receptor inhibitor sunitinib is rescued by endothelin receptor antagonism

International audienceThe growing field of cardio-oncology addresses the side effects of cancer treatment on the cardiovascular system. Here, we explored the cardiotoxicity of the antiangiogenic therapy, sunitinib, in the mouse heart from a diagnostic and therapeutic perspective. We showed that sunitinib induces an anaerobic switch of cellular metabolism within the myocardium which is associated with the development of myocardial fibrosis and reduced left ventricular ejection fraction as demonstrated by echocardiography. The capacity of positron emission tomography with [ 18 F]fluorodeoxyglucose to detect the changes in cardiac metabolism caused by sunitinib was dependent on fasting status and duration of treatment. Pan proteomic analysis in the myocardium showed that sunitinib induced (i) an early metabolic switch with enhanced glycolysis and reduced oxidative phosphorylation, and (ii) a metabolic failure to use glucose as energy substrate, similar to the insulin resistance found in type 2 diabetes. Co-administration of the endothelin receptor antagonist, macitentan, to sunitinib-treated animals prevented both metabolic defects, restored glucose uptake and cardiac function, and prevented myocardial fibrosis. These results support the endothelin system in mediating the cardiotoxic effects of sunitinib and endothelin receptor antagonism as a potential therapeutic approach to prevent cardiotoxicity. Furthermore, metabolic and functional imaging can monitor the cardiotoxic effects and the benefits of endothelin antagonism in a theranostic approach

Imaging of response to anti-angiogenic drugs

Les nouveaux traitements, comme les molécules antiangiogéniques, agissent sur des cibles spécifiques. Leur effet sur la taille tumorale est parfois absent ou retardé. De nouvelles techniques d’imagerie fonctionnelle s’intéressent à une caractéristique physiologique plutôt que la taille tumorale, et pourraient mettre en évidence des modifications en réponse au traitement apparaissant plus précocement. L’imagerie dynamique de la microcirculation suit la biodistribution d’un agent de contraste, et analyse la vascularisation tumorale. L’imagerie par résonance magnétique pondérée en diffusion permet de distinguer l’eau libre, de l’eau restreinte dans les tissus, reflétant la cellularité tumorale. L’imagerie par résonance magnétique par effet BOLD reflète l’oxygénation tissulaire en quantifiant le rapport déoxy/oxyhémoglobine. Les études testant ces techniques sont cependant préliminaires et nécessitent des études à grande échelle pour évaluer leur rôle dans la réponse aux traitements ciblés en oncologie.New therapies, such as anti-angiogenic drugs, target specific molecules. Their effect on tumor size is sometimes absent or delayed. New techniques of functional imaging do not detect changes in size, but rather a physiological characteristic, and could reveal changes in response to treatment which arise earlier. Dynamic contrast-enhanced (DCE) imaging follows the biodistribution of a contrast agent and analyzes tumor vascularization. Diffusion-weighted magnetic resonance imaging quantifies restriction to diffusion of water in tissues, reflecting tumor cellularity. BOLD magnetic resonance imaging reflects tissue oxygenation by quantifying the ratio between deoxygenated and oxygenated hemoglobin. Studies testing these techniques are still preliminary. It is therefore necessary to organize large scale studies to evaluate their potential role in response to targeted therapies in oncology

Machine Learning of Multi-Modal Tumor Imaging Reveals Trajectories of Response to Precision Treatment

"Funding: This work received funding from the Cancer Research for Personalized Medicine—CARPEM project (Site de Recherche Intégré sur le Cancer SIRIC), from the Plan Cancer Physicancer (grant C16025KS), and from the Région Ile-de-France. In vivo imaging was performed at the Life Imaging Facility of Université Paris Cité (Plateforme Imageries du Vivant - PIV), supported by France Life Imaging (grant ANR-11-INBS-0006) and Infrastructures Biologie-Santé (IBiSa). Nesrin Mansouri received a scholarship from the Ministère de l’Enseignement Supérieur et de la Recherche. This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement no. 101030046 of M. P.-L." "Acknowledgments: The authors thank Laure Fournier, Judith Favier, Charlotte Lussey-Lepoutre,Irène Buvat, Béatrice Berthon and J.M. Udías for rich scientific advice and discussions"The standard assessment of response to cancer treatments is based on gross tumor characteristics, such as tumor size or glycolysis, which provide very indirect information about the effect of precision treatments on the pharmacological targets of tumors. Several advanced imaging modalities allow for the visualization of targeted tumor hallmarks. Descriptors extracted from these images can help establishing new classifications of precision treatment response. We propose a machine learning (ML) framework to analyze metabolic–anatomical–vascular imaging features from positron emission tomography, ultrafast Doppler, and computed tomography in a mouse model of paraganglioma undergoing anti-angiogenic treatment with sunitinib. Imaging features from the follow-up of sunitinib-treated (n = 8, imaged once-per-week/6-weeks) and sham-treated (n = 8, imaged once-per-week/3-weeks) mice groups were dimensionally reduced and analyzed with hierarchical clustering Analysis (HCA). The classes extracted from HCA were used with 10 ML classifiers to find a generalized tumor stage prediction model, which was validated with an independent dataset of sunitinib-treated mice. HCA provided three stages of treatment response that were validated using the best-performing ML classifier. The Gaussian naive Bayes classifier showed the best performance, with a training accuracy of 98.7 and an average area under curve of 100. Our results show that metabolic–anatomical–vascular markers allow defining treatment response trajectories that reflect the efficacy of an anti-angiogenic drug on the tumor target hallmark.Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUECancer Research for Personalized Medicine—CARPEM project (Site de Recherche Intégré sur le Cancer SIRIC)Plan Cancer PhysicancerRégion Ile-de-Francee Life Imaging Facility of Université Paris Cité (Plateforme Imageries du Vivant - PIV)France Life ImagingInfrastructures Biologie-Santé (IBiSa)Ministère de l’Enseignement Supérieur et de la RechercheEuropean Union’s Horizon 2020 research and innovation programpu

Qualité de la modélisation en imagerie dynamique de la microcirculation avec injection d'un agent de contraste (nouveaux critères et applications en multimodalité)

L'imagerie dynamique de microcirculation dispose d'un potentiel important pour l'étude de nombreuses pathologies in vivo, en complément à l'imagerie conventionnelle. Or pour obtenir des cartes de paramètres microcirculatoires à partir des données dynamiques, une modélisation doit être effectuée. Les méthodes actuelles pour vérifier la qualité de cette modélisation n'étant pas satisfaisantes, le potentiel de l'imagerie dynamique en est fortement réduit. Nous montrons ici que pour étudier la modélisation, tant qualitativement que quantitativement, il est nécessaire de traiter séparément les questions de qualité d'ajustement et de robustesse,. Nous avons mis au point une nouvelle méthode, basée sur l'autocorrélation, pour estimer les amplitudes des composantes corrélées et non corrélées des signaux. Cette méthode nous a permis de corriger le coefficient de corrélation R et la matrice de covariance, et ainsi de définir de nouveaux critères de fiabilité et une matrice de covariance corrigée pour les remplacer. L'amélioration apportée par les nouveaux critères est démontrée sur simulations et sur données IRM dynamiques réelles. La matrice de covariance corrigée estime la robustesse et la redondance locale des paramètres. Elle peut être calculée conjointement pour compléter les nouveaux critères de fiabilité. Les améliorations apportées par les nouveaux indicateurs doivent faciliter le développement de l'imagerie de la microcirculation. L'intérêt des nouveaux indicateurs est illustré sur un grand panel de données d'imagerie. Ils constituent plus généralement de nouveaux outils de traitement du signal.The microcirculation dynamic imaging could be a relevant imaging when used in addition with more conventional medical imaging. The dynamic data are modeled, pixel by pixel, to provide microcirculation parameters maps. However there is no efficient tool to assess the modeling quality. The relevance of the parametric maps provided by the dynamic imaging is then limited. Here, we show that a qualitative and quantitative study of the modeling quality needs first to distinguish two questions : the quality of the data fits and the robusness for the random noise. To separate the questions, we designed a new autocorrelation based method which is able to estimate the amplitude of both the correlated and not correlated component of a signal. This method allowed us to correct the correlation coefficient R and the covariance matrix estimation. It allowed us to define new reliability criteria and a corrected covariance matrix to replace the more conventional indicators. It was shown, on simulated data and in MR data, that new reliabily criteria are obviously better than the R to assess fit quality. The corrected covariance matrix which assess the robustness and the redoundancy can be calculated in addition to the reliability criteria unlike conventional one which is limited to good data fits. Thus the modeling quality is obviously improved by the new indicators. It should improve the clinical use of microcirculation dynamic imaging where guaranties are needed against artefact. The interest of the new criteria is showed on many different dynamic data. More generaly the new indicators appear as new efficient tools for signal analysis.ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

Impacts humains sur les systèmes lacustres : exemple du Léman

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Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies

International audienceWe propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. In this approach we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is performed in a special way that selects factorial axes of Factor Correspondence Analysis in order to preserves contours. Then a spatio-temporal approach based on stochastic watershed is used to segment tumours. The results obtained are in accordance with the diagnosis of the medical doctors

Relations trophiques zooplancton-poissons dans le lac Leman : premiere approche de l'alimentation des larves de Coregonus lavaretus en conditions naturelles et semi-naturelles

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Anthropogenic and climate forcing on the long-term changes of planktonic rotifers in lake Geneva, Europe

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