13,729 research outputs found

    A preclinical model for the ATLL lymphoma subtype with insights into the role of microenvironment in HTLV-1-mediated lymphomagenesis

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    Abstract \uef7f View references (83) Adult T cell Leukemia/Lymphoma (ATLL) is a mature T cell malignancy associated with Human T cell Leukemia Virus type 1 (HTLV-1) infection. Among its four main clinical subtypes, the prognosis of acute and lymphoma variants remains poor. The long latency (3-6 decades) and low incidence (3-5%) of ATLL imply the involvement of viral and host factors in full-blown malignancy. Despite multiple preclinical and clinical studies, the contribution of the stromal microenvironment in ATLL development is not yet completely unraveled. The aims of this study were to investigate the role of the host microenvironment, and specifically fibroblasts, in ATLL pathogenesis and to propose a murine model for the lymphoma subtype. Here we present evidence that the oncogenic capacity of HTLV-1-immortalized C91/PL cells is enhanced when they are xenotransplanted together with human foreskin fibroblasts (HFF) in immunocompromised BALB/c Rag2-/-\u3b3c -/-mice. Moreover, cell lines derived from a developed lymphoma and their subsequent in vivo passages acquired the stable property to induce aggressive T cell lymphomas. In particular, one of these cell lines, C91/III cells, consistently induced aggressive lymphomas also in NOD/SCID/IL2R\u3b3c KO (NSG) mice. To dissect the mechanisms linked to this enhanced tumorigenic ability, we quantified 45 soluble factors released by these cell lines and found that 21 of them, mainly pro-inflammatory cytokines and chemokines, were significantly increased in C91/III cells compared to the parental C91/PL cells. Moreover, many of the increased factors were also released by human fibroblasts and belonged to the known secretory pattern of ATLL cells. C91/PL cells co-cultured with HFF showed features reminiscent of those observed in C91/III cells, including a similar secretory pattern and a more aggressive behavior in vivo. On the whole, our data provide evidence that fibroblasts, one of the major stromal components, might enhance tumorigenesis of HTLV-1-infected and immortalized T cells, thus throwing light on the role of microenvironment contribution in ATLL pathogenesis. We also propose that the lymphoma induced in NSG mice by injection with C91/III cells represents a new murine preclinical ATLL model that could be adopted to test novel therapeutic interventions for the aggressive lymphoma subtype

    Micro-CT Image-Derived Metrics Quantify Arterial Wall Distensibility Reduction in a Rat Model of Pulmonary Hypertension

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    We developed methods to quantify arterial structural and mechanical properties in excised rat lungs and applied them to investigate the distensibility decrease accompanying chronic hypoxia-induced pulmonary hypertension. Lungs of control and hypertensive (three weeks 11% O2) animals were excised and a contrast agent introduced before micro-CT imaging with a special purpose scanner. For each lung, four 3D image data sets were obtained, each at a different intra-arterial contrast agent pressure. Vessel segment diameters and lengths were measured at all levels in the arterial tree hierarchy, and these data used to generate features sensitive to distensibility changes. Results indicate that measurements obtained from 3D micro-CT images can be used to quantify vessel biomechanical properties in this rat model of pulmonary hypertension and that distensibility is reduced by exposure to chronic hypoxia. Mechanical properties can be assessed in a localized fashion and quantified in a spatially-resolved way or as a single parameter describing the tree as a whole. Micro-CT is a nondestructive way to rapidly assess structural and mechanical properties of arteries in small animal organs maintained in a physiological state. Quantitative features measured by this method may provide valuable insights into the mechanisms causing the elevated pressures in pulmonary hypertension of differing etiologies and should become increasingly valuable tools in the study of complex phenotypes in small-animal models of important diseases such as hypertension

    METTL13 methylation of eEF1A increases translational output to promote tumorigenesis

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    Increased protein synthesis plays an etiologic role in diverse cancers. Here, we demonstrate that METTL13 (methyltransferase-like 13) dimethylation of eEF1A (eukaryotic elongation factor 1A) lysine 55 (eEF1AK55me2) is utilized by Ras-driven cancers to increase translational output and promote tumorigenesis in vivo. METTL13-catalyzed eEF1A methylation increases eEF1A's intrinsic GTPase activity in vitro and protein production in cells. METTL13 and eEF1AK55me2 levels are upregulated in cancer and negatively correlate with pancreatic and lung cancer patient survival. METTL13 deletion and eEF1AK55me2 loss dramatically reduce Ras-driven neoplastic growth in mouse models and in patient-derived xenografts (PDXs) from primary pancreatic and lung tumors. Finally, METTL13 depletion renders PDX tumors hypersensitive to drugs that target growth-signaling pathways. Together, our work uncovers a mechanism by which lethal cancers become dependent on the METTL13-eEF1AK55me2 axis to meet their elevated protein synthesis requirement and suggests that METTL13 inhibition may constitute a targetable vulnerability of tumors driven by aberrant Ras signaling.We thank Pal Falnes, Jerry Pelletier, and Julien Sage for helpful discussion, Lauren Brown and William Devine for SDS-1-021, and members of the Gozani and Mazur laboratories for critical reading of the manuscript. This work was supported in part by grants from the NIH to S.M.C. (K99CA190803), M.P.K. (5K08CA218690-02), J.A.P. (R35GM118173), M.C.B. (1DP2HD084069-01), J.S. (1R35GM119721), I.T. (R01CA202021), P.K.M. (R00CA197816, P50CA070907, and P30CA016672), and O.G. (R01GM079641). J.E.E. received support from Stanford ChEM-H, and A.M. was supported by the MD Anderson Moonshot Program. I.T. is a Junior 2 Research Scholar of the Fonds de Recherche du Quebec - Sante (FRQ-S). P.K.M. is supported by the Neuroendocrine Tumor Research Foundation and American Association for Cancer Research and is the Andrew Sabin Family Foundation Scientist and CPRIT scholar (RR160078). S.H. is supported by a Deutsche Forschungsgemeinschaft Postdoctoral Fellowship. J.W.F. is supported by 5T32GM007276. (K99CA190803 - NIH; 5K08CA218690-02 - NIH; R35GM118173 - NIH; 1DP2HD084069-01 - NIH; 1R35GM119721 - NIH; R01CA202021 - NIH; R00CA197816 - NIH; P50CA070907 - NIH; P30CA016672 - NIH; R01GM079641 - NIH; Stanford ChEM-H; MD Anderson Moonshot Program; Neuroendocrine Tumor Research Foundation; American Association for Cancer Research; Deutsche Forschungsgemeinschaft Postdoctoral Fellowship; 5T32GM007276)Supporting documentationAccepted manuscrip

    Fractal feature analysis of the human brain structures in neuroanatomy changes

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    The paper presents a method to examine brain structures in neuroanatomy changes which result in dementia in magnetic resonance images by using fractal analysis techniques. We measured the relationship between the fractal dimensions of white matter and grey brain matter and the relationship between the fractal dimensions of the forebrain and the white brain matter. The investigation showed that the relationship between the fractal dimensions of brain in neuroanatomy changes differ from that in the normal brain

    Radioresistance in lung cancer: a molecular approach

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    Lung cancer represents one-quarter of the deaths caused by cancer. Despite the advances in treatments, chemo and radiotherapy remain the foundation of treatment for this disease, and the survival rate is still at 10%, mainly due to the recurrences after treatment. Tumors that re-emerge after a radiation treatment are usually radioresistant and challenging to treat. This research shows that lung cancer cells that have survived treatment with radiation therapy show characteristics of cancer stem cells and epithelial to mesenchymal transition. Moreover, radioresistant non-small cell lung cancer exhibited alterations of critical prosurvival signaling pathways, higher expression and activation of DNA repair genes, and altered expression of cytokines and receptors

    Cancer diagnosis using deep learning: A bibliographic review

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    In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements
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