The Roles of Tumor-Derived Exosomes in Cancer Pathogenesis

Exosomes are endosome-derived, 30–100 nm small membrane vesicles released by most cell types including tumor cells. They are enriched in a selective repertoire of proteins and nucleic acids from parental cells and are thought to be actively involved in conferring intercellular signals. Tumor-derived exosomes have been viewed as a source of tumor antigens that can be used to induce antitumor immune responses. However, tumor-derived exosomes also have been found to possess immunosuppressive properties and are able to facilitate tumor growth, metastasis, and the development of drug resistance. These different effects of tumor-derived exosomes contribute to the pathogenesis of cancer. This review will discuss the roles of tumor-derived exosomes in cancer pathogenesis, therapy, and diagnostics

Immunosuppressive Exosomes: A New Approach for Treating Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease and one of the leading causes of disability in the USA. Although certain biological therapies, including protein and antibodies targeting inflammatory factors such as the tumor necrosis factor, are effective in reducing symptoms of RA, these treatments do not reverse disease. Also, although novel gene therapy approaches have shown promise in preclinical and clinical studies to treat RA, it is still unclear whether gene therapy can be readily and safely applied to treat the large number of RA patients. Recently, nanosized, endocytic-derived membrane vesicles “exosomes” were demonstrated to function in cell-to-cell communication and to possess potent immunoregulatory properties. In particular, immunosuppressive DC-derived exosomes and blood plasma- or serum-derived exosomes have shown potent therapeutic effects in animal models of inflammatory and autoimmune disease including RA. This paper discusses the current knowledge on the production, efficacy, mechanism of action, and potential therapeutic use of immunosuppressive exosomes for arthritis therapy

Characterization of Tumor-Derived Exosomes and Their Role in Immune Regulation

Tumor cells usually express specific antigens that are potentially immunogenic; however, established tumors primarily induce immune tolerance. In the last decade, a population of small membrane vesicles, termed "exosomes", has gained increasing attention for their potential role in tumor immune regulation. Exosomes are formed in the late endocytic compartments and are released upon their fusion with the plasma membrane. They are secreted by various cell types, especially tumor cells and cells in the hematopoietic system. Although tumor-derived exosomes usually contain tumor antigens, they have been shown to exert diverse immunosuppressive effects. However, the ability of tumor-derived exosomes to induce antigen-specific immunosuppression has not been well examined. Also, the immunoregulatory effect of exosome-like vesicles in the blood circulation of tumor-bearing hosts remains unclear.In this thesis, we first investigate the role of tumor-derived exosomes in mediating antigen-specific immune suppression using ovalbumin (OVA) as a model tumor antigen. We demonstrate that exosomes derived from OVA-expressing tumor cell lines potently suppress OVA-specific delayed-type hypersensitivity (DTH) response. We also show that exosomes are mostly taken up by dendritic cells (DCs) after local administration, and the mRNA levels of TGF-beta1 and IL-4 in the draining lymph node were significantly elevated in correlation with suppression of the DTH response. Furthermore, tumor-derived exosomes affect the function of DCs in vitro by inhibiting their maturation and inducing TGF-beta1 production. These results suggest that tumor-derived exosomes are able to confer antigen-specific immune suppression possibly by DC-mediated mechanism.We further investigate the immunoregulatory effect of plasma-derived exosomes and demonstrate that plasma-derived exosomes isolated from mice bearing OVA-expressing tumors are able to suppress the OVA-specific DTH response. However, enrichment of tumor-derived exosomes in blood plasma was not identified and the suppressive effect is partially mediated by the MHC class II+ vesicle portion. The third part of the thesis discusses the B cell stimulatory and the consequent T cell inhibitory effect of exosomes derived from tumor cells with mycoplasma infection.The work presented in this thesis increases our understanding of the immunoregulatory role of tumor-derived exosomes, circulating exosomes in tumor-bearing hosts as well as exosomes derived from pathogen-infected tumor cells

Folding approach to topological order enriched by mirror symmetry

We develop a folding approach to study two-dimensional symmetry-enriched topological (SET) phases with the mirror reflection symmetry. Our folding approach significantly transforms the mirror SETs, such that their properties can be conveniently studied through previously known tools: (i) it maps the nonlocal mirror symmetry to an onsite Z[subscript 2] layer-exchange symmetry after folding the SET along the mirror axis, so that we can gauge the symmetry; (ii) it maps all mirror SET information into the boundary properties of the folded system, so that they can be studied by the anyon condensation theory—a general theory for studying gapped boundaries of topological orders; and (iii) it makes the mirror anomalies explicitly exposed in the boundary properties, i.e., strictly 2D SETs and those that can only live on the surface of a 3D system can be easily distinguished through the folding approach. With the folding approach, we derive a set of physical constraints on data that describes mirror SET, namely, mirror permutation and mirror symmetry fractionalization on the anyon excitations in the topological order. We conjecture that these constraints may be complete, in the sense that all solutions are realizable in physical systems. Several examples are discussed to justify this. Previously known general results on the classification and anomalies are also reproduced through our approach.China. Ministry of Science and Technology (Grant 2015CB921700)National Natural Science Foundation (China) (Grant 11874115)Perimeter Institute for Theoretical Physic

A spiking neural network model for obstacle avoidance in simulated prosthetic vision

Limited by visual percepts elicited by existing visual prosthesis, it’s necessary to enhance its functionality to fulfill some challenging tasks for the blind such as obstacle avoidance. This paper argues that spiking neural networks (SNN) are effective techniques for object recognition and introduces for the first time a SNN model for obstacle recognition to as- sist blind people wearing prosthetic vision devices by modelling and classifying spatio- temporal (ST) video data. The proposed methodology is based on a novel spiking neural network architecture, called NeuCube as a general framework for video data modelling in simulated prosthetic vision. As an integrated environment including spiking trains en- coding, input variable mapping, unsupervised reservoir training and supervised classifier training, the NeuCube consists of a spiking neural network reservoir (SNNr) and a dy- namic evolving spiking neural network classifier (deSNN). First, input data is captured by visual prosthesis, then ST feature extraction is utilized in the low-resolution prosthetic vi- sion generated by prostheses. Finally such ST features are fed to the NeuCube to output classification result of obstacle analysis for an early warning system to be activated. Ex- periments on collected video data and comparison with other computational intelligence methods indicate promising results. This makes it possible to directly utilize available neu- romorphic hardware chips, embedded in visual prostheses, to enhance significantly their functionality. The proposed NeuCube-based obstacle avoidance methodology provides use- ful guidance to the blind, thus offering a significant improvement of current prostheses and potentially benefiting future prosthesis wearers

Lattice model constructions for gapless domain walls between topological phases

Domain walls between different topological phases are one of the most interesting phenomena that reveal the non-trivial bulk properties of topological phases. Very recently, gapped domain walls between different topological phases have been intensively studied. In this paper, we systematically construct a large class of lattice models for gapless domain walls between twisted and untwisted gauge theories with arbitrary finite group $G$. As simple examples, we numerically study several finite groups(including both Abelian and non-Abelian finite group such as $S_3$) in $2$D using the state-of-the-art loop optimization of tensor network renormalization algorithm. We also propose a physical mechanism for understanding the gapless nature of these particular domain wall models. Finally, by taking advantage of the classification and construction of twisted gauge theories using group cohomology theory, we generalize such constructions into arbitrary dimensions, which might provide us a systematical way to understand gapless domain walls and topological quantum phase transitions.Comment: Non-Abelian examples adde

Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification

This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available glioma datasets are relatively moderate in size,and often accompanied with incomplete MRIs in different modalities. To tackle the commonly encountered problems of insufficiently large brain tumor datasets and incomplete modality of image for deep learning, we propose to add augmented brain MR images to enlarge the training dataset by employing a pairwise Generative Adversarial Network (GAN) model. The pairwise GAN is able to generate synthetic MRIs across different modalities. To achieve the patient-level diagnostic result, we propose a post-processing strategy to combine the slice-level glioma subtype classification results by majority voting. A two-stage course-to-fine training strategy is proposed to learn the glioma feature using GAN-augmented MRIs followed by real MRIs. To evaluate the effectiveness of the proposed scheme, experiments have been conducted on a brain tumor dataset for classifying glioma molecular subtypes: isocitrate dehydrogenase 1 (IDH1) mutation and IDH1 wild-type. Our results on the dataset have shown good performance (with test accuracy 88.82%). Comparisons with several state-of-the-art methods are also included

Deep semi-supervised learning for brain tumor classification

Background: This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently, many available glioma datasets often contain some unlabeled brain scans, and many datasets are moderate in size. Methods: We propose to exploit deep semi-supervised learning to make full use of the unlabeled data. Deep CNN features were incorporated into a new graph-based semi-supervised learning framework for learning the labels of the unlabeled data, where a new 3D-2D consistent constraint is added to make consistent classifications for the 2D slices from the same 3D brain scan. A deep-learning classifier is then trained to classify different glioma types using both labeled and unlabeled data with estimated labels. To alleviate the overfitting caused by moderate-size datasets, synthetic MRIs generated by Generative Adversarial Networks (GANs) are added in the training of CNNs. Results: The proposed scheme has been tested on two glioma datasets, TCGA dataset for IDH-mutation prediction (molecular-based glioma subtype classification) and MICCAI dataset for glioma grading. Our results have shown good performance (with test accuracies 86.53% on TCGA dataset and 90.70% on MICCAI dataset). Conclusions: The proposed scheme is effective for glioma IDH-mutation prediction and glioma grading, and its performance is comparable to the state-of-the-art