EGFR and EGFRvIII undergo stress- and EGFR kinase inhibitor-induced mitochondrial translocalization: A potential mechanism of EGFR-driven antagonism of apoptosis

<p>Abstract</p> <p>Background</p> <p>Epidermal growth factor receptor (EGFR) plays an essential role in normal development, tumorigenesis and malignant biology of human cancers, and is known to undergo intracellular trafficking to subcellular organelles. Although several studies have shown that EGFR translocates into the mitochondria in cancer cells, it remains unclear whether mitochondrially localized EGFR has an impact on the cells and whether EGFRvIII, a constitutively activated variant of EGFR, undergoes mitochondrial transport similar to EGFR.</p> <p>Results</p> <p>We report that both receptors translocate into the mitochondria of human glioblastoma and breast cancer cells, following treatments with the apoptosis inducers, staurosporine and anisomycin, and with an EGFR kinase inhibitor. Using mutant EGFR/EGFRvIII receptors engineered to undergo enriched intracellular trafficking into the mitochondria, we showed that glioblastoma cells expressing the mitochondrially enriched EGFRvIII were more resistant to staurosporine- and anisomycin-induced growth suppression and apoptosis and were highly resistant to EGFR kinase inhibitor-mediated growth inhibition.</p> <p>Conclusions</p> <p>These findings indicate that apoptosis inducers and EGFR-targeted inhibitors enhance mitochondrial translocalization of both EGFR and EGFRvIII and that mitochondrial accumulation of these receptors contributes to tumor drug resistance. The findings also provide evidence for a potential link between the mitochondrial EGFR pathway and apoptosis.</p

Digital Twin Based User-Centric Resource Management for Multicast Short Video Streaming

Multicast short video streaming (MSVS) can effectively reduce network traffic load by delivering identical video sequences to multiple users simultaneously. The existing MSVS schemes mainly rely on the aggregated video requests to reserve bandwidth and computing resources, which cannot satisfy users' diverse and dynamic service requirements, particularly when users' swipe behaviors exhibit spatiotemporal fluctuation. In this paper, we propose a user-centric resource management scheme based on the digital twin (DT) technique, which aims to enhance user satisfaction as well as reduce resource consumption. Firstly, we design a user DT (UDT)-assisted resource reservation framework. Specifically, UDTs are constructed for individual users, which store users' historical data for updating multicast groups and abstracting useful information. The swipe probability distributions and recommended video lists are abstracted from UDTs to predict bandwidth and computing resource demands. Parameterized sigmoid functions are leveraged to characterize multicast groups' user satisfaction. Secondly, we formulate a joint non-convex bandwidth and computing resource reservation problem which is transformed into a convex piecewise problem by utilizing a tangent function to approximately substitute the concave part. A low-complexity scheduling algorithm is then developed to find the optimal resource reservation decisions. Simulation results based on the real-world dataset demonstrate that the proposed scheme outperforms benchmark schemes in terms of user satisfaction and resource consumption.Comment: 13 pages, 11 figure

Identification of Cell Surface Proteins as Potential Immunotherapy Targets in 12 Pediatric Cancers

Technological advances now allow us to rapidly produce CARs and other antibody-derived therapeutics targeting cell surface receptors. To maximize the potential of these new technologies, relevant extracellular targets must be identified. The Pediatric Oncology Branch of the NCI curates a freely accessible database of gene expression data for both pediatric cancers and normal tissues, through which we have defined discrete sets of over-expressed transcripts in 12 pediatric cancer subtypes as compared to normal tissues. We coupled gene expression profiles to current annotation databases (i.e., Affymetrix, Gene Ontology, Entrez Gene), in order to categorize transcripts by their sub-cellular location. In this manner we generated a list of potential immune targets expressed on the cell surface, ranked by their difference from normal tissue. Global differences from normal between each of the pediatric tumor types studied varied, indicating that some malignancies expressed transcript sets that were more highly diverged from normal tissues than others. The validity of our approach is seen by our findings for pre-B cell ALL, where targets currently in clinical trials were top-ranked hits (CD19, CD22). For some cancers, reagents already in development could potentially be applied to a new disease class, as exemplified by CD30 expression on sarcomas. Moreover, several potential new targets shared among several pediatric solid tumors are herein identified, such as MCAM (MUC18), metadherin (MTDH), and glypican-2 (GPC2). These targets have been identified at the mRNA level and are yet to be validated at the protein level. The safety of targeting these antigens has yet to be demonstrated and therefore the identified transcripts should be considered preliminary candidates for new CAR and therapeutic antibody targets. Prospective candidate targets will be evaluated by proteomic analysis including Westerns and immunohistochemistry of normal and tumor tissues

Improving Conversational Recommendation Systems via Counterfactual Data Simulation

Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developing capable CRSs, they typically rely on sufficient training data for training. Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data. To address this issue, in this paper, we propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs. Our approach is developed based on the framework of counterfactual data augmentation, which gradually incorporates the rewriting to the user preference from a real dialogue without interfering with the entire conversation flow. To develop our approach, we characterize user preference and organize the conversation flow by the entities involved in the dialogue, and design a multi-stage recommendation dialogue simulator based on a conversation flow language model. Under the guidance of the learned user preference and dialogue schema, the flow language model can produce reasonable, coherent conversation flows, which can be further realized into complete dialogues. Based on the simulator, we perform the intervention at the representations of the interacted entities of target users, and design an adversarial training method with a curriculum schedule that can gradually optimize the data augmentation strategy. Extensive experiments show that our approach can consistently boost the performance of several competitive CRSs, and outperform other data augmentation methods, especially when the training data is limited. Our code is publicly available at https://github.com/RUCAIBox/CFCRS.Comment: Accepted by KDD 2023. Code: https://github.com/RUCAIBox/CFCR