Low-level expression of HER2 and CK19 in normal peripheral blood mononuclear cells: relevance for detection of circulating tumor cells

<p>Abstract</p> <p>Background</p> <p>Detection of circulating tumor cells (CTC) in the blood of cancer patients may have prognostic and predictive significance. However, background expression of 'tumor specific markers' in peripheral blood mononuclear cells (PBMC) may confound these studies. The goal of this study was to identify the origin of Cytokeratin 19 (CK19) and HER-2 signal in PBMC and suggest an approach to enhance techniques involved in detection of CTC in breast cancer patients.</p> <p>Methods</p> <p>PBMC from healthy donors were isolated and fractionated into monocytes, lymphocytes, natural killer cells/granulocytes and epithelial populations using immunomagnetic selection and fluorescent cell-sorting for each cell type. RNA isolated from each fraction was analyzed for CK19, HER2 and Beta 2 microglobulin (B2M) using real-time qRT-PCR. Positive selection for epithelial cells and negative selection for NK/granulocytes were used in an attempt to reduce background expression of CK19 and HER2 markers.</p> <p>Results</p> <p>In normal PBMC, CK19 was expressed in the lymphocyte population while HER-2 expression was highest in the NK/granulocyte population. Immunomagnetic selection for epithelial cells reduced background CK19 signal to a frequency of <5% in normal donors. Using negative selection, the majority (74–98%) of HER2 signal could be removed from PBMC. Positive selection methods are variably effective at reducing these background signals.</p> <p>Conclusion</p> <p>We present a novel method to improve the specificity of the traditional method of detecting CTC by identifying the source of the background signals and reducing them by negative immunoselection. Further studies are warranted to improve sensitivity and specificity of methods of detecting CTC will prove to be useful tools for clinicians in determining prognosis and monitoring treatment responses of breast cancer patients.</p

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

Dynamic Subcarrier Allocation for Real-Time Traffic over Multiuser OFDM Systems

<p/> <p>A dynamic resource allocation algorithm to satisfy the packet delay requirements for real-time services, while maximizing the system capacity in multiuser orthogonal frequency division multiplexing (OFDM) systems is introduced. Our proposed cross-layer algorithm, called Dynamic Subcarrier Allocation algorithm for Real-time Traffic (DSA-RT), consists of two interactive components. In the medium access control (MAC) layer, the users' expected transmission rates in terms of the number of subcarriers per symbol and their corresponding transmission priorities are evaluated. With the above MAC-layer information and the detected subcarriers' channel gains, in the physical (PHY) layer, a modified Kuhn-Munkres algorithm is developed to minimize the system power for a certain subcarrier allocation, then a PHY-layer resource allocation scheme is proposed to optimally allocate the subcarriers under the system signal-to-noise ratio (SNR) and power constraints. In a system where the number of mobile users changes dynamically, our developed MAC-layer access control and removal schemes can guarantee the quality of service (QoS) of the existing users in the system and fully utilize the bandwidth resource. The numerical results show that DSA-RT significantly improves the system performance in terms of the bandwidth efficiency and delay performance for real-time services.</p

Anticancer immunity induced by a synthetic tumor-targeted CD137 agonist

Background In contrast to immune checkpoint inhibitors, the use of antibodies as agonists of immune costimulatory receptors as cancer therapeutics has largely failed. We sought to address this problem using a new class of modular synthetic drugs, termed tumor-targeted immune cell agonists (TICAs), based on constrained bicyclic peptides (Bicycles).Methods Phage libraries displaying Bicycles were panned for binders against tumor necrosis factor (TNF) superfamily receptors CD137 and OX40, and tumor antigens EphA2, Nectin-4 and programmed death ligand 1. The CD137 and OX40 Bicycles were chemically conjugated to tumor antigen Bicycles with different linkers and stoichiometric ratios of binders to obtain a library of low molecular weight TICAs (MW &lt;8 kDa). The TICAs were evaluated in a suite of in vitro and in vivo assays to characterize their pharmacology and mechanism of action.Results Linking Bicycles against costimulatory receptors (e.g., CD137) to Bicycles against tumor antigens (e.g., EphA2) created potent agonists that activated the receptors selectively in the presence of tumor cells expressing these antigens. An EphA2/CD137 TICA (BCY12491) efficiently costimulated human peripheral blood mononuclear cells in vitro in the presence of EphA2 expressing tumor cell lines as measured by the increased secretion of interferon γ and interleukin-2. Treatment of C57/Bl6 mice transgenic for the human CD137 extracellular domain (huCD137) bearing EphA2-expressing MC38 tumors with BCY12491 resulted in the infiltration of CD8+ T cells, elimination of tumors and generation of immunological memory. BCY12491 was cleared quickly from the circulation (plasma t1/2 in mice of 1–2 hr), yet intermittent dosing proved effective.Conclusion Tumor target-dependent CD137 agonism using a novel chemical approach (TICAs) afforded elimination of tumors with only intermittent dosing suggesting potential for a wide therapeutic index in humans. This work unlocks a new path to effective cancer immunotherapy via agonism of TNF superfamily receptors

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions