CD171- and GD2-specific CAR-T cells potently target retinoblastoma cells in preclinical in vitro testing

BACKGROUND: Chimeric antigen receptor (CAR)-based T cell therapy is in early clinical trials to target the neuroectodermal tumor, neuroblastoma. No preclinical or clinical efficacy data are available for retinoblastoma to date. Whereas unilateral intraocular retinoblastoma is cured by enucleation of the eye, infiltration of the optic nerve indicates potential diffuse scattering and tumor spread leading to a major therapeutic challenge. CAR-T cell therapy could improve the currently limited therapeutic strategies for metastasized retinoblastoma by simultaneously killing both primary tumor and metastasizing malignant cells and by reducing chemotherapy-related late effects. METHODS: CD171 and GD2 expression was flow cytometrically analyzed in 11 retinoblastoma cell lines. CD171 expression and T cell infiltration (CD3+) was immunohistochemically assessed in retrospectively collected primary retinoblastomas. The efficacy of CAR-T cells targeting the CD171 and GD2 tumor-associated antigens was preclinically tested against three antigen-expressing retinoblastoma cell lines. CAR-T cell activation and exhaustion were assessed by cytokine release assays and flow cytometric detection of cell surface markers, and killing ability was assessed in cytotoxic assays. CAR constructs harboring different extracellular spacer lengths (short/long) and intracellular co-stimulatory domains (CD28/4-1BB) were compared to select the most potent constructs. RESULTS: All retinoblastoma cell lines investigated expressed CD171 and GD2. CD171 was expressed in 15/30 primary retinoblastomas. Retinoblastoma cell encounter strongly activated both CD171-specific and GD2-specific CAR-T cells. Targeting either CD171 or GD2 effectively killed all retinoblastoma cell lines examined. Similar activation and killing ability for either target was achieved by all CAR constructs irrespective of the length of the extracellular spacers and the co-stimulatory domain. Cell lines differentially lost tumor antigen expression upon CAR-T cell encounter, with CD171 being completely lost by all tested cell lines and GD2 further down-regulated in cell lines expressing low GD2 levels before CAR-T cell challenge. Alternating the CAR-T cell target in sequential challenges enhanced retinoblastoma cell killing. CONCLUSION: Both CD171 and GD2 are effective targets on human retinoblastoma cell lines, and CAR-T cell therapy is highly effective against retinoblastoma in vitro. Targeting of two different antigens by sequential CAR-T cell applications enhanced tumor cell killing and preempted tumor antigen loss in preclinical testing

Role of thioredoxin reductase 1 and thioredoxin interacting protein in prognosis of breast cancer

Introduction: The purpose of this work was to study the prognostic influence in breast cancer of thioredoxin reductase 1 (TXNRD1) and thioredoxin interacting protein (TXNIP), key players in oxidative stress control that are currently evaluated as possible therapeutic targets. Methods: Analysis of the association of TXNRD1 and TXNIP RNA expression with the metastasis-free interval (MFI) was performed in 788 patients with node-negative breast cancer, consisting of three individual cohorts (Mainz, Rotterdam and Transbig). Correlation with metagenes and conventional clinical parameters (age, pT stage, grading, hormone and ERBB2 status) was explored. MCF-7 cells with a doxycycline-inducible expression of an oncogenic ERBB2 were used to investigate the influence of ERBB2 on TXNRD1 and TXNIP transcription. Results: TXNRD1 was associated with worse MFI in the combined cohort (hazard ratio = 1.955; P < 0.001) as well as in all three individual cohorts. In contrast, TXNIP was associated with better prognosis (hazard ratio = 0.642; P < 0.001) and similar results were obtained in all three subcohorts. Interestingly, patients with ERBB2-status-positive tumors expressed higher levels of TXNRD1. Induction of ERBB2 in MCF-7 cells caused not only an immediate increase in TXNRD1 but also a strong decrease in TXNIP. A subsequent upregulation of TXNIP as cells undergo senescence was accompanied by a strong increase in levels of reactive oxygen species. Conclusions: TXNRD1 and TXNIP are associated with prognosis in breast cancer, and ERBB2 seems to be one of the factors shifting balances of both factors of the redox control system in a prognostic unfavorable manner

Marktanalyse und Betriebskonzept für den Next Generation Train CARGO

Im Rahmen der Marktanalyse werden NGT-affine Gutgruppen identifiziert, die besonders hohe Anforderungen an den Gütertransport stellen. Die identifizierten Gutgruppen bilden dabei eine breite Palette ab: angefangen bei eilbedürftigen landwirtschaftlichen Erzeugnissen bis hin zu besonders hochwertigen chemischen Erzeugnissen und Maschinen. Zur Untersuchung der betrieblichen Aspekte des NGT CARGO wird anhand der bestehenden Güterströme in Europa eine internationale Strecke zwischen Rumänien und Spanien mit einem geschätzten jährlichen Transportaufkommen zwischen 173.000 Tonnen und 1,45 Mio. Tonnen ausgewählt. In einem Vergleich werden zwei betriebliche Szenarien, ein Einzelwagensystem mit Rangiervorgängen zwischen Quelle und Ziel und ein Linienzugsystem mit Umladungen der Güter zwischen den Zügen, betrachtet. Der Vergleich zeigt, dass das Linienzugsystem einen effizienteren Betrieb ermöglicht. Ursache hierfür ist sowohl die Kleinteiligkeit der Gütermengen als auch die notwendige häufige Abfahrtfrequenz des NGT CARGO zur Vermeidung langer Sammel- und Verteilzeiten. Voraussetzung sind allerdings leistungsfähige Hub-Stationen, in denen große Gütermengen automatisch umgeladen werden können. Eine Kapazitätsuntersuchung im deutschen Verlauf der Referenzstrecke Spanien-Rumänien zeigt, dass insbesondere für Mischverkehrsstrecken die Bereitstellung von Trassen für weitere Hochgeschwindigkeitszüge die Planung vor Herausforderungen stellt

A Scalable Jointree Algorithm for Diagnosability ∗

Diagnosability is an essential property that determines how accurate any diagnostic reasoning can be on a system given any sequence of observations. An unobservable fault event in a discrete-event system is diagnosable iff its occurrence can always be deduced once sufficiently many subsequent observable events have occurred. A classical approach to diagnosability checking constructs a finite state machine known as a twin plant for the system, which has a critical path iff some fault event is not diagnosable. Recent work attempts to avoid the often impractical construction of the global twin plant by exploiting system structure. Specifically, local twin plants are constructed for components of the system, and synchronized with each other until diagnosability is decided. Unfortunately, synchronization of twin plants can remain a bottleneck for large systems; in the worst case, in particular, all local twin plants would be synchronized, again producing the global twin plant. We solve the diagnosability problem in a way that exploits the distributed nature of realistic systems. In our algorithm consistency among twin plants is achieved by message passing on a jointree. Scalability is significantly improved as the messages computed are generally much smaller than the synchronized product of the twin plants involved. Moreover we use an iterative procedure to search for a subset of the jointree that is sufficient to decide diagnosability. Finally, our algorithm is scalable in practice: it provides an approximate and useful solution if the computational resources are not sufficient

Minimizing User Involvement for Accurate Ontology Matching Problems

Many various types of sensors coming from different complex devices collect data from a city. Their underlying data representation follows specific manufacturer specifications that have possibly incomplete descriptions (in ontology) alignments. This paper addresses the problem of determining accurate and complete matching of ontologies given some common descriptions and their pre-determined high level alignments. In this context the problem of ontology matching consists of automatically determining all matching given the latter alignments, and manually verifying the matching results. Especially for applications where it is crucial that ontologies are matched correctly the latter can turn into a very time-consuming task for the user. This paper tackles this challenge and addresses the problem of computing the minimum number of user inputs needed to verify all matchings. We show how to represent this problem as a reasoning problem over a bipartite graph and how to encode it over pseudo Boolean constraints. Experiments show that our approach can be successfully applied to real-world data sets

From Semantic Models to Cognitive Buildings

Today's operation of buildings is either based on simple dashboards that are not scalable to thousands of sensor data or on rules that provide very limited fault information only. In either case considerable manual effort is required for diagnosing building operation problems related to energy usage or occupant comfort. We present a Cognitive Building demo that uses (i) semantic reasoning to model physical relationships of sensors and systems, (ii) machine learning to predict and detect anomalies in energy flow, occupancy and user comfort, and (iii) speech-enabled Augmented Reality interfaces for immersive interaction with thousands of devices. Our demo analyzes data from more than 3,300 sensors and shows how we can automatically diagnose building operation problems