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

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

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

Scalable diagnosability checking of event-driven system

Diagnosability of systems is an essential property that determines how accurate any diagnostic reasoning can be on a system given any sequence of observations. Generally, in the literature of dynamic event-driven systems, diagnosability analysis is performed by algorithms that consider a system as a whole and their response is either a positive answer or a counter example. In this paper, we present an original framework for diagnosability checking. The diagnosability problem is solved in a distributed way in order to take into account the distributed nature of realistic problems. As opposed to all other approaches, our algorithm also provides an exhaustive and synthetic view of the reasons why the system is not diagnosable. Finally, the presented algorithm is scalable in practice: it provides an approximate and useful solution if the computational resources are not sufficient.