Serpin B9 controls tumor cell killing by CAR T cells

BACKGROUND: Initial clinical responses with gene engineered chimeric antigen receptor (CAR) T cells in cancer patients are highly encouraging; however, primary resistance and also relapse may prevent durable remission in a substantial part of the patients. One of the underlying causes is the resistance mechanisms in cancer cells that limit effective killing by CAR T cells. CAR T cells exert their cytotoxic function through secretion of granzymes and perforin. Inhibition of granzyme B (GrB) can underlie resistance to T cell-mediated killing, and it has been shown that serine proteinase inhibitor serpin B9 can effectively inhibit GrB. We aimed to determine whether expression of serpin B9 by cancer cells can lead to resistance toward CAR T cells. METHODS: Serpin B9 gene and protein expression were examined by R2 or DepMap database mining and by western blot or flow cytometric analysis, respectively. Coculture killing experiments were performed with melanoma cell line MeWo, diffuse large B cell lymphoma (DLBCL) cell line OCI-Ly7 or primary chronic lymphocytic leukemia (CLL) cells as target cells and natural killer cell line YT-Indy, CD20 CAR T cells or CD19 CAR T cells as effector cells and analyzed by flow cytometry. RESULTS: Serpin B9 protein expression was previously shown to be associated with clinical outcome in melanoma patients and in line with these observations we demonstrate that enforced serpin B9 expression in melanoma cells reduces sensitivity to GrB-mediated killing. Next, we examined serpin B9 expression in a wide array of primary tumor tissues and human cell lines to find that serpin B9 is uniformly expressed in B-cell lymphomas and most prominently in DLBCL and CLL. Subsequently, using small interfering RNA, we silenced serpin B9 expression in DLBCL cells, which increased their sensitivity to CD20 CAR T cell-mediated killing. In addition, we showed that co-ulture of primary CLL cells with CD20 CAR T cells results in selection of serpin B9-high CLL cells, suggesting these cells resist CAR T-cell killing. CONCLUSIONS: Overall, the data indicate that serpin B9 is a resistance mediator for CAR T cell-mediated tumor cell killing that should be inhibited or bypassed to improve CAR T-cell responses

Molecular Implication of PP2A and Pin1 in the Alzheimer's Disease Specific Hyperphosphorylation of Tau

Tau phosphorylation and dephosphorylation regulate in a poorly understood manner its physiological role of microtubule stabilization, and equally its integration in Alzheimer disease (AD) related fibrils. A specific phospho-pattern will result from the balance between kinases and phosphatases. The heterotrimeric Protein Phosphatase type 2A encompassing regulatory subunit PR55/Bα (PP2A(T55α)) is a major Tau phosphatase in vivo, which contributes to its final phosphorylation state. We use NMR spectroscopy to determine the dephosphorylation rates of phospho-Tau by this major brain phosphatase, and present site-specific and kinetic data for the individual sites including the pS202/pT205 AT8 and pT231 AT180 phospho-epitopes.We demonstrate the importance of the PR55/Bα regulatory subunit of PP2A within this enzymatic process, and show that, unexpectedly, phosphorylation at the pT231 AT180 site negatively interferes with the dephosphorylation of the pS202/pT205 AT8 site. This inhibitory effect can be released by the phosphorylation dependent prolyl cis/trans isomerase Pin1. Because the stimulatory effect is lost with the dimeric PP2A core enzyme (PP2A(D)) or with a phospho-Tau T231A mutant, we propose that Pin1 regulates the interaction between the PR55/Bα subunit and the AT180 phospho-epitope on Tau.Our results show that phosphorylation of T231 (AT180) can negatively influence the dephosphorylation of the pS202/pT205 AT8 epitope, even without an altered PP2A pool. Thus, a priming dephosphorylation of pT231 AT180 is required for efficient PP2A(T55α)-mediated dephosphorylation of pS202/pT205 AT8. The sophisticated interplay between priming mechanisms reported for certain Tau kinases and the one described here for Tau phosphatase PP2A(T55α) may contribute to the hyperphosphorylation of Tau observed in AD neurons

Model validation: an integrated, fuzzy set and system theoretic approach

nrpages: 84status: publishe

A fuzzy-neural resemblance approach to validate simulation models

Validation is one of the most important steps in developing a reliable simulation model. It evaluates whether or not the model forms a representation of the simulated system accurate enough to satisfy the goals of the modelling study. The methods that are currently available for model validation are binary in nature in the sense that they only allow either to accept or reject the validity of the model. In this paper, we develop a new, fuzzy set theoretic method that allows to express degrees of model validity and that is hence continuous in nature. The method employs a fuzzy-neural machine learning algorithm and makes use of a new concept in fuzzy set theory known as resemblance relations. By a computational experiment, we demonstrate how our method can be used to discriminate more from less valid simulation models of a particular manufacturing process

A fuzzy set and resemblance relation approach to the validation of simulation models

Validation is no doubt one of the most important steps in the development of an effective and reliable simulation model of a real system. It aims at deciding whether the model forms a representation of the system accurate enough for credible analysis and decision making. The methods that are currently available for validation are binary in nature, in the sense that they can only be used to either reject or accept the validity of a model. Since it is a commonly accepted point of view that all models are invalid in the strict sense, we develop in this paper a new method for validation that allows to express degrees of validity on a continuous scale. The method makes use of a fuzzy inference algorithm and of a fairly new concept in the theory of fuzzy sets, known as resemblance relations. We demonstrate how our method can easily be used to discriminate more from less valid simulation models for a real-life airline network.status: publishe

An initial comparison of a fuzzy neural network classifier and a decision tree based classifier

As of this writing, an large number of AI methods have been developed in the fiel of pattern classification. In this paper, we will compare the performance of a well-known algorithm in machine learning (C4.5) with a recently proposed algorithm in the fuzzy set community (NEFCLASS). We will compare the algorithms both on the attained accuracy as on the size of the induced rule base. Additionally, we will investigate how the selected algorithms perform after they have been pre-processed by discretization and feature selection.status: publishe

An initial comparison of a fuzzy neural classifier and a decision tree based classifier

At the present time a large number of AI methods have been developed in the field of pattern classification. In this paper, we will compare the performance of a well-known algorithm in machine learning (C4.5) with a recently proposed algorithm in the fuzzy set community (NEFCLASS). We will compare the algorithms both on the accuracy attained and on the size of the induced rule base. Additionally, we will investigate how the selected algorithms perform after they have been pre-processed by discretization and feature selection

A computational pipeline for quantification of mouse myocardial stiffness parameters

The mouse is an important model for theoretical–experimental cardiac research, and biophysically based whole organ models of the mouse heart are now within reach. However, the passive material properties of mouse myocardium have not been much studied. We present an experimental setup and associated computational pipeline to quantify these stiffness properties. A mouse heart was excised and the left ventricle experimentally inflated from 0 to 1.44 kPa in eleven steps, and the resulting deformation was estimated by echocardiography and speckle tracking. An in silico counterpart to this experiment was built using finite element methods and data on ventricular tissue microstructure from diffusion tensor MRI. This model assumed a hyperelastic, transversely isotropic material law to describe the force–deformation relationship, and was simulated for many parameter scenarios, covering the relevant range of parameter space. To identify well-fitting parameter scenarios, we compared experimental and simulated outcomes across the whole range of pressures, based partly on gross phenotypes (volume, elastic energy, and short- and long-axis diameter), and partly on node positions in the geometrical mesh. This identified a narrow region of experimentally compatible values of the material parameters. Estimation turned out to be more precise when based on changes in gross phenotypes, compared to the prevailing practice of using displacements of the material points. We conclude that the presented experimental setup and computational pipeline is a viable method that deserves wider application.acceptedVersio

Sorafenib tosylate inhibits directly necrosome complex formation and protects in mouse models of inflammation and tissue injury

Necroptosis contributes to the pathophysiology of several inflammatory, infectious and degenerative disorders. TNF-induced necroptosis involves activation of the receptor-interacting protein kinases 1 and 3 (RIPK1/3) in a necrosome complex, eventually leading to the phosphorylation and relocation of mixed lineage kinase domain like protein (MLKL). Using a high-content screening of small compounds and FDA-approved drug libraries, we identified the anti-cancer drug Sorafenib tosylate as a potent inhibitor of TNF-dependent necroptosis. Interestingly, Sorafenib has a dual activity spectrum depending on its concentration. In murine and human cell lines it induces cell death, while at lower concentrations it inhibits necroptosis, without affecting NF-.B activation. Pull down experiments with biotinylated Sorafenib show that it binds independently RIPK1, RIPK3 and MLKL. Moreover, it inhibits RIPK1 and RIPK3 kinase activity. In vivo Sorafenib protects against TNF-induced systemic inflammatory response syndrome (SIRS) and renal ischemia-reperfusion injury (IRI). Altogether, we show that Sorafenib can, next to the reported Braf/Mek/Erk and VEGFR pathways, also target the necroptotic pathway and that it can protect in an acute inflammatory RIPK1/3-mediated pathology