CD123 expression levels in 846 acute leukemia patients based on standardized immunophenotyping

Background: While it is known that CD123 is normally strongly expressed on plasmacytoid dendritic cells and completely absent on nucleated red blood cells, detailed information regarding CD123 expression in acute leukemia is scarce and, if available, hard to compare due to different methodologies. Methods: CD123 expression was evaluated using standardized EuroFlow immunophenotyping in 139 pediatric AML, 316 adult AML, 193 pediatric BCP-ALL, 69 adult BCP-ALL, 101 pediatric T-A

On being a good Bayesian

Bayesianism is fast becoming the dominant paradigm in archaeological chronology construction. This paradigm shift has been brought about in large part by widespread access to tailored computer software which provides users with powerful tools for complex statistical inference with little need to learn about statistical modelling or computer programming. As a result, we run the risk that such software will be reduced to the status of black boxes. This would be a dangerous position for our community since good, principled use of Bayesian methods requires mindfulness when selecting the initial model, defining prior information, checking the reliability and sensitivity of the software runs and interpreting the results obtained. In this article, we provide users with a brief review of the nature of the care required and offer some comments and suggestions to help ensure that our community continues to be respected for its philosophically rigorous scientific approach

Neutrophils kill antibody-opsonized cancer cells by trogoptosis

Destruction of cancer cells by therapeutic antibodies occurs, at least in part, through antibody-dependent cellular cytotoxicity (ADCC), and this can be mediated by various Fc-receptor-expressing immune cells, including neutrophils. However, the mechanism(s) by which neutrophils kill antibody-opsonized cancer cells has not been established. Here, we demonstrate that neutrophils can exert a mode of destruction of cancer cells, which involves antibody-mediated trogocytosis by neutrophils. Intimately associated with this is an active mechanical disruption of the cancer cell plasma membrane, leading to a lytic (i.e., necrotic) type of cancer cell death. Furthermore, this mode of destruction of antibody-opsonized cancer cells by neutrophils is potentiated by CD47-SIRPa checkpoint blockade. Collectively, these findings show that neutrophil ADCC toward cancer cells occurs by a mechanism of cytotoxicity called trogoptosis, which can be further improved by targeting CD47-SIRPa interactions

IgA antibody immunotherapy targeting GD2 is effective in preclinical neuroblastoma models

BACKGROUND: Immunotherapy targeting GD2 is very effective against high-risk neuroblastoma, though administration of anti-GD2 antibodies induces severe and dose-limiting neuropathic pain by binding GD2-expressing sensory neurons. Previously, the IgG1 ch14.18 (dinutuximab) antibody was reformatted into the IgA1 isotype, which abolishes neuropathic pain and induces efficient neutrophil-mediated antibody-dependent cellular cytotoxicity (ADCC) via activation of the Fc alpha receptor (FcαRI/CD89). METHODS: To generate an antibody suitable for clinical application, we engineered an IgA molecule (named IgA3.0 ch14.18) with increased stability, mutated glycosylation sites and substituted free (reactive) cysteines. The following mutations were introduced: N45.2G and P124R (CH1 domain), C92S, N120T, I121L and T122S (CH2 domain) and a deletion of the tail piece P131-Y148 (CH3 domain). IgA3.0 ch14.18 was evaluated in binding assays and in ADCC and antibody-dependent cellular phagocytosis (ADCP) assays with human, neuroblastoma patient and non-human primate effector cells. We performed mass spectrometry analysis of N-glycans and evaluated the impact of altered glycosylation in IgA3.0 ch14.18 on antibody half-life by performing pharmacokinetic (PK) studies in mice injected intravenously with 5 mg/kg antibody solution. A dose escalation study was performed to determine in vivo efficacy of IgA3.0 ch14.18 in an intraperitoneal mouse model using 9464D-GD2 neuroblastoma cells as well as in a subcutaneous human xenograft model using IMR32 neuroblastoma cells. Binding assays and PK studies were compared with one-way analysis of variance (ANOVA), ADCC and ADCP assays and in vivo tumor outgrowth with two-way ANOVA followed by Tukey's post-hoc test. RESULTS: ADCC and ADCP assays showed that particularly neutrophils and macrophages from healthy donors, non-human primates and patients with neuroblastoma are able to kill neuroblastoma tumor cells efficiently with IgA3.0 ch14.18. IgA3.0 ch14.18 contains a more favorable glycosylation pattern, corresponding to an increased antibody half-life in mice compared with IgA1 and IgA2. Furthermore, IgA3.0 ch14.18 penetrates neuroblastoma tumors in vivo and halts tumor outgrowth in both 9464D-GD2 and IMR32 long-term tumor models. CONCLUSIONS: IgA3.0 ch14.18 is a promising new therapy for neuroblastoma, showing (1) increased half-life compared to natural IgA antibodies, (2) increased protein stability enabling effortless production and purification, (3) potent CD89-mediated tumor killing in vitro by healthy subjects and patients with neuroblastoma and (4) antitumor efficacy in long-term mouse neuroblastoma models

Report on the sixth blind test of organic crystal structure prediction methods.

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations. All of the targets, apart from a single potentially disordered Z' = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.The organisers and participants are very grateful to the crystallographers who supplied the candidate structures: Dr. Peter Horton (XXII), Dr. Brian Samas (XXIII), Prof. Bruce Foxman (XXIV), and Prof. Kraig Wheeler (XXV and XXVI). We are also grateful to Dr. Emma Sharp and colleagues at Johnson Matthey (Pharmorphix) for the polymorph screening of XXVI, as well as numerous colleagues at the CCDC for assistance in organising the blind test. Submission 2: We acknowledge Dr. Oliver Korb for numerous useful discussions. Submission 3: The Day group acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. We acknowledge funding from the EPSRC (grants EP/J01110X/1 and EP/K018132/1) and the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC through grant agreements n. 307358 (ERC-stG- 2012-ANGLE) and n. 321156 (ERC-AG-PE5-ROBOT). Submission 4: I am grateful to Mikhail Kuzminskii for calculations of molecular structures on Gaussian 98 program in the Institute of Organic Chemistry RAS. The Russian Foundation for Basic Research is acknowledged for financial support (14-03-01091). Submission 5: Toine Schreurs provided computer facilities and assistance. I am grateful to Matthew Habgood at AWE company for providing a travel grant. Submission 6: We would like to acknowledge support of this work by GlaxoSmithKline, Merck, and Vertex. Submission 7: The research was financially supported by the VIDI Research Program 700.10.427, which is financed by The Netherlands Organisation for Scientific Research (NWO), and the European Research Council (ERC-2010-StG, grant agreement n. 259510-KISMOL). We acknowledge the support of the Foundation for Fundamental Research on Matter (FOM). Supercomputer facilities were provided by the National Computing Facilities Foundation (NCF). Submission 8: Computer resources were provided by the Center for High Performance Computing at the University of Utah and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1053575. MBF and GIP acknowledge the support from the University of Buenos Aires and the Argentinian Research Council. Submission 9: We thank Dr. Bouke van Eijck for his valuable advice on our predicted structure of XXV. We thank the promotion office for TUT programs on advanced simulation engineering (ADSIM), the leading program for training brain information architects (BRAIN), and the information and media center (IMC) at Toyohashi University of Technology for the use of the TUT supercomputer systems and application software. We also thank the ACCMS at Kyoto University for the use of their supercomputer. In addition, we wish to thank financial supports from Conflex Corp. and Ministry of Education, Culture, Sports, Science and Technology. Submission 12: We thank Leslie Leiserowitz from the Weizmann Institute of Science and Geoffrey Hutchinson from the University of Pittsburgh for helpful discussions. We thank Adam Scovel at the Argonne Leadership Computing Facility (ALCF) for technical support. Work at Tulane University was funded by the Louisiana Board of Regents Award # LEQSF(2014-17)-RD-A-10 “Toward Crystal Engineering from First Principles”, by the NSF award # EPS-1003897 “The Louisiana Alliance for Simulation-Guided Materials Applications (LA-SiGMA)”, and by the Tulane Committee on Research Summer Fellowship. Work at the Technical University of Munich was supported by the Solar Technologies Go Hybrid initiative of the State of Bavaria, Germany. Computer time was provided by the Argonne Leadership Computing Facility (ALCF), which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. Submission 13: This work would not have been possible without funding from Khalifa University’s College of Engineering. I would like to acknowledge Prof. Robert Bennell and Prof. Bayan Sharif for supporting me in acquiring the resources needed to carry out this research. Dr. Louise Price is thanked for her guidance on the use of DMACRYS and NEIGHCRYS during the course of this research. She is also thanked for useful discussions and numerous e-mail exchanges concerning the blind test. Prof. Sarah Price is acknowledged for her support and guidance over many years and for providing access to DMACRYS and NEIGHCRYS. Submission 15: The work was supported by the United Kingdom’s Engineering and Physical Sciences Research Council (EPSRC) (EP/J003840/1, EP/J014958/1) and was made possible through access to computational resources and support from the High Performance Computing Cluster at Imperial College London. We are grateful to Professor Sarah L. Price for supplying the DMACRYS code for use within CrystalOptimizer, and to her and her research group for support with DMACRYS and feedback on CrystalPredictor and CrystalOptimizer. Submission 16: R. J. N. acknowledges financial support from the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. [EP/J017639/1]. R. J. N. and C. J. P. acknowledge use of the Archer facilities of the U.K.’s national high-performance computing service (for which access was obtained via the UKCP consortium [EP/K014560/1]). C. J. P. also acknowledges a Leadership Fellowship Grant [EP/K013688/1]. B. M. acknowledges Robinson College, Cambridge, and the Cambridge Philosophical Society for a Henslow Research Fellowship. Submission 17: The work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. The work at the University of Silesia was supported by the Polish National Science Centre Grant No. DEC-2012/05/B/ST4/00086. Submission 18: We would like to thank Constantinos Pantelides, Claire Adjiman and Isaac Sugden of Imperial College for their support of our use of CrystalPredictor and CrystalOptimizer in this and Submission 19. The CSP work of the group is supported by EPSRC, though grant ESPRC EP/K039229/1, and Eli Lilly. The PhD students support: RKH by a joint UCL Max-Planck Society Magdeburg Impact studentship, REW by a UCL Impact studentship; LI by the Cambridge Crystallographic Data Centre and the M3S Centre for Doctoral Training (EPSRC EP/G036675/1). Submission 19: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 20: The work at New York University was supported, in part, by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1-0387 (MET and LV) and, in part, by the Materials Research Science and Engineering Center (MRSEC) program of the National Science Foundation under Award Number DMR-1420073 (MET and ES). The work at the University of Delaware was supported by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. Submission 21: We thank the National Science Foundation (DMR-1231586), the Government of Russian Federation (Grant No. 14.A12.31.0003), the Foreign Talents Introduction and Academic Exchange Program (No. B08040) and the Russian Science Foundation, project no. 14-43-00052, base organization Photochemistry Center of the Russian Academy of Sciences. Calculations were performed on the Rurik supercomputer at Moscow Institute of Physics and Technology. Submission 22: The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC). Submission 24: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 25: J.H. and A.T. acknowledge the support from the Deutsche Forschungsgemeinschaft under the program DFG-SPP 1807. H-Y.K., R.A.D., and R.C. acknowledge support from the Department of Energy (DOE) under Grant Nos. DE-SC0008626. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DEAC02-05CH11231. Additional computational resources were provided by the Terascale Infrastructure for Groundbreaking Research in Science and Engineering (TIGRESS) High Performance Computing Center and Visualization Laboratory at Princeton University.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1107/S2052520616007447

Report on the sixth blind test of organic crystal-structure prediction methods

The sixth blind test of organic crystal-structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal, and a bulky flexible molecule. This blind test has seen substantial growth in the number of submissions, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and "best practices" for performing CSP calculations. All of the targets, apart from a single potentially disordered Z` = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms

The NOX toolbox: validating the role of NADPH oxidases in physiology and disease

Reactive oxygen species (ROS) are cellular signals but also disease triggers; their relative excess (oxidative stress) or shortage (reductive stress) compared to reducing equivalents are potentially deleterious. This may explain why antioxidants fail to combat diseases that correlate with oxidative stress. Instead, targeting of disease-relevant enzymatic ROS sources that leaves physiological ROS signaling unaffected may be more beneficial. NADPH oxidases are the only known enzyme family with the sole function to produce ROS. Of the catalytic NADPH oxidase subunits (NOX), NOX4 is the most widely distributed isoform. We provide here a critical review of the currently available experimental tools to assess the role of NOX and especially NOX4, i.e. knock-out mice, siRNAs, antibodies, and pharmacological inhibitors. We then focus on the characterization of the small molecule NADPH oxidase inhibitor, VAS2870, in vitro and in vivo, its specificity, selectivity, and possible mechanism of action. Finally, we discuss the validation of NOX4 as a potential therapeutic target for indications including stroke, heart failure, and fibrosis