Evaluation of PTPRZ1 and TMEM158 as potential new targets for a CAR-T-Cell-based approach for the treatment of glioblastoma

Glioblastoma (GBM) is the most frequent and lethal malignant brain tumor in adults. It emerges with an incidence of 3.2 per 100.000 in the US and 3.91 in Europe. Today, standard treatment after diagnosis consists of surgical removal of tumor tissue, followed by radiation therapy and adjuvant chemotherapy using temozolomide. Even after this rigorous therapy, patients show a median overall survival of only 15.6 months or 20.5 months when the tumor is additionally treated with so-called tumor treating fields. GBM is characterized by molecular heterogeneity within the same patient but also between different patients, which impedes development of novel therapeutics. During the last decades various immunotherapies including (multi-epitope) peptide vaccines, oncolytic viruses or immune checkpoint inhibitors against GBM were tested in small clinical studies, but failed to show a benefit in large studies. A novel kind of immunotherapies that showed great success in hematological tumors so far, is based on chimeric antigen receptors (CAR). These synthetic receptors can be introduced into immune cells to retarget their function towards tumor cells, independently of the major histocompatibility complex (MHC) that is often down regulated by tumors for immune evasion. A large hurdle for treatment of GBM using immunotherapies such as CAR-T cells, is antigen heterogeneity that limits the effect of therapies against single targets and renders the need for discovery of novel targets to enable treatment of a wide variety of patients with high success. Analyzing publicly available data and performing RT-qPCR experiments with RNA isolated from GBM tissue of a local cohort of patients, overexpression of two candidate GBM antigens, namely TMEM158 and PTPRZ1 were observed. Overexpression of both antigens in GBM in comparison to normal brain tissue and low-grade gliomas (only TMEM158) was revealed. In addition, a negative correlation between expression and patient survival was detected, as well as a correlation between TMEM158 and CD44 expression, the latter being a marker for GBM stem cells and the mesenchymal GBM subtype. Induction of chemoresistance by TMEM158 seems likely for GBM, since this was already discovered for several other tumor entities. Protein expression of TMEM158 was confirmed by Western blot analysis of different GBM cell lines. Since cell surface expression of a target protein is a prerequisite for targeting by a CAR-therapy, the expression of TMEM158 on cells from GBM cell lines was analyzed by flow cytometry. For this analysis a fluorescence-labeled peptide, based on sequence information of a known naturally occurring TMEM158 ligand (BINP) was designed. Binding to T98G and U-87 MG was observed, while only very low binding to the neuroblastoma cell line SH-SY5Y was seen in flow cytometry. Partial knockdown of TMEM158 was achieved using DsiRNAs, followed by Western blot (antibody staining) and flow cytometry (peptide staining), confirming the specificity of binding detectable by both methods. A recombinant fusion protein, consisting of the extracellular part of TMEM158 and a human Fc-antibody fragment was produced in 293T cells by transient transfection of an expression vector. The expected size of the protein produced was confirmed by Western blot. Furthermore, binding of the BINP-peptide to the recombinant protein was analyzed and compared to a scrambled BINP-peptide. In these experiments specific binding of the BINP-peptide was observed, also indicating the functionality of the recombinant protein. Next, CAR-constructs were designed using the original sequence information from BINP as binding domain and additional variants with amino acid exchanges at different positions. Significant cytotoxicity of all BINP-CAR-T cells was observed against T98G, which showed highest binding of BINP when analyzed by flow cytometry. A BINP-CAR version in which phenylalanine 11 was exchanged with alanine (BINP-F11A-CAR) showed significantly higher cytotoxicity against T98G than the BINP-CAR containing the original BINP sequence (BINP-WT-CAR). Against the U-87 MG cell line, only a version of the BINP-CAR containing an RGD- (arginine-glycine-aspartic acid) motif showed significant cytotoxicity. RGD-motifs are known to bind integrins like αVβ3, which was abundantly present on this cell line, as it was confirmed by flow cytometry within this work. Using this BINP-RGD-CAR version, targeting of both antigens at the same time seems possible. No significant cytotoxicity of the different CAR versions was observed against the TMEM158- and αVβ3-low cell line SH-SY5Y. In conclusion, overexpression of TMEM158 and PTPRZ1 and their negative influence on survival of patients, as found in recent literature, was confirmed for glioblastoma. Significantly higher expression of TMEM158 in GBM in comparison to low-grade gliomas as well as the correlation with CD44 hint at an association of TMEM158 with the aggressive phenotype of GBM. For all of these reasons, targeting of TMEM158 appears to be very feasible. Cytotoxicity of the produced BINP-CAR-T cells, which are the first CAR-T cells targeting TMEM158 so far, was demonstrated against GBM cells. Additional to cytotoxicity of the CAR-T cells, other in vitro assays and in vivo models should be utilized to determine more aspects of CAR-T cell function, in the future. For example, proliferation, cytokine release, invasion of tumor tissue, and inactivation of CAR-T cells by the tumor milieu should be quantified. To estimate how many patients could benefit from a therapy against it, percentage of patients and distribution within the tumors should be determined

Influence of murine mesenchymal stem cells on proliferation, phenotype, vitality, and cytotoxicity of murine cytokine-induced killer cells in coculture

Stimulating lymphocytes with Ifn-γ, anti-CD3, and interleukin-2 promotes the proliferation of a cell population coexpressing T-lymphocyte surface antigens such as CD3, CD8a, and CD25 as well as natural killer cell markers such as NK1.1, CD49, and CD69. These cells, referred to as cytokine-induced killer cells (CIKs), display cytotoxic activity against tumour cells, even without prior antigen presentation, and offer a new cell-based approach to the treatment of malignant diseases. Because CIKs are limited in vivo, strategies to optimize in vitro culture yield are required. In the last 10 years, mesenchymal stem cells (MSCs) have gathered considerable attention. Aside from their uses in tissue engineering and as support in haematopoietic stem cell transplantations, MSCs show notable immunomodulatory characteristics, providing further possibilities for therapeutic applications. In this study, we investigated the influence of murine MSCs on proliferation, phenotype, vitality, and cytotoxicity of murine CIKs in a coculture system. We found that CIKs in coculture proliferated within 7 days, with an average growth factor of 18.84, whereas controls grew with an average factor of 3.7 in the same period. Furthermore, higher vitality was noted in cocultured CIKs than in controls. Cell phenotype was unaffected by coculture with MSCs and, notably, coculture did not impact cytotoxicity against the tumour cells analysed. The findings suggest that cell-cell contact is primarily responsible for these effects. Humoral interactions play only a minor role. Furthermore, no phenotypical MSCs were detected after coculture for 4 h, suggesting the occurrence of immune reactions between CIKs and MSCs. Further investigations with DiD-labelled MSCs revealed that the observed disappearance of MSCs appears not to be due to differentiation processes. © 2014 Bach et al

Influence of murine mesenchymal stem cells on proliferation, phenotype, vitality, and cytotoxicity of murine cytokine-induced killer cells in coculture

Stimulating lymphocytes with Ifn-γ, anti-CD3, and interleukin-2 promotes the proliferation of a cell population coexpressing T-lymphocyte surface antigens such as CD3, CD8a, and CD25 as well as natural killer cell markers such as NK1.1, CD49, and CD69. These cells, referred to as cytokine-induced killer cells (CIKs), display cytotoxic activity against tumour cells, even without prior antigen presentation, and offer a new cell-based approach to the treatment of malignant diseases. Because CIKs are limited in vivo, strategies to optimize in vitro culture yield are required. In the last 10 years, mesenchymal stem cells (MSCs) have gathered considerable attention. Aside from their uses in tissue engineering and as support in haematopoietic stem cell transplantations, MSCs show notable immunomodulatory characteristics, providing further possibilities for therapeutic applications. In this study, we investigated the influence of murine MSCs on proliferation, phenotype, vitality, and cytotoxicity of murine CIKs in a coculture system. We found that CIKs in coculture proliferated within 7 days, with an average growth factor of 18.84, whereas controls grew with an average factor of 3.7 in the same period. Furthermore, higher vitality was noted in cocultured CIKs than in controls. Cell phenotype was unaffected by coculture with MSCs and, notably, coculture did not impact cytotoxicity against the tumour cells analysed. The findings suggest that cell-cell contact is primarily responsible for these effects. Humoral interactions play only a minor role. Furthermore, no phenotypical MSCs were detected after coculture for 4 h, suggesting the occurrence of immune reactions between CIKs and MSCs. Further investigations with DiD-labelled MSCs revealed that the observed disappearance of MSCs appears not to be due to differentiation processes. © 2014 Bach et al

GM2Calc: Precise MSSM prediction for (g−2)(g - 2) of the muon

We present GM2Calc, a public C++ program for the calculation of MSSM contributions to the anomalous magnetic moment of the muon, $(g-2)_\mu$. The code computes $(g-2)_\mu$ precisely, by taking into account the latest two-loop corrections and by performing the calculation in a physical on-shell renormalization scheme. In particular the program includes a $\tan\beta$ resummation so that it is valid for arbitrarily high values of $\tan\beta$, as well as fermion/sfermion-loop corrections which lead to non-decoupling effects from heavy squarks. GM2Calc can be run with a standard SLHA input file, internally converting the input into on-shell parameters. Alternatively, input parameters may be specified directly in this on-shell scheme. In both cases the input file allows one to switch on/off individual contributions to study their relative impact. This paper also provides typical usage examples not only in conjunction with spectrum generators and plotting programs but also as C++ subroutines linked to other programs.Comment: 27 pages, 4 figures, 4 listings; version sent to EPJ

Triplet Energy Transfer from Ruthenium Complexes to Chiral Eniminium Ions: Enantioselective Synthesis of Cyclobutanecarbaldehydes by [2+2] Photocycloaddition

Chiral eniminium salts, prepared from alpha,beta-unsaturated aldehydes and a chiral proline derived secondary amine, underwent, upon irradiation with visible light, a ruthenium-catalyzed (2.5 mol %) intermolecular [2+2] photocycloaddition to olefins, which after hydrolysis led to chiral cyclobutanecarbaldehydes (17 examples, 49-74 % yield), with high diastereo- and enantioselectivities. Ru(bpz)(3)(PF6)(2) was utilized as the ruthenium catalyst and laser flash photolysis studies show that the catalyst operates exclusively by triplet-energy transfer (sensitization). A catalytic system was devised with a chiral secondary amine co-catalyst. In the catalytic reactions, Ru(bpy)(3)(PF6)(2) was employed, and laser flash photolysis experiments suggest it undergoes both electron and energy transfer. However, experimental evidence supports the hypothesis that energy transfer is the only productive quenching mechanism. Control experiments using Ir(ppy)(3) showed no catalysis for the intermolecular [2+2] photocycloaddition of an eniminium ion

Digitalizing the Chemical Landscape: A Comprehensive Overview and Progress Report of NFDI4Chem

The Chemistry consortium NFDI4Chem aims to digitalise key steps in chemical research, supporting scientists in managing research data throughout its life cycle. The SmartLab, embedded in a federation of services, integrates various tools such as electronic lab notebooks, data repositories, and search services, to create a smart lab environment for structured data gathering. Utilizing terminology services and adhering to data format standards, NFDI4Chem promotes secure and FAIR data sharing, fostering collaboration and expediting scientific discoveries. This development is supported by community building measures, workshops, and training initiatives, along with collaboration on international minimum information standards

NFDI4Chem - A Research Data Network for International Chemistry

Research data provide evidence for the validation of scientific hypotheses in most areas of science. Open access to them is the basis for true peer review of scientific results and publications. Hence, research data are at the heart of the scientific method as a whole. The value of openly sharing research data has by now been recognized by scientists, funders and politicians. Today, new research results are increasingly obtained by drawing on existing data. Many organisations such as the Research Data Alliance (RDA), the goFAIR initiative, and not least IUPAC are supporting and promoting the collection and curation of research data. One of the remaining challenges is to find matching data sets, to understand them and to reuse them for your own purpose. As a consequence, we urgently need better research data management

From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making

Prediction algorithms are regularly used to support and automate high-stakes policy decisions about the allocation of scarce public resources. However, data-driven decision-making raises problems of algorithmic fairness and justice. So far, fairness and justice are frequently conflated, with the consequence that distributive justice concerns are not addressed explicitly. In this paper, we approach this issue by distinguishing (a) fairness as a property of the algorithm used for the prediction task from (b) justice as a property of the allocation principle used for the decision task in data-driven decision-making. The distinction highlights the different logic underlying concerns about fairness and justice and permits a more systematic investigation of the interrelations between the two concepts. We propose a new notion of algorithmic fairness called error fairness which requires prediction errors to not differ systematically across individuals. Drawing on sociological and philosophical discourse on local justice, we present a principled way to include distributive justice concerns into data-driven decision-making. We propose that allocation principles are just if they adhere to well-justified distributive justice principles. Moving beyond the one-sided focus on algorithmic fairness, we thereby make a first step toward the explicit implementation of distributive justice into data-driven decision-making

Rising Sound Intensity: An Intrinsic Warning Cue Activating the Amygdala

Human subjects overestimate the change of rising intensity sounds compared with falling intensity sounds. Rising sound intensity has therefore been proposed to be an intrinsic warning cue. In order to test this hypothesis, we presented rising, falling, and constant intensity sounds to healthy humans and gathered psychophysiological and behavioral responses. Brain activity was measured using event-related functional magnetic resonance imaging. We found that rising compared with falling sound intensity facilitates autonomic orienting reflex and phasic alertness to auditory targets. Rising intensity sounds produced neural activity in the amygdala, which was accompanied by activity in intraparietal sulcus, superior temporal sulcus, and temporal plane. Our results indicate that rising sound intensity is an elementary warning cue eliciting adaptive responses by recruiting attentional and physiological resources. Regions involved in cross-modal integration were activated by rising sound intensity, while the right-hemisphere phasic alertness network could not be supported by this stud

Humans versus machines: Who is perceived to decide fairer? Experimental evidence on attitudes toward automated decision-making

Human perceptions of fairness in (semi-)automated decision-making (ADM) constitute a crucial building block toward developing human-centered ADM solutions. However, measuring fairness perceptions is challenging because various context and design characteristics of ADM systems need to be disentangled. Particularly, ADM applications need to use the right degree of automation and granularity of data input to achieve efficiency and public acceptance. We present results from a large-scale vignette experiment that assessed fairness perceptions and the acceptability of ADM systems. The experiment varied context and design dimensions, with an emphasis on who makes the final decision. We show that automated recommendations in combination with a final human decider are perceived as fair as decisions made by a dominant human decider and as fairer than decisions made only by an algorithm. Our results shed light on the context dependence of fairness assessments and show that semi-automation of decision-making processes is often desirable