EZH2 Inhibition in Ewing Sarcoma Upregulates GD2 Expression for Targeting with Gene-Modified T Cells.

Chimeric antigen receptor (CAR) engineering of T cells allows one to specifically target tumor cells via cell surface antigens. A candidate target in Ewing sarcoma is the ganglioside GD2, but heterogeneic expression limits its value. Here we report that pharmacological inhibition of Enhancer of Zeste Homolog 2 (EZH2) at doses reducing H3K27 trimethylation, but not cell viability, selectively and reversibly induces GD2 surface expression in Ewing sarcoma cells. EZH2 in Ewing sarcoma cells directly binds to the promoter regions of genes encoding for two key enzymes of GD2 biosynthesis, and EZH2 inhibition enhances expression of these genes. GD2 surface expression in Ewing sarcoma cells is not associated with distinct in vitro proliferation, colony formation, chemosensitivity, or in vivo tumorigenicity. Moreover, disruption of GD2 synthesis by gene editing does not affect its in vitro behavior. EZH2 inhibitor treatment sensitizes Ewing sarcoma cells to effective cytolysis by GD2-specific CAR gene-modified T cells. In conclusion, we report a clinically applicable pharmacological approach for enhancing efficacy of adoptively transferred GD2-redirected T cells against Ewing sarcoma, by enabling recognition of tumor cells with low or negative target expression.

MicroRNA-21 versus microRNA-34: Lung cancer promoting and inhibitory microRNAs analysed in silico and in vitro and their clinical impact.

MicroRNAs are well-known strong RNA regulators modulating whole functional units in complex signaling networks. Regarding clinical application, they have potential as biomarkers for prognosis, diagnosis, and therapy. In this review, we focus on two microRNAs centrally involved in lung cancer progression. MicroRNA-21 promotes and microRNA-34 inhibits cancer progression. We elucidate here involved pathways and imbed these antagonistic microRNAs in a network of interactions, stressing their cancer microRNA biology, followed by experimental and bioinformatics analysis of such microRNAs and their targets. This background is then illuminated from a clinical perspective on microRNA-21 and microRNA-34 as general examples for the complex microRNA biology in lung cancer and its diagnostic value. Moreover, we discuss the immense potential that microRNAs such as microRNA-21 and microRNA-34 imply by their broad regulatory effects. These should be explored for novel therapeutic strategies in the clinic.

EMT, Stemness, and Drug Resistance in Biological Context: A 3D Tumor Tissue/In Silico Platform for Analysis of Combinatorial Treatment in NSCLC with Aggressive KRAS-Biomarker Signatures.

Epithelial-to-mesenchymal transition (EMT) is discussed to be centrally involved in invasion, stemness, and drug resistance. Experimental models to evaluate this process in its biological complexity are limited. To shed light on EMT impact and test drug response more reliably, we use a lung tumor test system based on a decellularized intestinal matrix showing more in vivo-like proliferation levels and enhanced expression of clinical markers and carcinogenesis-related genes. In our models, we found evidence for a correlation of EMT with drug resistance in primary and secondary resistant cells harboring KRASG12C or EGFR mutations, which was simulated in silico based on an optimized signaling network topology. Notably, drug resistance did not correlate with EMT status in KRAS-mutated patient-derived xenograft (PDX) cell lines, and drug efficacy was not affected by EMT induction via TGF-ß. To investigate further determinants of drug response, we tested several drugs in combination with a KRASG12C inhibitor in KRASG12C mutant HCC44 models, which, besides EMT, display mutations in P53, LKB1, KEAP1, and high c-MYC expression. We identified an aurora-kinase A (AURKA) inhibitor as the most promising candidate. In our network, AURKA is a centrally linked hub to EMT, proliferation, apoptosis, LKB1, and c-MYC. This exemplifies our systemic analysis approach for clinical translation of biomarker signatures.

Modular micro-physiological human tumor/tissue models based on decellularized tissue for improved preclinical testing

High attrition rates associated with drug testing in 2D cell culture and animal models stress the need for improved modeling of human tumor tissues. In previous studies, our 3D models on a decellularized tissue matrix have shown better predictivity and higher chemoresistance. A single porcine intestine yields material for 150 3D models of breast, lung, colorectal cancer (CRC) or leukemia. The uniquely preserved structure of the basement membrane enables physiological anchorage of endothelial cells and epithelial-derived carcinoma cells. The matrix provides different niches for cell growth: on top as monolayer, in crypts as aggregates, and within deeper layers. Dynamic culture in bioreactors enhances cell growth. Comparing gene expression between 2D and 3D cultures, we observed changes related to proliferation, apoptosis and stemness. For drug target predictions, we utilize tumor-specific sequencing data in our in silico model, finding an additive effect of metformin and gefitinib treatment for lung cancer in silico, validated in vitro. To analyze mode-of-action, immune therapies such as trispecific T-cell engagers in leukemia or toxicity on non-cancer cells, the model can be modularly enriched with human endothelial cells (hECs), immune cells and fibroblasts. Upon addition of hECs, transmigration of immune cells through the endothelial barrier can be investigated. In an allogenic CRC model, we observe a lower basic apoptosis rate after applying PBMCs in 3D compared to 2D, which offers new options to mirror antigen-specific immunotherapies in vitro. In conclusion, we present modular human 3D tumor models with tissue-like features for preclinical testing to reduce animal experiments.

ROR1-CAR T cells are effective against lung and breast cancer in advanced microphysiologic 3D tumor models.

Solid tumors impose immunologic and physical barriers to the efficacy of chimeric antigen receptor (CAR) T cell therapy that are not reflected in conventional preclinical testing against singularized tumor cells in 2-dimensional culture. Here, we established microphysiologic three-dimensional (3D) lung and breast cancer models that resemble architectural and phenotypical features of primary tumors and evaluated the antitumor function of receptor tyrosine kinase-like orphan receptor 1-specific (ROR1-specific) CAR T cells. 3D tumors were established from A549 (non-small cell lung cancer) and MDA-MB-231 (triple-negative breast cancer) cell lines on a biological scaffold with intact basement membrane (BM) under static and dynamic culture conditions, which resulted in progressively increasing cell mass and invasive growth phenotype (dynamic > static; MDA-MB-231 > A549). Treatment with ROR1-CAR T cells conferred potent antitumor effects. In dynamic culture, CAR T cells actively entered arterial medium flow and adhered to and infiltrated the tumor mass. ROR1-CAR T cells penetrated deep into tumor tissue and eliminated multiple layers of tumor cells located above and below the BM. The microphysiologic 3D tumor models developed in this study are standardized, scalable test systems that can be used either in conjunction with or in lieu of animal testing to interrogate the antitumor function of CAR T cells and to obtain proof of concept for their safety and efficacy before clinical application.

A combined tissue-engineered/in silico signature tool patient stratification in lung cancer.

Patient-tailored therapy based on tumor drivers is promising for lung cancer treatment. For this, we combined in vitro tissue models with in silico analyses. Using individual cell lines with specific mutations, we demonstrate a generic and rapid stratification pipeline for targeted tumor therapy. We improve in vitro models of tissue conditions by a biological matrix-based three-dimensional (3D) tissue culture that allows in vitro drug testing: It correctly shows a strong drug response upon gefitinib (Gef) treatment in a cell line harboring an EGFR-activating mutation (HCC827), but no clear drug response upon treatment with the HSP90 inhibitor 17AAG in two cell lines with KRAS mutations (H441, A549). In contrast, 2D testing implies wrongly KRAS as a biomarker for HSP90 inhibitor treatment, although this fails in clinical studies. Signaling analysis by phospho-arrays showed similar effects of EGFR inhibition by Gef in HCC827 cells, under both 2D and 3D conditions. Western blot analysis confirmed that for 3D conditions, HSP90 inhibitor treatment implies different p53 regulation and decreased MET inhibition in HCC827 and H441 cells. Using in vitro data (western, phospho-kinase array, proliferation, and apoptosis), we generated cell line-specific in silico topologies and condition-specific (2D, 3D) simulations of signaling correctly mirroring in vitro treatment responses. Networks predict drug targets considering key interactions and individual cell line mutations using the Human Protein Reference Database and the COSMIC database. A signature of potential biomarkers and matching drugs improve stratification and treatment in KRAS-mutated tumors. In silico screening and dynamic simulation of drug actions resulted in individual therapeutic suggestions, that is, targeting HIF1A in H441 and LKB1 in A549 cells. In conclusion, our in vitro tumor tissue model combined with an in silico tool improves drug effect prediction and patient stratification. Our tool is used in our comprehensive cancer center and is made now publicly available for targeted therapy decisions.

Characterizing Aging, Mild Cognitive Impairment, and Dementia with Blood-Based Biomarkers and Neuropsychology.

BACKGROUND: Current treatment in Alzheimer's disease (AD) is initiated at a stage where the brain already has irreversible structural deteriorations. Therefore, the concept of treatment prior to obvious cognitive deficits has become widely accepted, and simple biochemical tests to discriminate normal aging from prodromal or demented stages are now common practice. OBJECTIVE: The objective of the study was the differentiation of controls, mild cognitive impairment (MCI) and AD patients by novel blood-based assays in combination with neuropsychological tests. METHODS: In a cross-sectional study, 143 subjects aged 18 to 85 years were recruited. All participants were classified by a comprehensive neuropsychological assessment. Blood samples were analyzed for several amyloid-ß (Aß) species, pro-inflammatory markers, anti-Aß autoantibodies, and ApoE allele status, respectively. RESULTS: Plasma Aß1-42 was significantly decreased in MCI and AD compared to age-matched controls, whereas Aß1-40 did not differ, but increases with age in healthy controls. The Aß1-42 to Aß1-40 ratio was stepwise decreased from age-matched controls via MCI to AD, and shows a clear correlation with memory scores. Reduced Aß1-42 and Aß1-42 to Aß1-40 ratio have strongly correlated with carrying ApoE ɛ4 allele. Autoantibodies against pyroglutamate-modified Aß, but only a certain subclass, were significantly decreased in AD compared to MCI and age-matched controls, whereas autoantibodies against the unmodified N-terminus of Aß did not differ. CONCLUSION: Comprehensive sample preparation and assay standardization enable reliable usage of plasma Aß for diagnosis of MCI and AD. Anti-pGlu-Aß autoantibodies correlate with cognition, but not with ApoE, supporting the associated plasma Aß analysis with additional and independent information.

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds.

In the present study, we combined an in vitro 3D lung tumor model with an in silico model to optimize predictions of drug response based on a specific mutational background. The model is generated on a decellularized porcine scaffold that reproduces tissue-specific characteristics regarding extracellular matrix composition and architecture including the basement membrane. We standardized a protocol that allows artificial tumor tissue generation within 14 days including three days of drug treatment. Our article provides several detailed descriptions of 3D read-out screening techniques like the determination of the proliferation index Ki67 staining's, apoptosis from supernatants by M30-ELISA and assessment of epithelial to mesenchymal transition (EMT), which are helpful tools for evaluating the effectiveness of therapeutic compounds. We could show compared to 2D culture a reduction of proliferation in our 3D tumor model that is related to the clinical situation. Despite of this lower proliferation, the model predicted EGFR-targeted drug responses correctly according to the biomarker status as shown by comparison of the lung carcinoma cell lines HCC827 (EGFR -mutated, KRAS wild-type) and A549 (EGFR wild-type, KRAS-mutated) treated with the tyrosine-kinase inhibitor (TKI) gefitinib. To investigate drug responses of more advanced tumor cells, we induced EMT by long-term treatment with TGF-beta-1 as assessed by vimentin/pan-cytokeratin immunofluorescence staining. A flow-bioreactor was employed to adjust culture to physiological conditions, which improved tissue generation. Furthermore, we show the integration of drug responses upon gefitinib treatment or TGF-beta-1 stimulation - apoptosis, proliferation index and EMT - into a Boolean in silico model. Additionally, we explain how drug responses of tumor cells with a specific mutational background and counterstrategies against resistance can be predicted. We are confident that our 3D in vitro approach especially with its in silico expansion provides an additional value for preclinical drug testing in more realistic conditions than in 2D cell culture.

Establishment of a human 3D lung cancer model based on a biological tissue matrix combined with a Boolean in silico model.

For the development of new treatment strategies against cancer, understanding signaling networks and their changes upon drug response is a promising approach to identify new drug targets and biomarker profiles. Pre-requisites are tumor models with multiple read-out options that accurately reflect the clinical situation. Tissue engineering technologies offer the integration of components of the tumor microenvironment which are known to impair drug response of cancer cells. We established three-dimensional (3D) lung carcinoma models on a decellularized tissue matrix, providing a complex microenvironment for cell growth. For model generation, we used two cell lines with (HCC827) or without (A549) an activating mutation of the epidermal growth factor receptor (EGFR), exhibiting different sensitivities to the EGFR inhibitor gefitinib. EGFR activation in HCC827 was inhibited by gefitinib, resulting in a significant reduction of proliferation (Ki-67 proliferation index) and in the induction of apoptosis (TUNEL staining, M30-ELISA). No significant effect was observed in conventional cell culture. Results from the 3D model correlated with the results of an in silico model that integrates the EGFR signaling network according to clinical data. The application of TGFß1 induced tumor cell invasion, accompanied by epithelial-mesenchymal transition (EMT) both in vitro and in silico. This was confirmed in the 3D model by acquisition of mesenchymal cell morphology and modified expression of fibronectin, E-cadherin, ß-catenin and mucin-1. Quantitative read-outs for proliferation, apoptosis and invasion were established in the complex 3D tumor model. The combined in vitro and in silico model represents a powerful tool for systems analysis.