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Natural killer (NK) cells are a subset of lymphocytes that offer great potential for cancer immunotherapy due to their natural anti-tumor activity and the possibility to safely transplant cells from healthy donors to patients in a clinical setting. However, the efficacy of cell-based immunotherapies using both T and NK cells is often limited by a poor infiltration of immune cells into solid tumors. Importantly, regulatory immune cell subsets are frequently recruited to tumor sites. In this study, we overexpressed two chemokine receptors, CCR4 and CCR2B, that are naturally found on T regulatory cells and tumor-resident monocytes, respectively, on NK cells. Using the NK cell line NK-92 as well as primary NK cells from peripheral blood, we show that genetically engineered NK cells can be efficiently redirected using chemokine receptors from different immune cell lineages and migrate towards chemokines such as CCL22 or CCL2, without impairing the natural effector functions. This approach has the potential to enhance the therapeutic effect of immunotherapies in solid tumors by directing genetically engineered donor NK cells to tumor sites. As a future therapeutic option, the natural anti-tumor activity of NK cells at the tumor sites can be increased by co-expression of chemokine receptors with chimeric antigen receptors (CAR) or T cell receptors (TCR) on NK cells can be performed in the future.

High attrition-rates entailed by 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, as well as 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.

Treatment of terminal heart failure still poses a significant clinical problem. Cardiac tissue engineering could offer autologous solutions for the replacement of nonfunctional myocardial tissue. So far, soft matrix construction and missing large-scale prevascularization prevented the application of sizeable cardiac repair patches. We developed a novel bioreactor system for semi-automatic compression of a collagen I hydrogel applying 16 times higher pressure than in previous studies. Resistance towards compression stress was investigated for multiple cardiac-related cell types. For scaffold prevascuarization, a tubular cavity was imprinted during the compaction process. Primary cardiac-derived endothelial cells (ECs) were isolated from human left atrial appendages (HLAAs) and characterized by fluorescence-activated cell sorting (FACS) and immunocytology. EC were then seeded into the preformed channel with dermal fibroblasts as interstitial cell component of the fully cellularized patch. After 8 days of constant perfusion culture within the same bioreactor, scaffold dynamic modulus and cell viability were analyzed. Endothelial proliferation and vessel maturation were examined by immunohistochemistry and transmission electron microscopy. Our design allowed for scaffold production and dynamic culture in a one-stop-shop model. Enhanced compression and cell-mediated matrix remodeling induced a significant increase in scaffold stiffness while ensuring excellent cell survival. For the first time, we could isolate HLAA-derived EC with proliferative potential. ECs within the central channel proliferated during flow culture, continuously expressing endothelial markers (CD31) and displaying basal membrane synthesis (collagen IV, ultrastructural analysis). After 7 days of culture, a complete endothelial monolayer could be observed. Covering cells aligned themselves in flow direction and developed mature cell-cell contacts.

To improve and focus preclinical testing, we combine tumor models based on a decellularized tissue matrix with bioinformatics to stratify tumors according to stage-specific mutations that are linked to central cancer pathways. We generated tissue models with BRAF-mutant colorectal cancer (CRC) cells (HROC24 and HROC87) and compared treatment responses to two-dimensional (2D) cultures and xenografts. As the BRAF inhibitor vemurafenib is—in contrast to melanoma—not effective in CRC, we combined it with the EGFR inhibitor gefitinib. In general, our 3D models showed higher chemoresistance and in contrast to 2D a more active HGFR after gefitinib and combination-therapy. In xenograft models murine HGF could not activate the human HGFR, stressing the importance of the human microenvironment. In order to stratify patient groups for targeted treatment options in CRC, an in silico topology with different stages including mutations and changes in common signaling pathways was developed. We applied the established topology for in silico simulations to predict new therapeutic options for BRAF-mutated CRC patients in advanced stages. Our in silico tool connects genome information with a deeper understanding of tumor engines in clinically relevant signaling networks which goes beyond the consideration of single drivers to improve CRC patient stratification.

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.

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.

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.