Circular RNA circZFPM2 regulates cardiomyocyte hypertrophy and survival.

Hypertrophic cardiomyopathy (HCM) constitutes the most common genetic cardiac disorder. However, current pharmacotherapeutics are mainly symptomatic and only partially address underlying molecular mechanisms. Circular RNAs (circRNAs) are a recently discovered class of non-coding RNAs and emerged as specific and powerful regulators of cellular functions. By performing global circRNA-specific next generation sequencing in cardiac tissue of patients with hypertrophic cardiomyopathy compared to healthy donors, we identified circZFPM2 (hsa_circ_0003380). CircZFPM2, which derives from the ZFPM2 gene locus, is a highly conserved regulatory circRNA that is strongly induced in HCM tissue. In vitro loss-of-function experiments were performed in neonatal rat cardiomyocytes, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), and HCM-patient-derived hiPSC-CMs. A knockdown of circZFPM2 was found to induce cardiomyocyte hypertrophy and compromise mitochondrial respiration, leading to an increased production of reactive oxygen species and apoptosis. In contrast, delivery of recombinant circZFPM2, packaged in lipid-nanoparticles or using AAV-based overexpression, rescued cardiomyocyte hypertrophic gene expression and promoted cell survival. Additionally, HCM-derived cardiac organoids exhibited improved contractility upon CM-specific overexpression of circZFPM2. Multi-Omics analysis further promoted our hypothesis, showing beneficial effects of circZFPM2 on cardiac contractility and mitochondrial function. Collectively, our data highlight that circZFPM2 serves as a promising target for the treatment of cardiac hypertrophy including HCM.

Telomerase is essential for cardiac differentiation and sustained metabolism of human cardiomyocytes.

Telomeres as the protective ends of linear chromosomes, are synthesized by the enzyme telomerase (TERT). Critically short telomeres essentially contribute to aging-related diseases and are associated with a broad spectrum of disorders known as telomeropathies. In cardiomyocytes, telomere length is strongly correlated with cardiomyopathies but it remains ambiguous whether short telomeres are the cause or the result of the disease. In this study, we employed an inducible CRISPRi human induced pluripotent stem cell (hiPSC) line to silence TERT expression enabling the generation of hiPSCs and hiPSC-derived cardiomyocytes with long and short telomeres. Reduced telomerase activity and shorter telomere lengths of hiPSCs induced global transcriptomic changes associated with cardiac developmental pathways. Consequently, the differentiation potential towards cardiomyocytes was strongly impaired and single cell RNA sequencing revealed a shift towards a more smooth muscle cell like identity in the cells with the shortest telomeres. Poor cardiomyocyte function and increased sensitivity to stress directly correlated with the extent of telomere shortening. Collectively our data demonstrates a TERT dependent cardiomyogenic differentiation defect, highlighting the CRISPRi TERT hiPSCs model as a powerful platform to study the mechanisms and consequences of short telomeres in the heart and also in the context of telomeropathies.

L-arginine metabolism inhibits arthritis and inflammatory bone loss.

OBJECTIVES: To investigate the effect of the L-arginine metabolism on arthritis and inflammation-mediated bone loss. METHODS: L-arginine was applied to three arthritis models (collagen-induced arthritis, serum-induced arthritis and human TNF transgenic mice). Inflammation was assessed clinically and histologically, while bone changes were quantified by µCT and histomorphometry. In vitro, effects of L-arginine on osteoclast differentiation were analysed by RNA-seq and mass spectrometry (MS). Seahorse, Single Cell ENergetIc metabolism by profilIng Translation inHibition and transmission electron microscopy were used for detecting metabolic changes in osteoclasts. Moreover, arginine-associated metabolites were measured in the serum of rheumatoid arthritis (RA) and pre-RA patients. RESULTS: L-arginine inhibited arthritis and bone loss in all three models and directly blocked TNFα-induced murine and human osteoclastogenesis. RNA-seq and MS analyses indicated that L-arginine switched glycolysis to oxidative phosphorylation in inflammatory osteoclasts leading to increased ATP production, purine metabolism and elevated inosine and hypoxanthine levels. Adenosine deaminase inhibitors blocking inosine and hypoxanthine production abolished the inhibition of L-arginine on osteoclastogenesis in vitro and in vivo. Altered arginine levels were also found in RA and pre-RA patients. CONCLUSION: Our study demonstrated that L-arginine ameliorates arthritis and bone erosion through metabolic reprogramming and perturbation of purine metabolism in osteoclasts.

Illuminating the inner workings of a natural protein switch: Blue-light sensing in LOV-activated diguanylate cyclases.

Regulatory proteins play a crucial role in adaptation to environmental cues. Especially for lifestyle transitions, such as cell proliferation or apoptosis, switch-like characteristics are desirable. While nature frequently uses regulatory circuits to amplify or dampen signals, stand-alone protein switches are interesting for applications like biosensors, diagnostic tools, or optogenetics. However, such stand-alone systems frequently feature limited dynamic and operational ranges and suffer from slow response times. Here, we characterize a LOV-activated diguanylate cyclase (LadC) that offers precise temporal and spatial control of enzymatic activity with an exceptionally high dynamic range over four orders of magnitude. To establish this pronounced activation, the enzyme exhibits a two-stage activation process in which its activity is inhibited in the dark by caging its effector domains and stimulated upon illumination by the formation of an extended coiled-coil. These switch-like characteristics of the LadC system can be used to develop new optogenetic tools with tight regulation.

Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis.

The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell-cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.

Soluble CD83 modulates human-monocyte-derived macrophages toward alternative phenotype, function, and metabolism.

Alterations in macrophage (Mφ) polarization, function, and metabolic signature can foster development of chronic diseases, such as autoimmunity or fibrotic tissue remodeling. Thus, identification of novel therapeutic agents that modulate human Mφ biology is crucial for treatment of such conditions. Herein, we demonstrate that the soluble CD83 (sCD83) protein induces pro-resolving features in human monocyte-derived Mφ biology. We show that sCD83 strikingly increases the expression of inhibitory molecules including ILT-2 (immunoglobulin-like transcript 2), ILT-4, ILT-5, and CD163, whereas activation markers, such as MHC-II and MSR-1, were significantly downregulated. This goes along with a decreased capacity to stimulate alloreactive T cells in mixed lymphocyte reaction (MLR) assays. Bulk RNA sequencing and pathway analyses revealed that sCD83 downregulates pathways associated with pro-inflammatory, classically activated Mφ (CAM) differentiation including HIF-1A, IL-6, and cytokine storm, whereas pathways related to alternative Mφ activation and liver X receptor were significantly induced. By using the LXR pathway antagonist GSK2033, we show that transcription of specific genes (e.g., PPARG, ABCA1, ABCG1, CD36) induced by sCD83 is dependent on LXR activation. In summary, we herein reveal for the first time mechanistic insights into the modulation of human Mφ biology by sCD83, which is a further crucial preclinical study for the establishment of sCD83 as a new therapeutical agent to treat inflammatory conditions.

Soluble CD83 improves and accelerates wound healing by the induction of pro-resolving macrophages.

To facilitate the recovery process of chronic and hard-to-heal wounds novel pro-resolving treatment options are urgently needed. We investigated the pro-regenerative properties of soluble CD83 (sCD83) on cutaneous wound healing, where sCD83 accelerated wound healing not only after systemic but also after topical application, which is of high therapeutic interest. Cytokine profile analyses revealed an initial upregulation of inflammatory mediators such as TNFα and IL-1ß, followed by a switch towards pro-resolving factors, including YM-1 and IL-10, both expressed by tissue repair macrophages. These cells are known to mediate resolution of inflammation and stimulate wound healing processes by secretion of growth factors such as epidermal growth factor (EGF) and vascular endothelial growth factor (VEGF), which promote vascularization as well as fibroblast and keratinocyte differentiation. In conclusion, we have found strong wound healing capacities of sCD83 beyond the previously described role in transplantation and autoimmunity. This makes sCD83 a promising candidate for the treatment of chronic- and hard-to-heal wounds.

The soluble CD83 protein prevents bone destruction by inhibiting the formation of osteoclasts and inducing resolution of inflammation in arthritis.

Here we show that soluble CD83 induces the resolution of inflammation in an antigen-induced arthritis (AIA) model. Joint swelling and the arthritis-related expression levels of IL-1ß, IL-6, RANKL, MMP9, and OC-Stamp were strongly reduced, while Foxp3 was induced. In addition, we observed a significant inhibition of TRAP+ osteoclast formation, correlating with the reduced arthritic disease score. In contrast, cell-specific deletion of CD83 in human and murine precursor cells resulted in an enhanced formation of mature osteoclasts. RNA sequencing analyses, comparing sCD83- with mock treated cells, revealed a strong downregulation of osteoclastogenic factors, such as Oc-Stamp, Mmp9 and Nfatc1, Ctsk, and Trap. Concomitantly, transcripts typical for pro-resolving macrophages, e.g., Mrc1/2, Marco, Klf4, and Mertk, were upregulated. Interestingly, members of the metallothionein (MT) family, which have been associated with a reduced arthritic disease severity, were also highly induced by sCD83 in samples derived from RA patients. Finally, we elucidated the sCD83-induced signaling cascade downstream to its binding to the Toll-like receptor 4/(TLR4/MD2) receptor complex using CRISPR/Cas9-induced knockdowns of TLR4/MyD88/TRIF and MTs, revealing that sCD83 acts via the TRIF-signaling cascade. In conclusion, sCD83 represents a promising therapeutic approach to induce the resolution of inflammation and to prevent bone erosion in autoimmune arthritis.

Deciphering Pro-angiogenic Transcription Factor Profiles in Hypoxic Human Endothelial Cells by Combined Bioinformatics and in vitro Modeling.

Background: Constant supply of oxygen is crucial for multicellular tissue homeostasis and energy metabolism in cardiac tissue. As a first response to acute hypoxia, endothelial cells (ECs) promote recruitment and adherence of immune cells to the dysbalanced EC barrier by releasing inflammatory mediators and growth factors, whereas chronic hypoxia leads to the activation of a transcription factor (TF) battery, that potently induces expression of growth factors and cytokines including platelet-derived growth factor (PDGF) and vascular endothelial growth factor (VEGF). We report a hypoxia-minded, targeted bioinformatics approach aiming to identify and validate TFs that regulate angiogenic signaling. Results: A comprehensive RNA-Seq dataset derived from human ECs subjected to normoxic or hypoxic conditions was selected to identify significantly regulated genes based on (i) fold change (normoxia vs. hypoxia) and (ii) relative abundancy. Transcriptional regulation of this gene set was confirmed via qPCR in validation experiments where HUVECs were subjected to hypoxic conditions for 24 h. Screening the promoter and upstream regulatory elements of these genes identified two TFs, KLF5 and SP1, both with a potential binding site within these regions of selected target genes. In vitro, siRNA experiments confirmed SP1- and KLF5-mediated regulation of identified hypoxia-sensitive endothelial genes. Next to angiogenic signaling, we also validated the impact of TFs on inflammatory signaling, both key events in hypoxic sensing. Both TFs impacted on inflammatory signaling since endogenous repression led to increased NF-κB signaling. Additionally, SP1 silencing eventuated decreased angiogenic properties in terms of proliferation and tube formation. Conclusion: By detailed in silico analysis of promoter region and upstream regulatory elements for a list of hypoxia-sensitive genes, our bioinformatics approach identified putative binding sites for TFs of SP or KLF family in vitro. This strategy helped to identify TFs functionally involved in human angiogenic signaling and therefore serves as a base for identifying novel RNA-based drug entities in a therapeutic setting of vascularization.

IL-33-induced metabolic reprogramming controls the differentiation of alternatively activated macrophages and the resolution of inflammation.

Alternatively activated macrophages (AAMs) contribute to the resolution of inflammation and tissue repair. However, molecular pathways that govern their differentiation have remained incompletely understood. Here, we show that uncoupling protein-2-mediated mitochondrial reprogramming and the transcription factor GATA3 specifically controlled the differentiation of pro-resolving AAMs in response to the alarmin IL-33. In macrophages, IL-33 sequentially triggered early expression of pro-inflammatory genes and subsequent differentiation into AAMs. Global analysis of underlying signaling events revealed that IL-33 induced a rapid metabolic rewiring of macrophages that involved uncoupling of the respiratory chain and increased production of the metabolite itaconate, which subsequently triggered a GATA3-mediated AAM polarization. Conditional deletion of GATA3 in mononuclear phagocytes accordingly abrogated IL-33-induced differentiation of AAMs and tissue repair upon muscle injury. Our data thus identify an IL-4-independent and GATA3-dependent pathway in mononuclear phagocytes that results from mitochondrial rewiring and controls macrophage plasticity and the resolution of inflammation.

Engrailed 1 coordinates cytoskeletal reorganization to induce myofibroblast differentiation.

Transforming growth factor-ß (TGFß) is a key mediator of fibroblast activation in fibrotic diseases, including systemic sclerosis. Here we show that Engrailed 1 (EN1) is reexpressed in multiple fibroblast subpopulations in the skin of SSc patients. We characterize EN1 as a molecular amplifier of TGFß signaling in myofibroblast differentiation: TGFß induces EN1 expression in a SMAD3-dependent manner, and in turn, EN1 mediates the profibrotic effects of TGFß. RNA sequencing demonstrates that EN1 induces a profibrotic gene expression profile functionally related to cytoskeleton organization and ROCK activation. EN1 regulates gene expression by modulating the activity of SP1 and other SP transcription factors, as confirmed by ChIP-seq experiments for EN1 and SP1. Functional experiments confirm the coordinating role of EN1 on ROCK activity and the reorganization of cytoskeleton during myofibroblast differentiation, in both standard fibroblast culture systems and in vitro skin models. Consistently, mice with fibroblast-specific knockout of En1 demonstrate impaired fibroblast-to-myofibroblast transition and are partially protected from experimental skin fibrosis.

Reconstruction of the miR-506-Quaking axis in Idiopathic Pulmonary Fibrosis using integrative multi-source bioinformatics.

The family of RNA-binding proteins (RBP) functions as a crucial regulator of multiple biological processes and diseases. However, RBP function in the clinical setting of idiopathic pulmonary fibrosis (IPF) is still unknown. We developed a practical in silico screening approach for the characterization of RBPs using multi-sources data information and comparative molecular network bioinformatics followed by wet-lab validation studies. Data mining of bulk RNA-Sequencing data of tissues of patients with IPF identified Quaking (QKI) as a significant downregulated RBP. Cell-type specific expression was confirmed by single-cell RNA-Sequencing analysis of IPF patient data. We systematically analyzed the molecular interaction network around QKI and its functional interplay with microRNAs (miRs) in human lung fibroblasts and discovered a novel regulatory miR-506-QKI axis contributing to the pathogenesis of IPF. The in silico results were validated by in-house experiments applying model systems of miR and lung biology. This study supports an understanding of the intrinsic molecular mechanisms of IPF regulated by the miR-506-QKI axis. Initially applied to human lung disease, the herein presented integrative in silico data mining approach can be adapted to other disease entities, underlining its practical relevance in RBP research.

Protein kinase C-ß-dependent changes in the glucose metabolism of bone marrow stromal cells of chronic lymphocytic leukemia.

Survival of chronic lymphocytic leukemia (CLL) cells critically depends on the support of an adapted and therefore appropriate tumor microenvironment. Increasing evidence suggests that B-cell receptor-associated kinases such as protein kinase C-ß (PKCß) or Lyn kinase are essential for the formation of a microenvironment supporting leukemic growth. Here, we describe the impact of PKCß on the glucose metabolism in bone marrow stromal cells (BMSC) upon CLL contact. BMSC get activated by CLL contact expressing stromal PKCß that diminishes mitochondrial stress and apoptosis in CLL cells by stimulating glucose uptake. In BMSC, the upregulation of PKCß results in increased mitochondrial depolarization and leads to a metabolic switch toward oxidative phosphorylation. In addition, PKCß-deficient BMSC regulates the expression of Hnf1 promoting stromal insulin signaling after CLL contact. Our data suggest that targeting PKCß and the glucose metabolism of the leukemic niche could be a potential therapeutic strategy to overcome stroma-mediated drug resistance.

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.

Integrative Bioinformatic Analyses of Global Transcriptome Data Decipher Novel Molecular Insights into Cardiac Anti-Fibrotic Therapies.

Integrative bioinformatics is an emerging field in the big data era, offering a steadily increasing number of algorithms and analysis tools. However, for researchers in experimental life sciences it is often difficult to follow and properly apply the bioinformatical methods in order to unravel the complexity and systemic effects of omics data. Here, we present an integrative bioinformatics pipeline to decipher crucial biological insights from global transcriptome profiling data to validate innovative therapeutics. It is available as a web application for an interactive and simplified analysis without the need for programming skills or deep bioinformatics background. The approach was applied to an ex vivo cardiac model treated with natural anti-fibrotic compounds and we obtained new mechanistic insights into their anti-fibrotic action and molecular interplay with miRNAs in cardiac fibrosis. Several gene pathways associated with proliferation, extracellular matrix processes and wound healing were altered, and we could identify micro (mi) RNA-21-5p and miRNA-223-3p as key molecular components related to the anti-fibrotic treatment. Importantly, our pipeline is not restricted to a specific cell type or disease and can be broadly applied to better understand the unprecedented level of complexity in big data research.

Inflammatory Drivers of Cardiovascular Disease: Molecular Characterization of Senescent Coronary Vascular Smooth Muscle Cells.

The senescence of vascular smooth muscle cells (VSMCs) has been implicated as a causal pro-inflammatory mechanism for cardiovascular disease development and progression of atherosclerosis, the instigator of ischemic heart disease. Contemporary limitations related to studying this cellular population and senescence-related therapeutics are caused by a lack of specific markers enabling their detection. Therefore, we aimed to profile a phenotypical and molecular signature of senescent VSMCs to allow reliable identification. To achieve this goal, we have compared non-senescent and senescent VSMCs from two in vitro models of senescence, replicative senescence (RS) and DNA-damage induced senescence (DS), by analyzing the expressions of established senescence markers: cell cycle inhibitors- p16 INK4a, p14 ARF, p21 and p53; pro-inflammatory factors-Interleukin 1ß (IL-1ß), IL-6 and high mobility group box-1 (HMGB-1); contractile proteins-smooth muscle heavy chain- (MYH11), smoothelin and transgelin (TAGLN), as well as structural features (nuclear morphology and LMNB1 (Lamin B1) expression). The different senescence-inducing modalities resulted in a lack of the proliferative activity. Nucleomegaly was seen in senescent VSMC as compared to freshly isolated VSMC Phenotypically, senescent VSMC appeared with a significantly larger cell size and polygonal, non-spindle-shaped cell morphology. In line with the supposed switch to a pro-inflammatory phenotype known as the senescence associated secretory phenotype (SASP), we found that both RS and DS upregulated IL-1ß and released HMGB-1 from the nucleus, while RS also showed IL-6 upregulation. In regard to cell cycle-regulating molecules, we detected modestly increased p16 levels in both RS and DS, but largely inconsistent p21, p14ARF, and p53 expressions in senescent VSMCs. Since these classical markers of senescence showed insufficient deregulation to warrant senescent VSMC detection, we have conducted a non-biased proteomics and in silico analysis of RS VSMC demonstrating altered RNA biology as the central molecular feature of senescence in this cell type. Therefore, key proteins involved with RNA functionality, HMGB-1 release, LMNB-1 downregulation, in junction with nuclear enlargement, can be used as markers of VSMC senescence, enabling the detection of these pathogenic pro-inflammatory cells in future therapeutic studies in ischemic heart disease and atherosclerosis.

Comprehensive Bioinformatics Identifies Key microRNA Players in ATG7-Deficient Lung Fibroblasts.

BACKGROUND: Deficient autophagy has been recently implicated as a driver of pulmonary fibrosis, yet bioinformatics approaches to study this cellular process are lacking. Autophagy-related 5 and 7 (ATG5/ATG7) are critical elements of macro-autophagy. However, an alternative ATG5/ATG7-independent macro-autophagy pathway was recently discovered, its regulation being unknown. Using a bioinformatics proteome profiling analysis of ATG7-deficient human fibroblasts, we aimed to identify key microRNA (miR) regulators in autophagy. METHOD: We have generated ATG7-knockout MRC-5 fibroblasts and performed mass spectrometry to generate a large-scale proteomics dataset. We further quantified the interactions between various proteins combining bioinformatics molecular network reconstruction and functional enrichment analysis. The predicted key regulatory miRs were validated via quantitative polymerase chain reaction. RESULTS: The functional enrichment analysis of the 26 deregulated proteins showed decreased cellular trafficking, increased mitophagy and senescence as the major overarching processes in ATG7-deficient lung fibroblasts. The 26 proteins reconstitute a protein interactome of 46 nodes and miR-regulated interactome of 834 nodes. The miR network shows three functional cluster modules around miR-16-5p, miR-17-5p and let-7a related to multiple deregulated proteins. Confirming these results in a biological setting, serially passaged wild-type and autophagy-deficient fibroblasts displayed senescence-dependent expression profiles of miR-16-5p and miR-17-5p. CONCLUSIONS: We have developed a bioinformatics proteome profiling approach that successfully identifies biologically relevant miR regulators from a proteomics dataset of the ATG-7-deficient milieu in lung fibroblasts, and thus may be used to elucidate key molecular players in complex fibrotic pathological processes. The approach is not limited to a specific cell-type and disease, thus highlighting its high relevance in proteome and non-coding RNA research.

In silico signaling modeling to understand cancer pathways and treatment responses.

Precision medicine has changed thinking in cancer therapy, highlighting a better understanding of the individual clinical interventions. But what role do the drivers and pathways identified from pan-cancer genome analysis play in the tumor? In this letter, we will highlight the importance of in silico modeling in precision medicine. In the current era of big data, tumor engines and pathways derived from pan-cancer analysis should be integrated into in silico models to understand the mutational tumor status and individual molecular pathway mechanism at a deeper level. This allows to pre-evaluate the potential therapy response and develop optimal patient-tailored treatment strategies which pave the way to support precision medicine in the clinic of the future.