Individualized neoantigen peptide immunization of a metastatic pancreatic cancer patient: a case report of combined tumor and liquid biopsy.

This report details a case of pancreatic cancer with liver metastasis that exhibited a positive immune response to personalized immunization therapy. Our study involved the identification of neoantigens and their corresponding immunogenic peptides using an in-house bioinformatic pipeline. This process included the identification of somatic mutations through DNA/RNA sequencing of solid tumor tissue and blood liquid biopsy. Computational prediction techniques were then employed to identify novel epitopes, followed by the design and manufacture of patient-specific immunization peptides. In combination with standard-of-care chemotherapy, the patient received a sequence of 5 biweekly prime injections followed by 2 boost injections 2 and 5 months later. The peptides were emulsified in Montanide and the injection-site was conditioned with nivolumab and imiquimod. The combined regimen of peptide immunization and chemotherapy resulted in a notable decline in CA19-9 tumor marker levels following both prime and boost applications. Subsequent MRI assessments revealed a reduction in the size of liver metastases several months post-immunization initiation. Importantly, the patient showed and improved overall survival and reported an improved quality of life without experiencing significant treatment-related adverse effects. This case underscores the potential benefits of personalized peptide-based immunization as an adjunctive therapy in the treatment of advanced pancreatic cancer, showcasing promising outcomes in tumor marker reduction, tumor shrinkage, and enhanced patient well-being.

Attenuation of fibroblast activation and fibrosis by adropin in systemic sclerosis.

Fibrotic diseases impose a major socioeconomic challenge on modern societies and have limited treatment options. Adropin, a peptide hormone encoded by the energy homeostasis-associated (ENHO) gene, is implicated in metabolism and vascular homeostasis, but its role in the pathogenesis of fibrosis remains enigmatic. Here, we used machine learning approaches in combination with functional in vitro and in vivo experiments to characterize adropin as a potential regulator involved in fibroblast activation and tissue fibrosis in systemic sclerosis (SSc). We demonstrated consistent down-regulation of adropin/ENHO in skin across multiple cohorts of patients with SSc. The prototypical profibrotic cytokine TGFß reduced adropin/ENHO expression in a JNK-dependent manner. Restoration of adropin signaling by therapeutic application of bioactive adropin34-76 peptides in turn inhibited TGFß-induced fibroblast activation and fibrotic tissue remodeling in primary human dermal fibroblasts, three-dimensional full-thickness skin equivalents, mouse models of bleomycin-induced pulmonary fibrosis and sclerodermatous chronic graft-versus-host-disease (sclGvHD), and precision-cut human skin slices. Knockdown of GPR19, an adropin receptor, abrogated the antifibrotic effects of adropin in fibroblasts. RNA-seq demonstrated that the antifibrotic effects of adropin34-76 were functionally linked to deactivation of GLI1-dependent profibrotic transcriptional networks, which was experimentally confirmed in vitro, in vivo, and ex vivo using cultured human dermal fibroblasts, a sclGvHD mouse model, and precision-cut human skin slices. ChIP-seq confirmed adropin34-76-induced changes in TGFß/GLI1 signaling. Our study characterizes the TGFß-induced down-regulation of adropin/ENHO expression as a potential pathomechanism of SSc as a prototypical systemic fibrotic disease that unleashes uncontrolled activation of profibrotic GLI1 signaling.

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.

Improved Survival of a HER2-Positive Metastatic Breast Cancer Patient Following a Personalized Peptide Immunization.

Cancer neoantigens that arise from somatic mutations have emerged as important targets for personalized immunization. Here, we report an improved overall survival of a HER2-positive metastatic breast cancer patient using a bioinformatic-based personalized peptide immunization called BITAP (BioInformatic Tumor Address Peptides). The epitopes were predicted using our in-house bioinformatic pipeline, and the immunogenicity was tested by IFN-γ ELISPOT and intracellular cytokine staining assays. In total, a significant peptide-specific T-cell response was detected against 18 out of the 76 (≈24%) tested peptides. The patient's follow-up by measuring serologic markers showed a significant reduction in the tumor marker levels following BITAP immunization. Along with standard treatment, the patient treated with the BITAP showed stable disease with a remarkably improved overall survival, and no serious treatment-related adverse effects. In conclusion, our findings suggest that BITAP immunization is feasible, and safe, and may induce tumor regressions in patients with HER2-positive subsets of breast cancer.

Transcription factor combinations that define human astrocyte identity encode significant variation of maturity and function.

Increasing evidence indicates that cellular identity can be reduced to the distinct gene regulatory networks controlled by transcription factors (TFs). However, redundancy exists in these states as different combinations of TFs can induce broadly similar cell types. We previously demonstrated that by overcoming gene silencing, it is possible to deterministically reprogram human pluripotent stem cells directly into cell types of various lineages. In the present study we leverage the consistency and precision of our approach to explore four different TF combinations encoding astrocyte identity, based on previously published reports. Analysis of the resulting induced astrocytes (iAs) demonstrated that all four cassettes generate cells with the typical morphology of in vitro astrocytes, which expressed astrocyte-specific markers. The transcriptional profiles of all four iAs clustered tightly together and displayed similarities with mature human astrocytes, although maturity levels differed between cells. Importantly, we found that the TF cassettes induced iAs with distinct differences with regards to their cytokine response and calcium signaling. In vivo transplantation of selected iAs into immunocompromised rat brains demonstrated long term stability and integration. In conclusion, all four TF combinations were able to induce stable astrocyte-like cells that were morphologically similar but showed subtle differences with respect to their transcriptome. These subtle differences translated into distinct differences with regards to cell function, that could be related to maturation state and/or regional identity of the resulting cells. This insight opens an opportunity to precision-engineer cells to meet functional requirements, for example, in the context of therapeutic cell transplantation.

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.

A translational study: Involvement of miR-21-5p in development and maintenance of neuropathic pain via immune-related targets CCL5 and YWHAE.

Neuropathic pain occurs in more than half of the patients suffering from peripheral neuropathies. We investigated the role of microRNA (miR)-21 in neuropathic pain using a murine-human translational approach. We applied the spared nerve injury (SNI) model at the sciatic nerve of mice and assessed the potential analgesic effect of perineurial miR-21-5p inhibitor application. Immune-related targets of miR-21-5p were determined by a qRT-PCR based cytokine and chemokine array. Bioinformatical analysis identified potential miR-21-5p targets interacting with CC-chemokine ligand (CCL)5. We validated CCL5 and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein (YWHAE), an interaction partner of miR-21-5p and CCL5, by qRT-PCR in murine common peroneal and tibial nerves. Validated candidates were then investigated in white blood cell and sural nerve biopsy samples of patients with focal to generalized pain syndromes, i.e. small fiber neuropathy (SFN), polyneuropathy (PNP), and nerve lesion (NL). We showed that perineurial miR-21-5p inhibition reverses SNI-induced mechanical and heat hypersensitivity in mice and found a reduction of the SNI-induced increase of the pro-inflammatory mediators CCL5 (p < 0.01), CCL17 (p < 0.05), and IL-12ß (p < 0.05) in miR-21-5p inhibitor-treated mice. In silico analysis revealed several predicted and validated targets for miR-21-5p with CCL5 interaction. Among these, we found lower YWHAE gene expression in mice after SNI and perineurial injections of a scrambled oligonucleotide compared to naïve mice (p < 0.05), but this was not changed by miR-21-5p inhibition. Furthermore, miR-21-5p inhibition led to a further increase of the SNI-induced increase in TGFß (p < 0.01). Patient biomaterial revealed different systemic expression patterns of miR-21-5p, with higher expression in SFN and lower expression in NL. Further, we showed higher systemic expression of pro-inflammatory mediators in white blood cells of SFN patients compared to healthy controls. We have conducted a translational study comparing results from animal models to human patients with three different neuropathic pain syndromes. We identified CCL5 as a miR-21 dependent common player in the mouse SNI model and the human painful disease SFN.

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.

Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors.

Context: Pheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at metabolome level have been described after surgical PPGL removal. The value of metabolomics for the diagnosis of PPGL has not been studied yet. Objective: Evaluation of quantitative metabolomics as a diagnostic tool for PPGL. Design: Targeted metabolomics by liquid chromatography-tandem mass spectrometry of plasma specimens and statistical modeling using ML-based feature selection approaches in a clinically well characterized cohort study. Patients: Prospectively enrolled patients (n=36, 17 female) from the Prospective Monoamine-producing Tumor Study (PMT) with hormonally active PPGL and 36 matched controls in whom PPGL was rigorously excluded. Results: Among 188 measured metabolites, only without considering false discovery rate, 4 exhibited statistically significant differences between patients with PPGL and controls (histidine p=0.004, threonine p=0.008, lyso PC a C28:0 p=0.044, sum of hexoses p=0.018). Weak, but significant correlations for histidine, threonine and lyso PC a C28:0 with total urine catecholamine levels were identified. Only the sum of hexoses (reflecting glucose) showed significant correlations with plasma metanephrines.By using ML-based feature selection approaches, we identified diagnostic signatures which all exhibited low accuracy and sensitivity. The best predictive value (sensitivity 87.5%, accuracy 67.3%) was obtained by using Gradient Boosting Machine Modelling. Conclusions: The diabetogenic effect of catecholamine excess dominates the plasma metabolome in PPGL patients. While curative surgery for PPGL led to normalization of catecholamine-induced alterations of metabolomics in individual patients, plasma metabolomics are not useful for diagnostic purposes, most likely due to inter-individual variability.

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.

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.

Connecting Cancer Pathways to Tumor Engines: A Stratification Tool for Colorectal Cancer Combining Human In Vitro Tissue Models with Boolean In Silico Models.

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.

The New Genetic Landscape of Cushing's Disease: Deubiquitinases in the Spotlight.

Cushing's disease (CD) is a rare condition caused by adrenocorticotropic hormone (ACTH)-producing adenomas of the pituitary, which lead to hypercortisolism that is associated with high morbidity and mortality. Treatment options in case of persistent or recurrent disease are limited, but new insights into the pathogenesis of CD are raising hope for new therapeutic avenues. Here, we have performed a meta-analysis of the available sequencing data in CD to create a comprehensive picture of CD's genetics. Our analyses clearly indicate that somatic mutations in the deubiquitinases are the key drivers in CD, namely USP8 (36.5%) and USP48 (13.3%). While in USP48 only Met415 is affected by mutations, in USP8 there are 26 different mutations described. However, these different mutations are clustering in the same hotspot region (affecting in 94.5% of cases Ser718 and Pro720). In contrast, pathogenic variants classically associated with tumorigenesis in genes like TP53 and BRAF are also present in CD but with low incidence (12.5% and 7%). Importantly, several of these mutations might have therapeutic potential as there are drugs already investigated in preclinical and clinical setting for other diseases. Furthermore, network and pathway analyses of all somatic mutations in CD suggest a rather unified picture hinting towards converging oncogenic pathways.

A Toolbox for Functional Analysis and the Systematic Identification of Diagnostic and Prognostic Gene Expression Signatures Combining Meta-Analysis and Machine Learning.

The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.

Plasma steroid metabolome profiling for the diagnosis of adrenocortical carcinoma.

Objective Current workup for the pre-operative distinction between frequent adrenocortical adenomas (ACAs) and rare but aggressive adrenocortical carcinomas (ACCs) combines imaging and biochemical testing. We here investigated the potential of plasma steroid hormone profiling by liquid chromatography tandem mass spectrometry (LC-MS/MS) for the diagnosis of malignancy in adrenocortical tumors. Design Retrospective cohort study of prospectively collected EDTA-plasma samples in a single tertiary reference center. Methods Steroid hormone profiling by liquid chromatography tandem mass spectrometry (LC-MS/MS) in random plasma samples and logistic regression modeling. Results Fifteen steroid hormones were quantified in 66 ACAs (29 males; M) and 42 ACC (15 M) plasma samples. Significantly higher abundances in ACC vs ACA were observed for 11-deoxycorticosterone, progesterone, 17-hydroxyprogesterone, 11-deoxycortisol, DHEA, DHEAS and estradiol (all P < 0.05). Maximal areas under the curve (AUC) for discrimination between ACA and ACC for single analytes were only 0.76 (estradiol) and 0.77 (progesterone), respectively. Logistic regression modeling enabled the discovery of diagnostic signatures composed of six specific steroids for male and female patients with AUC of 0.95 and 0.94, respectively. Positive predictive values in males and females were 92 and 96%, negative predictive values 90 and 86%, respectively. Conclusion This study in a large adrenal tumor patient cohort demonstrates the value of plasma steroid hormone profiling for diagnosis of ACC. Application of LC-MS/MS analysis and of our model may facilitate diagnosis of malignancy in non-expert centers. We propose to continuously evaluate and improve diagnostic accuracy of LC-MS/MS profiling by applying machine-learning algorithms to prospectively obtained steroid hormone profiles.

Systems Biology Analysis to Understand Regulatory miRNA Networks in Lung Cancer.

Lung cancer has currently the highest cancer-related mortality rate worldwide. MicroRNAs (miRNAs) are small noncoding RNAs that play a fundamental role in gene expression and are linked to disease progression of different cancer types such as lung cancer. However, functional characterization is made difficult by the fact that miRNAs generally regulate several mRNA interaction partners, resulting in complex regulatory networks. Thus, analysis of the network biology of miRNAs is essential for comprehensive understanding of their regulatory effects in lung cancer. A deeper understanding of miRNA networks in cancer could finally serve as a basis for the development of new therapeutic interventions. Here, we present a systems biology approach to analyze regulatory miRNA interaction networks to get better insight into their function.