Stromal NRG1 in luminal breast cancer defines pro-fibrotic and migratory cancer-associated fibroblasts.

HER3 is highly expressed in luminal breast cancer subtypes. Its activation by NRG1 promotes activation of AKT and ERK1/2, contributing to tumour progression and therapy resistance. HER3-targeting agents that block this activation, are currently under phase 1/2 clinical studies, and although they have shown favorable tolerability, their activity as a single agent has proven to be limited. Here we show that phosphorylation and activation of HER3 in luminal breast cancer cells occurs in a paracrine manner and is mediated by NRG1 expressed by cancer-associated fibroblasts (CAFs). Moreover, we uncover a HER3-independent NRG1 signaling in CAFs that results in the induction of a strong migratory and pro-fibrotic phenotype, describing a subtype of CAFs with elevated expression of NRG1 and an associated transcriptomic profile that determines their functional properties. Finally, we identified Hyaluronan Synthase 2 (HAS2), a targetable molecule strongly correlated with NRG1, as an attractive player supporting NRG1 signaling in CAFs.

Integrating proteomics into precision oncology.

DNA sequencing and RNA sequencing are increasingly applied in precision oncology, where molecular tumor boards evaluate the actionability of genetic events in individual tumors to guide targeted treatment. To work toward an additional level of patient characterization, we assessed the abundance and activity of 27 proteins in 134 patients whose tumors had previously undergone whole-exome and RNA sequencing within the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) program of National Center for Tumor Diseases, Heidelberg. Proteomic and phosphoproteomic targets were selected to reflect the most relevant therapeutic baskets in MASTER. Among six different therapeutic baskets, the proteomic data supported treatment recommendations that were based on DNA and RNA analyses in 10% to 57% and frequently suggested alternative treatment options. In several cases, protein activities explained the patients' clinical course and provided potential explanations for treatment failure. Our study indicates that the integrative analysis of DNA, RNA and protein data may refine therapeutic stratification of individual patients and, thus, holds potential to increase the success rate of precision cancer therapy. Prospective validation studies are needed to advance the integration of proteomic analysis into precision oncology.

Co-Inflammatory Roles of TGFß1 in the Presence of TNFα Drive a Pro-inflammatory Fate in Mesenchymal Stem Cells.

High plasticity is a hallmark of mesenchymal stem cells (MSCs), and as such, their differentiation and activities may be shaped by factors of their microenvironment. Bones, tumors, and cardiomyopathy are examples of niches and conditions that contain MSCs and are enriched with tumor necrosis factor α (TNFα) and transforming growth factor ß1 (TGFß1). These two cytokines are generally considered as having opposing roles in regulating immunity and inflammation (pro- and anti-inflammatory, respectively). Here, we performed global gene expression analysis of human bone marrow-derived MSCs and identified overlap in half of the transcriptional programs that were modified by TNFα and TGFß1. The two cytokines elevated the mRNA expression of soluble factors, including mRNAs of pro-inflammatory mediators. Accordingly, the typical pro-inflammatory factor TNFα prominently induced the protein expression levels of the pro-inflammatory mediators CCL2, CXCL8 (IL-8), and cyclooxygenase-2 (Cox-2) in MSCs, through the NF-κB/p65 pathway. In parallel, TGFß1 did not elevate CXCL8 protein levels and induced the protein expression of CCL2 at much lower levels than TNFα; yet, TGFß1 readily induced Cox-2 and acted predominantly via the Smad3 pathway. Interestingly, combined stimulation of MSCs by TNFα + TGFß1 led to a cooperative induction of all three inflammatory mediators, indicating that TGFß1 functioned as a co-inflammatory cytokine in the presence of TNFα. The cooperative activities of TNFα + TGFß1 that have led to CCL2 and CXCL8 induction were almost exclusively dependent on p65 activation and were not regulated by Smad3 or by the upstream regulator TGFß-activated kinase 1 (TAK1). In contrast, the TNFα + TGFß1-induced cooperative elevation in Cox-2 was mostly dependent on Smad3 (demonstrating cooperativity with activated NF-κB) and was partly regulated by TAK1. Studies with MSCs activated by TNFα + TGFß1 revealed that they release factors that can affect other cells in their microenvironment and induce breast tumor cell elongation, migration, and scattering out of spheroid tumor masses. Thus, our findings demonstrate a TNFα + TGFß1-driven pro-inflammatory fate in MSCs, identify specific molecular mechanisms involved, and propose that TNFα + TGFß1-stimulated MSCs influence the tumor niche. These observations suggest key roles for the microenvironment in regulating MSC functions, which in turn may affect different health-related conditions.

miRNA-1246 induces pro-inflammatory responses in mesenchymal stem/stromal cells by regulating PKA and PP2A.

The tumor microenvironment (TME) has an impact on breast cancer progression by creating a pro-inflammatory milieu within the tumor. However, little is known about the roles of miRNAs in cells of the TME during this process. We identified six putative oncomiRs in a breast cancer dataset, all strongly correlating with poor overall patient survival. Out of the six candidates, miR-1246 was upregulated in aggressive breast cancer subtypes and expressed at highest levels in mesenchymal stem/stroma cells (MSCs). Functionally, miR-1246 led to a p65-dependent increase in transcription and release of pro-inflammatory mediators IL-6, CCL2 and CCL5 in MSCs, and increased NF-κB activity. The pro-inflammatory phenotype of miR-1246 in MSCs was independent of TNFα stimulations and mediated by direct targeting of the tumor-suppressors PRKAR1A and PPP2CB. In vitro recapitulation of the TME revealed increased Stat3 phosphorylation in breast epithelial (MCF10A) and cancer cells (SK-BR-3, MCF7, T47D) upon incubation with conditioned medium (CM) of MSCs overexpressing miR-1246. Additionally, this stimulation enhanced proliferation of MCF10A cells, increased migration of MDA-MB-231 cells and induced attraction of THP-1 monocytic cells. Our data shows that miR-1246 acts as both key-enhancer of pro-inflammatory responses in MSCs and putative oncomiR in breast cancer, suggesting its influence on cancer-related inflammation and breast cancer progression.

Changes in serum miRNAs following generalized convulsive seizures in human mesial temporal lobe epilepsy.

MicroRNAs (miRNAs) are key regulators of gene expression and are involved in the pathomechanisms of epilepsy. MiRNAs may also serve as peripheral biomarkers of epilepsy. We investigated the miRNA profile in the blood serum of patients suffering from mesial temporal lobe epilepsy (mTLE) following a single focal seizure evolving to a bilateral convulsive seizure (BCS) during video-EEG monitoring. Data of 15 patients were included in the final analysis. MiRNA expression was determined using Real Time-PCR followed by thorough bioinformatical analysis of expression levels. We found that more than 200 miRNAs were differentially expressed in the serum of patients within 30 min after a single seizure. Validation of the 20 top miRNA candidates confirmed that 4 miRNAs (miR-143, miR-145, miR-532, miR-365a) were significantly deregulated. Interestingly, in a sub-group of patients with seizures occurring during sleep, we found 10 miRNAs to be deregulated up to 20-28 h after the seizure. In this group of patients, miR-663b was significantly deregulated. We conclude that single seizures are associated with detectable transient miRNA alterations in blood serum in the early postictal phase. The significant upregulation of miR-663b following BCS arising during sleep indicates potential suitability of this miRNA as a potential biomarker for seizure diagnostics.

Different microRNA profiles in chronic epilepsy versus acute seizure mouse models.

Epilepsy affects around 50 million people worldwide, and in about 65% of patients, the etiology of disease is unknown. MicroRNAs are small non-coding RNAs that have been suggested to play a role in the pathophysiology of epilepsy. Here, we compared microRNA expression patterns in the hippocampus using two chronic models of epilepsy characterised by recurrent spontaneous seizures (pilocarpine and self-sustained status epilepticus (SSSE)) and an acute 6-Hz seizure model. The vast majority of microRNAs deregulated in the acute model exhibited increased expression with 146 microRNAs up-regulated within 6 h after a single seizure. In contrast, in the chronic models, the number of up-regulated microRNAs was similar to the number of down-regulated microRNAs. Three microRNAs-miR-142-5p, miR-331-3p and miR-30a-5p-were commonly deregulated in all three models. However, there is a clear overlap of differentially expressed microRNAs within the chronic models with 36 and 15 microRNAs co-regulated at 24 h and at 28 days following status epilepticus, respectively. Pathway analysis revealed that the altered microRNAs are associated with inflammation, innate immunity and cell cycle regulation. Taken together, the identified microRNAs and the pathways they modulate might represent candidates for novel molecular approaches for the treatment of patients with epilepsy.

TGF-ß stimulation in human and murine cells reveals commonly affected biological processes and pathways at transcription level.

BACKGROUND: The TGF-ß signaling pathway is a fundamental pathway in the living cell, which plays a key role in many central cellular processes. The complex and sometimes contradicting mechanisms by which TGF-ß yields phenotypic effects are not yet completely understood. In this study we investigated and compared the transcriptional response profile of TGF-ß1 stimulation in different cell types. For this purpose, extensive experiments are performed and time-course microarray data are generated in human and mouse parenchymal liver cells, human mesenchymal stromal cells and mouse hematopoietic progenitor cells at different time points. We applied a panel of bioinformatics methods on our data to uncover common patterns in the dynamic gene expression response in respective cells. RESULTS: Our analysis revealed a quite variable and multifaceted transcriptional response profile of TGF-ß1 stimulation, which goes far beyond the well-characterized classical TGF-ß1 signaling pathway. Nonetheless, we could identify several commonly affected processes and signaling pathways across cell types and species. In addition our analysis suggested an important role of the transcription factor EGR1, which appeared to have a conserved influence across cell-types and species. Validation via an independent dataset on A549 lung adenocarcinoma cells largely confirmed our findings. Network analysis suggested explanations, how TGF-ß1 stimulation could lead to the observed effects. CONCLUSIONS: The analysis of dynamical transcriptional response to TGF-ß treatment experiments in different human and murine cell systems revealed commonly affected biological processes and pathways, which could be linked to TGF-ß1 via network analysis. This helps to gain insights about TGF-ß pathway activities in these cell systems and its conserved interactions between the species and tissue types.

TGF-beta1 does not induce senescence of multipotent mesenchymal stromal cells and has similar effects in early and late passages.

Transforming growth factor-beta 1 (TGF-ß1) stimulates a broad range of effects which are cell type dependent, and it has been suggested to induce cellular senescence. On the other hand, long-term culture of multipotent mesenchymal stromal cells (MSCs) has a major impact on their cellular physiology and therefore it is well conceivable that the molecular events triggered by TGF-ß1 differ considerably in cells of early and late passages. In this study, we analyzed the effect of TGF-ß1 on and during replicative senescence of MSCs. Stimulation with TGF-ß1 enhanced proliferation, induced a network like growth pattern and impaired adipogenic and osteogenic differentiation. TGF-ß1 did not induce premature senescence. However, due to increased proliferation rates the cells reached replicative senescence earlier than untreated controls. This was also evident, when we analyzed senescence-associated DNA-methylation changes. Gene expression profiles of MSCs differed considerably at relatively early (P 3-5) and later passages (P 10). Nonetheless, relative gene expression differences provoked by TGF-ß1 at individual time points or in a time course dependent manner (stimulation for 0, 1, 4 and 12 h) were very similar in MSCs of early and late passage. These results support the notion that TGF-ß1 has major impact on MSC function, but it does not induce senescence and has similar molecular effects during culture expansion.

Joint Bayesian inference of condition-specific miRNA and transcription factor activities from combined gene and microRNA expression data.

MOTIVATION: There have been many successful experimental and bioinformatics efforts to elucidate transcription factor (TF)-target networks in several organisms. For many organisms, these annotations are complemented by miRNA-target networks of good quality. Attempts that use these networks in combination with gene expression data to draw conclusions on TF or miRNA activity are, however, still relatively sparse. RESULTS: In this study, we propose Bayesian inference of regulation of transcriptional activity (BIRTA) as a novel approach to infer both, TF and miRNA activities, from combined miRNA and mRNA expression data in a condition specific way. That means our model explains mRNA and miRNA expression for a specific experimental condition by the activities of certain miRNAs and TFs, hence allowing for differentiating between switches from active to inactive (negative switch) and inactive to active (positive switch) forms. Extensive simulations of our model reveal its good prediction performance in comparison to other approaches. Furthermore, the utility of BIRTA is demonstrated at the example of Escherichia coli data comparing aerobic and anaerobic growth conditions, and by human expression data from pancreas and ovarian cancer. AVAILABILITY AND IMPLEMENTATION: The method is implemented in the R package birta, which is freely available for Bio-conductor (>=2.10) on http://www.bioconductor.org/packages/release/bioc/html/birta.html.