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One of the major challenges in cancer research is finding models that closely resemble tumors within patients. Human tissue slice cultures are a promising approach to provide a model of the patient's tumor biology ex vivo. Recently, it was shown that these slices can be successfully analyzed by whole transcriptome sequencing as well as automated histochemistry, increasing their usability as preclinical model. Glioblastoma multiforme (GBM) is a highly malignant brain tumor with poor prognosis and little is known about its genetic background and heterogeneity regarding therapy success. In this study, tissue from the tumors of 25 patients with primary GBM was processed into slice cultures and treated with standard therapy (irradiation and temozolomide). Total RNA sequencing and automated histochemistry were performed to enable analysis of treatment effects at a transcriptional and histological level. Slice cultures from long‐term survivors (overall survival [OS] > 24 months) exhibited more apoptosis than cultures from patients with shorter OS. Proliferation within these slices was slightly increased in contrast to other groups, but not significantly. Among all samples, 58 protein‐coding genes were upregulated and 32 downregulated in treated vs. untreated slice cultures. In general, an upregulation of DNA damage‐related and cell cycle checkpoint genes as well as enrichment of genotoxicity pathways and p53‐dependent signaling was found after treatment. Overall, the current study reproduces knowledge from former studies regarding the feasibility of transcriptomic analyses and automated histology in tissue slice cultures. We further demonstrate that the experimental data merge with the clinical follow‐up of the patients, which improves the applicability of our model system., In this study, we investigated mRNA expression and histological effects of radiochemotherapy treatment on freshly resected and cultivated tissue slices of 25 patients diagnosed with primary glioblastoma. We demonstrate that the experimental data obtained from these slice cultures can be correlated to the clinical course of a particular patient, thereby increasing their suitability as a preclinical model system.

BackgroundWith increasing clinical use of NK-92 cells and their CAR-modified derivatives in cancer immunotherapy, there is a growing demand for efficient production processes of these “off-the-shelf” therapeutics. In order to ensure safety and prevent the occurrence of secondary tumors, (CAR-)NK-92 cell proliferation has to be inactivated before transfusion. This is commonly achieved by gamma irradiation. Recently, we showed proof of concept that low energy electron irradiation (LEEI) is a new method for NK-92 inactivation. LEEI has several advantages over gamma irradiation, including a faster reaction time, a more reproducible dose rate and much less requirements on radiation shielding. Here, LEEI was further evaluated as a promising alternative to gamma irradiation yielding cells with highly maintained cytotoxic effector function.MethodsEffectiveness and efficiency of LEEI and gamma irradiation were analyzed using NK-92 and CD123-directed CAR-NK-92 cells. LEE-irradiated cells were extensively characterized and compared to gamma-irradiated cells via flow cytometry, cytotoxicity assays, and comet assays, amongst others.ResultsOur results show that both irradiation methods caused a progressive decrease in cell viability and are, therefore, suitable for inhibition of cell proliferation. Notably, the NK-mediated specific lysis of tumor cells was maintained at stable levels for three days post-irradiation, with a trend towards higher activities after LEEI treatment as compared to gamma irradiation. Both gamma irradiation as well as LEEI led to substantial DNA damage and an accumulation of irradiated cells in the G2/M cell cycle phases. In addition, transcriptomic analysis of irradiated cells revealed approximately 12-fold more differentially expressed genes two hours after gamma irradiation, compared to LEEI. Analysis of surface molecules revealed an irradiation-induced decrease in surface expression of CD56, but no changes in the levels of the activating receptors NKp46, NKG2D, or NKp30.ConclusionsThe presented data show that LEEI inactivates (CAR-)NK-92 cells as efficiently as gamma irradiation, but with less impact on the overall gene expression. Due to logistic advantages, LEEI might provide a superior alternative for the manufacture of (CAR-)NK-92 cells for clinical application.

Background Prostate cancer (PCa) is the most prevalent solid cancer among men in Western Countries. The clinical behavior of localized PCa is highly variable. Some cancers are aggressive leading to death, while others can even be monitored safely. Hence, there is a high clinical need for precise biomarkers for identification of aggressive disease in addition to established clinical parameters. Objective To develop an RNA expression-based score for the prediction of PCa prognosis that facilitates clinical decision making. Design, setting, and participants We assessed 233 tissue specimens of PCa patients with long-term follow-up data from fresh-frozen radical prostatectomies (RPs), from formalin-fixed and paraffin-embedded RP specimens and biopsies by transcriptome-wide next-generation sequencing and customized expression microarrays. Outcome measurements and statistical analysis We applied Cox proportional hazard models to the cohorts from different platforms and specimen types. Evidence from these models was combined by fixed-effect meta-analysis to identify genes predictive of the time to death of disease (DoD). Genes were combined by a weighted median approach into a prognostic score called ProstaTrend and transferred for the prediction of biochemical recurrence (BCR) after RP in an independent cohort of The Cancer Genome Atlas (TCGA). Results and limitations ProstaTrend comprising ∼1400 genes was significantly associated with DoD in the training cohort of PCa patients treated by RP (leave-one-out cross-validation, Cox regression: p = 2e-09) and with BCR in the TCGA validation cohort (Cox regression: p = 3e-06). The prognostic impact persisted after multivariable Cox regression analysis adjusting for Gleason grading group (GG) ≥3 and resection status (p = 0.001; DoD, training cohort) and for GG ≥ 3, pathological stage ≥T3, and resection state (p = 0.037; BCR, validation cohort). Conclusions ProstaTrend is a transcriptome-based score that predicts DoD and BCR in cohorts of PCa patients treated with RP. Patient summary ProstaTrend provides molecular patient risk stratification after radical prostatectomy.

measured using RT-qPCR. Results from cohort A were used to develop a model with (combinations of) the four genes based on the comparative CT method, and the model was validated in the independent cohort B. RESULTS: The HOXC6-DLX1 score model resulted in the highest area-under-the-curve (AUC) of 0.75 (95% confidence interval: 0.70-0.80) for the diagnosis of high-grade PCa upon prostate biopsy, with a specificity of 36% and 91% sensitivity. The HOXC6-DLX1 score outperformed PCA3 in both cohorts (cohort A: AUC 0.75 vs. 0.65; cohort B: 0.73 vs. 0.62). Furthermore, the HOXC6-DLX1 score was significantly correlated with the Gleason score upon biopsy. Subgroup analysis confirmed the added value of this urine test for the prediction of high-grade PCa in patients with low serum PSA values (

The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6.

Signal transducer and activator of transcription 3 (Stat3) is the major mediator of interleukin-6 (IL-6) family cytokines. In addition, Stat3 is known to be involved in the pathophysiology of many malignancies. Here, we show that the cis-trans peptidyl-prolyl isomerase cyclophilin (Cyp) B specifically interacts with Stat3, whereas the highly related CypA does not. CypB knockdown inhibited the IL-6-induced transactivation potential but not the tyrosine phosphorylation of Stat3. Binding of CypB to Stat3 target promoters and alteration of the intranuclear localization of Stat3 on CypB depletion suggested a nuclear function of Stat3/CypB interaction. By contrast, CypA knockdown inhibited Stat3 IL-6-induced tyrosine phosphorylation and nuclear translocation. The Cyp inhibitor cyclosporine A (CsA) caused similar effects. However, Stat1 activation in response to IL-6 or interferon-gamma was not affected by Cyp silencing or CsA treatment. As a result, Cyp knockdown shifted IL-6 signaling to a Stat1-dominated pathway. Furthermore, Cyp depletion or treatment with CsA induced apoptosis in IL-6-dependent multiple myeloma cells, whereas an IL-6-independent line was not affected. Thus, Cyps support the anti-apoptotic action of Stat3. Taken together, CypA and CypB both play pivotal roles, yet at different signaling levels, for Stat3 activation and function. These data also suggest a novel mechanism of CsA action.

Signal transducer and activator of transcription 3 (Stat3) is implicated in the pathogenesis of many malignancies and essential for IL-6–dependent survival and growth of multiple myeloma cells. Here, we demonstrate that the gene encoding oncogenic microRNA-21 (miR-21) is controlled by an upstream enhancer containing 2 Stat3 binding sites strictly conserved since the first observed evolutionary appearance of miR-21 and Stat3. MiR-21 induction by IL-6 was strictly Stat3 dependent. Ectopically raising miR-21 expression in myeloma cells in the absence of IL-6 significantly reduced their apoptosis levels. These data provide strong evidence that miR-21 induction contributes to the oncogenic potential of Stat3.

Background: The genome is pervasively transcribed but most transcripts do not code for proteins, constituting non-protein-coding RNAs. Despite increasing numbers of functional reports of individual long non-coding RNAs (lncRNAs), assessing the extent of functionality among the non-coding transcriptional output of mammalian cells remains intricate. In the protein-coding world, transcripts differentially expressed in the context of processes essential for the survival of multicellular organisms have been instrumental in the discovery of functionally relevant proteins and their deregulation is frequently associated with diseases. We therefore systematically identified lncRNAs expressed differentially in response to oncologically relevant processes and cell-cycle, p53 and STAT3 pathways, using tiling arrays. Results: We found that up to 80% of the pathway-triggered transcriptional responses are non-coding. Among these we identified very large macroRNAs with pathway-specific expression patterns and demonstrated that these are likely continuous transcripts. MacroRNAs contain elements conserved in mammals and sauropsids, which in part exhibit conserved RNA secondary structure. Comparing evolutionary rates of a macroRNA to adjacent protein-coding genes suggests a local action of the transcript. Finally, in different grades of astrocytoma, a tumor disease unrelated to the initially used cell lines, macroRNAs are differentially expressed. Conclusions: It has been shown previously that the majority of expressed non-ribosomal transcripts are non-coding. We now conclude that differential expression triggered by signaling pathways gives rise to a similar abundance of non-coding content. It is thus unlikely that the prevalence of non-coding transcripts in the cell is a trivial consequence of leaky or random transcription events.

Signal transducer and activator of transcription 3 (STAT3) is activated by a variety of cytokines and growth factors. To generate a comprehensive data set of proteins interacting specifically with STAT3, we applied stable isotope labeling with amino acids in cell culture (SILAC). For high-affinity pull-down using streptavidin, we fused STAT3 with a short peptide tag allowing biotinylation in situ (bio-tag), which did not affect STAT3 functions. By this approach, 3642 coprecipitated proteins were detected in human embryonic kidney-293 cells. Filtering using statistical and functional criteria finally extracted 136 proteins as putative interaction partners of STAT3. Both, a physical interaction network analysis and the enrichment of known and predicted interaction partners suggested that our filtering criteria successfully enriched true STAT3 interactors. Our approach identified numerous novel interactors, including ones previously predicted to associate with STAT3. By reciprocal coprecipitation, we were able to verify the physical association between STAT3 and selected interactors, including the novel interaction with TOX4, a member of the TOX high mobility group box family. Applying the same method, we next investigated the activation-dependency of the STAT3 interactome. Again, we identified both known and novel interactions. Thus, our approach allows to study protein-protein interaction effectively and comprehensively. Biological significance The location, activity, function, degradation, and synthesis of proteins are significantly regulated by interactions of proteins with other proteins, biopolymers and small molecules. Thus, the comprehensive characterization of interactions of proteins in a given proteome is the next milestone on the path to understanding the biochemistry of the cell. In order to generate a comprehensive interactome dataset of proteins specifically interacting with a selected bait protein, we fused our bait protein STAT3 with a short peptide tag allowing biotinylation in situ (bio-tag). This bio-tag allows an affinity pull-down using streptavidin but affected neither the activation of STAT3 by tyrosine phosphorylation nor its transactivating potential. We combined SILAC for accurate relative protein quantification, subcellular fractionation to increase the coverage of interacting proteins, high-affinity pull-down and a stringent filtering method to successfully analyze the interactome of STAT3. With our approach we confirmed several already known and identified numerous novel STAT3 interactors. The approach applied provides a rapid and effective method, which is broadly applicable for studying protein-protein interactions and their dependency on post-translational modifications.

SRC (steroid receptor co-activator)-1 has been reported to interact with and to be an essential co-activator for several members of the STAT (signal transducer and activator of transcription) family, including STAT3, the major signal transducer of IL (interleukin)-6. We addressed the question of whether SRC-1 is crucial for IL-6- and STAT3-mediated physiological responses such as myeloma cell survival and acute-phase protein induction. In fact, silencing of SRC-1 by RNA interference rapidly induced apoptosis in IL-6-dependent INA-6 human myeloma cells, comparable with what was observed upon silencing of STAT3. Using chromatin immunoprecipitation at STAT3 target regions of various genes, however, we observed constitutive binding of SRC-1 that decreased when INA-6 cells were treated with IL-6. The same held true for STAT3 target genes analysed in HepG2 human hepatocellular carcinoma cells. SRC-1-knockdown studies demonstrated that STAT3-controlled promoters require neither SRC-1 nor the other p160 family members SRC-2 or SRC-3 in HepG2 cells. Furthermore, microarray expression profiling demonstrated that the responsiveness of IL-6 target genes is not affected by SRC-1 silencing. In contrast, co-activators of the CBP [CREB (cAMP-response element-binding protein)-binding protein]/p300 family proved functionally important for the transactivation potential of STAT3 and bound inducibly to STAT3 target regions. This recruitment did not depend on the presence of SRC-1. Altogether, this suggests that functional impairment of STAT3 is not involved in the induction of myeloma cell apoptosis by SRC-1 silencing. We therefore conclude that STAT3 transactivates its target genes by the recruitment of CBP/p300 co-activators and that this process generally does not require the contribution of SRC-1.

Stable isotope labeling with amino acids in cell culture (SILAC) has emerged as a major technique for quantitative proteomics using cell culture. It has been applied to the investigation of many different biological processes such diverse as the characterization of signaling pathways and the determination of protein interactions. For investigation of proteins, interacting with the Signal Transducer and activator of transcription 3 (STAT3), we combined SILAC with stringent precipitation of the biotinylated proteins and quantitative mass spectrometry analyses, to detect cellular interaction partners of STAT3 in mammalian cells. STAT3 is a well-known protein, which plays crucial roles in different biological responses including early embryonic development as well as cell growth and apoptosis. Moreover, STAT3 is constitutively activated in oncogene-transformed cells and various primary tumors. Although the JAK-STAT signaling pathway is one of the best known, there appears to be STAT3-specific regulatory proteins, which are not established until now. Hence, finding of novel STAT3-interacting proteins can encourage the knowledge of STAT3-protein network. For metabolic labeling using SILAC, cells were grown in medium containing either only 'light' arginine or only 'heavy' 13C6-arginine, resulting in incorporation of labeled arginine in all sites. In mass spectrometry analyses the peptides containing the 13C6-arginine, has produced a 6 Da difference in mass, relative to the light form. STAT3 fused with a tag for its biotinylation was expressed and biotinylated in HEK-293 cells, which were fully substituted with 13C-labeled arginine in place of the light arginine. In the parallel approach HEK cells, grown in normal medium, were transfected with STAT3 without the tag for biotinylation. After combining the cultured cells, STAT3 and its interacting proteins were specific precipitated, separated by SDS/PAGE, and analyzed by mass spectrometry. This method bears a strong potential to recover now protein interactions.