Wachter, Astrid

[["dc.bibliographiccitation.firstpage","549"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Cancer Cell"],["dc.bibliographiccitation.lastpage","+"],["dc.bibliographiccitation.volume","31"],["dc.contributor.author","Mohr, Sebastian"],["dc.contributor.author","Döbele, Carmen"],["dc.contributor.author","Comoglio, Federico"],["dc.contributor.author","Berg, Tobias"],["dc.contributor.author","Beck, Julia"],["dc.contributor.author","Bohnenberger, Hanibal"],["dc.contributor.author","Alexe, Gabriela"],["dc.contributor.author","Corso, Jasmin"],["dc.contributor.author","Ströbel, Philipp"],["dc.contributor.author","Wachter, Astrid"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Schnuetgen, Frank"],["dc.contributor.author","Cremer, Anjali"],["dc.contributor.author","Haetscher, Nadine"],["dc.contributor.author","Goellner, Stefanie"],["dc.contributor.author","Rouhi, Arefeh"],["dc.contributor.author","Palmqvist, Lars"],["dc.contributor.author","Rieger, Michael A."],["dc.contributor.author","Schroeder, Timm"],["dc.contributor.author","Boenig, Halvard"],["dc.contributor.author","Meuller-Tidow, Carsten"],["dc.contributor.author","Kuchenbauer, Florian"],["dc.contributor.author","Schuetz, Ekkehard"],["dc.contributor.author","Green, Anthony R."],["dc.contributor.author","Urlaub, Henning"],["dc.contributor.author","Stegmaier, Kimberly"],["dc.contributor.author","Humphries, R. Keith"],["dc.contributor.author","Serve, Hubert"],["dc.contributor.author","Oellerich, Thomas"],["dc.date.accessioned","2018-11-07T10:25:02Z"],["dc.date.available","2018-11-07T10:25:02Z"],["dc.date.issued","2017"],["dc.description.abstract","The transcription factor Meis1 drives myeloid leukemogenesis in the context of Hox gene overexpression but is currently considered undruggable. We therefore investigated whether myeloid progenitor cells transformed by Hoxa9 and Meis1 become addicted to targetable signaling pathways. A comprehensive (phospho) proteomic analysis revealed that Meis1 increased Syk protein expression and activity. Syk upregulation occurs through a Meis1-dependent feedback loop. By dissecting this loop, we show that Syk is a direct target of miR-146a, whose expression is indirectly regulated by Meis1 through the transcription factor PU. 1. In the context of Hoxa9 overexpression, Syk signaling induces Meis1, recapitulating several leukemogenic features of Hoxa9/Meis1-driven leukemia. Finally, Syk inhibition disrupts the identified regulatory loop, prolonging survival of mice with Hoxa9/Meis1-driven leukemia."],["dc.identifier.doi","10.1016/j.ccell.2017.03.001"],["dc.identifier.isi","000398670600010"],["dc.identifier.pmid","28399410"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14438"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/42772"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Cell Press"],["dc.relation.issn","1878-3686"],["dc.relation.issn","1535-6108"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Hoxa9 and Meis1 Cooperatively Induce Addiction to Syk Signaling by Suppressing miR-146a in Acute Myeloid Leukemia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","112"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Breast Cancer Research"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Bernhardt, Stephan"],["dc.contributor.author","Bayerlová, Michaela"],["dc.contributor.author","Vetter, Martina"],["dc.contributor.author","Wachter, Astrid"],["dc.contributor.author","Mitra, Devina"],["dc.contributor.author","Hanf, Volker"],["dc.contributor.author","Lantzsch, Tilmann"],["dc.contributor.author","Uleer, Christoph"],["dc.contributor.author","Peschel, Susanne"],["dc.contributor.author","John, Jutta"],["dc.contributor.author","Buchmann, Jörg"],["dc.contributor.author","Weigert, Edith"],["dc.contributor.author","Bürrig, Karl-Friedrich"],["dc.contributor.author","Thomssen, Christoph"],["dc.contributor.author","Korf, Ulrike"],["dc.contributor.author","Beissbarth, Tim"],["dc.contributor.author","Wiemann, Stefan"],["dc.contributor.author","Kantelhardt, Eva Johanna"],["dc.date.accessioned","2020-12-10T18:39:04Z"],["dc.date.available","2020-12-10T18:39:04Z"],["dc.date.issued","2017"],["dc.description.abstract","Abstract Background Breast cancer tumors are known to be highly heterogeneous and differences in their metabolic phenotypes, especially at protein level, are less well-understood. Profiling of metabolism-related proteins harbors the potential to establish new patient stratification regimes and biomarkers promoting individualized therapy. In our study, we aimed to examine the relationship between metabolism-associated protein expression profiles and clinicopathological characteristics in a large cohort of breast cancer patients. Methods Breast cancer specimens from 801 consecutive patients, diagnosed between 2009 and 2011, were investigated using reverse phase protein arrays (RPPA). Patients were treated in accordance with national guidelines in five certified German breast centers. To obtain quantitative expression data, 37 antibodies detecting proteins relevant to cancer metabolism, were applied. Hierarchical cluster analysis and individual target characterization were performed. Clustering results and individual protein expression patterns were associated with clinical data. The Kaplan-Meier method was used to estimate survival functions. Univariate and multivariate Cox regression models were applied to assess the impact of protein expression and other clinicopathological features on survival. Results We identified three metabolic clusters of breast cancer, which do not reflect the receptor-defined subtypes, but are significantly correlated with overall survival (OS, p ≤ 0.03) and recurrence-free survival (RFS, p ≤ 0.01). Furthermore, univariate and multivariate analysis of individual protein expression profiles demonstrated the central role of serine hydroxymethyltransferase 2 (SHMT2) and amino acid transporter ASCT2 (SLC1A5) as independent prognostic factors in breast cancer patients. High SHMT2 protein expression was significantly correlated with poor OS (hazard ratio (HR) = 1.53, 95% confidence interval (CI) = 1.10–2.12, p ≤ 0.01) and RFS (HR = 1.54, 95% CI = 1.16–2.04, p ≤ 0.01). High protein expression of ASCT2 was significantly correlated with poor RFS (HR = 1.31, 95% CI = 1.01–1.71, p ≤ 0.05). Conclusions Our data confirm the heterogeneity of breast tumors at a functional proteomic level and dissects the relationship between metabolism-related proteins, pathological features and patient survival. These observations highlight the importance of SHMT2 and ASCT2 as valuable individual prognostic markers and potential targets for personalized breast cancer therapy. Trial registration ClinicalTrials.gov, NCT01592825 . Registered on 3 May 2012."],["dc.identifier.doi","10.1186/s13058-017-0905-7"],["dc.identifier.eissn","1465-542X"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15161"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77532"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","In goescholar not merged with http://resolver.sub.uni-goettingen.de/purl?gs-1/16987 but duplicate"],["dc.publisher","BioMed Central"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)."],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Proteomic profiling of breast cancer metabolism identifies SHMT2 and ASCT2 as prognostic factors"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","351"],["dc.bibliographiccitation.journal","FRONTIERS IN GENETICS"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Wachter, Astrid"],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2018-11-07T10:19:27Z"],["dc.date.available","2018-11-07T10:19:27Z"],["dc.date.issued","2016"],["dc.description.abstract","Identification of dynamic signaling mechanisms on different cellular layers is now facilitated as the increased usage of various high-throughput techniques goes along with decreasing costs for individual experiments. A lot of these signaling mechanisms are known to be coordinated by their dynamics, turning time course data sets into valuable information sources for inference of regulatory mechanisms. However, the combined analysis of parallel time-course measurements from different high-throughput platforms still constitutes a major challenge requiring sophisticated bioinformatic tools in order to ease biological interpretation. We developed a new pathway-based integration approach for the analysis of coupled omics time-series data, which we implemented in the R package pwOmics. Unlike many other approaches, our approach acknowledges the role of the different cellular layers of measurement and infers consensus profiles and time profile clusters for further biological interpretation. We investigated a time-course data set on epidermal growth factor stimulation of human mammary epithelial cells generated on the two layers of RNA and proteins. The data was analyzed using our new approach with a focus on feedback signaling and pathway crosstalk. We could confirm known regulatory patterns relevant in the physiological cellular response to epidermal growth factor stimulation as well as identify interesting new interactions in this signaling context, such as the regulatory influence of the connective tissue growth factor on transferrin receptor or the influence of growth arrest and DNA-damage-inducible alpha on the connective tissue growth factor. Thus, we show that integrated cross-platform analysis provides a deeper understanding of regulatory signaling mechanisms. Combined with time-course information it enables the characterization of dynamic signaling processes and leads to the identification of important regulatory interactions which might be dysregulated in disease with adverse effects."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2015"],["dc.identifier.doi","10.3389/fgene.2015.00351"],["dc.identifier.isi","000367644200001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12766"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/41662"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Media Sa"],["dc.relation.issn","1664-8021"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Decoding Cellular Dynamics in Epidermal Growth Factor Signaling Using a New Pathway-Based Integration Approach for Proteomics and Transcriptomics Data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]