Heyde, Silvia von der

[["dc.bibliographiccitation.journal","European Journal of Cancer"],["dc.bibliographiccitation.volume","49"],["dc.contributor.author","Sonntag, J."],["dc.contributor.author","Bender, Christian"],["dc.contributor.author","Aulmann, S."],["dc.contributor.author","Heyde, Silvia von der"],["dc.contributor.author","Burwinkel, B."],["dc.contributor.author","Wiemann, Stefan"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Sinn, H. P."],["dc.contributor.author","Schneeweiss, A."],["dc.contributor.author","Korf, Ulrike"],["dc.date.accessioned","2018-11-07T09:20:20Z"],["dc.date.available","2018-11-07T09:20:20Z"],["dc.date.issued","2013"],["dc.format.extent","S212"],["dc.identifier.isi","000326843601235"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/28857"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Sci Ltd"],["dc.publisher.place","Oxford"],["dc.relation.conference","European Cancer Congress 2013 - 17th ECCO / 38th ESMO / 32nd ESTRO"],["dc.relation.eventlocation","Amsterdam, NETHERLANDS"],["dc.relation.issn","1879-0852"],["dc.relation.issn","0959-8049"],["dc.title","Protein biomarker signature for risk classification of hormone receptor positive breast cancer identified by reverse phase protein array based tumor profiling"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","75"],["dc.bibliographiccitation.journal","BMC Systems Biology"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Heyde, Silvia von der"],["dc.contributor.author","Bender, Christian"],["dc.contributor.author","Henjes, Frauke"],["dc.contributor.author","Sonntag, Johanna"],["dc.contributor.author","Korf, Ulrike"],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2018-11-07T09:38:48Z"],["dc.date.available","2018-11-07T09:38:48Z"],["dc.date.issued","2014"],["dc.description.abstract","Background: Despite promising progress in targeted breast cancer therapy, drug resistance remains challenging. The monoclonal antibody drugs trastuzumab and pertuzumab as well as the small molecule inhibitor erlotinib were designed to prevent ErbB-2 and ErbB-1 receptor induced deregulated protein signalling, contributing to tumour progression. The oncogenic potential of ErbB receptors unfolds in case of overexpression or mutations. Dimerisation with other receptors allows to bypass pathway blockades. Our intention is to reconstruct the ErbB network to reveal resistance mechanisms. We used longitudinal proteomic data of ErbB receptors and downstream targets in the ErbB-2 amplified breast cancer cell lines BT474, SKBR3 and HCC1954 treated with erlotinib, trastuzumab or pertuzumab, alone or combined, up to 60 minutes and 30 hours, respectively. In a Boolean modelling approach, signalling networks were reconstructed based on these data in a cell line and time course specific manner, including prior literature knowledge. Finally, we simulated network response to inhibitor combinations to detect signalling nodes reflecting growth inhibition. Results: The networks pointed to cell line specific activation patterns of the MAPK and PI3K pathway. In BT474, the PI3K signal route was favoured, while in SKBR3, novel edges highlighted MAPK signalling. In HCC1954, the inferred edges stimulated both pathways. For example, we uncovered feedback loops amplifying PI3K signalling, in line with the known trastuzumab resistance of this cell line. In the perturbation simulations on the short-term networks, we analysed ERK1/2, AKT and p70S6K. The results indicated a pathway specific drug response, driven by the type of growth factor stimulus. HCC1954 revealed an edgetic type of PIK3CA-mutation, contributing to trastuzumab inefficacy. Drug impact on the AKT and ERK1/2 signalling axes is mirrored by effects on RB and RPS6, relating to phenotypic events like cell growth or proliferation. Therefore, we additionally analysed RB and RPS6 in the long-term networks. Conclusions: We derived protein interaction models for three breast cancer cell lines. Changes compared to the common reference network hint towards individual characteristics and potential drug resistance mechanisms. Simulation of perturbations were consistent with the experimental data, confirming our combined reverse and forward engineering approach as valuable for drug discovery and personalised medicine."],["dc.identifier.doi","10.1186/1752-0509-8-75"],["dc.identifier.isi","000338582700001"],["dc.identifier.pmid","24970389"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33140"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1752-0509"],["dc.title","Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","125"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","BioTechniques"],["dc.bibliographiccitation.lastpage","135"],["dc.bibliographiccitation.volume","57"],["dc.contributor.author","Heyde, Silvia von der"],["dc.contributor.author","Sonntag, Johanna"],["dc.contributor.author","Kaschek, Daniel"],["dc.contributor.author","Bender, Christian"],["dc.contributor.author","Bues, Johannes"],["dc.contributor.author","Wachter, Astrid"],["dc.contributor.author","Timmer, Jens"],["dc.contributor.author","Korf, Ulrike"],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2018-11-07T09:35:59Z"],["dc.date.available","2018-11-07T09:35:59Z"],["dc.date.issued","2014"],["dc.description.abstract","Analysis of large-scale proteomic data sets requires specialized software tools, tailored toward the requirements of individual approaches. Here we introduce an extension of an open-source software solution for analyzing reverse phase protein array (RPPA) data. The R package RPPanalyzer was designed for data preprocessing followed by basic statistical analyses and proteomic data visualization. In this update, we merged relevant data preprocessing steps into a single user-friendly function and included a new method for background noise correction as well as new methods for noise estimation and averaging of replicates to transform data in such a way that they can be used as input for a new time course plotting function. We demonstrate the robustness of our enhanced RPPanalyzer platform by analyzing longitudinal RPPA data of MET receptor signaling upon stimulation with different hepatocyte growth factor concentrations."],["dc.description.sponsorship","German Federal Ministry of Education and Research [0315396, 0316173]"],["dc.identifier.doi","10.2144/000114205"],["dc.identifier.isi","000341635700005"],["dc.identifier.pmid","25209047"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32510"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biotechniques Office"],["dc.relation.issn","1940-9818"],["dc.relation.issn","0736-6205"],["dc.title","RPPanalyzer Toolbox: An improved R package for analysis of reverse phase protein array data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]