Heyde, Silvia von der

[["dc.bibliographiccitation.firstpage","52"],["dc.bibliographiccitation.journal","Translational Proteomics"],["dc.bibliographiccitation.lastpage","59"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Sonntag, Johanna"],["dc.contributor.author","Bender, Christian"],["dc.contributor.author","Soons, Zita"],["dc.contributor.author","Heyde, Silvia von der"],["dc.contributor.author","König, Rainer"],["dc.contributor.author","Wiemann, Stefan"],["dc.contributor.author","Sinn, Hans-Peter"],["dc.contributor.author","Schneeweiss, Andreas"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Korf, Ulrike"],["dc.date.accessioned","2019-07-09T11:40:49Z"],["dc.date.available","2019-07-09T11:40:49Z"],["dc.date.issued","2014"],["dc.description.abstract","A robust subclassification of luminal breast cancer, the most common molecular subtype of human breast cancer, is crucial for therapy decisions. While a part of patients is at higher risk of recurrence and requires chemo-endocrine treatment, the other part is at lower risk and also poorly responds to chemotherapeutic regimens. To approximate the risk of cancer recurrence, clinical guidelines recommend determining histologic grading and abundance of a cell proliferation marker in tumor specimens. However, this approach assigns an intermediate risk to a substantial number of patients and in addition suffers from a high interobserver variability. Therefore, the aim of our study was to identify a quantitative protein biomarker signature to facilitate risk classification. Reverse phase protein arrays (RPPA) were used to obtain quantitative expression data for 128 breast cancer relevant proteins in a set of hormone receptor-positive tumors (n = 109). Proteomic data for the subset of histologic G1 (n = 14) and G3 (n = 22) samples were used for biomarker discovery serving as surrogates of low and high recurrence risk, respectively. A novel biomarker selection workflow based on combining three different classification methods identified caveolin-1, NDKA, RPS6, and Ki-67 as top candidates. NDKA, RPS6, and Ki-67 were expressed at elevated levels in high risk tumors whereas caveolin-1 was observed as downregulated. The identified biomarker signature was subsequently analyzed using an independent test set (AUC = 0.78). Further evaluation of the identified biomarker panel by Western blot and mRNA profiling confirmed the proteomic signature obtained by RPPA. In conclusion, the biomarker signature introduced supports RPPA as a tool for cancer biomarker discovery."],["dc.identifier.doi","10.1016/j.trprot.2014.02.001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11379"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58260"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY-NC-ND 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/3.0"],["dc.title","Reverse phase protein array based tumor profiling identifies a biomarker signature for risk classification of hormone receptor-positive breast cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["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.artnumber","UNSP e16"],["dc.bibliographiccitation.journal","Oncogenesis"],["dc.bibliographiccitation.volume","1"],["dc.contributor.author","Henjes, Frauke"],["dc.contributor.author","Bender, Christian"],["dc.contributor.author","Heyde, Silvia von der"],["dc.contributor.author","Braun, L."],["dc.contributor.author","Mannsperger, Heiko A."],["dc.contributor.author","Schmidt, C."],["dc.contributor.author","Wiemann, Stefan"],["dc.contributor.author","Hasmann, M."],["dc.contributor.author","Aulmann, S."],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Korf, Ulrike"],["dc.date.accessioned","2018-11-07T09:08:57Z"],["dc.date.available","2018-11-07T09:08:57Z"],["dc.date.issued","2012"],["dc.description.abstract","Increasing the efficacy of targeted cancer therapies requires the identification of robust biomarkers suitable for patient stratification. This study focused on the identification of molecular mechanisms causing resistance against the anti-ERBB2-directed therapeutic antibodies trastuzumab and pertuzumab presently used to treat patients with ERBB2-amplified breast cancer. Immunohistochemistry and clinical data were evaluated and yielded evidence for the existence of ERBB2-amplified breast cancer with high-level epidermal growth-factor receptor (EGFR) expression as a separate tumor entity. Because the proto-oncogene EGFR tightly interacts with ERBB2 on the protein level, the hypothesis that high-level EGFR expression might contribute to resistance against ERBB2-directed therapies was experimentally validated. SKBR3 and HCC1954 cells were chosen as model systems of EGFR-high/ERBB2-amplified breast cancer and exposed to trastuzumab, pertuzumab and erlotinib, respectively, and in combination. Drug impact was quantified in cell viability assays and on the proteomic level using reverse-phase protein arrays. Phosphoprotein dynamics revealed a significant downregulation of AKT signaling after exposure to trastuzumab, pertuzumab or a coapplication of both antibodies in SKBR3 cells but no concomitant impact on ERK1/2, RB or RPS6 phosphorylation. On the other hand, signaling was fully downregulated in SKBR3 cells after coinhibition of EGFR and ERBB2. Inhibitory effects in HCC1954 cells were driven by erlotinib alone, and a significant upregulation of RPS6 and RB phosphorylation was observed after coincubation with pertuzumab and trastuzumab. In summary, proteomic data suggest that high-level expression of EGFR in ERBB2-amplified breast cancer cells attenuates the effect of anti-ERBB2-directed antibodies. In conclusion, EGFR expression may serve as diagnostic and predictive biomarker to advance personalized treatment concepts of patients with ERBB2-amplified breast cancer."],["dc.identifier.doi","10.1038/oncsis.2012.16"],["dc.identifier.isi","000209219200001"],["dc.identifier.pmid","23552733"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10849"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/26148"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Nature Publishing Group"],["dc.relation.issn","2157-9024"],["dc.rights","CC BY-NC-ND 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/3.0"],["dc.title","Strong EGFR signaling in cell line models of ERBB2-amplified breast cancer attenuates response towards ERBB2-targeting drugs"],["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.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"]]