@article{UmmanniMundtPospisiletal.2011, author = {Ummanni, Ramesh and Mundt, Frederike and Pospisil, Heike and Venz, Simone and Scharf, Christian and Barett, Christine and F{\"a}lth, Maria and K{\"o}llermann, Jens and Walther, Reinhard and Schlomm, Thorsten and Sauter, Guido and Bokemeyer, Carsten and S{\"u}ltmann, Holger and Schuppert, Andreas and Br{\"u}mmendorf, Tim H. and Balabanov, Stefan}, title = {Identification of Clinically Relevant Protein Targets in Prostate Cancer with 2D-DIGE Coupled Mass Spectrometry and Systems Biology Network Platform}, series = {PLoS ONE}, volume = {6}, journal = {PLoS ONE}, number = {2}, issn = {1932-6203}, doi = {10.1371/journal.pone.0016833}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-6432}, pages = {14}, year = {2011}, abstract = {Prostate cancer (PCa) is the most common type of cancer found in men and among the leading causes of cancer death in the western world. In the present study, we compared the individual protein expression patterns from histologically characterized PCa and the surrounding benign tissue obtained by manual micro dissection using highly sensitive two-dimensional differential gel electrophoresis (2D-DIGE) coupled with mass spectrometry. Proteomic data revealed 118 protein spots to be differentially expressed in cancer (nā€Š=ā€Š24) compared to benign (nā€Š=ā€Š21) prostate tissue. These spots were analysed by MALDI-TOF-MS/MS and 79 different proteins were identified. Using principal component analysis we could clearly separate tumor and normal tissue and two distinct tumor groups based on the protein expression pattern. By using a systems biology approach, we could map many of these proteins both into major pathways involved in PCa progression as well as into a group of potential diagnostic and/or prognostic markers. Due to complexity of the highly interconnected shortest pathway network, the functional sub networks revealed some of the potential candidate biomarker proteins for further validation. By using a systems biology approach, our study revealed novel proteins and molecular networks with altered expression in PCa. Further functional validation of individual proteins is ongoing and might provide new insights in PCa progression potentially leading to the design of novel diagnostic and therapeutic strategies.}, language = {en} } @article{BalabanovWilhelmVenzetal.2013, author = {Balabanov, Stefan and Wilhelm, Thomas and Venz, Simone and Keller, Gunhild and Scharf, Christian and Pospisil, Heike and Braig, Melanie and Barett, Christine and Bokemeyer, Carsten and Walther, Reinhard and Br{\"u}mmendorf, Tim H. and Schuppert, Andreas}, title = {Combination of a Proteomics Approach and Reengineering of Meso Scale Network Models for Prediction of Mode-of-Action for Tyrosine Kinase Inhibitors}, series = {PLoS ONE}, volume = {8}, journal = {PLoS ONE}, number = {1}, issn = {1932-6203}, doi = {10.1371/journal.pone.0053668}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-6370}, pages = {14}, year = {2013}, abstract = {In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs.}, language = {en} }