@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 (n = 24) compared to benign (n = 21) 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} } @article{DuscharlaBhumireddyLakshettietal.2016, author = {Duscharla, Divya and Bhumireddy, Sudarshana Reddy and Lakshetti, Sridhar and Pospisil, Heike and Murthy, P. V. L. N. and Walther, Reinhard and Sripadi, Prabhakar and Ummanni, Ramesh}, title = {Prostate Cancer Associated Lipid Signatures in Serum Studied by ESI-Tandem Mass Spectrometryas Potential New Biomarkers}, series = {PLoS ONE}, volume = {11}, journal = {PLoS ONE}, number = {3}, issn = {1932-6203}, doi = {10.1371/journal.pone.0150253}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-5863}, pages = {21}, year = {2016}, abstract = {Prostate cancer (PCa) is one amongst the most common cancersin western men. Incidence rate ofPCa is on the rise worldwide. The present study deals with theserum lipidome profiling of patients diagnosed with PCa to identify potential new biomarkers. We employed ESI-MS/MS and GC-MS for identification of significantly altered lipids in cancer patient's serum compared to controls. Lipidomic data revealed 24 lipids are significantly altered in cancer patinet's serum (n = 18) compared to normal (n = 18) with no history of PCa. By using hierarchical clustering and principal component analysis (PCA) we could clearly separate cancer patients from control group. Correlation and partition analysis along with Formal Concept Analysis (FCA) have identified that PC (39:6) and FA (22:3) could classify samples with higher certainty. Both the lipids, PC (39:6) and FA (22:3) could influence the cataloging of patients with 100\% sensitivity (all 18 control samples are classified correctly) and 77.7\% specificity (of 18 tumor samples 4 samples are misclassified) with p-value of 1.612×10-6 in Fischer's exact test. Further, we performed GC-MS to denote fatty acids altered in PCa patients and found that alpha-linolenic acid (ALA) levels are altered in PCa. We also performed an in vitro proliferation assay to determine the effect of ALA in survival of classical human PCa cell lines LNCaP and PC3. We hereby report that the altered lipids PC (39:6) and FA (22:3) offer a new set of biomarkers in addition to the existing diagnostic tests that could significantly improve sensitivity and specificity in PCa diagnosis.}, language = {en} }