Identifying differentially regulated subnetworks from phosphoproteomic data

Klammer, Martin; Godl, Klaus; Tebbe, Andreas; Schaab, Christoph

doi:10.1186/1471-2105-11-351

Cited by 28 publications

(43 citation statements)

References 24 publications

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“…To visualize and interpret the data in a network context, the SubExtractor algorithm was applied (26). In brief, SubExtractor combines phosphoproteomic data with protein-protein interaction data via a Bayesian probabilistic model.…”

Section: Detection Of Significantly Different Subnetworkmentioning

confidence: 99%

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Phosphosignature Predicts Dasatinib Response in Non-small Cell Lung Cancer

Klammer

Kaminski

Zedler

et al. 2012

Molecular & Cellular Proteomics

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102

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Targeted drugs are less toxic than traditional chemotherapeutic therapies; however, the proportion of patients that benefit from these drugs is often smaller. A marker that confidently predicts patient response to a specific therapy would allow an individual therapy selection most likely to benefit the patient. Here, we used quantitative mass spectrometry to globally profile the basal phosphoproteome of a panel of non-small cell lung cancer cell lines. The effect of the kinase inhibitor dasatinib on cellular growth was tested against the same panel. From the phosphoproteome profiles, we identified 58 phosphorylation sites, which consistently differ between sensitive and resistant cell lines. Many of the corresponding proteins are involved in cell adhesion and cytoskeleton organization. We showed that a signature of only 12 phosphorylation sites is sufficient to accurately predict dasatinib sensitivity. The introduction of targeted drugs for treating cancer is a major biomedical achievement of the past decade (1, 2). Because these drugs selectively block molecular pathways that are typically overactivated in tumor cells, they are more precise and less toxic than traditional chemotherapeutics. However, although many cancer patients benefit from a specific targeted therapy, many others do not. Therefore, predictive molecular markers are needed to confidently predict patient response to a specific therapy. Such markers would facilitate therapy personalization, where the selected therapy is based on the molecular profile of the patient.Predictive tests currently used in the clinic are frequently based on one particular marker that is often linked to the drug target. A well known example for a predictive test is assessing HER2/neu overexpression using immunohistochemistry or fluorescent in situ hybridization to predict the response to therapy with trastuzumab (Herceptinா; Roche) (3, 4). However, in some cases the expression or mutational status of the target or other singleton markers might not be sufficient to predict a therapeutic response. Recently, several studies tried to identify molecular signatures comprising multiple markers for response predictions, usually based on gene expression profiling (5, 6). To our knowledge, no study successfully identified a signature from global phosphoproteomic profiles so far.Recent advances in mass spectrometry, methods for enriching phosphorylated proteins or peptides, and computer algorithms for analyzing proteomics data have enabled the application of mass spectrometry-based proteomics to monitor phosphorylation events in a global and unbiased manner. These methods have become sufficiently sensitive and robust to localize and quantify the phosphorylation sites within a peptide sequence (7-9). Phosphorylation events are important in signal transduction, where signals caused by external stimuli are transmitted from the cell membrane to the nucleus. Aberrations in these signal transduction pathways are particularly important for understanding the mechanisms of certain diseases,...

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Section: Detection Of Significantly Different Subnetworkmentioning

confidence: 99%

“…Subsequently, the pair-wise differences could be computed along with the estimated global standard deviation as suggested in Ref. 26, and finally the z scores were calculated.…”

Section: Detection Of Significantly Different Subnetworkmentioning

confidence: 99%

Phosphosignature Predicts Dasatinib Response in Non-small Cell Lung Cancer

Klammer

Kaminski

Zedler

et al. 2012

Molecular & Cellular Proteomics

Self Cite

102

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“…z-Score calculation z-Scores were calculated for each phosphorylation site based on the phosphosite's mean ratio and a globally estimated standard deviation (20). In addition, a 2-sided P-value was computed for each z-score, which was used for the in-depth analysis of enriched pathways.…”

Section: Functional Enrichment Analysismentioning

confidence: 99%

“…The program is described in detail in ref. 20. In brief, SubExtractor combines phosphoproteomic data with protein-protein interaction data via a Bayesian probabilistic model.…”

Section: Subnetwork Detectionmentioning

confidence: 99%

“…In addition, all 8,820 quantified class-1 sites were used as input for the SubExtractor algorithm (20), which detects significantly regulated subnetworks in STRING. The main idea of this tool is to combine local as well as topological information, that is, information about the regulation of a certain node (represented by the protein's strongest regulated phosphorylation site) and information about the connectivity with its neighbors.…”

Section: Bioinformatics Analysismentioning

confidence: 99%

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Global Quantitative Phosphoproteome Analysis of Human Tumor Xenografts Treated with a CD44 Antagonist

Weigand

Herting²,

Maisel³

et al. 2012

Cancer Research

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The cell surface glycoprotein CD44 plays an important role in the development and progression of various tumor types. RG7356 is a humanized antibody targeting the constant region of CD44 that shows antitumor efficacy in mice implanted with CD44-expressing tumors such as MDA-MB-231 breast cancer cells. CD44 receptor seems to function as the main receptor for hyaluronic acid and osteopontin, serving as coreceptor for growth factor pathways like cMet, EGFR, HER-2, and VEGFR and by cytoskeletal modulation via ERM and Rho kinase signaling. To assess the direct impact of RG7356 binding to the CD44 receptor, a global mass spectrometry-based phosphoproteomics approach was applied to freshly isolated MDA-MB-231 tumor xenografts. Results from a global phosphoproteomics screen were further corroborated by Western blot and ELISA analyses of tumor lysates from CD44-expressing tumors. Short-term treatment of tumor-bearing mice with RG7356 resulted in modifications of the MAPK pathway in the responsive model, although no effects on downstream phosphorylation were observed in a nonresponsive xenograft model. Taken together, our approach augments the value of other high throughput techniques to identify biomarkers for clinical development of targeted agents.

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Proteome-wide analysis of temporal phosphorylation dynamics in lysophosphatidic acid-induced signaling

et al. 2012

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Most growth factor receptors trigger phosphorylation-based signal transduction to translate environmental stimuli into defined biological responses. In addition to comprehensive and reliable assessment of growth factor-induced phosphoregulation, temporal resolution is needed to gain insights into the organizing principles of the cellular signaling machinery. Here, we introduce a refined experimental design for MS-based phosphoproteomics to reconcile the need for high comprehensiveness and temporal resolution with the key requirement of monitoring biological reproducibility. We treated SILAC-labeled SCC-9 cells with the seven transmembrane receptor ligand lysophosphatidic acid (LPA) and identified more than 17 000 phosphorylation sites. Filtering for biological replicate quantification yielded five-time point profiles for 6292 site-specific phosphorylations, which we analyzed for statistically significant regulation. Notably, about 30% of these sites changed significantly upon LPA stimulation, indicating extensive phosphoproteome regulation in response to this growth factor. Analysis of time series data identified distinct temporal profiles for different kinase substrate motifs, likely reflecting temporal orchestration of cellular kinase activities. Our data further indicated coordinated regulation of biological processes and phosphoprotein networks upon LPA stimulation. Finally, we detected regulation of functionally characterized phosphorylation sites not yet implicated in LPA signaling, which may foster a better understanding how LPA regulates cellular physiology on the molecular level.

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Identifying differentially regulated subnetworks from phosphoproteomic data

Cited by 28 publications

References 24 publications

Phosphosignature Predicts Dasatinib Response in Non-small Cell Lung Cancer

Phosphosignature Predicts Dasatinib Response in Non-small Cell Lung Cancer

Global Quantitative Phosphoproteome Analysis of Human Tumor Xenografts Treated with a CD44 Antagonist

Proteome-wide analysis of temporal phosphorylation dynamics in lysophosphatidic acid-induced signaling

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