PXD012073
PXD012073 is an original dataset announced via ProteomeXchange.
Dataset Summary
| Title | Kinome-centric pharmacoproteomics identifies signaling pathways underlying cellular responses to targeted cancer drugs |
| Description | Kinase-dependent signaling networks are frequently dysregulated in cancer, driving disease progression. While kinase inhibition has become an important therapeutic approach many cancers resist drug treatment. Therefore, we need both reliable biomarkers that predict drug responses and new targets to overcome drug resistance. Determining the kinase(s) that control cancer progression in individual cancers can pose a significant challenge. Genomics has identified important, yet limited numbers of kinase driver mutations. Transcriptomics can quantify aberrant gene expression, but it cannot measure the protein phosphorylation that regulates kinase-dependent signaling network activity. Proteomics measures protein expression and phosphorylation and, therefore, quantifies aberrant signaling network activity directly. We developed a kinome-centric pharmacoproteomics platform to study signaling pathways that determine cancer drug response. Using hepatocellular carcinoma (HCC) as our model, we determined kinome activity with kinobead/LC-MS profiling, and screened 299 kinase inhibitors for growth inhibition. Integrating kinome activity with drug responses, we obtained a comprehensive database of predictive biomarkers, and kinase targets that promote drug sensitivity and resistance. Our dataset specified pathway-based biomarkers for the clinical HCC drugs sorafenib, regorafenib and lenvatinib, and we found these biomarkers enriched in human HCC specimens. Strikingly, our database also revealed signaling pathways that promote HCC cell epithelialmesenchymal transition (EMT) and drug resistance, and that NUAK1 and NUAK2 regulate these pathways. Inhibition of these kinases reversed the EMT and sensitized HCC cells to kinase inhibition. These results2 demonstrate that our kinome pharmacoproteomics platform discovers both predictive biomarkers for personalized oncology and novel cancer drug targets. |
| HostingRepository | MassIVE |
| AnnounceDate | 2019-11-29 |
| AnnouncementXML | Submission_2019-11-29_21:43:22.xml |
| DigitalObjectIdentifier | |
| ReviewLevel | Non peer-reviewed dataset |
| DatasetOrigin | Original dataset |
| RepositorySupport | Unsupported dataset by repository |
| PrimarySubmitter | Martin Golkowski |
| SpeciesList | scientific name: Homo sapiens; common name: human; NCBI TaxID: 9606; |
| ModificationList | Phospho |
| Instrument | LTQ Orbitrap Elite; Orbitrap Fusion ETD |
Dataset History
| Revision | Datetime | Status | ChangeLog Entry |
|---|---|---|---|
| 0 | 2018-12-15 10:54:36 | ID requested | |
| ⏵ 1 | 2019-11-29 21:43:23 | announced |
Publication List
| no publication |
Keyword List
| submitter keyword: hepatocellular carcinoma, kinase inhibitor, kinobeads, epithelial-mesenchymal transition, drug resistance |
Contact List
| Shao-En Ong | |
|---|---|
| contact affiliation | University of Washington |
| contact email | shaoen@uw.edu |
| lab head | |
| Martin Golkowski | |
| contact affiliation | University of Washington |
| contact email | golkom@uw.edu |
| dataset submitter | |
Full Dataset Link List
| MassIVE dataset URI |
| Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://massive.ucsd.edu/MSV000083236/ |
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