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Journal Article

Phosphosignature Predicts Dasatinib Response in Non-small Cell Lung Cancer


Schaab,  Christoph
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

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Klammer, M., Kaminski, M., Zedler, A., Oppermann, F., Blencke, S., Marx, S., et al. (2012). Phosphosignature Predicts Dasatinib Response in Non-small Cell Lung Cancer. MOLECULAR & CELLULAR PROTEOMICS, 11(9), 651-668. doi:10.1074/mcp.M111.016410.

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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. Four of the phosphorylation sites belong to integrin beta 4, a protein that mediates cell-matrix or cell-cell adhesion. The signature was validated in cross-validation and label switch experiments and in six independently profiled breast cancer cell lines. The study supports that the phosphorylation of integrin beta 4, as well as eight further proteins comprising the signature, are candidate biomarkers for predicting response to dasatinib in solid tumors. Furthermore, our results show that identifying predictive phosphorylation signatures from global, quantitative phosphoproteomic data is possible and can open a new path to discovering molecular markers for response prediction. Molecular & Cellular Proteomics 11: 10.1074/mcp.M111.016410, 651-668, 2012.