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Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors
1 Analytical Signalling Group, Centre for Cell Signalling, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1B 6BQ, UK
2 European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus - Cambridge CB10 1SD, UK
3 Cancer Genome Project, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus - Cambridge CB10 1SD, UK
4 Cell Signalling Group, Centre for Cell Signalling, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1B 6BQ, UK
5 Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1B 6BQ, UK
6 Current address: MRC Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
Genome Biology 2013, 14:R37 doi:10.1186/gb-2013-14-4-r37
Published: 29 April 2013Additional files
Additional file 1:
Table S1 - Hematological cell lines used to compare phosphoproteomes of different hematological cancers. Table S2 - AML cell lines used to correlate sensitivity to kinase inhibitors with phosphoproteomics data.
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Additional file 2:
Figure S1 - Workflow and distribution of the identified phosphorylation sites.
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Additional file 3:
Dataset 1 - Identities and quantitative values of all phosphopeptides identified in AML, lymphoma, and multiple myeloma cell lines.
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Additionla file 4:
Figure S2 - Protein classes represented in the phosphoproteomes of hematological cancer cell lines.
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Additional file 5:
Figure S3 - Representative examples of phosphopeptides differentially regulated in AML, lymphoma, and multiple myeloma cell lines.
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Additional file 6:
Dataset 2 - Correlation of phosphoprotein data with responses to kinase inhibitors in AML.
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Additional file 7:
Figure S4 - Scatter plots between predicted/observed viability scores for individual drugs with cell lines identifiers, correlations scores, and P values.
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Additional file 8:
Dataset 3 - Final descriptive models of drug responses as resulting from the lasso regression analysis. Listed are predictive phosphopeptides together with their average coefficients and inclusion frequency.
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Additional file 9:
Figure S5 - Association between the markers of sensitivity to kinase inhibitors found for AML cells with the sensitivity to the same inhibitors in lymphoma and multiple myeloma cells.
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Additional file 10:
Figure S6 - Pathway analysis of phosphopeptides that correlate with the responses to PI-103.
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Additional file 11:
Figure S7 - An inhibitor of PKC reduced the viability of AML cells resistant to PI-103 inhibition and had an additive effect with PI-103.
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