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Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors

Pedro Casado1, Maria P Alcolea1, Francesco Iorio23, Juan-Carlos Rodríguez-Prados1, Bart Vanhaesebroeck4, Julio Saez-Rodriguez2, Simon Joel5 and Pedro R Cutillas16*

Author Affiliations

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

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Genome Biology 2013, 14:R37  doi:10.1186/gb-2013-14-4-r37

Published: 29 April 2013

Additional 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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