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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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Citation and License

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

Published: 29 April 2013

Abstract

Background

Tumor classification based on their predicted responses to kinase inhibitors is a major goal for advancing targeted personalized therapies. Here, we used a phosphoproteomic approach to investigate biological heterogeneity across hematological cancer cell lines including acute myeloid leukemia, lymphoma, and multiple myeloma.

Results

Mass spectrometry was used to quantify 2,000 phosphorylation sites across three acute myeloid leukemia, three lymphoma, and three multiple myeloma cell lines in six biological replicates. The intensities of the phosphorylation sites grouped these cancer cell lines according to their tumor type. In addition, a phosphoproteomic analysis of seven acute myeloid leukemia cell lines revealed a battery of phosphorylation sites whose combined intensities correlated with the growth-inhibitory responses to three kinase inhibitors with remarkable correlation coefficients and fold changes (> 100 between the most resistant and sensitive cells). Modeling based on regression analysis indicated that a subset of phosphorylation sites could be used to predict response to the tested drugs. Quantitative analysis of phosphorylation motifs indicated that resistant and sensitive cells differed in their patterns of kinase activities, but, interestingly, phosphorylations correlating with responses were not on members of the pathway being targeted; instead, these mainly were on parallel kinase pathways.

Conclusion

This study reveals that the information on kinase activation encoded in phosphoproteomics data correlates remarkably well with the phenotypic responses of cancer cells to compounds that target kinase signaling and could be useful for the identification of novel markers of resistance or sensitivity to drugs that target the signaling network.