MassIVE MSV000080700

Imported Reanalysis Dataset Public PXD000901

Phospho-networks in Ovarian Cancer Cell Lines

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Description

Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling processes. While thousands of mutations have been identified, mostly by genome-wide sequencing, systematic interpretation of their role in cancer and impact on cellular information processing is presently missing. Here, we propose a computational approach (ReKINect) to identify mutations attacking signaling networks. We demonstrate six types of network-attacking mutations (NAMs) including changes in kinase modulation, network rewiring as well as the genesis and extinction of specific phosphorylation sites. Through global, quantitative analysis of the exomes and (phospho-)proteomes of five ovarian cancer cell lines we identify and validate numerous NAMs. Finally, we explore the entire cancer genome repertoire and predict hundreds of NAMs affecting kinase and SH2 driven signaling. Our approach is scalable with the complexity of cancer genomes and cell signaling, and can be readily applied in personalized precision medicine. [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: SILAC ; Ovarian ; Cancer ; TiO2 ; Proteome ; Phospho

Contact

Principal Investigators:
(in alphabetical order) 		Rune Linding, Technical University of Denmark, N/A
Submitting User: ccms

Publications

Creixell P, Schoof EM, Simpson CD, Longden J, Miller CJ, Lou HJ, Perryman L, Cox TR, Zivanovic N, Palmeri A, Wesolowska-Andersen A, Helmer-Citterich M, Ferkinghoff-Borg J, Itamochi H, Bodenmiller B, Erler JT, Turk BE, Linding R.
Kinome-wide decoding of network-attacking mutations rewiring cancer signaling.
Cell. 2015 Sep 24;163(1):202-17. Epub 2015 Sep 17.

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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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