Project PXD000901

PRIDE Assigned Tags:
Biomedical Dataset



Phospho-networks in Ovarian Cancer Cell Lines


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.

Sample Processing Protocol

Es2, Ovas, Ovise, Tov-21 and Koc-7c cells were labeled with medium Stable Isotope Labeling by Amino acids in cell Culture (SILAC) and grown to ~80% confluency in sufficient 15cm dishes to provide 12mg of protein as starting material in triplicate. Each medium-labeled sample would be subsequently mixed 1:1 with a Spike-in SILAC{Geiger et al., 2011, Nat Protoc, 6, 147-57} sample labeled with heavy SILAC containing a mix of peptides from the different ovarian cell lines, so that such internal standard would allow inter-sample comparisons. After synchronization, cells were lysed using ice-cold modified RIPA buffer supplemented with Roche complete protease inhibitor cocktail tablets and ß-glycerophosphate (5mM), NaF (5mM), Na-orthovanadate (1mM, activated). The samples were processed further as detailed in the Extended Experimental Procedures section and analyzed by liquid chromatography-mass spectrometry.

Data Processing Protocol

MaxQuant 1.3 analysis, default settings. Genome specific database used for searching, with cell-line specific FASTA file being generated.


Pau Creixell, Technical University of Denmark (DTU)
Rune Linding, Technical University of Denmark ( lab head )

Submission Date


Publication Date



    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 PubMed: 26388441