Dataset Information

Phospho-networks in Ovarian Cancer Cell Lines

ABSTRACT: 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.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Permanent Cell Line Cell, Cell Culture

DISEASE(S): Malignant Neoplasm Of Ovary

SUBMITTER: Pau Creixell

LAB HEAD: Rune Linding

PROVIDER: PXD000901 | Pride | 2016-07-08

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	20121110_FJ_PC_KOC7C_1_P01.raw	Raw
	20121110_FJ_PC_KOC7C_1_P02.raw	Raw
	20121110_FJ_PC_KOC7C_1_P03.raw	Raw
	20121110_FJ_PC_KOC7C_1_P04.raw	Raw
	20121110_FJ_PC_KOC7C_1_P05.raw	Raw

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Publications

Kinome-wide decoding of network-attacking mutations rewiring cancer signaling.

Creixell Pau P Schoof Erwin M EM Simpson Craig D CD Longden James J Miller Chad J CJ Lou Hua Jane HJ Perryman Lara L Cox Thomas R TR Zivanovic Nevena N Palmeri Antonio A Wesolowska-Andersen Agata A Helmer-Citterich Manuela M Ferkinghoff-Borg Jesper J Itamochi Hiroaki H Bodenmiller Bernd B Erler Janine T JT Turk Benjamin E BE Linding Rune R

Cell 20150917 1

Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-) ...[more]

PMID: 26388441

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