Open AccessPredicting selective drug targets in cancer through metabolic networks
Author(s) -
Folger Ori,
Jerby Livnat,
Frezza Christian,
Gottlieb Eyal,
Ruppin Eytan,
Shlomi Tomer
Publication year - 2011
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.1038/msb.2011.35
Subject(s) - biology , cancer , computational biology , cancer cell , gene , somatic cell , drug , cancer cell lines , drug discovery , cancer research , bioinformatics , genetics , pharmacology
The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome‐scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI‐60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type‐specific downregulation of gene expression and somatic mutations are compiled.