Predicting selective drug targets in cancer through metabolic networks

Folger, O., Jerby, L., Frezza, C., Gottlieb, E. , Ruppin, E. and Shlomi, T. (2011) Predicting selective drug targets in cancer through metabolic networks. Molecular Systems Biology, 7, p. 501. (doi: 10.1038/msb.2011.35)

Abstract

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 genomescale 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. Molecular Systems Biology 7: 501; published online 21 June 2011; doi:10.1038/msb.2011.35

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Frezza, Mr Christian and Gottlieb, Professor Eyal
• Authors: Folger, O., Jerby, L., Frezza, C., Gottlieb, E., Ruppin, E., and Shlomi, T.
• College/School: College of Medical Veterinary and Life Sciences > School of Cancer Sciences
• Journal Name: Molecular Systems Biology
• ISSN: 1744-4292