Predicting selective drug targets in cancer through metabolic networks

Ori Folger, Livnat Jerby, Christian Frezza, Eyal Gottlieb, Eytan Ruppin*, Tomer Shlomi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number501
Number of pages10
JournalMolecular Systems Biology
StatePublished - 2011


  • cancer
  • metabolic
  • metabolism
  • modeling
  • selectivity


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