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Mol Syst Biol. 2014 Aug 1;10:744. doi: 10.15252/msb.20134993.

A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration.

Author information

The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
Division of Toxicology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
Gene Expression Analysis Laboratory, Cancer Research UK, London Research Institute, London, UK.
Laboratory Genetic Metabolic Diseases, Academic Medical Center, Amsterdam, The Netherlands.
MRC Cancer Unit, Hutchison/MRC Research Centre, University of Cambridge, Cambridge, UK.
The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel The Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel

Erratum in

  • Mol Syst Biol. 2014 Nov;10(11):765.


Over the last decade, the field of cancer metabolism has mainly focused on studying the role of tumorigenic metabolic rewiring in supporting cancer proliferation. Here, we perform the first genome-scale computational study of the metabolic underpinnings of cancer migration. We build genome-scale metabolic models of the NCI-60 cell lines that capture the Warburg effect (aerobic glycolysis) typically occurring in cancer cells. The extent of the Warburg effect in each of these cell line models is quantified by the ratio of glycolytic to oxidative ATP flux (AFR), which is found to be highly positively associated with cancer cell migration. We hence predicted that targeting genes that mitigate the Warburg effect by reducing the AFR may specifically inhibit cancer migration. By testing the anti-migratory effects of silencing such 17 top predicted genes in four breast and lung cancer cell lines, we find that up to 13 of these novel predictions significantly attenuate cell migration either in all or one cell line only, while having almost no effect on cell proliferation. Furthermore, in accordance with the predictions, a significant reduction is observed in the ratio between experimentally measured ECAR and OCR levels following these perturbations. Inhibiting anti-migratory targets is a promising future avenue in treating cancer since it may decrease cytotoxic-related side effects that plague current anti-proliferative treatments. Furthermore, it may reduce cytotoxic-related clonal selection of more aggressive cancer cells and the likelihood of emerging resistance.


cancer cell migration; cellular metabolism; genome‐scale metabolic modeling

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