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

Keren Yizhak,Almut Schulze,Franziska Baenke,Eytan Ruppin,Sylvia E Le Dévédec,Bob Van De Water,Vincent C De Boer,Christian Frezza,Vasiliki Maria Rogkoti

doi:10.15252/msb.20134993

Abstract

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.

Highlights

Altered tumor metabolism has become a generally regarded hallmark of cancer (Hanahan & Weinberg, 2011)
As a starting point for this study, we developed a set of metabolic models specific for each of the NCI-60 cell lines
A comparison of our new ATP flux ratio (AFR) metric versus the aforementioned state-of-the-art extracellular acidification rate (ECAR)/oxygen consumption rate (OCR) ratio (EOR) (Materials and Methods and Supplementary Dataset S2) showed a significant correlation across the NCI-60 models (Spearman correlation R = 0.66, P-value = 2eÀ8). Testing both measures using a genome-wide NCI-60 drug response dataset (Scherf et al, 2000), we find that the model-predicted wildtype AFR levels across all cell line models are significantly correlated (Spearman P-value < 0.05; FDR corrected with a = 0.05) with Gi50 values of 30% of the compounds across these cell lines, whereas the model-predicted EOR measure accomplish this task for only 19% of the compounds (Materials and Methods)

Summary

Introduction

Altered tumor metabolism has become a generally regarded hallmark of cancer (Hanahan & Weinberg, 2011). The initial recognition that metabolism is altered in cancer can be traced back to Otto Warburg’s early studies, showing that transformed cells consume glucose at an abnormally high rate and largely reduce it to lactate, even in the presence of oxygen (Warburg, 1956). Much of the field of cancer metabolism has focused on the role of the Warburg effect in supporting cancer proliferation (Vander Heiden et al, 2009). Drug targets that can inhibit migration but leave cellular proliferation relatively spared may be able to avoid such side effects. Such targets may have the additional benefit of reducing the selection for more resistant clones that occurs due to the elimination of treatment-sensitive cells.

Methods

Results

Conclusion