Predicting selective drug targets in cancer through metabolic networks

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

doi:10.1038/msb.2011.35

Cited by 422 publications

(349 citation statements)

References 40 publications

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“…GSMMs can also be used to predict the phenotypic effects of genetic and environmental perturbations on the cell's flux distribution and viability. Such modeling studies have been employed in recent years to describe human metabolism (Duarte et al , 2007) in general and in cancer (Folger et al , 2011; Agren et al , 2012, 2014; Nam et al , 2014; Yizhak et al , 2014). …”

Section: Introductionmentioning

confidence: 99%

An integrated computational and experimental study uncovers FUT 9 as a metabolic driver of colorectal cancer

Auslander

Cunningham

Toosi

et al. 2017

Molecular Systems Biology

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Metabolic alterations play an important role in cancer and yet, few metabolic cancer driver genes are known. Here we perform a combined genomic and metabolic modeling analysis searching for metabolic drivers of colorectal cancer. Our analysis predicts FUT9, which catalyzes the biosynthesis of Ley glycolipids, as a driver of advanced‐stage colon cancer. Experimental testing reveals FUT9's complex dual role; while its knockdown enhances proliferation and migration in monolayers, it suppresses colon cancer cells expansion in tumorspheres and inhibits tumor development in a mouse xenograft models. These results suggest that FUT9's inhibition may attenuate tumor‐initiating cells (TICs) that are known to dominate tumorspheres and early tumor growth, but promote bulk tumor cells. In agreement, we find that FUT9 silencing decreases the expression of the colorectal cancer TIC marker CD44 and the level of the OCT4 transcription factor, which is known to support cancer stemness. Beyond its current application, this work presents a novel genomic and metabolic modeling computational approach that can facilitate the systematic discovery of metabolic driver genes in other types of cancer.

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Section: Introductionmentioning

confidence: 99%

An integrated computational and experimental study uncovers FUT 9 as a metabolic driver of colorectal cancer

Auslander

Cunningham

Toosi

et al. 2017

Molecular Systems Biology

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“…[2][3][4]25,27 We used COBRA Toolbox v2.0 28 for constraint-based modeling of metabolic networks, with GLPK as the linear programming solver (http://www.gnu.org/software/glpk). Briefly, metabolic fluxes are assumed to be under certain constraints.…”

Section: Modeling Cell Metabolism and Growthmentioning

confidence: 99%

“…The S2011 model 4 had been reconstructed assuming that the cells are grown in RPMI-1640 medium. Instead, we used the growth medium by which Recon1 has been evaluated (representing body fluid 25 ).…”

Section: Using Body Fluid As the Growth Medium Of S2011mentioning

confidence: 99%

“…In our study, modeling gene deletion and reaction deletion were performed by ''deleteModelGenes'' and ''removeRxns'' functions in the COBRA Toolbox. A biomass reaction with a previously defined composition 4,27 was added to the model. Although it is not easy to determine the objective of human cells, biomass production can be used as a proxy cellular objective, reflecting the cell requirement to replenish metabolites continuously.…”

Section: Gene Deletion and Reaction Deletion Studiesmentioning

confidence: 99%

“…A generic metabolic network of cancer cells has been previously reconstructed 4 to investigate the differences between healthy cells and cancer cells. This network will be referred to as ''S2011'' in this text.…”

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Reconstruction of a generic metabolic network model of cancer cells

Hadi

Marashi

2014

Mol. BioSyst.

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A promising strategy for finding new cancer drugs is to use metabolic network models to investigate the essential reactions or genes in cancer cells. In this study, we present a generic constraint-based model of cancer metabolism, which is able to successfully predict the metabolic phenotypes of cancer cells. This model is reconstructed by collecting the available data on tumor suppressor genes. Notably, we show that the activation of oncogene related reactions can be explained by the inactivation of tumor suppressor genes. We show that in a simulated growth medium similar to the body fluids, our model outperforms the previously proposed model of cancer metabolism in predicting expressed genes.

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Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma

et al. 2018

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Retinoblastoma (RB) is a childhood eye cancer. Currently, chemotherapy, local therapy, and enucleation are the main ways in which these tumors are managed. The present work is the first study that uses constraint‐based reconstruction and analysis approaches to identify and explain RB‐specific survival strategies, which are RB tumor specific. Importantly, our model‐specific secretion profile is also found in RB1‐depleted human retinal cells in vitro and suggests that novel biomarkers involved in lipid metabolism may be important. Finally, RB‐specific synthetic lethals have been predicted as lipid and nucleoside transport proteins that can aid in novel drug target development.

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Predicting selective drug targets in cancer through metabolic networks

Abstract: The authors develop a genome-scale model of cancer metabolism and use it to predict genes that are essential for cancer cell growth. An array of target combinations are then identified that could potentially provide novel selective treatments for specific cancers.

Cited by 422 publications

References 40 publications

An integrated computational and experimental study uncovers FUT 9 as a metabolic driver of colorectal cancer

An integrated computational and experimental study uncovers FUT 9 as a metabolic driver of colorectal cancer

Reconstruction of a generic metabolic network model of cancer cells

Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma

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