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Review
. 2015 Jun 30;11(6):817.
doi: 10.15252/msb.20145307.

Modeling cancer metabolism on a genome scale

Keren Yizhak  1 , Barbara Chaneton  2 , Eyal Gottlieb  2 , Eytan Ruppin  3
Affiliations
Review

Modeling cancer metabolism on a genome scale

Keren Yizhak et al. Mol Syst Biol. .

Abstract

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field.

Keywords: Cancer metabolism; Genome‐scale simulations; Metabolic modeling.

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Figures

Figure 1
Figure 1
Central metabolic pathways and their association with key metabolic enzymes Enzymes marked in red have been implicated with tumor initiation and progression and/or serve as potential therapeutic targets. G6P, glucose-6-phosphate; F6P, fructose-6-phosphate; F1,6P, fructose-1,6-bisphosphate; F2,6P, fructose-2,6-bisphosphate; G3P, glyceraldehyde 3-phosphate; 1,3BPG, 1,3 biphosphoglycerate; 3PG, 3-phosphoglycerate; 2PG, 2-phosphoglycerate; PEP, phosphoenolpyruvate; 3PHP, 3-phosphohydroxypyruvate; Ac-CoA, acetyl-CoA; 6PGL, 6-phospho-glucono-1,5-lactone; 6PGC, 6-phospho-D-gluconate; Ru5P, ribulose 5-phosphate; R5P, ribose 5-phosphate. PRPP, 5-phospho-alpha-D-ribose 1-diphosphate. S7P, sedoheptulose 7-phosphate; Xu5P, xylulose 5-phosphate; E4P, erythrose 4-phosphate; THF, tetrahydrofolate; mTHF, 5,10-methylenetetrahydrofolate; DHF, dihydrofolate; Mal-CoA, malonyl-CoA; αKG, α-ketoglutarate; dTMP, deoxythymidine monophosphate; dUMP, deoxyuridine monophosphate; TCA, tricarboxylic acid; GLUT1, glucose transporter 1; HK2, hexokinase 2; GPI, glucose-6-phosphate isomerase; PFKFB2, 6-phosphofructo-2-kinase; PFK1, phosphofructokinase 1; PGAM, phosphoglycerate mutase; PKM2, pyruvate kinase M2 isoform; LDHA, lactate dehydrogenase A; PHGDH, phosphoglycerate dehydrogenase; PDH, pyruvate dehydrogenase; PDK, pyruvate dehydrogenase kinase; FH, fumarate hydratase; SDH, succinate dehydrogenase; IDH, isocitrate dehydrogenase; GDH, glutamate dehydrogenase; GLS, glutaminase; GS, glutathione synthetase; ASCT2, solute carrier family 1, member 5; ACL, ATP citrate lyase; ACC, acetyl-CoA carboxylase; FASN, fatty acid synthase; ASNS, asparagine synthetase; ASL, argininosuccinate lyase; ASS, argininosuccinate synthetase; DHFR, dihydrofolate reductase; TYMS, thymidylate synthase.
Figure 2
Figure 2
Genome-scale metabolic modeling as a platform for predicting flux distributions and simulating cellular perturbations Genome-scale metabolic modelings (GSMMs) provide an opportunity to characterize a cellular metabolic state by predicting the distribution of the network's reaction flux rates on a genome-scale level. For the analysis of microorganisms, this has been mostly achieved by assuming a pre-defined cellular objective function such as maximization of biomass yield or ATP production (left section, upper panel). Such an objective function cannot always be assumed when analyzing human metabolism, and therefore, omics data are utilized to derive a reduced specific model or characterize a metabolic flux state that best fits the context-specific omics data. The data can be used either in a discrete manner (left section, middle panel), trying to activate the flux thorough reactions associated with highly expressed genes (green) while removing those associated with lowly expressed genes (red), or constraining the model more quantitatively by considering the absolute expression levels (as depicted by the different colors, left section, lower panel). The network can be further studied by simulating genetic and environmental perturbations (right section). Similarly, the flux through the perturbed network can be derived based on a pre-defined objective function (right section, upper panel) or by utilizing the omics data to define the differential expression signature that can then be used to constrain the model in various ways (right section, lower panel).
Figure 3
Figure 3
Metabolic processes, enzymes and metabolites that have been studied via Genome-scale metabolic modeling (GSMM) Some of the processes studied include the Warburg effect, the regulation of p53 on gluconeogenesis, one-carbon metabolism and nutrient exchange rates in cancer cell lines. A subset of the metabolic enzymes predicted by GSMM and validated experimentally appear in red. Additionally, the role of one-carbon metabolism in contributing to the cell's NADPH pool has been studied deeply. Leukotrienes and prostaglandins have been suggested as reporter metabolites in different cancer cell lines.
Figure 4
Figure 4
Current and future applications of GSMMs In the context of cancer metabolism, Genome-scale metabolic modelings (GSMMs) have been applied for studying fundamental cancer phenotypes that are either generic or tumor/cell-specific and for identifying drug targets that inhibit cancer-related phenotypes such as proliferation and migration in a specific and selective manner. GSMMs can also be used for addressing emerging challenges in cancer therapy such as drug resistance. Furthermore, the analysis of GSMMs can be extended by integrating additional omics data such as genomics and metabolomics and by utilizing the information on post-transcriptional and post-translational integration as well as incorporating allosteric regulation effects. Another challenge is the modeling of the interaction between cancer cells and supporting cells in their environment. Environmental effects can also be modeled by integrating structural analysis and predicting the effects of environmental conditions (which cannot be modeled directly) on enzyme activities.
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