Dataset Information

A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism.

ABSTRACT: Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.

SUBMITTER: Jamialahmadi O

PROVIDER: S-EPMC6497301 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism.

Jamialahmadi Oveis O Hashemi-Najafabadi Sameereh S Motamedian Ehsan E Romeo Stefano S Bagheri Fatemeh F

PLoS computational biology 20190422 4

Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the ...[more]

PMID: 31009458

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Project description:BACKGROUND:Pancreatic ductal adenocarcinomas (PDA) activate a glutamine-dependent pathway of cytosolic nicotinamide adenine dinucleotide phosphate (NADPH) production to maintain redox homeostasis and support proliferation. Enzymes involved in this pathway (GLS1 (mitochondrial glutaminase 1), GOT1 (cytoplasmic glutamate oxaloacetate transaminase 1), and GOT2 (mitochondrial glutamate oxaloacetate transaminase 2)) are highly upregulated in PDA, and among these, inhibitors of GLS1 were recently deployed in clinical trials to target anabolic glutamine metabolism. However, single-agent inhibition of this pathway is cytostatic and unlikely to provide durable benefit in controlling advanced disease. RESULTS:Here, we report that reducing NADPH pools by genetically or pharmacologically (bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide (BPTES) or CB-839) inhibiting glutamine metabolism in mutant Kirsten rat sarcoma viral oncogene homolog (KRAS) PDA sensitizes cell lines and tumors to ß-lapachone (ß-lap, clinical form ARQ761). ß-Lap is an NADPH:quinone oxidoreductase (NQO1)-bioactivatable drug that leads to NADPH depletion through high levels of reactive oxygen species (ROS) from the futile redox cycling of the drug and subsequently nicotinamide adenine dinucleotide (NAD)+ depletion through poly(ADP ribose) polymerase (PARP) hyperactivation. NQO1 expression is highly activated by mutant KRAS signaling. As such, ß-lap treatment concurrent with inhibition of glutamine metabolism in mutant KRAS, NQO1 overexpressing PDA leads to massive redox imbalance, extensive DNA damage, rapid PARP-mediated NAD+ consumption, and PDA cell death-features not observed in NQO1-low, wild-type KRAS expressing cells. CONCLUSIONS:This treatment strategy illustrates proof of principle that simultaneously decreasing glutamine metabolism-dependent tumor anti-oxidant defenses and inducing supra-physiological ROS formation are tumoricidal and that this rationally designed combination strategy lowers the required doses of both agents in vitro and in vivo. The non-overlapping specificities of GLS1 inhibitors and ß-lap for PDA tumors afford high tumor selectivity, while sparing normal tissue.

Dataset Information

A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism.

Publications

A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism.

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