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A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines.

ABSTRACT: The metabolic phenotype of cancer cells is reflected by the metabolites they consume and by the byproducts they release. Here, we use quantitative, extracellular metabolomic data of the NCI-60 panel and a novel computational method to generate 120 condition-specific cancer cell line metabolic models. These condition-specific cancer models used distinct metabolic strategies to generate energy and cofactors. The analysis of the models' capability to deal with environmental perturbations revealed three oxotypes, differing in the range of allowable oxygen uptake rates. Interestingly, models based on metabolomic profiles of melanoma cells were distinguished from other models through their low oxygen uptake rates, which were associated with a glycolytic phenotype. A subset of the melanoma cell models required reductive carboxylation. The analysis of protein and RNA expression levels from the Human Protein Atlas showed that IDH2, which was an essential gene in the melanoma models, but not IDH1 protein, was detected in normal skin cell types and melanoma. Moreover, the von Hippel-Lindau tumor suppressor (VHL) protein, whose loss is associated with non-hypoxic HIF-stabilization, reductive carboxylation, and promotion of glycolysis, was uniformly absent in melanoma. Thus, the experimental data supported the predicted role of IDH2 and the absence of VHL protein supported the glycolytic and low oxygen phenotype predicted for melanoma. Taken together, our approach of integrating extracellular metabolomic data with metabolic modeling and the combination of different network interrogation methods allowed insights into the metabolism of cells.

SUBMITTER: Aurich MK

PROVIDER: S-EPMC5570491 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCancer is a complex disease where various types of molecular aberrations drive the development and progression of malignancies. Large-scale screenings of multiple types of molecular aberrations (e.g., mutations, copy number variations, DNA methylations, gene expressions) become increasingly important in the prognosis and study of cancer. Consequently, a computational model integrating multiple types of information is essential for the analysis of the comprehensive data.ResultsWe propose an integrated modeling framework to identify the statistical and putative causal relations of various molecular aberrations and gene expressions in cancer. To reduce spurious associations among the massive number of probed features, we sequentially applied three layers of logistic regression models with increasing complexity and uncertainty regarding the possible mechanisms connecting molecular aberrations and gene expressions. Layer 1 models associate gene expressions with the molecular aberrations on the same loci. Layer 2 models associate expressions with the aberrations on different loci but have known mechanistic links. Layer 3 models associate expressions with nonlocal aberrations which have unknown mechanistic links. We applied the layered models to the integrated datasets of NCI-60 cancer cell lines and validated the results with large-scale statistical analysis. Furthermore, we discovered/reaffirmed the following prominent links: (1) Protein expressions are generally consistent with mRNA expressions. (2) Several gene expressions are modulated by composite local aberrations. For instance, CDKN2A expressions are repressed by either frame-shift mutations or DNA methylations. (3) Amplification of chromosome 6q in leukemia elevates the expression of MYB, and the downstream targets of MYB on other chromosomes are up-regulated accordingly. (4) Amplification of chromosome 3p and hypo-methylation of PAX3 together elevate MITF expression in melanoma, which up-regulates the downstream targets of MITF. (5)Mutations of TP53 are negatively associated with its direct target genes.ConclusionsThe analysis results on NCI-60 data justify the utility of the layered models for the incoming flow of cancer genomic data. Experimental validations on selected prominent links and application of the layered modeling framework to other integrated datasets will be carried out subsequently.

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A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines.

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