Applications of metabolomics to study cancer metabolism

Abstract

Reprogrammed metabolism supports tumor growth and provides a potential source of therapeutic targets and disease biomarkers. Mass spectrometry-based metabolomics has emerged as a broadly informative technique for profiling metabolic features associated with specific oncogenotypes, disease progression, therapeutic liabilities and other clinically relevant aspects of tumor biology. In this review, we introduce the applications of metabolomics to study deregulated metabolism and metabolic vulnerabilities in cancer. We provide examples of studies that used metabolomics to discover novel metabolic regulatory mechanisms, including processes that link metabolic alterations with gene expression, protein function, and other aspects of systems biology. Finally, we discuss emerging applications of metabolomics for in vivo isotope tracing and metabolite imaging, both of which hold promise to advance our understanding of the role of metabolic reprogramming in cancer.

Keywords: And metabolite imaging; Cancer metabolism; Isotope tracing; Metabolic subtypes; Metabolomics; Systems biology.

Figure 1. Oncometabolites inhibit α-KG-dependent dioxygenases

α-KG is required for the function of a family of dioxygenase enzymes including histone demethylases, which remove methyl groups from lysine residues in histone proteins; 5-methylcytosine hydroxylases, which initiate demethylation of cytosine bases; and prolyl hydroxylases, which hydroxylate proline residues in proteins such as the α subunits of hypoxia inducible factors (HIFs). These dioxygenases can be inhibited by high levels of other dicarboxylic acids, which compete with α-KG. Dicarboxylic acids demonstrated to inhibit dioxygenases include D-HG (a product of mutant IDH1/2) and fumarate and succinate, which accumulate due to loss-of-function mutations in FH and SDH, respectively.

Figure 2. Examples of genotype-driven metabolic reprogramming in cancer

a. Non-small cell lung cancer (NSCLC) with concomitant mutations in KRAS and LKB1 use an unusual form of pyrimidine biosynthesis initiated by carbamoylphosphate synthetase-1 (CPS1). NSCLC with mutations in KRAS and p53 display glucose-dependent glutathione (GSH) biosynthesis. b. KRAS-mutant pancreatic ductal adenocarcinoma (PDAC) requires MAPK signaling to regulate glucose flux into the hexosamine biosynthesis pathway (HBP) and non-oxidative pentose phosphate pathway (Non-Oxidative PPP). These pathways contribute to protein glycosylation and nucleic acid synthesis, respectively.

Figure 3. Metabolic rewiring during cancer progression

In prostate cancer, elevated sarcosine is associated with metastasis while downregulation of the hexosamine biosynthesis pathway (HBP) is associated with castration-resistance. In clear cell renal cell carcinoma (ccRCC), elevated glutathione (GSH), dipeptide metabolites, and metabolites from the 1-carbon/folate pathway are associated with metastasis while α-hydroxybutyrate is associated with disease recurrence. Decreased levels of lipids and citrate are observed as lower-grade tumors progress to high-grade ccRCC. In melanoma, trimethyllysine, dimethylarginine, and induction of the 1-carbon/folate pathway are associated with metastasis, while elevated ROS is associated with inhibition of metastasis.

Figure 4. Systems biology approaches to understand biological interactions between the metabolome and other regulatory networks

Metabolic changes define many phenotypic aspects of genetically-determined diseases. These diseases generally originate with genomic mutations and are executed through changes in the transcriptome, proteome and metabolome. Recent work has emphasized the importance of signaling effects caused by perturbed metabolic states, resulting in changes in protein function, transcription, and other effects. Examples include post-translational protein modification or regulation of these modifications by 2-HG, Acetyl-CoA, and UDP-GlcNac, all of which can impact cell signaling. Other metabolites regulate epigenetic control of the transcriptome or promote further genomic alterations. Systems biology provides systematic techniques to interrogate the complex interaction of genes and proteins with metabolites. Broadly, high throughput data generated from multiple compartments can be integrated with metabolomics using three different approaches. Concordance analysis uses direct information from the transcript/protein expression of enzymes and levels of product and substrate of the reaction. As an example, high levels of glucose and glucose 6-phosphate (Glucose-6-P) correlate with elevated hexokinase 1 (HK1) expression. Pathway based enrichment analysis uses statistical tests, such as Fisher’s exact test, to determine the likelihood of observing alterations in groups of metabolites/genes associated with specific metabolic pathways. In the corresponding figure, node size represents the number of metabolites in a pathway, and enrichment score represents directionality of enriched pathways based on composite score of differential metabolites. Network based integration uses interaction information about genes, proteins and metabolites as well as stoichiometry information of reactomes to design networks to test enrichment of metabolic pathways using several mathematical models. In the figure, node size corresponds to number of metabolites in a pathway, and interaction between pathways and directionality of flux are represented by arrows of varying width.

Figure 5. Advanced in vivo applications of metabolomics

Isotope tracing (left) and metabolite imaging (right) are two examples of advanced applications of metabolomics. Isotope tracing studies in lung cancer patients have established that glucose and lactate are oxidized in the TCA cycle in vivo. These studies have also revealed the activity of both pyruvate dehydrogenase and pyruvate carboxylase (PDH and PC) in vivo. In the illustration, PDH activity results in TCA cycle intermediates with two ¹³C nuclei and PC activity results in TCA cycle intermediates with three ¹³C nuclei. Metabolite imaging (right) using matrix assisted laser desorption/ionization (MALDI) provides temporal and spatial resolution of metabolite abundance to observe metabolic differences across tissue sections. Metabolite imaging has been used in murine glioma models to assess changes in glycolytic and TCA cycle intermediates.