Metabolomics in paraganglioma: applications and perspectives from genetics to therapy

Abstract

Metabolites represent the highest layer of biological information. Their diverse chemical nature enables networks of chemical reactions that are critical for maintaining life by providing energy and building blocks. Quantification by targeted and untargeted analytical methods using either mass spectrometry or nuclear magnetic resonance spectroscopy has been applied to pheochromocytoma/paraganglioma (PPGL) with the long-term goal to improve diagnosis and therapy. PPGLs have unique features that provide useful biomarkers and clues for targeted treatments. First, high production rates of catecholamines and metanephrines allow for specific and sensitive detection of the disease in plasma or urine. Secondly, PPGLs are associated with heritable pathogenic variants (PVs) in around 40% of cases, many of which occur in genes encoding enzymes, such as succinate dehydrogenase (SDH) and fumarate hydratase (FH). These genetic aberrations lead to the overproduction of oncometabolites succinate or fumarate, respectively, and are detectable in tumors and blood. Such metabolic dysregulation can be exploited diagnostically, with the aim to ensure appropriate interpretation of gene variants, especially those with unknown significance, and facilitate early tumor detection through regular patient follow-up. Furthermore, SDHx and FH PV alter cellular pathways, including DNA hypermethylation, hypoxia signaling, redox homeostasis, DNA repair, calcium signaling, kinase cascades, and central carbon metabolism. Pharmacological interventions targeted toward such features have the potential to uncover treatments against metastatic PPGL, around 50% of which are associated with germline PV in SDHx. With the availability of omics technologies for all layers of biological information, personalized diagnostics and treatment is in close reach.

Keywords: fumarate hydratase; mass spectrometry; nuclear magnetic resonance spectroscopy; oncometabolites; succinate dehydrogenase.

Figure 1

How to choose your metabolomics method. GC-MS, gas chromatography with mass spectrum; LC-MS, liquid chromatography with mass spectrometry; MALDI-MS, matrix-assisted laser desorption/ionization mass spectrometry; MRI, magnetic resonance imaging; NMR, nuclear resonance spectroscopy.

Figure 2

Cellular consequences of oncometabolites and targeted therapies for metastatic PPGL. (A) Oncometabolites, such as succinate, fumarate, and 2-hydroxyglutarate, change many cellular signaling pathways that can be potential treatment targets. Succinate stimulates the receptor SUCNR1 and results in the activation of cyclin-dependent kinase 5 (CDK5). Oncometabolites inhibit α-ketoglutarate-dependent enzymes, including DNA and histone demethylases (TET and JmjC, respectively), prolyl hydroxylases (PHD) that lead to hypoxia-inducible factor (HIF) stabilization, and lysine demethylases KDM4A/B that are involved in DNA repair. (B) Aberrations in mitochondrial metabolism increase the production of reactive oxygen species (ROS), resulting in increased oxidative stress and glutathione consumption as a mechanism of compensation. (C) Loss of SDH leads to increased polyamine synthesis and (D) dependence on glutamine usage as well as on respiratory complex I for oxidation of NADH to maintain flux through the Krebs cycle. Blue boxes depict PPGL-susceptibility genes involved in mitochondrial metabolism (other susceptibility genes are listed in the manuscript text): DLST, dihydrolipoyllysine-residue succinyltransferase; FH, fumarate hydratase; GOT2, glutamic-oxaloacetic transaminase; IDHx, isocitrate dehydrogenase 1 or 2; MDH2, malate dehydrogenase 2; SDHx, succinate dehydrogenase subunit genes; SLC25A11, mitochondrial α-ketoglutarate/malate carrier; SUCLG2, succinate-CoA ligase GDP-forming subunit β; gene mutations known to increase oncometabolites (red letters) are shown in black letters. Targeted therapies are highlighted in blue, with clinically approved therapies for other cancers marked with drug capsules.

Figure 3

Succinate:fumarate ratios (SFR) for fresh-frozen PPGL tissues using a novel multi-purpose extraction method. (A) Comparison of the SFR between two extraction methods in the same tissue samples (n = 69). SFR_Master refers to the recently published method by proprietary buffer composition (Bechmann et al.); SFR refers to methanol extraction (Richter et al.). Blue, SDHx wildtype tumors; red, SDHx-mutated tumors. (B) Receiver operator characteristic curve calculations for a training set of 69 PPGLs (20 SDHx-mutated and 49 SDHx wild type) to obtain the best cutoff for SDHx-mutated samples. Cutoff was optimized to avoid false-positives (SFR_Master cutoff 40). (C) Application of the cutoff (dashed line) to a validation cohort of 76 PPGLs (6 SDHx-mutated and 70 SDHx wild type). (D) Four of seven false-negatives (training + validation set) fall into the category of head-and-neck paragangliomas (HNP). AUC, area under the curve; PHEO, pheochromocytoma; PGL, abdominal or thoracic paraganglioma.

Figure 4

Metabolic assessment of PPGL now (left) and future perspectives (right). ¹H-MRS, proton magnetic resonance spectroscopy; VUS, genetic variants of unknown significance.