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.
Introduction
The metabolome constitutes the entirety of metabolites present in a cell and is the result of the cell’s genetic, transcriptomic, and proteomic characteristics. Thereby, metabolism is responsible for maintaining life by conversion of energy in a form suitable for cellular reactions, generation of cellular building blocks, and removal of end products. Nevertheless, metabolites are not only important as precursors and products of enzymatic reactions, but they can also act as signaling molecules in the cell as well as extracellularly as paracrine or/and endocrine factors. Examples are succinate and lactate; both act through G protein-coupled receptors and regulate inflammatory responses (Kes et al. 2020). The amino acids glycine, glutamate, and γ-aminobutyric acid are also players in central carbon metabolism and additionally function as neurotransmitters. Intracellularly, metabolites regulate enzymatic reactions, influence gene expression through the modulation of epigenetic processes, for example through the so-called oncometabolites 2-hydroxyglutarate, succinate, and fumarate, and alter protein function and stability through posttranslational modifications, one well-known example being the regulation of hypoxia-inducible factor (HIF) proteins (Liu & Wellen 2020).
Metabolites comprise diverse groups of chemical classes, from small organic acids to derivatives of amino acids, complex ring structures, and long carboxyl chains. The Human Metabolome Database (HMDB) lists more than 220,000 endogenous metabolites. These diverse chemical structures dictate the analytical approach necessary for detection. Broadly speaking, there are two techniques used for the quantification of metabolites, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) coupled either to gas chromatography or to liquid chromatography (LC). Targeted approaches focus on a defined group of metabolites, while untargeted analyses detect a wider spectrum of metabolites. Depending on the goal of the analysis, the appropriate technique has to be selected (Fig. 1). NMR relies on inherent magnetic properties of atomic nuclei to identify compounds and their structures, whereas MS detects the mass-charge-ratio (m/z) and identifies compounds based on their characteristic fragmentation in a targeted approach or by their mass accuracy in an untargeted approach (Markley et al. 2017). MS usually provides better sensitivity as well as a much broader set of metabolites and lipids that can be measured compared to NMR. NMR has the advantage of easy sample preparation without any requirement for chemical derivatization or downstream chromatographical separation as well as short measuring times and very high reproducibility. Furthermore, both methodologies can be linked to imaging modalities. NMR combined with anatomical imaging by magnetic resonance imaging (MRI) is a tool to non-invasively measure metabolites in patient tissues in vivo. Matrix-assisted laser desorption ionization (MALDI)-MS imaging can be applied to tissue sections to visualize the spatial distribution of metabolites.
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.
Citation: Endocrine-Related Cancer 30, 6; 10.1530/ERC-22-0376
For metabolomics using MS, the approaches (targeted or untargeted) are typically conducted on different instruments. For targeted metabolomics, most methods rely on the use of triple quadrupole instrumentation and are operated under selected reaction monitoring (SRM) or multiple reaction monitoring (MRM), a form of tandem MS/MS. In these modes, the precursor metabolite is isolated in the first quadrupole and then subjected to collision-induced dissociation in the collision cell, which induces fragmentation causing product ions to be formed. In the next quadrupole, one or more product ions are isolated. Thus, the reported measurement is a precursor/product ion pair that is associated with each metabolite, allowing for a very specific and confident identification of each metabolite. An example is the analysis of catecholamine metabolites in the diagnosis of pheochromocytoma/paraganglioma (PPGL) (Eisenhofer & Peitzsch 2014). A triple quadrupole operating in the SRM/MRM mode is typically the most sensitive approach for measurements and is often employed when very low levels of an analyte are expected. Such a targeted method is limited to only the metabolites for which SRM/MRM pairs are assigned and thus cannot expand to new metabolites that could be biomarkers of disease or expand on our knowledge of metabolism.
Untargeted or global metabolomics most often relies on high-resolution mass spectrometry using a quadrupole-time-of-flight or an Orbitrap mass spectrometer that can measure masses with high accuracy, typically 5 ppm or less (Xian et al. 2012). Metabolites are identified either by searching databases such as the HMDB using the mass accuracy of the instrument or by developing an internal library that combines both m/z and retention time from chromatographic separation (Alseekh et al. 2021). In this manner, a metabolite is represented by the m/z and retention time pair, which is often called a feature. Statistical analysis relies on comparing the same signal across multiple samples in the peak list to identify metabolites that are significantly different between the groups. Not all metabolites can be identified, and some features remain ambiguous, representing one of the main challenges in metabolomics. Nevertheless, metabolomics has the potential to identify new metabolic pathways, shed light on disease states, or identify new biomarkers for diagnosis.
For targeted and untargeted LC-MS approaches, the choice of chromatography is essential to achieve specificity and sensitivity. The two most common separation approaches for LC are reversed-phase (RP) and hydrophilic interaction liquid chromatograph (HILIC). RP is the most common approach in metabolomics as it provides the best peak capacity and reliability covering semi-polar to non-polar metabolites. The performance on polar analytes is limited, even for polar embedded stationary phases. HILIC is a nowadays more widely used alternative to RP, because it can be employed to separate very polar analytes, like sugars or sugar-containing metabolites. Both LC methods are highly compatible with electrospray ionization (ESI) as they use a combination of water, methanol, and/or acetonitrile as mobile phases. Alternatively, atmospheric pressure chemical ionization (APCI) can be used. ESI is better suited for more polar, non-volatile molecules, while APCI has an advantage for non-polar, volatile compounds. MALDI as a third ionization approach most often is applied in imaging MS.
PPGL and catecholamine metabolism
Non-epithelial neuroendocrine neoplasms originating from neural crest-derived cells are referred to as paragangliomas according to the fifth series of the WHO Classification of Endocrine and Neuroendocrine Tumours (Mete et al. 2022). They can arise either along sympathetic and parasympathetic ganglia (PGL) or in the adrenal medulla (pheochromocytomas, PHEO). These rare and mostly catecholamine-producing tumors can lead to diverse symptoms related to either mass effects or the actions of catecholamines on blood pressure, heart rate, sweating and other body functions (Prejbisz et al. 2011, Zuber et al. 2011). Life-threatening complications, such as hypertensive crisis, can occur when PPGLs are undiagnosed during surgical procedures (Riester et al. 2015).
The catecholamine metabolism with high production rates in PHEO and sympathetic PGL tumor cells can be exploited diagnostically and aid in patient stratification according to underlying genetic drivers. As this topic was reviewed extensively in previous publications, we direct the reader to the following reviews (Eisenhofer et al. 2004, Eisenhofer et al. 2017). In chromaffin cells of the adrenal, catecholamines are not continuously secreted; instead, they are stored in vesicles waiting for stimuli that trigger secretion. In many PPGLs, catecholamines are released in intervals leading to fluctuating plasma levels. Importantly though, degradation of about 10% of catecholamines is a continuous process that takes place within chromaffin cells. Degradation products, the so-called metanephrines, hence reflect catecholamine production rates of PPGLs. Targeted LC-MS/MS-based measurements of metanephrines in either plasma or urine show high diagnostic sensitivity and specificity and are considered gold standard in the diagnostic workup to exclude or confirm PPGLs (Eisenhofer & Peitzsch 2014, Lenders et al. 2014).
Further research in this area identified the dopamine metabolite 3-methoxytyramine as a plasma biomarker for metastatic PPGL (Eisenhofer et al. 2012). Higher levels of 3-methoxytyramine are indicative of shorter disease-specific survival in patients with parasympathetic PGL, also called head-and-neck PGL, or metastases (Pamporaki et al. 2022). Head-and-neck PGLs produce only very low levels of catecholamines; hence, metanephrines are for the most part within normal limits for these patients (Richter et al. 2022). With improved assay sensitivity, however, plasma 3-methoxytyramine might be useful for screening of head-and-neck PGLs in the future. In situ derivatization, for example, enabled the simultaneous measurement of several amines with increased assay sensitivity for 3-methoxytyramine by LC-MS/MS (van Faassen et al. 2020). The use of such extended metabolite panels might identify further markers for patient stratification.
PPGL genetics and tumorigenic effects of oncometabolites
PPGLs have the highest number of hereditary cases amongst all neoplasms. About 40% arise due to germline pathogenic variants (PVs) in 1 of 21 known susceptibility genes. A majority of these hereditary PPGLs are associated with PVs in one of five genes, each connected to a specific syndrome: the von Hippel–Lindau (VHL) gene, rearranged during transfection (RET) protooncogene, the neurofibromatosis type 1 (NF1) gene, and succinate dehydrogenase (SDH) genes SDHB and SDHD. Less commonly, PVs occur in other SDH genes: SDHA, SDHC, and assembly factor SDHAF2, as well as in transmembrane protein 127 (TMEM127), MYC associated factor X (MAX), fumarate hydratase (FH), mitochondrial malate dehydrogenase (MDH2), succinate-CoA ligase GDP-forming subunit beta (SUCLG2), solute carrier family 25 (SLC25A11), mitochondrial glutamic-oxaloacetic transaminase (GOT2), dihydrolipoamide S-succinyltransferase (DLST), Egl nine homolog 1 and 2 (EGLN1/2), kinesin family member 1B (KIF1B), DNA methyltransferase 3α (DNMT3A), and endothelial PAS domain-containing protein 1 (EPAS1 or HIF2A) (Jhawar et al. 2022). Additionally, somatic mutations in these or other cancer genes were described, explaining the cause of roughly 80% of PPGLs (Curras-Freixes et al. 2017, Fishbein et al. 2017).
Since SDHB mutations are linked to a higher rate of metastatic disease and curative treatments are lacking, early tumor detection and removal are paramount to the prevention of metastases (Gimenez-Roqueplo et al. 2003, Davidoff et al. 2022). This has been recognized in clinical guidelines, in that patients who already have a tumor should undergo genetic testing to identify the underlying cause, and patients who have a confirmed mutation in an SDHx gene but no PPGL yet should undergo regular follow-up (Lenders et al. 2014, Amar et al. 2021). Besides PPGL, SDHx PVs predispose to renal cell carcinomas (Vanharanta et al. 2004) and gastrointestinal stromal tumors (Janeway et al. 2011). DNA sequencing technologies have been optimized to the point that the majority of gene variants can be detected by next-generation sequencing, either using a preselected panel of target genes or through sequencing the entire exome or genome. The problem, however, is that most gene variants are neutral and have no impact on health. Classifying gene variants according to their pathogenicity follows a standardized procedure that considers different information, including the nature of the mutation, in silico predictions, population frequency, and functional information (Richards et al. 2015). The latter becomes especially important for missense variants in disease-causing genes, since their effect on function is less predictable than for truncating variants. Metabolomics can contribute valuable information about gene variants of metabolic enzymes, such as SDHx and FH, by providing measurements of precursors and products and hence aids in the classification process.
Genetic alterations have amongst others also metabolic consequences in a cell (Fig. 2). Especially mutations affecting SDH, FH, or isocitrate dehydrogenase (IDH) lead to strong increases in succinate, fumarate, and 2-hydroxyglutarate, respectively. IDH1 and IDH2 hotspot mutations occur somatically in rare instances in PPGL (Richter et al. 2019, Li et al. 2022). Those oncometabolites cause global cellular changes by affecting epigenetic reprogramming and hypoxic signaling (Pollard et al. 2005, Yang & Pollard 2013). Similar to downstream consequences of VHL mutations, oncometabolites inhibit prolyl hydroxylases (PHDs), leading to accumulation of HIFs and subsequent activation of pathways associated with pseudohypoxia, which creates opportunities for angiogenesis and cellular proliferation under normal oxygen pressures. Additionally, histone lysine demethylases and enzymes of the ten-eleven translocation family are inhibited by oncometabolites, resulting in histone and DNA hypermethylation, respectively (Xiao et al. 2012, Letouze et al. 2013). Together, these signaling pathways promote a more aggressive pro-metastatic behavior of tumor cells (Bechmann et al. 2020, Morin et al. 2020). Additional effects of oncometabolites involve altered posttranslational modifications, including protein succinylation in SDHx- and IDH1-mutated cells (Li et al. 2015, Smestad et al. 2018) and protein succination in FH-mutated tumors (Yang et al. 2014). The latter is now being used for diagnostic purposes to detect tumors with pathogenic variants in FH based on 2-succinocysteine immunohistochemistry (Chen et al. 2014).
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.
Citation: Endocrine-Related Cancer 30, 6; 10.1530/ERC-22-0376
Metabolic changes in tumor cells with SDHx PV go beyond elevations of succinate, as a number of MS- and NMR-based studies have been found. Krebs cycle intermediates and amino acids, such as glutamate and aspartate, are generally lower in SDHx-mutated tumors compared to other PPGLs, whereas methionine and glutamine are more abundant (Imperiale et al. 2013, Richter et al. 2014, Imperiale et al. 2015, Richter et al. 2019). In vitro studies demonstrated that the lack of SDHx function leads to reductive carboxylation of glutamine and increased pyruvate consumption to replenish aspartate pools through pyruvate carboxylation (Cardaci et al. 2015, Lussey-Lepoutre et al. 2015). In line with these results, higher glutaminase 1 levels were reported in PPGLs with low SDHB expression and inhibition reduced cell growth (Sarkadi et al. 2020). SDHx-mutated tumors have lower levels of ATP/ADP/AMP compared to VHL and other PPGLs (Imperiale et al. 2013, Rao et al. 2013, 2015). To partially compensate for the loss of SDH or complex II from the respiratory chain, enzyme activities of complexes I, III, and IV were increased compared to sporadic RET and NF1 tumors (Rao et al. 2013). Especially complex I was shown to be important for the oxidation of NADH to support continued Krebs cycle function (Kluckova et al. 2020). Inhibition of complex I resulted in reduced tumor growth in a preclinical model (Moog et al. 2022). Contrary to these findings, another study suggested that loss of SDH and complex I are required to recapitulate the metabolic phenotype of SDHx-mutant tumors (Lorendeau et al. 2017). These divergent results may be related to the use of cell line models originating from different tissues. Changes in respiratory chain activities can increase reactive oxygen species (ROS), which may signal oxygen insufficiency to PHDs in addition to succinate (Chandel et al. 2000, Guzy et al. 2008).
Recent data have shown that excessive succinate can lead to the disruption of Ca2+–calpain–cyclin-dependent kinase 5 (CDK5) signaling and aberrant activation of the protein kinase CDK5 (Gupta et al. 2022). Aberrant CDK5 initiates a phospho-signaling cascade that inactivates energy sensing by AMP kinase through dephosphorylation of AMP kinase and creates opportunities for cellular proliferation. Finally, a metabolomic analysis of PPGL tissues has found an increase in the polyamine pathway in SDHx-mutated tumors (Rai et al. 2020). The diamine putrescine and the polyamines spermidine and spermine are small organic cations integral to several cellular biochemical pathways and implicated in oncogenic signaling. Increased concentrations of polyamines support cell growth and inhibition suppresses xenograft growth in mice (Rai et al. 2020).
FH germline PVs are known to cause hereditary leiomyomatosis and renal cell carcinoma as well as familial non-medullary thyroid cancer (Alzahrani et al. 2022, Tomlinson et al. 2002). They also predispose to malignant PPGL and cause DNA hypermethylation similar to SDHx PVs (Letouze et al. 2013, Castro-Vega et al. 2014). Metabolic profiling of FH- and SDHx-mutated renal cell carcinoma identified elevated guanine pools in both tumor types, but changes in urea cycle metabolites were only present in tumors harboring FH PVs (Yoo et al. 2022). Similar results were obtained in FH-mutated leiomyomas (Heinonen et al. 2017) and are most likely similar in PPGL. High levels of the urea cycle metabolite argininosuccinate in FH-mutated tumors arise due to reverse activity of argininosuccinate lyase, making these cells dependent on extracellular arginine (Zheng et al. 2013). Additionally, NADH produced in the Krebs cycle is regenerated to NAD in the heme degradation pathway, leading to elevated bilirubin excretion and therapeutic vulnerabilities (Frezza et al. 2011).
Another PPGL-susceptibility gene is malate dehydrogenase 2 (MDH2) (Cascon et al. 2015). Loss of MDH2 hinders the conversion of malate to oxaloacetate in mitochondria and can cause accumulation of malate and fumarate (Ait-El-Mkadem et al. 2017), although metabolite accumulation in MDH2-mutated tumors occurs at much lower levels than in FH- and SDHx-mutated PPGLs (Richter et al. 2019). Similar to succinate and fumarate, malate can inhibit prolyl hydroxylation of HIFα (Pan et al. 2007), and global DNA hypermethylation was also detected in MDH2-mutated PPGLs (Cascon et al. 2015).
Metabolome-guided diagnostics and future directions
Metabolomics is a useful tool for exploiting the metabolic features of PPGLs for diagnostic purposes. This section introduces a number of available methods that can aid in the metabolic stratification of PPGLs and variant classification in a subset of PPGL-susceptibility genes, thereby improving genetic diagnosis and patient management. Furthermore, methodological advances in preanalytics and the use of untargeted analytical approaches will be discussed to envisage future perspectives in the field.
Using an LC-MS/MS-based targeted quantification of Krebs cycle metabolites proved useful to identify and confirm PVs in SDHx and FH genes of PPGL samples by measuring the ratios of succinate to fumarate or fumarate to malate, respectively (Lendvai et al. 2014, Richter et al. 2014, Richter et al. 2019, Garrett et al. 2022). The same method is also useful in screening for hotspot mutations in IDH1/2 through the detection of elevations in 2-hydroxyglutarate and especially its D-enantiomer but has less relevance in confirming the pathogenicity of these variants, since they are well described in the literature for many different tumor types and occur somatically (Dang et al. 2016). Beyond PPGL, the method is also applicable to other tumor types, including renal cell carcinoma and gastrointestinal stromal tumors (Kim et al. 2017, William et al. 2021).
Metabolic screening of tumors could become important, when specific therapies for metabolically dysregulated cancers with SDHx, FH, or IDH1/2 PV become available, as targeted LC-MS/MS analysis is faster and cheaper than sequencing analyses. In this case, tumors with somatic PVs in these genes or epigenetic alterations that result in a similar phenotype are also of interest, for example SDHC promoter methylation in PPGL and gastrointestinal stromal tumors causing loss of SDHC expression and increased succinate levels (Killian et al. 2014, Richter et al. 2016). Additionally, PVs in other susceptibility genes, such as SUCLG2 and GOT2, lead to elevations in succinate and might respond to similar treatments (Remacha et al. 2017, Hadrava Vanova et al. 2022). Routine metabolic screening could also aid in identifying patients with other rare variants affecting the Krebs cycle. In the future, pathologist would be best suited to extract a couple of tissue pieces per tumor of interest for further metabolite extraction, which can be accomplished from fresh-frozen or archival formalin-fixed and paraffin-embedded tissues (Richter et al. 2014). Depending on the tumor entity, the aim of metabolite profiling could then be to identify cancers with a possibly hereditary basis and to suggest targeted treatments when available.
Beyond succinate and fumarate, many other metabolite levels are altered in SDHx-mutated PPGLs and could be important diagnostically. Both LC-MS/MS and NMR studies have discovered different possible markers, including adenine nucleotides, amino acids, Krebs cycle intermediates, and polyamines (Imperiale et al. 2013, Rao et al. 2013, Richter et al. 2019, Rai et al. 2020). With the application of machine learning algorithms on LC-MS/MS tissue metabolite profiles, PPGL classification according to SDH status was improved with at least four different metabolites, especially for the more difficult-to- classify head-and-neck PGLs (Wallace et al. 2020). The latter contain a high percentage of non-transformed sustentacular cells that are assumed to lower succinate levels in the measured sample. For artificial intelligence to be incorporated into diagnostic procedures, larger study populations than that used in Wallace et al. are required to build robust algorithms without overfitting, which should be validated in prospective trials afterward (de Hond et al. 2022).
Recently, we reported a methodological improvement of LC-MS/MS-mediated tissue metabolomics, by showing that a commercial buffer enables analysis by multiple targeted and untargeted LC-MS/MS assays, as well as RNA and protein analyses, including enzyme activities, from the same piece of tissue (Bechmann et al. 2021). Other groups have also published on simultaneous tissue extraction methods for multi-omics focusing on proteins or RNA in combination with metabolites and lipids (Coman et al. 2016, Leuthold et al. 2018, Kang et al. 2021). Such developments are extremely important for procedures that integrate information from different assays to overcome effects due to tumor heterogeneity. Additionally, procedures that require minimal tissue input are critical for research of rare tumor entities, such as PPGLs, and for animal studies. We further validated our new multi-omics extraction method by comparing the resulting succinate:fumarate ratio (SFR) to those generated with the previously employed methanol extraction (Fig. 3). The SFR is generally lower with the new extraction, but PPGLs with SDHx mutation are mostly identified, resulting in an area under the curve of 0.987. An SFR cutoff of 40 for the multi-purpose extraction classified all SDHx-wildtype PPGLs correctly, but three of six SDHx-mutated tumors of a validation set were false-negatives. From the seven false-negatives that were present in the training and the validation sets, four belonged to the group of head-and-neck PGLs, two were PGLs, and one was a PHEO. Selection of an appropriate piece of tissue is similar to the methanol extraction method paramount to receiving the right classification. Hence, parallel testing of at least two different tissue pieces and selection by a pathologist might increase the reliability of classifications. With such a multi-purpose method, additional analyses looking at the expression of specific chromaffin markers vs those of sustentacular cells could be helpful in assessing the suitability of the tissue piece.
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.
Citation: Endocrine-Related Cancer 30, 6; 10.1530/ERC-22-0376
In some cases, an operation is not possible or not indicated, for example for slow-growing head-and-neck PGLs, where sometimes a wait-and-scan approach is recommended, less invasive methods for the assessment of metabolite profiles are required (Jansen et al. 2017). One such method is NMR with respect to hydrogen-1 nuclei, also referred to as proton magnetic resonance spectroscopy (1H-MRS), in combination with MRI. It was demonstrated that this technique detects succinate elevations exclusively in SDHx-mutated PPGLs (Varoquaux et al. 2015, Lussey-Lepoutre et al. 2016). Larger tumors give higher quality spectra, whereas small head-and-neck PGLs, for example of the jugular region, are more difficult to analyze due to current detection limits of instruments. Further limitations include tumors with hemorrhagic or necrotic spots. A study with 49 patients calculated a sensitivity of 87% and specificity of 100% for succinate detection in SDHx-mutated PPGLs (Lussey-Lepoutre et al. 2020). Since operation costs are high and radiological expertise and experience is required, this technique is most appropriate for specialized PPGL clinical centers. In the future, 7-tesla MRIs might improve post-processing of spectra to increase sensitivity.
Whereas 1H-MRS is useful to identify SDHx-mutated tumors in vivo, it is not of course applicable to SDHx PV carriers who have not yet developed tumors. A recent publication reported that carriers of SDHB PV have elevated levels of serum succinate (Lamy et al. 2022). Patients with SDHC or SDHD PV, of which very few were tested, had smaller increases of succinate. To validate this method for diagnostic use in PV carriers, reference intervals from a large group of healthy individuals of different ages and body mass index should be generated, since succinate elevations in plasma were also linked to the risk of cardiovascular disease and obesity (Serena et al. 2018, Osuna-Prieto et al. 2021). Furthermore, serum succinate correlated with the metastatic load of patients with tumors due to an SDHB PV and can be used as a biomarker to monitor treatment success and recurrence. Since blood samples are easily obtainable, they are the preferred diagnostic materials and hold a lot of potential for the future. As cell line models of PPGL with loss of about half of the SDH protein show only moderate succinate increases, it is rather surprising that succinate differences are measurable in the serum (Richter et al. 2018). Further studies will show whether other metabolites improve the classification of SDHx mutation carriers in the blood and whether this method is applicable for patients with other germline PV, such as FH. Table 1 summarizes all currently available methods for metabolic assessment of PPGLs with variants in SDHx genes.
Overview of analytical methods for the measurement of succinate for functional validation of variants in SDHx genes.
| Material | Platform | Advantages | Disadvantages |
|---|---|---|---|
| Tissue ex vivo | LC-MS/MS | –Validated on a large patient cohort –Known to be applicable also for other rare gene mutations (FH, IDH1/2) –Instrument is widely available (same as metanephrine analytic) –Fast and cheap |
–OP necessary –Not applicable for SDHx mutation carriers without tumor |
| In vivo tumor | 1H-MRS + MRI | –Pre-OP measurement possible | –Not widely available instrumentation –Expensive –Not applicable for SDHx mutation carriers without tumor |
| Serum | LC-MS/MS | –No OP necessary –Sampling most comfortable for patient –Possibility of evaluation of SDHB mutation carriers –Follow-up of treatment efficacy –Fast and cheap |
–Only shown in one cohort for patients with SDHB mutations |
1H-MRS, proton magnetic resonance spectroscopy; LC-MS/MS, liquid chromatography with mass spectrometry; MRI, magnetic resonance imaging; NMR, nuclear resonance spectroscopy; OP, operation.
Diagnostic methods discussed herein for the metabolic assessment of PPGLs are all targeted analyses focusing either on metanephrines or on succinate as the main metabolites and aid in stratifying patients suffering from PPGLs (Fig. 4). The presented techniques of metabolome characterization are not only important for diagnosis, which includes tumor screening by metanephrines and functional validation of variants of unknown significance in SDHx genes, but they also guide patient management and prognosis. Depending on germline SDHx status, certain follow-up times for patients with PPGL or mutation carriers are recommended (Amar et al. 2021). In addition, serum succinate analysis has the potential as a marker for treatment efficacy in SDHx-mutated PPGLs, and plasma 3-methoxytyramine has prognostic value for risk assessment of metastatic disease. Recently, an untargeted metabolomics study using MALDI-MS imaging of formalin-fixed paraffin-embedded PPGL specimens indicated that the abundance of kynurenine pathway metabolites that originate from tryptophan catabolism was significantly lower in PPGLs with activation of pseudohypoxia pathways compared to kinase-driven PPGLs (Murakami et al. 2021). Lower abundance of the metabolite xanthurenic acid was associated with shorter metastasis-free survival and identified as a risk factor for metastasis independent of the genetic status. Although not yet validated in other cohorts or with targeted MS assays, xanthurenic acid could become another important biomarker for the management of PPGL patients.
Metabolic assessment of PPGL now (left) and future perspectives (right). 1H-MRS, proton magnetic resonance spectroscopy; VUS, genetic variants of unknown significance.
Citation: Endocrine-Related Cancer 30, 6; 10.1530/ERC-22-0376
In the future, metabolomic assessment of patients with PPGL, in the tumor tissue and blood, will most likely move toward broader metabolic signatures using bigger targeted panels or untargeted approaches that will be combined with genomic and possibly transcriptomic or proteomic information. In this respect, the methods for efficient extraction of different molecules from the same piece of tumor become very important (Bechmann et al. 2021). NMR-based untargeted metabolomics studies have so far only been performed on a very limited number of PPGL tissues. The technique was useful in classifying PPGLs based on their mutational backgrounds, which applied not only to SDHx-mutated tumors but also to VHL and RET mutations (Imperiale et al. 2015, Rao et al. 2015). Metabolites that may help to better classify SDHx- and FH-mutated PPGLs are guanine, acylcarnitines for SDHx and urea cycle metabolites like argininosuccinate for FH mutant tumors. Although data have so far been presented only for renal cell carcinomas, it is highly likely that these features are also present in PPGLs (Yoo et al. 2022). Omics setups that recognize patterns rather than isolated alterations could further improve patient stratification, for example according to risk of metastasis or recurrence and eligibility for targeted therapies. Two recent studies of plasma from patients with PPGL used targeted LC-MS/MS with an extended panel of over 100 metabolites. Unfortunately, low patient numbers and high interindividual differences may have prevented the identification of suitable signatures for PPGL diagnosis, but it was demonstrated that the metabolic aberrations that occurred with PPGL were reversed after surgery (Erlic et al. 2019, Marz et al. 2021).
In order to gather meaningful data suitable for computational analysis, a number of critical steps have to be taken, which are especially important when different medical and analytical centers are involved in data collection and processing. Preanalytical procedures of sample collection, handling, and storage have to be standardized to avoid strong confounding factors that might mask underlying effects. A recent study using NMR metabolomics in plasma reported sample age and origin as major confounders (Bliziotis et al. 2022). Furthermore, analytical methods used in different centers have to be harmonized to further avoid confounders and ensure reproducibility, as it was described for LC-MS/MS-based plasma metanephrine measurements (Peitzsch et al. 2021). Data formats and outputs should be compatible, and all required clinical information has to be provided in appropriate data sheets. Furthermore, ethical considerations have to be resolved to avoid misuse of omics data against the patient’s consent. The setup of such an omics-based diagnostic approach requires intense communication between medical disciplines and clinical centers to establish the necessary infrastructure. The European cooperation in science and technology funded the harmonization of clinical care and research on adrenal tumors in European countries (CA20122), providing the first step into a future of standardized medical care involving multi-omics-based diagnostic for PPGL. Due to the high costs connected to the computational requirements for bioinformatics analyses and data storage, not all PPGL patients might be examined by multi-omics, but selected cases with inoperable tumors or risk of metastases could benefit. Additionally, rare gene mutations might be identified based on unusual omics patterns.
Therapeutic strategies for PPGLs and oncometabolite-driven tumors
The mainstay for treatment in PPGL is surgical with preoperative antihypertensive treatment with alpha receptor antagonists a few weeks prior to tumor resection. If a patient has rapidly growing metastatic disease, the patient will have to undergo conventional chemotherapy with cyclophosphamide, vincristine, and dacarbazine that interfere with DNA replication and mitosis (Jawed et al. 2018). Another option may be the alkylating agent temozolomide alone or combined with the antimetabolite capecitabine, which inhibits DNA synthesis (Hadoux et al. 2014, Perez et al. 2022). For patients that have more slowly growing tumors, radiopharmaceutical agents, such as 131I-metaiodobenzylguanidine targeting the norepinephrine transport system or peptide receptor radionuclide therapy (PRRT) taking advantage of somatostatin receptor type 2 expression, can be an option (Pryma et al. 2019, Vyakaranam et al. 2019). Despite these available therapies, a complete cure for metastatic PPGL is still not available. Investigators have turned now toward specific alterations in tumor signaling and metabolism to identify new treatment targets that may help to achieve a durable and satisfactory response to aggressive disease.
Since roughly 50% of metastatic PPGLs carry PVs in SDHB (Pamporaki et al. 2022), oncometabolite-driven signaling pathways and metabolic vulnerabilities are a focal point of new therapeutic approaches (Fig. 2). Recent research has uncovered that the oncometabolites fumarate and succinate suppress the homologous recombination DNA repair pathway responsible for maintaining genomic integrity through the inhibition of lysine demethylases KDM4A/B (Sulkowski et al. 2018). This observation renders affected tumor cells vulnerable to targeting with poly-(ADP-ribose) polymerase (PARP) inhibitors. Mouse PPGL models confirmed that a combination of temozolomide and PARP inhibitor reduced metastatic lesions and improved overall survival (Pang et al. 2018). Combination of these two agents is currently evaluated in a phase II clinical study (NCT04394858). Additional opportunities may arise with agents targeting DNA or histone methylation (Pang et al. 2019); however, since DNA hypermethylation alone is not sufficient to drive mesenchymal transition, monotherapy may not be effective against metastatic PPGL (Morin et al. 2020). Instead, it may be useful as adjuvant therapy supporting PRRT (Ullrich et al. 2023).
Further opportunities have emerged with the understanding that increases in unhydroxylated HIF through either oncometabolite mediated inhibition of PHDs, loss of VHL, or mutations in the hydroxylation domain of EPAS1 activate vascular endothelial growth factor, a driver of angiogenesis. Inhibiting the angiogenic pathway with the receptor tyrosine kinase inhibitor sunitinib showed moderate tumor regression and stability with treatment (Joshua et al. 2009). Newer data from the First International Randomized Study in Malignant Progressive Pheochromocytoma and Paragangliomas (FIRSTMAPPP, NCT01371201) indicate that 35.9% of patients with progressive malignant PPGL treated with sunitinib achieved the primary endpoint of progression-free survival at 12 months (Baudin et al. 2021). The median progression-free survival was 8.9 months with sunitinib vs 3.6 months in the placebo arm. Another approach is based on the inhibition of HIF2A by the specific inhibitor belzutifan, which disrupts binding to its heterodimerization partner aryl hydrocarbon receptor nuclear translocator (Ren et al. 2022). Belzutifan was approved last year for cancers associated with VHL disease but may also show benefits in patients with PPGLs caused by EPAS1 (Kamihara et al. 2021) and other mutations causing a pseudohypoxia phenotype. In preclinical models for SDHx-mutated PPGL, an earlier generation HIF2A inhibitor or belzutifan did not show antitumor activity, while sunitinib induced a significant reduction of tumor volume (Bechmann et al. 2020, Moog et al. 2022).
Specifically in SDHB-mutated PPGLs and cell line models, intracellular iron accumulation leads to the generation of oxidative stress and increased mitochondrial ROS (Liu et al. 2020b , Goncalves et al. 2021). As a result, treatment with ascorbic acid may be a promising strategy but requires clinical testing and investigations into administration and dosage. In another approach, researchers have targeted nuclear factor erythroid 2-related factor 2-guided glutathione synthesis in preclinical models of SDHB-mutated PPGL (Liu et al. 2020a ).
Another promising target that has been investigated preclinically is the polyamine pathway. Application of the polyamine analogue N 1 , N 11-bis(ethyl) norspermine (BENSpm/DENSPM) induced growth arrest in a xenograft mouse model and was also highly effective in cells with loss of SDHB (Rai et al. 2020). Phase 1 clinical trials in other cancers demonstrated that DENSPM can be administered with minimal toxicities, but single therapy showed no objective disease responses, indicating the need for identifying effective drug combinations (Streiff & Bender 2001, Hahm et al. 2002). With the recognition of elevated succinate causing aberrant activation of CDK5, the anti-CDK5 inhibitor MRT3-007 was shown to suppress tumor growth in vitro as well as in xenograft mouse models with SDHB knockout (Gupta et al. 2022). Other approaches to inhibit aberrations in SDHx-mutated PPGL that might warrant further exploration include inhibition of glutaminase, respiratory complex I with IACS-010759, or pyruvate carboxylase (Cardaci et al. 2015, Sarkadi et al. 2020, Moog et al. 2022). For the latter, unfortunately, no promising inhibitors have been identified. A phase I study of glutaminase inhibitor CB-839 in patients with SDHx-, FH- or IDHx-mutated tumors completed recruitment in 2022 (NCT02071862). Further opportunities may arise from investigations of synthetically lethal pathway aberrations, as demonstrated by the loss of FH in combination with inhibition of heme oxygenation (Frezza et al. 2011).
Other metabolic drugs, including antidiabetic agent metformin, pyruvate dehydrogenase inhibitor dichloroacetate, and peroxisome proliferator-activated receptor α inhibitor GW6471, reduce cell viability and clonogenicity in vitro; however, whether these treatments are specific to cells with SDHx mutations is unclear (Florio et al. 2018). Interestingly, metformin lowers the migratory capacity specifically in SDHB-silenced cells cocultured with fibroblasts but not in wildtype tumor cells (Martinelli et al. 2022). Since metformin activates AMP kinase by phosphorylation, it may counteract the inhibitory effect of succinate-dependent CDK5 activity on AMP kinase (Zhou et al. 2001, Gupta et al. 2022).
Different combinations of established cancer therapies and new experimental agents should be investigated in the future, focusing on targeting two or more pathways important for tumor maintenance or metastasis at the same time. These combinations can be tailored to the specific mutations and clinical presentations of the individual patient. Metabolomics of tissue samples or liquid biopsies could be a very useful tool for accompanying clinical trials to identify biomarkers for treatment responders or non-responders. Additionally, metabolic profiles of these patients might give new clues about which further treatments are promising.
Conclusions
Cellular metabolism is key to all processes of life, and especially cancer cells have developed a multitude of strategies to support proliferation, ensure survival by adapting to changing environments, and escape anti-cancer treatments. Exploring metabolic alterations by targeted or untargeted approaches resulted in clinical biomarkers for diagnosis of PPGLs in urine or blood and of subforms with mutations in Krebs cycle genes. Metabolomic information from tumors have provided clues for potential treatment targets. These include pathways affecting angiogenesis, ROS, DNA repair, polyamines, and kinase cascades. With further advances, tailored therapies for subgroups of PPGLs, for example those with specific gene mutations and transcriptomic or metabolomic signatures, will become available. Quantification of the oncometabolite succinate in the blood will be a useful tool for following therapy effects in SDHB-mutated metastatic PPGL. Metabolomics will play an important role together with the other omics disciplines in paving the way towards a better future for patients with metastatic PPGL.
Declaration of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
Funding
SR and NB are supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project number: 314061271 – TRR 205. HKG is supported by the NIH R21 TR003044-01A1.
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