Abstract
The genetics of pheochromocytoma and paraganglioma (PPGL) has become increasingly complex over the last two decades. The list of genes involved in the development of these tumors has grown steadily, and there are currently more than 20 driver genes implicated in either the hereditary or the sporadic nature of the disease. Although genetic diagnosis is achieved in about 75–80% of patients, genetic etiology remains unexplained in a significant percentage of cases. Patients lacking a genetic diagnosis include not only those with apparently sporadic PPGL but also patients with a family history of the disease or with multiple tumors, that meet the criteria to be considered as candidates for carrying germline mutations in yet undiscovered genes. Mutations in known PPGL genes deregulate three main signaling pathways (hypoxia, kinase signaling, and Wnt-signaling pathways), which could be the starting point for the development of personalized treatment for PPGL patients. Furthermore, the integration of results from several genomic high-throughput platforms enables the discovery of regulatory mechanisms that cannot be identified by analyzing each piece of information separately. These strategies are powerful tools for elucidating optimal therapeutic options based on molecular biomarkers in PPGL and represent an important step toward the achievement of precision medicine for patients with metastatic PPGL.
Pheochromocytomas (PCCs) and paragangliomas (PGLs), all together called PPGLs, are rare neuroendocrine tumors with a complex genetic etiology. PCCs develop in the adrenal medulla, and the term PGL is reserved for tumors that arise from sympathetic paraganglia in the thorax, abdomen, and pelvis, as well as from parasympathetic paraganglia in the head and neck (H&N) area (Koopman et al. 2019). PPGLs are considered the tumors with the highest degree of heritability within all human neoplasia. Approximately 40% of PPGL patients harbor an autosomal dominant germline mutation in 1 of the 20 susceptibility genes described so far. Some of these familial cases have clinical features that point to heritability (such as early age of onset, tumor multiplicity, and/or family history of PPGL), but up to 12% of the mutation carriers develop a single tumor without family history of PPGL, thus showing an apparently sporadic presentation of the disease (Brito et al. 2015).
Somatic mutations in one of the genes involved in the disease are responsible for tumor development in 30% of patients. Lastly, there are still 30% of PPGLs for which a pathogenic gene mutation has not been identified, including some exhibiting clinical signs indicative of an inherited condition. This latter suggests that the number of PPGL susceptibility genes will continue to increase in the near future due to ongoing genetic/genomic efforts (NGS in PPGL (NGSnPPGL) Study Group et al. 2017). This unexplained heritability represents a major challenge for the proper genetic classification of patients in order to provide the most adequate clinical follow-up.
Genetic basis of inherited PPGLs
The major PPGL susceptibility genes found mutated in the germline include classic driver genes, such as RET, NF1, VHL, SDHD, SDHC, and SDHB, as well as more recently identified genes such as SDHA, SDHAF2, TMEM127, MAX, FH, MDH2, EGLN1, EGLN2, KIF1B, MET, IDH3B, GOT2, SLC25A11, DNMT3A, and DLST (Schlisio et al. 2008, Toledo et al. 2016, Dahia 2017, Remacha et al. 2017, Buffet et al. 2018, Remacha et al. 2019) (Fig. 1).
Schematic representation of the susceptibility genes involved in hereditary (yellow color) and sporadic (blue color) PPGL development. Genes colored in yellow and blue have been identified in both hereditary and sporadic PPGLs, and the proportion of colors indicates the percentage of cases reported in each condition. Genes affected by somatic postzygotic mutations are denoted in gray. *Apparently sporadic cases and patients with clinical features of heritability.
Citation: Journal of Molecular Endocrinology 70, 3; 10.1530/JME-22-0167
Schematic representation of the susceptibility genes involved in hereditary (yellow color) and sporadic (blue color) PPGL development. Genes colored in yellow and blue have been identified in both hereditary and sporadic PPGLs, and the proportion of colors indicates the percentage of cases reported in each condition. Genes affected by somatic postzygotic mutations are denoted in gray. *Apparently sporadic cases and patients with clinical features of heritability.
Citation: Journal of Molecular Endocrinology 70, 3; 10.1530/JME-22-0167
Schematic representation of the susceptibility genes involved in hereditary (yellow color) and sporadic (blue color) PPGL development. Genes colored in yellow and blue have been identified in both hereditary and sporadic PPGLs, and the proportion of colors indicates the percentage of cases reported in each condition. Genes affected by somatic postzygotic mutations are denoted in gray. *Apparently sporadic cases and patients with clinical features of heritability.
Citation: Journal of Molecular Endocrinology 70, 3; 10.1530/JME-22-0167
Somatic mutations in some of these driver genes (mainly RET, VHL, NF1, and MAX) and in others (EPAS1, HRAS, IDH1, IDH2, CSDE1,FGFR1, and BRAF) are found in 30% of PPGLs without a known germline variant (Favier et al. 2015, Fishbein et al. 2017, Welander et al. 2018). In addition, EPAS1 (or HIF2A) and H3-3A may be affected by postzygotic somatic mosaicism events in early developmental stages; therefore, patients may develop multiple tumors, even if they do not carry a germline mutation (Zhuang et al. 2012, Toledo et al. 2016). Finally, translocations affecting the MAML3 gene have also been described, mainly involving the UBTF gene (Fishbein et al. 2017). Since these fusions are not detectable by the canonical methods used for genetic testing, their presence had not been identified until recently. These fusions may account for about 5–7% of cases, and they are of particular interest because they are associated with an increased risk of metastasis (Fishbein et al. 2017, Alzofon et al. 2021).
In almost all cases, tumor development is associated with the presence of a single mutation in one of the driver genes, regardless of whether it is germline or somatic (Dahia 2017). However, anecdotal reports of PPGLs carrying mutations in two driver genes have been reported (NF1 along with RET, VHL, or FGFR1; RET with SDHA or MAX with HRAS mutations) (Burnichon et al. 2012a , Ben Aim et al. 2019). Other non-driver somatic mutations, such as those in the ATRX and TERT genes, may influence the course of the disease and may even predict prognosis (Fishbein et al. 2015, Dwight et al. 2018, Job et al. 2019, Dariane et al. 2021). Considering all these data, it is advised that all patients with PPGL undergo genetic screening since the patient's management and genetic counseling are significantly impacted by the identification of the underlying mutation (Lenders et al. 2020).
Moreover, a particular expression profile is tightly linked to the driver gene involved in tumor development. Until 2016, PPGLs were classified into two main clusters (C1 and C2) according to their transcriptomic signatures. Specifically, the cluster C1 (subdivided into C1A and C1B) includes tumors carrying mutations in the tricarboxylic acid (TCA) cycle genes (SDHB, SDHA, SDHC, SDHD, SDHAF2, FH, MDH2, DLST, and IDH1) and in genes involved in the regulation of hypoxia-inducible transcription factors HIF1α and/or HIF2α (VHL, EPAS1, and EGLN1/2). This cluster is enriched in tumors showing a transcriptomic profile that mirrors the cellular response to hypoxia (‘pseudohypoxic’ signature) and a predominantly noradrenergic phenotype (Cascon et al. 2019). Cluster C2, comprising RET-, NF1-, TMEM127-, HRAS-, MAX-, and FGFR1-mutated tumors, is enriched in PPGLs that exhibit a kinase receptor activated profile, increased protein translation, and an adrenergic/noradrenergic phenotype (Castro-Vega et al. 2016). From 2017 onwards, PPGL classification was fine-tuned with the publication of the PPGL TCGA Project (Fishbein et al. 2017). Fishbein et al. identified, for the first time in PPGLs, a third transcriptional cluster composed of tumors with Wnt signaling-pathway activation carrying MAML3 fusions and CSDE1 mutations.
PPGL syndromes
PPGLs frequently occur together with other clinical features within a family and even in the same individual, which is indicative of a strong genetic etiology (Fig. 2). Thus, PCCs are associated with multiple endocrine neoplasia type 2 (MEN2), von Hippel-Lindau (VHL) disease or neurofibromatosis type 1 (NF1) and, to a lesser extent, with Carney triad (CT), Carney–Stratakis síndrome (CSS), and multiple endocrine neoplasia type 1. In some of these syndromes, the presence of additional clinical signs in the patient usually precedes the development of PPGL (‘cafe au lait’ spots in NF1 patients and medullary thyroid cancer (MTC) in MEN2 cases), while in others, there are no previous clinical evidence to suggest a genetic disorder (type 2C VHL disease).
Diagram illustrating the frequency of clinical manifestations related to the presence of mutations in the PPGL genes. *Postzygotic somatic mutation (mosaic); CALMs, café-au-lait macules; GCT, giant cell tumor of bone; H&N-PGL, head and neck paraganglioma; Hbs, hemangioblastomas; MTC, medullary thyroid carcinoma; Nfs, neurofibromas; PCC, pheochromocytoma; PNET, pituitary NET; Polycyth, polycythemia; PT, pancreatic tumor; RCC, renal cell carcinoma; Sls, skin leiomyomas; TAP-PGL, thoracic-abdominal paraganglioma; Uls, uterine leiomyomas.
Citation: Journal of Molecular Endocrinology 70, 3; 10.1530/JME-22-0167
Diagram illustrating the frequency of clinical manifestations related to the presence of mutations in the PPGL genes. *Postzygotic somatic mutation (mosaic); CALMs, café-au-lait macules; GCT, giant cell tumor of bone; H&N-PGL, head and neck paraganglioma; Hbs, hemangioblastomas; MTC, medullary thyroid carcinoma; Nfs, neurofibromas; PCC, pheochromocytoma; PNET, pituitary NET; Polycyth, polycythemia; PT, pancreatic tumor; RCC, renal cell carcinoma; Sls, skin leiomyomas; TAP-PGL, thoracic-abdominal paraganglioma; Uls, uterine leiomyomas.
Citation: Journal of Molecular Endocrinology 70, 3; 10.1530/JME-22-0167
Diagram illustrating the frequency of clinical manifestations related to the presence of mutations in the PPGL genes. *Postzygotic somatic mutation (mosaic); CALMs, café-au-lait macules; GCT, giant cell tumor of bone; H&N-PGL, head and neck paraganglioma; Hbs, hemangioblastomas; MTC, medullary thyroid carcinoma; Nfs, neurofibromas; PCC, pheochromocytoma; PNET, pituitary NET; Polycyth, polycythemia; PT, pancreatic tumor; RCC, renal cell carcinoma; Sls, skin leiomyomas; TAP-PGL, thoracic-abdominal paraganglioma; Uls, uterine leiomyomas.
Citation: Journal of Molecular Endocrinology 70, 3; 10.1530/JME-22-0167
PPGL genes associated with a pseudohypoxic signature (cluster 1)
The SDHx genes (SDHA, SDHB, SDHC, and SDHD) encode the four subunits of the succinate dehydrogenase complex, a component of both the mitochondrial respiratory chain (complex II) and the TCA cycle. In addition, a fifth gene, SDHAF2, encodes a mitochondrial assembly factor responsible for the flavination of SDHA, essential for the function of the aforementioned complex. In the last decade, other genes belonging or related to the TCA cycle have also been identified to be involved in the susceptibility to develop PPGL: FH, MDH2, SLC25A11, GOT2,and DLST (Remacha et al. 2017, Buffet et al. 2018, Remacha et al. 2019). Most PPGLs associated with mutations in TCA cycle genes exhibit a pseudohypoxic transcriptional profile, global DNA hypermethylation, and activation of angiogenesis signaling (Letouze et al. 2013, Cascon et al. 2019).
SDH genes
Mutations in any of the SDH genes cause accumulation of succinate due to defects in the activity of the complex, detected by the absence of SDHB protein by immunohistochemical (IHC) techniques. In fact, this technique is used to select patients as candidates for carrying mutations in the SDH genes, as well as to interpret the significance of a variant of unknown significance (VUS) in any of these SDH genes. Moreover, negative staining for SDHB and SDHA will focus the genetic study on SDHA (Korpershoek et al. 2011, Papathomas et al. 2015). If SDHB IHC results positive, the genetic diagnosis should be focused on other genes, excluding the SDH genes. Tumors associated with mutations in the SDH genes show a ‘CpG island methylator phenotype’ (CIMP), which is the consequence of the accumulation of the succinate, leading to inhibition of multiple alpha-ketoglutarate-dependent dioxygenases, and dysregulation of DNA and histone methylation (Letouze et al. 2013).
Mutations in the SDHD gene are the cause of the familial paragangliomas-1 (PGL1) tumor predisposition syndrome (OMIM 168000). Patients develop tumors mainly in the H&N region (84% of cases), and in the thoraco-abdominal region (up to 22%), and 12–24% may also develop PCC. Penetrance is 86% at 50 years of age (Andrews et al. 2018), and the gene follows an autosomal dominant pattern of inheritance, modified by maternal imprinting. This means that mutation carriers will develop the disease if they inherit the mutation from their father, although there are some exceptions (Burnichon et al. 2017).
SDHB gene mutation carriers present familial paragangliomas-4 (PGL4, OMIM 115310), associated with the development of thoraco-abdominal PGLs, H&N PGLs, and PCC in 67%, 27%, and 17–29% of the patients, respectively. SDHB mutations are found in around 8–10% of PPGL patients and have a very low penetrance (30% at age of 80 years). It is not uncommon to find patients carrying SDHB mutations with a single retroperitoneal tumor and no family history of the disease (Andrews et al. 2018). All genetic study algorithms for PPGL include SDHB because it is considered to be a poor prognostic factor. However, a recent study aiming to validate prognostic parameters of overall survival in metastatic PPGL (mPPGL) patients using a well-characterized series of 169 cases was not able to confirm SDHB as a major prognostic factor (Hescot et al. 2019).
Mutations in SDHC, found in less than 2% of PPGL patients, are the cause of hereditary paragangliomas-3 (PGL3, OMIM 605373) that lead to the development of H&N PGLs (93% of cases) and are associated with a low metastatic risk. SDHC mutations have incomplete penetrance, the mean age at diagnosis is 38 years, and only 25% of cases develop multiple PGLs or have a family history of the disease.
SDHA likely pathogenic or pathogenic germline variants are identified in up to 10% of patients with PPGL (Hanson et al. 2022). Most of the mutation carriers present with an apparently sporadic tumor without a relevant family history. These patients inherit the germline alteration from parents without clinical features of the disease (Casey et al. 2017a ). SDHA mutation carriers (PGL5; OMIM 614165) develop PPGLs at any location, with a penetrance at the age of 70 years estimated to be 10% (van der Tuin et al. 2018), and with a poorly established risk of developing metastasis. The low penetrance associated with SDHA explains the presence of gene variants in the control population. For this reason, it is especially relevant to use complementary assays (i.e. SDHA IHC) (Korpershoek et al. 2011) to predict the pathogenicity of the genetic changes detected in PPGL patients.
About 4% of patients carrying SDHB mutations develop renal tumors (Andrews et al. 2018). In addition, carriers of defects in the SDH genes may develop PGLs and gastrointestinal stromal tumors (CSS; OMIM 606864) or PGLs and pulmonary chondromas (CT; OMIM 604287). While CSS affects both genders equally and is inherited in an autosomal dominant manner with incomplete penetrance, CT may present as a mosaic disorder. Although both syndromes are due to SDH deficiency, CSS is caused by germline mutations that inactivate the SDH subunits, whereas CT is primarily caused by a particular methylation pattern of the SDHC gene and may be caused by germline mosaicism of this alteration (Pitsava et al. 2021).
Patients with germline mutations in SDH may also develop pituitary neuroendocrine tumors (pituitary NETs). The co-existence of PPGLs and pituitary NETs is known as 3PA. SDH-deficient pituitary NETs as part of 3PAs are more frequently macroadenomas and they commonly exhibit different phenotypes within the same family, such as prolactinomas, somatotropinomas, and non-functional adenomas (Xekouki et al. 2019).
SDHAF2 mutations have been described in young patients with multiple H&N PGLs or with previous family histories (PGL2; OMIM 601650). Mutations in SDHAF2 explain less than 1% of cases, and its genetic study is indicated in patients with H&N PGLs, with negative IHC for SDHB, and negative in the screening for the rest of the SDH genes (Bayley et al. 2010).
Other TCA-related genes
Mutations in the FH gene lead to fumarate accumulation in the TCA cycle, CIMP, and HIF stabilization. Clinically, metastatic phenotype and multiple tumors were frequent in patients with FH mutations (Letouze et al. 2013, Castro-Vega et al. 2014). The gene is also known to be responsible for hereditary leiomyomatosis (cutaneous and uterine) and papillary renal cancer type 2 (HLRCC) syndrome (OMIM 150800), which follows an autosomal dominant mode of inheritance. Although there are still few cases described so far with PPPGL caused by FH mutations, there seems to be no relationship between the type and location of the mutations in the gene and the development of hereditary PPGL or HLRCC.
The MDH2 gene encodes another enzyme of the TCA cycle, and it is involved in the reversible conversion of malate to oxaloacetate. As occurs with SDH- and FH-related PPGLs, tumors associated with mutations in MDH2 also show CIMP (Cascon et al. 2015). Mutations in MDH2 (less than 1% of patients) tend to appear in individuals with multiple PGLs, noradrenergic phenotype, metastatic disease, and incomplete penetrance (Cascon et al. 2015, Calsina et al. 2018).
The SLC25A11 gene encodes one of the members of the malate–asparate shuttle that is involved in the exchange between α-ketoglutarate and other dicarboxylates. It catalyzes the transport of 2-oxoglutarate across the inner mitochondrial membrane in an electroneutral exchange for malate. Mutations in SLC25A11 account for 1% of patients (PGL6; OMIM 618464), are mainly associated with thoraco-abdominal PGL, and present a marked risk of developing metastases (Buffet et al. 2018).
DLST encodes the E2 subunit of the mitochondrial alpha-ketoglutarate (αKG) dehydrogenase (OGDH) complex, which catalyzes the overall conversion of αKG to succinyl-CoA and CO2 in the TCA cycle. Mutations in DLST cause the PGL7 tumor predisposition syndrome (OMIM 618475) and have been found in 0.6–3% of PPGL patients (all of them with multiple tumors in the thoraco-abdominal region) without mutations in other PPGL-related genes (Remacha et al. 2019, Buffet et al. 2021).
VHL-associated PPGL
The VHL syndrome (OMIM 193300), caused by mutations in the VHL gene, is a rare hereditary tumor syndrome (incidence of 1 in 36,000 individuals) with variable clinical manifestation. There are two clinical subtypes of VHL disease, and only subtype 2 develops PCC. Approximately 20% of patients with VHL develop PCC or PGL (sympathetic and parasympathetic), although PGL is much less frequent. VHL-related tumors are associated with typical noradrenergic biochemical phenotype, are multifocal or bilateral in 43–45% of cases, and metastatic in less than 5% of the cases (Eisenhofer et al. 2004, Gimm et al. 2004). The median age at diagnosis of VHL-related PPGLs is 29 years, which is lower than the observed for other syndromes. It is particularly relevant for genetic testing since between 12 and 32% of patients with PCC diagnosed during childhood are found to carry a germline mutation in VHL. It is noteworthy that PCC (mostly) and PGL (rarely) are the first symptoms in 30–50% of patients with VHL. The latter makes VHL mutation screening crucial in patients diagnosed before the age of 18 years. Additionally, VHL has a high de novo mutation rate of ~20% (Sgambati et al. 2000, Evans et al. 2010); hence, mutation testing of this gene is advised also for patients that appear to be sporadic and non-syndromic. Regarding this, histological features that may indicate an underlying VHL aberrancy have been described (Juhlin & Mete 2022).
PPGL genes related to kinase signature (cluster 2)
MEN2-associated PCCs
MEN2 (OMIM 171400) has an estimated annual incidence of 0.5 × 10−6 and a prevalence of 1 out of 30,000 individuals. MEN2 is an autosomal dominant inherited disease caused by germline mutations in the RET proto-oncogene, which encodes a receptor tyrosine kinase for members of the glial cell line-derived neurotrophic factor family of extracellular signaling molecules. RET-activating mutations are associated with variable clinical manifestation and penetrance, according to their transforming capacity. MEN2 patients may develop MTC, PCC, and primary hyperparathyroidism; the latter resulting from multiglandular disease or from parathyroid adenomas. This syndrome is classified into three clinical subtypes: MEN2a, MEN2b, and familial MTC, each one defined according to the combination of pathologies developed by the affected individuals.
Approximately 50% of MEN2 patients develop PCC in their lifetime, and the mean age at diagnosis is 35 years. Between 50 and 80% of PCC developed by MEN2 patients are bilateral, often exhibit an adrenergic biochemical phenotype, and only a small percentage of the tumors metastasize. A PCC is the first manifestation of MEN2 in only 12–15% of cases, and therefore, RET mutations explain relatively few cases of non-syndromic disease (around 5%), compared to other syndromes (Milos et al. 2008). A study carried out by an international consortium reported the presence of RET (and VHL) somatic mutations in 4–5% of sporadic PCCs (Curras-Freixes et al. 2017). This finding highlights the importance of working with both germline and tumor DNA from the same patient in order to provide a comprehensive genetic diagnosis.
PCCs associated with NF1
NF1, formerly known as von Recklinghausen disease (OMIM 162200), is a common hereditary disease (incidence of 1 in 2500–3300 newborns) caused by germline mutations in the NF1 gene leading to dysregulation of the RAS/MAPK pathway. An estimated 0.1–5.7% of NF1 patients develop PPGL, although this number is 3.3–13% based on autopsy studies. NF1-associated PPGLs appear at a later age (mean age of 41 years), are usually unilateral and infrequently extra-adrenal, and manifest metastatic disease more frequently (up to 10%) than VHL and MEN2 cases. A significant percentage of sporadic PCCs (14–20% of tumors that appear to be sporadic) are caused by somatic mutations in NF1 (Curras-Freixes et al. 2017). To note, NF1 can show genetic mosaicism further complicating the diagnosis of the disease. This finding highlights that, as happens with RET, in order to conduct a thorough genetic diagnostic useful for genetic counseling, it is essential to evaluate both normal and tumor tissue from the same patient.
PCC related to TMEM127 gene mutations
An integrated investigation using different genomic platforms, including linkage analysis, gene expression profiling, and mapping of chromosomal gains and losses, led to the discovery of TMEM127 (2q11) as a new PCC susceptibility gene in 2010 (Qin et al. 2010). TMEM127, acting as a classic tumor suppressor gene, encodes a transmembrane protein that modulates mTOR complex 1, which stimulates cell proliferation, protein translation, and the phosphorylation of 4EBP1 and S6K. Mutations in this gene account for 2% of patients with PCC or PGL (OMIM 171300). Most patients harboring mutations in TMEM127 develop PCC (85.5%), although PGL (9%) and renal cell carcinoma (5.4%) are also detected, either alone or in combination with PCC, which should be taken into account during the monitoring and treatment of carriers (Casey et al. 2017b, Deng et al. 2017, Armaiz-Pena et al. 2021).
The penetrance of mutations in this gene is not complete, as suggested by a mean age at diagnosis of 45 years. Though one-third of the cases have multiple tumors, family history is reported only in 15.4% of mutation carriers (Armaiz-Pena et al. 2021). Incomplete penetrance can mask the underlying inherited condition and cause patients to sometimes fail to meet screening criteria for genetic testing.
PCC related to MAX gene mutations
MAX encodes a component of the MYC signaling pathway, and it is essential for controlling cell proliferation, differentiation, and apoptosis. Less than 2% of patients have germline mutations in MAX (OMIM 154950), 21% develop thoraco-abdominal PGLs in addition to PCC, and though the mean age at diagnosis is 32 years, 21% of patients are diagnosed at pediatric age. Similar to SDHD and SDHAF2, MAX exhibits a dominant autosomal inheritance with preferential paternal transmission of the disease making it difficult to identify a family history. Mutations in MAX are associated with a distinctive biochemical profile with elevated levels of normetanephrins and normal or slightly increased levels of metanephrins (Burnichon et al. 2012b ). In addition, pituitary NETs, pancreatic NET, erythrocytosis, and renal oncocytomas have been reported in the setting of germline MAX alterations (Burnichon et al. 2012b , Korpershoek et al. 2016, Petignot et al. 2020, Mamedova et al. 2021).
Postzygotic mutations in PPGLs: a twist on the genetic complexity of the disease
The presence of multiple tumors without family history is not always explained by germline mutations. This is the case of postzogotic somatic mutations in EPAS1 and H3-3A that have been reported in PPGLs (Zhuang et al. 2012, Toledo et al. 2016). The development of polycythemia and somatostatinomas (in EPAS1-mutated cases) and giant cell tumors of bone (in H3-3A-mutated cases) can guide the genetic diagnosis in these patients. Once more, the study of the tumor tissue for the identification of this uncommon mechanism is essential.
Sporadic PPGLs
Somatic mutations in some of the genes involved in the hereditary predisposition to PPGL (NF1, VHL, RET, and MAX) are frequent in PPGLs, especially in PCCs. Less common is the presence of somatic mutations, including epi-mutations (i.e. specific DNA methylation), affecting the SDH genes (van Nederveen et al. 2007, Richter et al. 2016, Remacha et al. 2017). Furthermore, somatic mutations in HRAS or FGFR1 mainly, and BRAF rarely, drive tumor development in a significant percentage of PPGLs included in the transcriptional cluster 2 (Fishbein et al. 2017). The TCGA project identified the existence of a third cluster, which grouped PCCs carrying CSDE1 somatic mutations together with cases showing rearrangements involving the MAML3 gene (Fishbein et al. 2017). This study suggested MAML3 fusion as a novel marker associated with metastatic disease since 37.5% of tumors carrying these rearrangements developed metastases. Further studies are needed to decipher the mechanisms driving the aggressive behavior of this tumor type.
Genetic diagnosis of PPGL
Genetic testing is recommended for all patients with PPGLs and for family members of patients with hereditary forms of these tumors, as the identification of the underlying mutation has important implications in patient management and genetic counselling. However, given the genetic heterogeneity that characterizes this disease, the gene-by-gene study is not a reasonable option for the genetic diagnosis of PPGL patients. Currently, targeted next-generation sequencing (NGS) custom panels are the gold standard for the genetic diagnosis of PPGL, due to its high throughput, accuracy, speed, and flexibility (Curras-Freixes et al. 2017, Ben Aim et al. 2019). The NGS assay significantly improves the performance of PPGL genetic testing, increasing the rate of mutations identified (NGS in PPGL (NGSnPPGL) Study Group et al. 2017).
As reviewed earlier, not all known genes related to the development of PPGL explain the same proportion of patients (Fig. 3), and therefore, the genetic diagnosis could be carried out in two steps. In the first step, the genes that explain most PPGLs should be analyzed, and after that, a study using a complementary panel including the last identified genes (each of which explains less than 1% of the cases) may be performed. However, NGS assays are robust enough to consider analyzing all genes simultaneously, thus saving important resources. This methodology reduces the turnaround time in molecular diagnosis but entails the identification of a considerable number of VUS. The accurate interpretation of VUS is one of the key labor-intensive tasks associated with NGS, and the most challenging part of this process, as these types of variants should not be used for clinical management of patients and families. The NGS in PPGL Study Group has published a consensus statement on NGS PPGL diagnosis which includes a framework for variant categorization based on multiple criteria to classify each variant into one from five classes relying on the likelyhood of pathogenicity ('pathogenic', 'likely pathogenic', 'VUS', 'likely not pathogenic', and 'not pathogenic') (NGS in PPGL (NGSnPPGL) Study Group et al. 2017). Several tests carried out on tumor tissue as well as functional studies using in vitro models have been reported for the classification of VUS in the different susceptibility genes. If tumor tissue is available, LOH (loss of heterozigosity) events or a negative SDHB IHC in SDHx VUS, are widely used tools in VUS classification in most PPGL diagnostic laboratories, (Knudson & Strong 1972, Papathomas et al. 2015). Moreover, the use of other specific IHC stainings for certain target genes has also been reported. This is the case for alterations in SDHA (negative SDHA IHC), MAX (negative MAX IHC), FH (positive 2-SC IHC), cluster 1 PPGLs (positive alpha-inhibin IHC), and TCA-cycle genes (negative 5-hmC IHC and positive DLST IHC) (Comino-Mendez et al. 2011, Korpershoek et al. 2011, Castro-Vega et al. 2014, Remacha et al. 2019, Mete et al. 2021). Loss of enzymatic activity or accumulation of certain metabolites in the case of tumors carrying a VUS in a TCA-cycle gene could also be a useful marker (Letouze et al. 2013, Cascon et al. 2015, Remacha et al. 2017). When tumor tissue is unavailable, some in vitro assays have been proposed to test the pathogenicity of VUS from MAX, TMEM127, SDHx, MDH2, or DLST genes (Comino-Mendez et al. 2015, Kim et al. 2015, Calsina et al. 2018, Remacha et al. 2019, Flores et al. 2020, Wallace et al. 2020).
Distribution of mutations in PPGL susceptibility genes: 40% of PPGL patients carry germline mutations in predisposition genes; 30% of PPGLs are spordic and carry somatic mutations; 30% of patients do not carry mutations in any known gene, including apparently sporadic cases and patients with clinical features of heritability.
Citation: Journal of Molecular Endocrinology 70, 3; 10.1530/JME-22-0167
Distribution of mutations in PPGL susceptibility genes: 40% of PPGL patients carry germline mutations in predisposition genes; 30% of PPGLs are spordic and carry somatic mutations; 30% of patients do not carry mutations in any known gene, including apparently sporadic cases and patients with clinical features of heritability.
Citation: Journal of Molecular Endocrinology 70, 3; 10.1530/JME-22-0167
Distribution of mutations in PPGL susceptibility genes: 40% of PPGL patients carry germline mutations in predisposition genes; 30% of PPGLs are spordic and carry somatic mutations; 30% of patients do not carry mutations in any known gene, including apparently sporadic cases and patients with clinical features of heritability.
Citation: Journal of Molecular Endocrinology 70, 3; 10.1530/JME-22-0167
As the number of new susceptibility genes increases and their role in the disease becomes more clear, there is an urgent need to develop additional screening methods for each gene, which may facilitate future functional assessments. To note, it is also important to maintain adequate communication between physicians and genetic diagnosis specialists in order to obtain updated clinical information and facilitate access to patients’ samples.
Taking into account the expected somatic mutation rate, together with the probability of finding a germline mutation that explains the pathology, the most efficient way to perform genetic diagnosis is with tumor DNA. Using this approach, we can identify both germline and somatic mutations in a single experiment, and thereafter determine whether the mutation detected in the tumor is also present in the patient's blood. The major benefit of identifying a somatic mutation is to avoid family surveillance while finding a germline mutation offers the opportunity to identify patients at risk of developing a syndrome. In this way, clinical follow-up can be tailored to each individual case. Due to the low penetrance associated with mutations in some of the PPGL susceptibility genes, the utility of offering predictive genetic testing to relatives has been questioned (Hanson et al. 2022). However, the risks associated with low penetrance genes could only be established if genetic studies on at-risk family members are systematically performed. International initiatives are needed to reach a consensus on the specific clinical follow-up to be offered to each patient according to the gene responsible for their disease. A good example of this is the consensus focused on the management of asymptomatic SDHx mutation carriers (Amar et al. 2021).
Beyond genetic counseling: is it possible to predict metastasis?
Approximately 5% of patients with H&N PGLs, 10% with PCC, and 40% with sympathetic PGL will develop metastases in their lifetime (Ayala-Ramirez et al. 2011, Patel et al. 2020). While synchronous metastases are common (35–50% of cases), metachronous metastases may develop months to a decade after diagnosis of the primary tumor (Baudin et al. 2014), so these patients require long-term follow-up. According to the latest World Health Organization classification of endocrine tumors, all PPGLs are considered to have a ‘potential metastatic risk’, replacing the previous term ‘malignant’ applied to only a subset of tumors (Mete et al. 2022). The metastatic niche mainly affects bones (64%), lymph nodes (40%), lungs (29%), and liver (26%), whereas metastases are rarely identified in the pancreas, breast, CNS, or skin (Hescot et al. 2019). Five years overall survival ranges from 40 to 77% (De Filpo et al. 2021), and median survival is approximately 6 years (Dahia et al. 2020), underscoring the highly variable rate of progression of mPPGL.
Risk factors for predicting the development of metastases in patients with PPGL are scarce, imprecise, and remain poorly defined. The identification of prognostic markers in PPGL has been hampered by the low prevalence of the disease. However, there are some clinical and pathological factors that have been associated with increased risk of metastasis: the development of thoraco-abdominal PGL, large tumor size (≥5 cm in greatest dimension), invasion of surrounding tissues, elevated plasma methoxytyramine (Lam 2017, Lenders et al. 2020, Pamporaki et al. 2022), or the Ki67 labeling index (Kimura et al. 2014). An additional marker is the niche where metastatic disease develops. Thus, the presence of only bone metastases is likely to be more indolent (median survival of 12 years) compared to non-bone metastases (median survival of 7.5 years) (Ayala-Ramirez et al. 2013).
Molecular risk markers of metastasis
In the 2000s, germline mutations in SDHB were uncovered as a poor prognostic risk factor (Gimenez-Roqueplo et al. 2003, Amar et al. 2007). Later, with the identification of more PPGL susceptibility genes, it was shown that other genes related to the TCA cycle (SDHA, SDHC, SDHD, SDHF2, FH, MDH2, SLC25A11, and GOT2) were also associated with a higher risk for metastasis (Castro-Vega et al. 2014, Cascon et al. 2015, Remacha et al. 2017, Buffet et al. 2018, Calsina et al. 2018). These PPGLs are classified in the C1A pseudohypoxic transcriptional subgroup, and they exhibit global DNA hypermethylation, gene expression signatures characteristic of neuroendocrine to mesenchymal transition (neuroendoMT), and activation of angiogenesis/hypoxia signaling (Loriot et al. 2012, Cascon et al. 2019). In 2013, Letouzé et al. established that the inactivation of the succinate dehydrogenase activity in PPGLs with SDHx mutations led to a marked accumulation of succinate (Letouze et al. 2013), which inhibits the activity of multiple 2-oxoglutarate-dependent dioxygenases (Xiao et al. 2012), promoting a global DNA hypermethylation. Moreover, inhibition of certain prolyl-hydroxylases promotes the HIF1-alpha and HIF2-alpha stabilization, and subsequent expression of hypoxia-inducible genes (Pollard et al. 2005, Morin et al. 2020). Hypoxia is a known inducer of epithelial-to-mesenchymal transition and metastasis, and pseudohypoxia has been considered a driver mechanism for invasive and metastatic behavior of tumors driven by oncometabolites (Morin et al. 2020). In addition, new findings provide evidence that pseudohypoxia cooperates with hypermethylation in the induction of the neuroendoMT phenotype, suggesting synergistic requirements for tumor initiation and progression of aggressive SDHB-mutated tumors (Morin et al. 2020). The fact that only 50% of PPGLs with SDHB mutations develop metastases suggests that additional molecular event(s) confer properties involved in the tumor progression of mPPGL. In relation to the latter, one of the processes that have been recently proposed is the contribution of immortalization mechanisms to metastatic progression. Telomerase activation, whose enzymatic subunit is encoded by the TERT gene, is a well-known mechanism for the maintenance of telomere length in many tumor types. Several events have been linked to TERT reactivation, including TERT promoter mutations, TERT promoter rearrangements, TERT promoter hypermethlylation, and TERT DNA copy number gains. Alternative lengthening of telomeres (ALT) counteracts the absence of telomerase activation in tumors with a lack of TERT alterations. Recent studies suggest that the loss of ATRX is a major contributor to the ALT phenotype (Barthel et al. 2017, Fishbein et al. 2017, Dwight et al. 2018).
In 2019, Job et al. published a report analyzing systematically these immortalization events in a large and well-characterized tumor series (Job et al. 2019). This study describes that 70% of mPPGLs, including every metastatic case classified into subcluster C1A, become immortalized either by telomerase activation or by an ATRX loss of function mutation. The presence of these events appears to be more accurate in metastatic risk stratification than SDHB status and is strongly associated with both metastasis-free and overall survivals (Job et al. 2019). A more recent study describes alterations in an additional gene, NOP10, which is involved in telomere maintenance, to be associated with PPGL poor prognosis in combination with TERT or ATRX events (Monteagudo et al. 2021). Even though these alterations seem to be promising candidates for improving high-risk patient stratification and understanding metastatic transformation in mPPGLs, there is still room to evaluate if alterations in other genes involved in telomere maintenance are also involved in immortalization. Moreover, further studies are needed to clarify why ATRX and TERT mutational events are more frequent in some genotypes over others.
The use of omics in treatment selection
Despite the increasing knowledge about the genetics and molecular biology of PPGL, treatment options, especially against irresectable tumors, cases with high tumor burden status or metastatic disease, remain limited. It seems reasonable to propose PPGL treatment to be personalized and, therefore, it should rely on the identification of the disease-causing gene in each patient, given the existence of drugs that especially target critical points of the pathways involved in PPGL development (reviewed in Ilanchezhian et al. 2020, Sarkadi et al. 2022). However, it is challenging to draw clear conclusions about these treatments, considering the toxicity of some of the drugs and because most of the evidence come from small and/or retrospective trials (Ilanchezhian et al. 2020). Progress toward systemic therapies for patients with mPPGLs has been slow due to the absence of cell and animal models recapitulating mPPGL phenotype (Jimenez et al. 2017). Some of the pending tasks in mPPGL management are the identification of accurate biomarkers capable of predicting therapeutic response, as well as the optimization of clinical trials to test the effectiveness of monotherapy and combination therapies. These goals could only be achieved by expanding our knowledge of the molecular mechanisms behind PPGL pathogenesis (Pang et al. 2019). In the genomics era, integrative studies of clinical variables and multi-omics data on the same set of subjects can aid in the identification of biologically relevant features, and novel patient subgroups for tailored therapy and monitoring. They represent a significant step toward the development of precision medicine for patients with mPPGL. (Calsina et al. 2019). There is a need to incorporate tumor genetics and genomics into future clinical studies, which would lead to the identification of potential tumor vulnerabilities and to the development of effective molecular treatments tailored to stratified patients.
Declaration of interest
The authors declare no conflict of interest.
Funding
M.R.is funded by grants from the Instituto de Salud Carlos III (ISCIII), through the ‘Acción Estratégica en Salud’ (AES) (project PI20/01169), Paradifference Foundation, and the Pheipas Association. A.C. is funded by the Instituto de Salud Carlos III (ISCIII), through the ‘Acción Estratégica en Salud’ (AES) (project PI18/00454). M.M. and S.M. are supported by the Spanish Ministry of Science, Innovation and Universities ‘Formación del Profesorado Universitario—FPU’ fellowship with ID numbers FPU18/00064 and FPU19/04940, respectively. A.D.-T. is supported by the Centro de Investigacion Biomédica en Red de Enfermedades Raras (CIBERER).
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