Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas

Genetic and Molecular Biomarkers in Aggressive Pheochromocytomas and Paragangliomas

Pheochromocytomas and paragangliomas (PPGLs) are rare neoplasms producing catecholamines that occur as hereditary syndromes in 25-40% of cases. To date, PPGLs are no longer classified as benign and malignant tumors since any lesion could theoretically metastasize, even if it occurs only in a minority of cases (approximately 10-30%). Over the last decades, several attempts were made to develop a scoring system able to predict the risk of aggressive behavior at diagnosis, including the risk of metastases and disease recurrence; unfortunately, none of the available scores is able to accurately predict the risk of aggressive behavior, even including clinical, biochemical, and histopathological features. Thus, life-long follow-up is required in PPGL patients. Some recent studies focusing on genetic and molecular markers (involved in hypoxia regulation, gene transcription, cellular growth, differentiation, signaling pathways, and apoptosis) seem to indicate they are promising prognostic factors, even though their clinical significance needs to be further evaluated. The most involved pathways in PPGLs with aggressive behavior are represented by Krebs cycle alterations caused by succinate dehydrogenase subunits (SDHx), especially when caused by SDHB mutations, and by fumarate hydratase mutations that lead to the activation of hypoxia pathways and DNA hypermethylation, suggesting a common pathway in tumorigenesis. Conversely, PPGLs showing mutations in the kinase cascade (cluster 2) tend to display less aggressive behavior. Finally, establishing pathways of tumorigenesis is also fundamental to developing new drugs targeted to specific pathways and improving the survival of patients with metastatic disease. Unfortunately, the rarity of these tumors and the scarce number of cases enrolled in the available studies represents an obstacle to validating the role of molecular markers as reliable predictors of aggressiveness.

Ensemble Machine Learning Model Incorporating Radiomics and Body Composition for Predicting Intraoperative HDI in PPGL

CONTEXT

Intraoperative hemodynamic instability (HDI) can lead to cardiovascular and cerebrovascular complications during surgery for pheochromocytoma/paraganglioma (PPGL).

OBJECTIVES

We aimed to assess the risk of intraoperative HDI in patients with PPGL to improve surgical outcome.

METHODS

A total of 199 consecutive patients with PPGL confirmed by surgical pathology were retrospectively included in this study. This cohort was separated into 2 groups according to intraoperative systolic blood pressure, the HDI group (n = 101) and the hemodynamic stability (HDS) group (n = 98). It was also divided into 2 subcohorts for predictive modeling: the training cohort (n = 140) and the validation cohort (n = 59). Prediction models were developed with both the ensemble machine learning method (EL model) and the multivariate logistic regression model using body composition parameters on computed tomography, tumor radiomics, and clinical data. The efficiency of the models was evaluated with discrimination, calibration, and decision curves.

RESULTS

The EL model showed good discrimination between the HDI group and HDS group, with an area under the curve of (AUC) of 96.2% (95% CI, 93.5%-99.0%) in the training cohort, and an AUC of 93.7% (95% CI, 88.0%-99.4%) in the validation cohort. The AUC values from the EL model were significantly higher than the logistic regression model, which had an AUC of 74.4% (95% CI, 66.1%-82.6%) in the training cohort and an AUC of 74.2% (95% CI, 61.1%-87.3%) in the validation cohort. Favorable calibration performance and clinical applicability of the EL model were observed.

CONCLUSION

The EL model combining preoperative computed tomography-based body composition, tumor radiomics, and clinical data could potentially help predict intraoperative HDI in patients with PPGL.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Biochemical Assessment of Pheochromocytoma and Paraganglioma

Pheochromocytoma and paraganglioma (PPGL) require prompt consideration and efficient diagnosis and treatment to minimize associated morbidity and mortality. Once considered, appropriate biochemical testing is key to diagnosis. Advances in understanding catecholamine metabolism have clarified why measurements of the O-methylated catecholamine metabolites rather than the catecholamines themselves are important for effective diagnosis. These metabolites, normetanephrine and metanephrine, produced respectively from norepinephrine and epinephrine, can be measured in plasma or urine, with choice according to available methods or presentation of patients. For patients with signs and symptoms of catecholamine excess, either test will invariably establish the diagnosis, whereas the plasma test provides higher sensitivity than urinary metanephrines for patients screened due to an incidentaloma or genetic predisposition, particularly for small tumors or in patients with an asymptomatic presentation. Additional measurements of plasma methoxytyramine can be important for some tumors, such as paragangliomas, and for surveillance of patients at risk of metastatic disease. Avoidance of false-positive test results is best achieved by plasma measurements with appropriate reference intervals and preanalytical precautions, including sampling blood in the fully supine position. Follow-up of positive results, including optimization of preanalytics for repeat tests or whether to proceed directly to anatomic imaging or confirmatory clonidine tests, depends on the test results, which can also suggest likely size, adrenal vs extra-adrenal location, underlying biology, or even metastatic involvement of a suspected tumor. Modern biochemical testing now makes diagnosis of PPGL relatively simple. Integration of artificial intelligence into the process should make it possible to fine-tune these advances.

Applications of Deep Learning in Endocrine Neoplasms

Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multiple disease areas. In cancer, and particularly endocrine cancer, DL approaches have been shown to be useful in tasks ranging from tumor grading to gene expression prediction. This review summarizes the current state of DL research in endocrine cancer histopathology with an emphasis on experimental design, significant findings, and key limitations.

Metabolomics-A Tool to Find Metabolism of Endocrine Cancer

Clinical endocrinology entails an understanding of the mechanisms involved in the regulation of tumors that occur in the endocrine system. The exact cause of endocrine cancers remains an enigma, especially when discriminating malignant lesions from benign ones and early diagnosis. In the past few years, the concepts of personalized medicine and metabolomics have gained great popularity in cancer research. In this systematic review, we discussed the clinical metabolomics studies in the diagnosis of endocrine cancers within the last 12 years. Cancer metabolomic studies were largely conducted using nuclear magnetic resonance (NMR) and mass spectrometry (MS) combined with separation techniques such as gas chromatography (GC) and liquid chromatography (LC). Our findings revealed that the majority of the metabolomics studies were conducted on tissue, serum/plasma, and urine samples. Studies most frequently emphasized thyroid cancer, adrenal cancer, and pituitary cancer. Altogether, analytical hyphenated techniques and chemometrics are promising tools in unveiling biomarkers in endocrine cancer and its metabolism disorders.

Succinate dehydrogenase and MYC-associated factor X mutations in pituitary neuroendocrine tumours

Pituitary neuroendocrine tumours (PitNETs) associated with paragangliomas or phaeochromocytomas are rare. SDHx variants are estimated to be associated with 0.3-1.8% of PitNETs. Only a few case reports have documented the association with MAX variants. Prolactinomas are the most common PitNETs occurring in patients with SDHx variants, followed by somatotrophinomas, clinically non-functioning tumours and corticotrophinomas. One pituitary carcinoma has been described. SDHC, SDHB and SDHA mutations are inherited in an autosomal dominant fashion and tumorigenesis seems to adhere to Knudson's two-hit hypothesis. SDHD and SDHAF2 mutations most commonly have paternal inheritance. Immunohistochemistry for SDHB or MAX and loss of heterozygosity analysis can support the assessment of pathogenicity of the variants. Metabolomics is promising in the diagnosis of SDHx-related disease. Future research should aim to further clarify the role of SDHx and MAX variants or other genes in the molecular pathogenesis of PitNETs, including pseudohypoxic and kinase signalling pathways along with elucidating epigenetic mechanisms to predict tumour behaviour.

Succinate: A Serum Biomarker of SDHB-Mutated Paragangliomas and Pheochromocytomas

CONTEXT

Pheochromocytomas and paragangliomas (PPGL) are rare neuroendocrine tumors that are frequently associated with succinate dehydrogenase (SDH) germline mutations. When mutated, SDH losses its function, thus leading to succinate accumulation.

OBJECTIVE

In this study, we evaluated serum succinate levels as a new metabolic biomarker in SDHx-related carriers.

METHODS

Retrospective monocentric study of 88 PPGL patients (43 sporadic, 35 SDHB, 10 SDHA/C/D), 17 tumor-free familial asymptomatic carriers (13 SDHB, 4 SDHC/D), and 60 healthy controls. Clinical, biological, and imaging data were reviewed. Serum succinate levels (n = 280) were quantified by an ultra-performance liquid chromatography coupled to a tandem mass spectrometry method and correlated to SDHx mutational status, disease extension, and other biological biomarkers.

RESULTS

Serum succinate levels > 7 μM allowed identification of tumor-free asymptomatic SDHB-mutated cases compared to a healthy control group (100% specificity; 85% sensitivity). At PPGL diagnosis, SDHB-mutated patients had a significantly increased median succinate level (14 μM) compared to sporadic patients (8 μM) (P < 0.01). Metastatic disease extension was correlated to serum succinate levels (r = 0.81). In the SDHB group, patients displaying highest tumor burdens showed significant increased succinate levels compared to the sporadic group (P < 0.0001).

CONCLUSIONS

In this pilot study, we showed that serum succinate level is an oncometabolic biomarker that should be useful to identify SDHB-related carriers. Succinate levels are also a marker of metabolic tumor burden in patients with a metastatic PPGL and a potential marker of treatment response and follow-up.

Succinate Dehydrogenase, Succinate, and Superoxides: A Genetic, Epigenetic, Metabolic, Environmental Explosive Crossroad

Research focused on succinate dehydrogenase (SDH) and its substrate, succinate, culminated in the 1950s accompanying the rapid development of research dedicated to bioenergetics and intermediary metabolism. This allowed researchers to uncover the implication of SDH in both the mitochondrial respiratory chain and the Krebs cycle. Nowadays, this theme is experiencing a real revival following the discovery of the role of SDH and succinate in a subset of tumors and cancers in humans. The aim of this review is to enlighten the many questions yet unanswered, ranging from fundamental to clinically oriented aspects, up to the danger of the current use of SDH as a target for a subclass of pesticides.

Personalized Management of Pheochromocytoma and Paraganglioma

Pheochromocytomas/paragangliomas are characterized by a unique molecular landscape that allows their assignment to clusters based on underlying genetic alterations. With around 30% to 35% of Caucasian patients (a lower percentage in the Chinese population) showing germline mutations in susceptibility genes, pheochromocytomas/paragangliomas have the highest rate of heritability among all tumors. A further 35% to 40% of Caucasian patients (a higher percentage in the Chinese population) are affected by somatic driver mutations. Thus, around 70% of all patients with pheochromocytoma/paraganglioma can be assigned to 1 of 3 main molecular clusters with different phenotypes and clinical behavior. Krebs cycle/VHL/EPAS1-related cluster 1 tumors tend to a noradrenergic biochemical phenotype and require very close follow-up due to the risk of metastasis and recurrence. In contrast, kinase signaling-related cluster 2 tumors are characterized by an adrenergic phenotype and episodic symptoms, with generally a less aggressive course. The clinical correlates of patients with Wnt signaling-related cluster 3 tumors are currently poorly described, but aggressive behavior seems likely. In this review, we explore and explain why cluster-specific (personalized) management of pheochromocytoma/paraganglioma is essential to ascertain clinical behavior and prognosis, guide individual diagnostic procedures (biochemical interpretation, choice of the most sensitive imaging modalities), and provide personalized management and follow-up. Although cluster-specific therapy of inoperable/metastatic disease has not yet entered routine clinical practice, we suggest that informed personalized genetic-driven treatment should be implemented as a logical next step. This review amalgamates published guidelines and expert views within each cluster for a coherent individualized patient management plan.

New Insights on the Genetics of Pheochromocytoma and Paraganglioma and Its Clinical Implications

Simple Summary

Pheochromocytoma and paraganglioma (together PPGL) are rare neuroendocrine tumors that arise from chromaffin tissue and produce catecholamines. Approximately 40% of cases of PPGL carry a germline mutation, suggesting that they have a high degree of heritability. The underlying mutation influences the PPGL clinical presentation such as cell differentiation, specific catecholamine production, tumor location, malignant potential and genetic anticipation, which helps to better understand the clinical course and tailor treatment accordingly. Genetic testing for pheochromocytoma and paraganglioma allows an early detection of hereditary syndromes and facilitates a close follow-up of high-risk patients. In this review article, we present the most recent advances in the field of genetics and we discuss the latest guidelines on the surveillance of asymptomatic SDHx mutation carriers.

Abstract

Pheochromocytomas (PHEOs) and paragangliomas (PGLs) are rare neuroendocrine tumors that arise from chromaffin cells. PHEOs arise from the adrenal medulla, whereas PGLs arise from the neural crest localized outside the adrenal gland. Approximately 40% of all cases of PPGLs (pheochromocytomas/paragangliomas) are associated with germline mutations and 30–40% display somatic driver mutations. The mutations associated with PPGLs can be classified into three groups. The pseudohypoxic group or cluster I includes the following genes: SDHA, SDHB, SDHC, SDHD, SDHAF2, FH, VHL, IDH1/2, MHD2, EGLN1/2 and HIF2/EPAS; the kinase group or cluster II includes RET, NF1, TMEM127, MAX and HRAS; and the Wnt signaling group or cluster III includes CSDE1 and MAML3. Underlying mutations can help understand the clinical presentation, overall prognosis and surveillance follow-up. Here we are discussing the new genetic insights of PPGLs.

Precision Medicine in Phaeochromocytoma and Paraganglioma

Precision medicine is a term used to describe medical care, which is specifically tailored to an individual patient or disease with the aim of ensuring the best clinical outcome whilst reducing the risk of adverse effects. Phaeochromocytoma and paraganglioma (PPGL) are rare neuroendocrine tumours with uncertain malignant potential. Over recent years, the molecular profiling of PPGLs has increased our understanding of the mechanisms that drive tumorigenesis. A high proportion of PPGLs are hereditary, with non-hereditary tumours commonly harbouring somatic mutations in known susceptibility genes. Through detailed interrogation of genotype-phenotype, correlations PPGLs can be classified into three different subgroups or clusters. Thus, PPGLs serve as an ideal paradigm for developing, testing and implementing precision medicine concepts in the clinic. In this review, we provide an overview of PPGLs and highlight how detailed molecular characterisation of these tumours provides current and future opportunities for precision oncology.

Targeted Quantification of Carbon Metabolites Identifies Metabolic Progression Markers and an Undiagnosed Case of SDH-Deficient Clear Cell Renal Cell Carcinoma in a German Cohort

Renal cell carcinoma (RCC) is among the 10 most common cancer entities and can be categorised into distinct subtypes by differential expression of Krebs cycle genes. We investigated the predictive value of several targeted metabolites with regards to tumour stages and patient survival in an unselected cohort of 420 RCCs. Unsupervised hierarchical clustering of metabolite ratios identified two main clusters separated by α-ketoglutarate (α-KG) levels and sub-clusters with differential levels of the oncometabolite 2-hydroxyglutarate (2HG). Sub-clusters characterised by high 2HG were enriched in higher tumour stages, suggesting metabolite profiles might be suitable predictors of tumour stage or survival. Bootstrap forest models based on single metabolite signatures showed that lactate, 2HG, citrate, aspartate, asparagine, and glutamine better predicted the cancer-specific survival (CSS) of clear cell RCC patients, whereas succinate and α-ketoglutarate were better CSS predictors for papillary RCC patients. Additionally, this assay identifies rare cases of tumours with SDHx mutations, which are caused predominantly by germline mutations and which predispose to development of different neoplasms. Hence, analysis of selected metabolites should be further evaluated for potential utility in liquid biopsies, which can be obtained using less invasive methods and potentially facilitate disease monitoring for both patients and caregivers.

Significance of Alpha-inhibin Expression in Pheochromocytomas and Paragangliomas

Alpha-inhibin expression has been reported in pheochromocytomas and paragangliomas (PPGLs). We analyzed alpha-inhibin immunohistochemistry in 77 PPGLs (37 pheochromocytomas [PCCs] and 40 paragangliomas) and correlated the results with catecholamine profile, tumor size, Ki-67 labeling index, succinate dehydrogenase B subunit and carbonic anhydrase IX (CAIX) staining, and genetic pathogenesis. PPGLs were classified as pseudohypoxic cluster 1 disease with documented VHL mutation or SDHx mutation or biochemical phenotype, whereas NF1-driven and RET-driven PPGLs and those with a mature secretory (adrenergic or mixed adrenergic and noradrenergic) phenotype were classified as cluster 2 disease. The Cancer Genome Atlas data on INHA expression in PPGLs was examined. Alpha-inhibin was positive in 43 PPGLs (56%). Ki-67 labeling indices were 8.07% and 4.43% in inhibin-positive and inhibin-negative PPGLs, respectively (P<0.05). Alpha-inhibin expression did not correlate with tumor size. Alpha-inhibin was expressed in 92% of SDHx-related and 86% of VHL-related PPGLs. CAIX membranous staining was found in 8 of 51 (16%) tumors, including 1 SDHx-related PCC and all 5 VHL-related PCCs. NF1-driven and RET-driven PPGLs were negative for alpha-inhibin and CAIX. Alpha-inhibin was expressed in 77% of PPGLs with a pseudohypoxia signature, and 20% of PPGLs without a pseudohypoxia signature (P<0.05). PPGLs with a mature secretory phenotype were negative for CAIX. The Cancer Genome Atlas data confirmed higher expression of INHA in cluster 1 than in cluster 2 PPGLs. This study identifies alpha-inhibin as a highly sensitive (90.3%) marker for SDHx/VHL-driven pseudohypoxic PPGLs. Although CAIX has low sensitivity, it is the most specific biomarker of VHL-related pathogenesis. While alpha-inhibin cannot replace succinate dehydrogenase B subunit immunohistochemistry for detection of SDHx-related disease, it adds value in prediction of cluster 1 disease. Importantly, these data emphasize that alpha-inhibin is not a specific marker of adrenal cortical differentiation, as it is also expressed in PCCs.

What Have We Learned from Molecular Biology of Paragangliomas and Pheochromocytomas?

Recent advances in molecular genetics and genomics have led to increased understanding of the aetiopathogenesis of pheochromocytomas and paragangliomas (PPGLs). Thus, pan-genomic studies now provide a comprehensive integrated genomic analysis of PPGLs into distinct molecularly defined subtypes concordant with tumour genotypes. In addition, new embryological discoveries have refined the concept of how normal paraganglia develop, potentially establishing a developmental basis for genotype-phenotype correlations for PPGLs. The challenge for modern pathology is to translate these scientific discoveries into routine practice, which will be based largely on histopathology for the foreseeable future. Here, we review recent progress concerning the cell of origin and molecular pathogenesis of PPGLs, including pathogenetic mechanisms, genetic susceptibility and molecular classification. The current roles and tools of pathologists are considered from a histopathological perspective, including differential diagnoses, genotype-phenotype correlations and the use of immunohistochemistry in identifying hereditary predisposition and validating genetic variants of unknown significance. Current and potential molecular prognosticators are also presented with the hope that predictive molecular biomarkers will be integrated into risk stratification scoring systems to assess the metastatic potential of these intriguing neoplasms and identify potential drug targets.

Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors

CONTEXT

Pheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at metabolome level have been described after surgical PPGL removal. The value of metabolomics for the diagnosis of PPGL has not been studied yet.

OBJECTIVE

Evaluation of quantitative metabolomics as a diagnostic tool for PPGL.

DESIGN

Targeted metabolomics by liquid chromatography-tandem mass spectrometry of plasma specimens and statistical modeling using ML-based feature selection approaches in a clinically well characterized cohort study.

PATIENTS

Prospectively enrolled patients (n=36, 17 female) from the Prospective Monoamine-producing Tumor Study (PMT) with hormonally active PPGL and 36 matched controls in whom PPGL was rigorously excluded.

RESULTS

Among 188 measured metabolites, only without considering false discovery rate, 4 exhibited statistically significant differences between patients with PPGL and controls (histidine p=0.004, threonine p=0.008, lyso PC a C28:0 p=0.044, sum of hexoses p=0.018). Weak, but significant correlations for histidine, threonine and lyso PC a C28:0 with total urine catecholamine levels were identified. Only the sum of hexoses (reflecting glucose) showed significant correlations with plasma metanephrines.By using ML-based feature selection approaches, we identified diagnostic signatures which all exhibited low accuracy and sensitivity. The best predictive value (sensitivity 87.5%, accuracy 67.3%) was obtained by using Gradient Boosting Machine Modelling.

CONCLUSIONS

The diabetogenic effect of catecholamine excess dominates the plasma metabolome in PPGL patients. While curative surgery for PPGL led to normalization of catecholamine-induced alterations of metabolomics in individual patients, plasma metabolomics are not useful for diagnostic purposes, most likely due to inter-individual variability.

An update on adult forms of hereditary pheochromocytomas and paragangliomas

PURPOSE OF REVIEW

Pheochromocytomas and paragangliomas (PPGL) display a strong genetic determinism with 40% of inherited forms. The purpose of this review is to provide an update on current knowledge on adult forms of hereditary PPGL and their management.

RECENT FINDINGS

PPGL are genetically-driven in 70% of cases, with germline and/or somatic mutations identified in more than 20 genes. Although eight new susceptibility genes have recently emerged, mutations on SDHx genes remain the most frequent. In addition to SDHB, mutations in SLC25A11, FH and MDH2 may predispose to a metastatic disease and somatic alterations including TERT and ATRX mutations, and the differential expression on noncoding RNAs are also associated with the occurrence of metastases.The biochemical diagnosis remains the mainstay of functional PPGL and does not differ between hereditary PPGL while the choice of the best nuclear imaging approach is dictated by the tumor type and can be influenced by the presence of a germline mutation (18F-DOPA PET/CT for cluster 2 mutation and Ga-DOTATATE PET/CT for cluster 1 mutation).

SUMMARY

A systematic genetic testing and counselling is recommended for all PPGL patients and should lead to conservative surgery and an adapted follow up, in case of hereditary form.

Immunohistochemistry and Mutation Analysis of SDHx Genes in Carotid Paragangliomas

Carotid paragangliomas (CPGLs) are rare neuroendocrine tumors often associated with mutations in SDHx genes. The immunohistochemistry of succinate dehydrogenase (SDH) subunits has been considered a useful instrument for the prediction of SDHx mutations in paragangliomas/pheochromocytomas. We compared the mutation status of SDHx genes with the immunohistochemical (IHC) staining of SDH subunits in CPGLs. To identify pathogenic/likely pathogenic variants in SDHx genes, exome sequencing data analysis among 42 CPGL patients was performed. IHC staining of SDH subunits was carried out for all CPGLs studied. We encountered SDHx variants in 38% (16/42) of the cases in SDHx genes. IHC showed negative (5/15) or weak diffuse (10/15) SDHB staining in most tumors with variants in any of SDHx (94%, 15/16). In SDHA-mutated CPGL, SDHA expression was completely absent and weak diffuse SDHB staining was detected. Positive immunoreactivity for all SDH subunits was found in one case with a variant in SDHD. Notably, CPGL samples without variants in SDHx also demonstrated negative (2/11) or weak diffuse (9/11) SDHB staining (42%, 11/26). Obtained results indicate that SDH immunohistochemistry does not fully reflect the presence of mutations in the genes; diagnostic effectiveness of this method was 71%. However, given the high sensitivity of SDHB immunohistochemistry, it could be used for initial identifications of patients potentially carrying SDHx mutations for recommendation of genetic testing.