Adrenocortical tumors and pheochromocytoma/paraganglioma initially mistaken as neuroblastoma — experiences from the GPOH-MET registry

In children and adolescents, neuroblastoma (NBL), pheochromocytoma (PCC), and adrenocortical tumors (ACT) can arise from the adrenal gland. It may be difficult to distinguish between these three entities including associated extra-adrenal tumors (paraganglioma, PGL). Precise discrimination, however, is of crucial importance for management. Biopsy in ACT or PCC is potentially harmful and should be avoided whenever possible. We herein report data on 10 children and adolescents with ACT and five with PCC/PGL, previously mistaken as NBL. Two patients with adrenocortical carcinoma died due to disease progression. Two (2/9, missing data in one patient) patients with a final diagnosis of ACT clearly presented with obvious clinical signs and symptoms of steroid hormone excess, while seven patients did not. Blood analyses indicated increased levels of steroid hormones in one additional patient; however, urinary steroid metabolome analysis was not performed in any patient. Two (2/10) patients underwent tumor biopsy, and in two others tumor rupture occurred intraoperatively. In 6/10 patients, ACT diagnosis was only established by a reference pediatric pathology laboratory. Four (4/5) patients with a final diagnosis of PCC/PGL presented with clinical signs and symptoms of catecholamine excess. Urine tests indicated possible catecholamine excess in two patients, while no testing was carried out in three patients. Measurements of plasma metanephrines were not performed in any patient. None of the five patients with PCC/PGL received adrenergic blockers before surgery. In four patients, PCC/PGL diagnosis was established by a local pathologist, and in one patient diagnosis was revised to PGL by a pediatric reference pathologist. Genetic testing, performed in three out of five patients with PCC/PGL, indicated pathogenic variants of PCC/PGL susceptibility genes. The differential diagnosis of adrenal neoplasias and associated extra-adrenal tumors in children and adolescents may be challenging, necessitating interdisciplinary and multidisciplinary efforts. In ambiguous and/or hormonally inactive cases through comprehensive biochemical testing, microscopical complete tumor resection by an experienced surgeon is vital to preventing poor outcome in children and adolescents with ACT and/or PCC/PGL. Finally, specimens need to be assessed by an experienced pediatric pathologist to establish diagnosis

Targeted metabolomics as a tool in discriminating endocrine from primary hypertension

Context Identification of patients with endocrine forms of hypertension (EHT) (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL], and Cushing syndrome [CS]) provides the basis to implement individualized therapeutic strategies. Targeted metabolomics (TM) have revealed promising results in profiling cardiovascular diseases and endocrine conditions associated with hypertension. Objective Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability. Methods Retrospective analyses of PHT and EHT patients from a European multicenter study (ENSAT-HT). TM was performed on stored blood samples using liquid chromatography mass spectrometry. To identify discriminating metabolites a “classical approach” (CA) (performing a series of univariate and multivariate analyses) and a “machine learning approach” (MLA) (using random forest) were used. The study included 282 adult patients (52% female; mean age 49 years) with proven PHT (n = 59) and EHT (n = 223 with 40 CS, 107 PA, and 76 PPGL), respectively. Results From 155 metabolites eligible for statistical analyses, 31 were identified discriminating between PHT and EHT using the CA and 27 using the MLA, of which 16 metabolites (C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC16:0, lysoPCaC20:4, lysoPCaC24:0, PCaeC42:0, SM C18:1, SM C20:2) were found by both approaches. The receiver operating characteristic curve built on the top 15 metabolites from the CA provided an area under the curve (AUC) of 0.86, which was similar to the performance of the 15 metabolites from MLA (AUC 0.83). Conclusion TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance

Machine learning for classification of hypertension subtypes using multi-omics: a multi-centre, retrospective, data-driven study

Background: Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. Methods: This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. Findings: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers. Interpretation: We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment. Funding: European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1)

Reference intervals for LC-MS/MS measurements of plasma free, urinary free and urinary acid-hydrolyzed deconjugated normetanephrine, metanephrine and methoxytyramine

Contains fulltext : 201350.pdf (publisher's version ) (Open Access

Supine or Sitting? Economic and other considerations for use of plasma metanephrines for diagnosis of phaeochromocytoma

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Silent pheochromocytoma and paraganglioma: Systematic review and proposed definitions for standardized terminology

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors with heterogeneous clinical presentations and potential lethal outcomes. The diagnosis is based on clinical suspicion, biochemical testing, imaging and histopathological confirmation. Increasingly widespread use of imaging studies and surveillance of patients at risk of PPGL due to a hereditary background or a previous tumor is leading to the diagnosis of these tumors at an early stage. This has resulted in an increasing use of the term "silent" PPGL. This term and other variants are now commonly found in the literature without any clear or unified definition. Among the various terms, "clinically silent" is often used to describe the lack of signs and symptoms associated with catecholamine excess. Confusion arises when these and other terms are used to define the tumors according to their ability to synthesize and/or release catecholamines in relation to biochemical test results. In such cases the term "silent" and other variants are often inappropriately and misleadingly used. In the present analysis we provide an overview of the literature and propose standardized terminology in an attempt at harmonization to facilitate scientific communication

Plasma metanephrines and prospective prediction of tumor location, size and mutation type in patients with pheochromocytoma and paraganglioma

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