TY - JOUR A1 - Sbiera, Silviu A1 - Kunz, Meik A1 - Weigand, Isabel A1 - Deutschbein, Timo A1 - Dandekar, Thomas A1 - Fassnacht, Martin T1 - The new genetic landscape of Cushing’s disease: deubiquitinases in the spotlight JF - Cancers N2 - Cushing’s disease (CD) is a rare condition caused by adrenocorticotropic hormone (ACTH)-producing adenomas of the pituitary, which lead to hypercortisolism that is associated with high morbidity and mortality. Treatment options in case of persistent or recurrent disease are limited, but new insights into the pathogenesis of CD are raising hope for new therapeutic avenues. Here, we have performed a meta-analysis of the available sequencing data in CD to create a comprehensive picture of CD’s genetics. Our analyses clearly indicate that somatic mutations in the deubiquitinases are the key drivers in CD, namely USP8 (36.5%) and USP48 (13.3%). While in USP48 only Met415 is affected by mutations, in USP8 there are 26 different mutations described. However, these different mutations are clustering in the same hotspot region (affecting in 94.5% of cases Ser718 and Pro720). In contrast, pathogenic variants classically associated with tumorigenesis in genes like TP53 and BRAF are also present in CD but with low incidence (12.5% and 7%). Importantly, several of these mutations might have therapeutic potential as there are drugs already investigated in preclinical and clinical setting for other diseases. Furthermore, network and pathway analyses of all somatic mutations in CD suggest a rather unified picture hinting towards converging oncogenic pathways. KW - Cushing’s disease KW - pathogenesis KW - somatic mutations KW - deubiquitinases Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193194 SN - 2072-6694 VL - 11 IS - 11 ER - TY - JOUR A1 - März, Juliane A1 - Kurlbaum, Max A1 - Roche-Lancaster, Oisin A1 - Deutschbein, Timo A1 - Peitzsch, Mirko A1 - Prehn, Cornelia A1 - Weismann, Dirk A1 - Robledo, Mercedes A1 - Adamski, Jerzy A1 - Fassnacht, Martin A1 - Kunz, Meik A1 - Kroiss, Matthias T1 - Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors JF - Frontiers in Endocrinology N2 - 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. KW - adrenal KW - pheochromocytoma KW - paraganglioma KW - targeted metabolomics KW - mass spectronomy KW - catecholamines KW - machine learning KW - feature selection Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-245710 SN - 1664-2392 VL - 12 ER -