TY - JOUR A1 - Minner, S. A1 - Schreiner, J. A1 - Saeger, W. T1 - Adrenal cancer: relevance of different grading systems and subtypes JF - Clinical and Translational Oncology N2 - Purpose The subclassification of adrenal cancers according to the WHO classification in ordinary, myxoid, oncocytic, and sarcomatoid as well as pediatric types is well established, but the criteria for each subtype are not sufficiently determined and the relative frequency of the different types of adrenal cancers has not been studied in large cohorts. Therefore, our large collection of surgically removed adrenal cancers should be reviewed o establish the criteria for the subtypes and to find out the frequency of the various types. Methods In our series of 521 adrenal cancers the scoring systems of Weiss et al., Hough et al., van Slooten et al. and the new Helsinki score system were used for the ordinary type of cancer (97% of our series) and the myxoid type (0.8%). For oncocytic carcinomas (2%), the scoring system of Bisceglia et al. was applied. Results Discrepancies between benign and malignant diagnoses from the first thee classical scoring systems are not rare (22% in our series) and could be resolved by the Helsinki score especially by Ki-67 index (more than 8% unequivocally malignant). Since all our cancer cases are positive in the Helsinki score, this system can replace the three elder systems. For identification of sarcomatoid cancer as rarest type in our series (0.2%), the scoring systems are not practical but additional immunostainings used for soft tissue tumors and in special cases molecular pathology are necessary to differentiate these cancers from adrenal sarcomas. According to the relative frequencies of the different subtypes of adrenal cancers the main type is the far most frequent (97%) followed by the oncocytic type (2%), the myxoid type (0.8%) and the very rare sarcomatoid type (0.2%). Conclusions The Helsinki score is the best for differentiating adrenal carcinomas of the main, the oncocytic, and the myxoid type in routine work. Additional scoring systems for these carcinomas are generally not any longer necessary. Signs of proliferation (mitoses and Ki-67 index) and necroses are the most important criteria for diagnosis of malignancy. KW - adrenal KW - cancer KW - cancer types KW - classification Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-308479 SN - 1699-048X SN - 1699-3055 VL - 23 IS - 7 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 -