Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors

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. Evaluatio...

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Vydané v:	Frontiers in endocrinology (Lausanne) Ročník 12; s. 722656
Hlavní autori:	März, Juliane, Kurlbaum, Max, Roche-Lancaster, Oisin, Deutschbein, Timo, Peitzsch, Mirko, Prehn, Cornelia, Weismann, Dirk, Robledo, Mercedes, Adamski, Jerzy, Fassnacht, Martin, Kunz, Meik, Kroiss, Matthias
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Media S.A 07.09.2021
Predmet:	adrenal Adrenal Gland Neoplasms - blood Adrenal Gland Neoplasms - diagnosis Adrenal Gland Neoplasms - metabolism Adult Blood Cells - metabolism Blood Chemical Analysis - methods Case-Control Studies catecholamines Catecholamines - metabolism Chromatography, Liquid Endocrinology Female Germany Humans Male mass spectronomy Metabolome Metabolomics - methods Middle Aged paraganglioma Paraganglioma - blood Paraganglioma - diagnosis Paraganglioma - metabolism pheochromocytoma Pheochromocytoma - blood Pheochromocytoma - diagnosis Pheochromocytoma - metabolism Tandem Mass Spectrometry targeted metabolomics Germany catecholamines adrenal pheochromocytoma targeted metabolomics mass spectronomy machine learning feature selection paraganglioma
ISSN:	1664-2392, 1664-2392
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Abstract	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. Evaluation of quantitative metabolomics as a diagnostic tool for PPGL. 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. 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. 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. 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.
AbstractList	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.ContextPheochromocytomas 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.Evaluation of quantitative metabolomics as a diagnostic tool for PPGL.ObjectiveEvaluation of quantitative metabolomics as a diagnostic tool for PPGL.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.DesignTargeted 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.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.PatientsProspectively 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.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.ResultsAmong 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.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.ConclusionsThe 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. ContextPheochromocytomas 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.ObjectiveEvaluation of quantitative metabolomics as a diagnostic tool for PPGL.DesignTargeted 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.PatientsProspectively 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.ResultsAmong 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.ConclusionsThe 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. 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. Evaluation of quantitative metabolomics as a diagnostic tool for PPGL. 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. 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. 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. 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.
Author	Kroiss, Matthias Adamski, Jerzy Deutschbein, Timo Kurlbaum, Max Roche-Lancaster, Oisin Kunz, Meik Prehn, Cornelia März, Juliane Weismann, Dirk Robledo, Mercedes Fassnacht, Martin Peitzsch, Mirko
AuthorAffiliation	3 Chair of Medical Informatics, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg , Erlangen , Germany 10 Hereditary Endocrine Cancer Group, Spanish National Cancer Research Center and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER) , Madrid , Spain 6 Medicover Oldenburg Medizinisches Versorgungszentrum (MVZ) , Oldenburg , Germany 8 Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health , Neuherberg , Germany 2 Core Unit Clinical Mass Spectrometry, University Hospital , Würzburg , Germany 13 Institute of Biochemistry, Faculty of Medicine, University of Ljubljana , Ljubljana , Slovenia 1 Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg , Würzburg , Germany 5 Comprehensive Cancer Center Erlangen-Europäische Metropolregion Nürnberg (CCC ER-EMN) , Erlangen , Germany 4 Department of Pediatrics and Adolescent Medicine, University Hospital Erlang
AuthorAffiliation_xml	– name: 8 Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health , Neuherberg , Germany – name: 10 Hereditary Endocrine Cancer Group, Spanish National Cancer Research Center and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER) , Madrid , Spain – name: 5 Comprehensive Cancer Center Erlangen-Europäische Metropolregion Nürnberg (CCC ER-EMN) , Erlangen , Germany – name: 1 Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg , Würzburg , Germany – name: 11 Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health , Neuherberg , Germany – name: 3 Chair of Medical Informatics, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg , Erlangen , Germany – name: 7 Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Carl Gustav Carus at Technische Universität (TU) Dresden , Dresden , Germany – name: 2 Core Unit Clinical Mass Spectrometry, University Hospital , Würzburg , Germany – name: 6 Medicover Oldenburg Medizinisches Versorgungszentrum (MVZ) , Oldenburg , Germany – name: 14 Cancer Center Mainfranken, University of Würzburg , Würzburg , Germany – name: 15 Fraunhofer Institute of Toxicology and Experimental Medicine , Hannover , Germany – name: 13 Institute of Biochemistry, Faculty of Medicine, University of Ljubljana , Ljubljana , Slovenia – name: 4 Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen , Erlangen , Germany – name: 9 Hereditary Endocrine Cancer Group, Spanish National Cancer Research Center , Madrid , Spain – name: 16 Department of Internal Medicine IV, University Hospital Munich, Ludwig-Maximilians-Universität München , Munich , Germany – name: 12 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore , Singapore , Singapore
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Copyright	Copyright © 2021 März, Kurlbaum, Roche-Lancaster, Deutschbein, Peitzsch, Prehn, Weismann, Robledo, Adamski, Fassnacht, Kunz and Kroiss. Copyright © 2021 März, Kurlbaum, Roche-Lancaster, Deutschbein, Peitzsch, Prehn, Weismann, Robledo, Adamski, Fassnacht, Kunz and Kroiss 2021 März, Kurlbaum, Roche-Lancaster, Deutschbein, Peitzsch, Prehn, Weismann, Robledo, Adamski, Fassnacht, Kunz and Kroiss
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Keywords	catecholamines adrenal pheochromocytoma targeted metabolomics mass spectronomy machine learning feature selection paraganglioma
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Snippet	Pheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at metabolome... ContextPheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at...
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SubjectTerms	adrenal Adrenal Gland Neoplasms - blood Adrenal Gland Neoplasms - diagnosis Adrenal Gland Neoplasms - metabolism Adult Blood Cells - metabolism Blood Chemical Analysis - methods Case-Control Studies catecholamines Catecholamines - metabolism Chromatography, Liquid Endocrinology Female Germany Humans Male mass spectronomy Metabolome Metabolomics - methods Middle Aged paraganglioma Paraganglioma - blood Paraganglioma - diagnosis Paraganglioma - metabolism pheochromocytoma Pheochromocytoma - blood Pheochromocytoma - diagnosis Pheochromocytoma - metabolism Tandem Mass Spectrometry targeted metabolomics
Title	Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors
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