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

Paal Wallace; Catleen Conrad; Sascha  Bruckmann; Ying Pang; Eduardo Caleiras; Masanori Murakami; Esther Korpershoek; Zhenping Zhuang; Elena Rapizzi; Matthias Kroiss; Volker Gudziol; Henri J L M Timmers; Massimo Mannelli; Jens Pietzsch; Felix Beuschlein; Karel Pacak; Mercedes Robledo; Barbara Klink; Mirko Peitzsch; Anthony J Gill; Arthur S Tischler; Ronald De Krijger; Thomas Papathomas; Daniela Aust; Graeme Eisenhofer; Susan Richter

doi:10.1002/path.5472

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

Paal Wallace, Catleen Conrad, Sascha Bruckmann, Ying Pang, Eduardo Caleiras, Masanori Murakami, Esther Korpershoek, Zhenping Zhuang, Elena Rapizzi, Matthias Kroiss, Volker Gudziol, Henri J L M Timmers, Massimo Mannelli, Jens Pietzsch, Felix Beuschlein, Karel Pacak, Mercedes Robledo, Barbara Klink, Mirko Peitzsch, Anthony J GillArthur S Tischler, Ronald De Krijger, Thomas Papathomas, Daniela Aust, Graeme Eisenhofer, Susan Richter

Metabolism and Systems Science

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Original language	English
Pages (from-to)	378-387
Journal	Journal of Pathology
Volume	251
Issue number	4
DOIs	https://doi.org/10.1002/path.5472
Publication status	Published - 27 May 2020

Keywords

Krebs cycle metabolites
LC–MS/MS
diagnostics
linear discriminant analysis
mass spectrometry
metabolite profiling
multi-observer
prediction models
succinate to fumarate ratio
variants of unknown significance

ASJC Scopus subject areas

Pathology and Forensic Medicine

Access to Document

10.1002/path.5472

Cite this

Wallace, P., Conrad, C., Bruckmann, S., Pang, Y., Caleiras, E., Murakami, M., Korpershoek, E., Zhuang, Z., Rapizzi, E., Kroiss, M., Gudziol, V., Timmers, H. J. L. M., Mannelli, M., Pietzsch, J., Beuschlein, F., Pacak, K., Robledo, M., Klink, B., Peitzsch, M., ... Richter, S. (2020). Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas. Journal of Pathology, 251(4), 378-387. https://doi.org/10.1002/path.5472

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

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Wallace, P, Conrad, C, Bruckmann, S, Pang, Y, Caleiras, E, Murakami, M, Korpershoek, E, Zhuang, Z, Rapizzi, E, Kroiss, M, Gudziol, V, Timmers, HJLM, Mannelli, M, Pietzsch, J, Beuschlein, F, Pacak, K, Robledo, M, Klink, B, Peitzsch, M, Gill, AJ, Tischler, AS, De Krijger, R, Papathomas, T, Aust, D, Eisenhofer, G & Richter, S 2020, 'Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas', Journal of Pathology, vol. 251, no. 4, pp. 378-387. https://doi.org/10.1002/path.5472

TY - JOUR

T1 - Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas

AU - Wallace, Paal

AU - Conrad, Catleen

AU - Bruckmann, Sascha

AU - Pang, Ying

AU - Caleiras, Eduardo

AU - Murakami, Masanori

AU - Korpershoek, Esther

AU - Zhuang, Zhenping

AU - Rapizzi, Elena

AU - Kroiss, Matthias

AU - Gudziol, Volker

AU - Timmers, Henri J L M

AU - Mannelli, Massimo

AU - Pietzsch, Jens

AU - Beuschlein, Felix

AU - Pacak, Karel

AU - Robledo, Mercedes

AU - Klink, Barbara

AU - Peitzsch, Mirko

AU - Gill, Anthony J

AU - Tischler, Arthur S

AU - De Krijger, Ronald

AU - Papathomas, Thomas

AU - Aust, Daniela

AU - Eisenhofer, Graeme

AU - Richter, Susan

PY - 2020/5/27

Y1 - 2020/5/27

KW - Krebs cycle metabolites

KW - LC–MS/MS

KW - diagnostics

KW - linear discriminant analysis

KW - mass spectrometry

KW - metabolite profiling

KW - multi-observer

KW - prediction models

KW - succinate to fumarate ratio

KW - variants of unknown significance

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U2 - 10.1002/path.5472

DO - 10.1002/path.5472

M3 - Article

SN - 0022-3417

VL - 251

SP - 378

EP - 387

JO - Journal of Pathology

JF - Journal of Pathology

IS - 4

ER -