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Molecular classification of neuroendocrine tumors of the thymus

Introduction - The WHO classification of pulmonary neuroendocrine tumors (PNETs) is also used to classify thymic NETs (TNETs) into typical and atypical carcinoid (TC and AC), large cell neuroendocrine carcinoma (LCNEC), and small cell carcinoma (SCC), but little is known about the usability of alter...

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Main Authors: Dinter, Helen (Author) , Bohnenberger, Hanibal (Author) , Beck, Julia (Author) , Bornemann-Kolatzki, Kirsten (Author) , Schütz, Ekkehard (Author) , Küffer, Stefan (Author) , Klein, Lukas (Author) , Franks, Teri J. (Author) , Roden, Anja (Author) , Emmert, Alexander (Author) , Hinterthaner, Marc (Author) , Marino, Mirella (Author) , Brcic, Luka (Author) , Popper, Helmut (Author) , Weis, Cleo-Aron Thias (Author) , Pelosi, Giuseppe (Author) , Marx, Alexander (Author) , Ströbel, Philipp (Author)
Format: Article (Journal)
Language:English
Published: 28 April 2019
In: Journal of thoracic oncology
Year: 2019, Volume: 14, Issue: 8, Pages: 1472-1483
ISSN:1556-1380
DOI:10.1016/j.jtho.2019.04.015
Online Access:Verlag, Volltext: https://doi.org/10.1016/j.jtho.2019.04.015
Verlag, Volltext: http://www.sciencedirect.com/science/article/pii/S1556086419303120
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Author Notes:Helen Dinter, Hanibal Bohnenberger, Julia Beck, Kirsten Bornemann-Kolatzki, Ekkehard Schütz, Stefan Küffer, Lukas Klein, Teri J. Franks, Anja Roden, Alexander Emmert, Marc Hinterthaner, Mirella Marino, Luka Brcic, Helmut Popper, Cleo-Aron Weis, Giuseppe Pelosi, Alexander Marx, Philipp Ströbel
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Summary:Introduction - The WHO classification of pulmonary neuroendocrine tumors (PNETs) is also used to classify thymic NETs (TNETs) into typical and atypical carcinoid (TC and AC), large cell neuroendocrine carcinoma (LCNEC), and small cell carcinoma (SCC), but little is known about the usability of alternative classification systems. - Methods - One hundred seven TNET (22 TC, 51 AC, 28 LCNEC, and 6 SCC) from 103 patients were classified according to the WHO, the European Neuroendocrine Tumor Society, and a grading-related PNET classification. Low coverage whole-genome sequencing and immunohistochemical studies were performed in 63 cases. A copy number instability (CNI) score was applied to compare tumors. Eleven LCNEC were further analyzed using targeted next-generation sequencing. Morphologic classifications were tested against molecular features. - Results - Whole-genome sequencing data fell into three clusters: CNIlow, CNIint, and CNIhigh. CNIlow and CNIint comprised not only TC and AC, but also six LCNECs. CNIhigh contained all SCC and nine LCNEC, but also three AC. No morphologic classification was able to predict the CNI cluster. Cases where primary tumors and metastases were available showed progression from low-grade to higher-grade histologies. Analysis of LCNEC revealed a subgroup of intermediate NET G3 tumors that differed from LCNEC by carcinoid morphology, expression of chromogranin, and negativity for enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2). - Conclusions - TNETs fall into three molecular subgroups that are not reflected by the current WHO classification. Given the large overlap between TC and AC on the one hand, and AC and LCNEC on the other, we propose a morphomolecular grading system, Thy-NET G1-G3, instead of histologic classification for patient stratification and prognostication.
Item Description:Gesehen am 23.01.2020
Physical Description:Online Resource
ISSN:1556-1380
DOI:10.1016/j.jtho.2019.04.015

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