共 50 条
Molecular Classification of Neuroendocrine Tumors of the Thymus
被引:47
|作者:
Dinter, Helen
[1
]
Bohnenberger, Hanibal
[1
]
Beck, Julia
[2
]
Bornemann-Kolatzki, Kirsten
[2
]
Schuetz, Ekkehard
[2
]
Kueffer, Stefan
[1
]
Klein, Lukas
[1
]
Franks, Teri J.
[3
]
Roden, Anja
[4
]
Emmert, Alexander
[5
]
Hinterthaner, Marc
[5
]
Marino, Mirella
[6
]
Brcic, Luka
[7
]
Popper, Helmut
[7
]
Weis, Cleo-Aron
[8
]
Pelosi, Giuseppe
[9
,10
]
Marx, Alexander
[8
]
Stroebel, Philipp
[1
]
机构:
[1] Univ Med Ctr Gottingen, Inst Pathol, Robert Koch Str 40, D-37075 Gottingen, Germany
[2] Chronix Biomed, Gottingen, Germany
[3] Joint Pathol Ctr, Pulm & Mediastinal Pathol, Silver Spring, MD USA
[4] Mayo Clin, Dept Lab Med & Pathol, Rochester, MN USA
[5] Georg August Univ, Univ Med Ctr, Dept Thorac & Cardiovasc Surg, Gottingen, Germany
[6] IRCCS Regina Elena Natl Canc Inst, Dept Pathol, Rome, Italy
[7] Med Univ Graz, Inst Pathol, Diagnost & Res Ctr, Graz, Austria
[8] Heidelberg Univ, Univ Med Ctr Mannheim, Inst Pathol, Heidelberg, Germany
[9] Univ Milan, Dept Oncol & Hematooncol, Milan, Italy
[10] IRCCS MultiMed, Interhosp Pathol Div, Milan, Italy
关键词:
Neuroendocrine;
Carcinoid;
Thymus;
Molecular;
Genetic;
Classification;
HIGH-GRADE;
LUNG;
EXPRESSION;
CARCINOIDS;
MUTATIONS;
NEOPLASMS;
SURVIVAL;
11Q;
RB1;
G3;
D O I:
10.1016/j.jtho.2019.04.015
中图分类号:
R73 [肿瘤学];
学科分类号:
100214 ;
摘要:
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, CNIl(ow) 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. (C) 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc.
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页码:1472 / 1483
页数:12
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