Helen Dinter1, Hanibal Bohnenberger1, Julia Beck2, Kirsten Bornemann-Kolatzki2, Ekkehard Schütz2, Stefan Küffer1, Lukas Klein1, Teri J Franks3, Anja Roden4, Alexander Emmert5, Marc Hinterthaner5, Mirella Marino6, Luka Brcic7, Helmut Popper7, Cleo-Aron Weis8, Giuseppe Pelosi9, Alexander Marx8, Philipp Ströbel10. 1. Institute of Pathology, University Medical Center Göttingen, Göttingen, Germany. 2. Chronix Biomedical, Göttingen, Germany. 3. Pulmonary & Mediastinal Pathology, The Joint Pathology Center, Silver Spring, Maryland. 4. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota. 5. Department of Thoracic and Cardiovascular Surgery, University Medical Center, Georg-August University, Göttingen, Germany. 6. Department of Pathology, IRCCS Regina Elena National Cancer Institute, Rome, Italy. 7. Diagnostic and Research Center, Institute of Pathology, Medical University of Graz, Graz, Austria. 8. Institute of Pathology, University Medical Center Mannheim, University of Heidelberg, Germany. 9. Department of Oncology and Hemato-Oncology, University of Milan, and Inter-Hospital Pathology Division, IRCCS MultiMedica, Milan, Italy. 10. Institute of Pathology, University Medical Center Göttingen, Göttingen, Germany. Electronic address: philipp.stroebel@med.uni-goettingen.de.
Abstract
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.
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.
Authors: Sebastian C B Bremer; Gabi Bittner; Omar Elakad; Helen Dinter; Jochen Gaedcke; Alexander O König; Ahmad Amanzada; Volker Ellenrieder; Alexander Freiherr von Hammerstein-Equord; Philipp Ströbel; Hanibal Bohnenberger Journal: Cancers (Basel) Date: 2022-06-08 Impact factor: 6.575
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