Molecular classification of neuroendocrine tumors of the thymus
Menée à partir de l'analyse de 107 échantillons tumoraux provenant de 103 patients atteints d'une tumeur neuroendocrine du thymus, cette étude identifie trois sous-groupes moléculaires non réprésentés par le système de classification des tumeurs pulmonaires neuroendocrines de l'Organisation Mondiale de la Santé
Introduction : The World Health Organization (WHO) Classification of pulmonary NET (PNET) is also used to classify thymic NET (TNET) 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 : 107 TNET (22 TC, 51 AC, 28 LCNEC, 6 SCC) from 103 patients were classified according to WHO, the European Neuroendocrine Tumor Society (ENETS), and a grading-related PNET classification. Low coverage whole genome sequencing (WGS) 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. Morphological classifications were tested against molecular features.
Results : WGS data fell into three clusters CNIlow, CNIint, and CNIhigh. CNIlow and CNIint comprised not only TC and AC, but also 6 LCNEC. CNIhigh contained all SCC and 9 LCNEC, but also 3 AC. No morphological 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 EZH2.
Conclusions : TNET fall into 3 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 morpho-molecular grading system, Thy-NET G1-G3, instead of histological classification for patient stratification and prognostication.
Journal of Thoracic Oncology , article en libre accès, 2018