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Published on: August 25, 2023
Lineage-specific transcription factor landscape of thymic neuroendocrine tumors
Seiji Omura1, Yutaka Kurebayashi2, Junko Hamamoto3
1Division of Thoracic Surgery, Keio University School of Medicine, Tokyo, Japan.
Introduction:
Thymic neuroendocrine tumors (TNETs) are rare malignancies characterized by aggressive clinical behavior and limited therapeutic options. In small cell lung cancer (SCLC), molecular subtypes based on the expression of lineage-defining transcription factors (TFs)-ASCL1, NEUROD1, POU2F3, and YAP1-have been proposed. However, the TF landscape of TNETs remains poorly defined. Given the pathological similarities among neuroendocrine tumors across organs, we aimed to investigate whether the TF-based classification system used in SCLC is applicable to TNETs.
Methods:
Sixteen pathologically confirmed TNETs-including large cell neuroendocrine carcinoma (LCNEC), thymic small cell carcinoma (TSCC), atypical carcinoid (AC), and typical carcinoid (TC)-were retrospectively analyzed. Immunohistochemistry was performed to evaluate classical neuroendocrine (NE) markers (synaptophysin, chromogranin A, CD56) and TFs (ASCL1, NEUROD1, POU2F3, YAP1). H-scores were calculated, and tumors were categorized according to TFs expression profiles.
Results:
Synaptophysin was strongly expressed in all cases, while chromogranin A and CD56 showed variable expression, with reduced levels in LCNEC and TSCC. The combined NE score was significantly higher in carcinoid tumors compared to LCNEC and TSCC. For TFs, ASCL1 expression was observed in 93.8 % of cases, whereas NEUROD1 and POU2F3 were rarely or not expressed. YAP1 expression was confined to LCNEC cases, all of which co-expressed ASCL1 and YAP1. Based on H-scores, TNETs were classified into three subgroups: (1) ASCL1-positive/YAP1-negative (n = 12, 75 %), (2) ASCL1/YAP1 double-positive (n = 3, 19 %), and (3) double-negative (n = 1, 6 %).
Conclusion:
This study reveals molecular heterogeneity among TNETs. Notably, ASCL1 and YAP1 co-expression characterizes all LCNEC cases, making a distinct TF landscape in high-grade TNETs.
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