p53+/-Rb1-ASCL1low phenotype predicts better prognosis after surgical resection in large cell neuroendocrine carcinoma: a molecular characteristic and clinical-pathological investigation of lung neuroendocrine tumors
Research Square (Research Square)(2020)
Abstract
Abstract Background: Based on morphology, necrosis degree, and proliferation status, lung neuroendocrine tumors (NETs) are commonly divided into four subtypes: typical carcinoid (TC), atypical carcinoid (AC), large cell neuroendocrine carcinoma (LCNEC), and small cell lung carcinoma (SCLC). However, cases difficult to classify still exist in diagnosis. Methods: We immunohistochemically investigated the molecular phenotypes in lung NETs and assessed their prognostic value. Results: After morphological re-analysis of 179 NETs, 19 cases were classified as undefined, which included 3 cases showing carcinoid morphology with high proliferation (Ki67>20% or mitotic count>10/2mm2), and 16 cases showing intermediate differentiated morphology with Ki67 among 20% to 60%. Furthermore, molecular phenotypes were determined by expression of p53, Rb1, ASCL1, STK11, and gene mutation status of KRAS. Interestingly, p53wtRb1+ distinguished an unique subcategory from undefined lung NET cases which differ from SCLC or LCNEC in prognosis, indicating WD-NET G3 existed in lung NETs. Additionally, in LCNEC, high ASCL1 expression was only relevant to lymph node metastasis rather than overall survival. However, in p53+/-Rb1- LCNEC phenotype, low ASCL1 predicted better outcomes, along with less risk of lymph node metastasis. Conclusion: This study provided evidence for existence of WD-NET G3 in NETs and revealed promising prognosis of p53+/-Rb1- ASCL1low subcategory in LCNEC, which could be beneficial to the evaluation of patient status in future clinic practice.
MoreTranslated text
Key words
large cell neuroendocrine carcinoma,p53+/-rb1-ascl1low phenotype predicts,tumors,clinical-pathological
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined