胰腺神经内分泌肿瘤(PanNETs),第二常见的原发性胰腺肿瘤,在侵袭性方面表现出显著的异质性。关于基因组改变的最新知识,包括DAXX/ATRX,MEN1突变,和拷贝数变异(CNVs),提供了一些关于肿瘤侵袭性的见解。然而,胰岛素瘤和其他类型的PanNETs之间侵袭性显著差异的根本原因仍不清楚.构建综合预后分层模型,我们对已建立的PanNETsRip1-Tag2(RT2)小鼠模型和具有各种功能类型的人PanNETs进行了分析。首先,通过在不同年龄和品系的RT2小鼠和人类PanNETs中应用单细胞和批量RNA测序,我们引入了二维(2D)分类系统。基于二维分类系统,人类PanNETs主要分为良性胰岛素瘤或非胰岛素瘤亚簇.非胰岛素瘤亚型主要包括胃泌素瘤,胰高血糖素,VIPmas,和NF-PanNETs,都表现出潜在的侵入性。此外,我们在相应的人类PanNET亚簇中发现了特定CNV模式和突变的富集。然后,我们将体细胞DAXX/ATRX表示为“第二次打击”和侵袭性的混杂因素。最后,通过结合2D系统,DAXX/ATRX突变状态,和肿瘤直径,确定了一组复发风险最小的惰性PanNETs.总之,我们目前的工作构建了一个综合模型来阐明PanNETs侵袭性的异质性,并改善预后分层.
Pancreatic neuroendocrine tumors (PanNETs), the second most common type of primary pancreatic tumors, display notable heterogeneity in invasiveness. Current knowledge regarding genomic alterations, including DAXX/ATRX, MEN1 mutations, and copy number variations (CNVs), provides some insights into tumor invasiveness. However, the underlying reasons for the significant variation in invasiveness between insulinoma and other types of PanNETs remain unclear. To construct a comprehensive model for the stratification of prognosis, we employed analysis of both the well-established Rip1-Tag2 (RT2) mouse model of PanNETs and human PanNETs with various functional types. Firstly, by applying single-cell and bulk RNA sequencing in PanNETs from different ages and strains of RT2 mice and human PanNETs, we introduced a 2-dimensional (2D) classification system. Based on the 2-D classification system, human PanNETs were mainly classified as benign insulinomas or non-insulinomas subclusters. Non-insulinomas subtypes mainly included gastrinomas, glucagonomas, VIPomas, and NF-PanNETs, which all exhibited potential invasiveness. In addition, we discovered an enrichment of specific CNV patterns and mutations in corresponding human PanNET subclusters. Then we denoted somatic DAXX/ATRX as the \'second hit\' and confounding factors for invasiveness. Finally, by combining the 2D system, DAXX/ATRX mutation status, and tumor diameter, a group of indolent PanNETs with minimal recurrence risk was identified. In conclusion, our current work constructed a comprehensive model to elucidate the heterogeneity of invasiveness in PanNETs and improve prognostic stratification.