PO.BCS01.03 · 生物信息与计算

整合bulk与单细胞转录组学揭示转移性小肠神经内分泌肿瘤中的肝细胞样重编程

Integrated Bulk and Single-Cell Transcriptomics Reveal Hepatocyte-Like Reprogramming in Metastatic Small Intestinal Neuroendocrine Tumors

海报缩略图:整合bulk与单细胞转录组学揭示转移性小肠神经内分泌肿瘤中的肝细胞样重编程
编号 2679 展板 4 时间 4/20 02:00–05:00 区域 Section 1 主讲 James Madigan, PhD
分会场 Application of Bioinformatics to Cancer Biology 3
查看 PDF 下载 PDF 🔒 查看 / 下载完整 PDF 需登录并开通下载套餐 · 查看套餐 / 开通 AACR 官方页面

作者与单位 Authors & Affiliations

Sunanda Biswas Mukherjee1, James P. Madigan2, Samira M. Sadowski1

1Surgical Oncology Program, National Cancer Institute, NIH, Bethesda, MD,2Postdoctoral Fellow, National Cancer Institute, Rockville, MD

摘要 Abstract

中文摘要
肝转移是小肠神经内分泌肿瘤(siNET)预后不良的首要决定因素,然而使转移适应成为可能的分子机制在很大程度上仍属未知。在此,我们整合bulk和单细胞转录组学分析,以剖析这一过程背后的肿瘤内在程序。对44例原发和37例转移siNET患者样本的bulk RNA-seq分析揭示了肝转移过程中广泛的转录重塑。差异表达和通路富集分析证明,转移肿瘤中代谢及各种肝脏相关通路上调,同时肠道特异性程序下调。这些结果提示存在一种朝向肝脏功能状态的肿瘤内在表型转变。此外,应用ESTIMATE算法(该算法根据基质和免疫转录组分的相对丰度推断肿瘤纯度)证明转移样本纯度持续较高,表明所观察到的转录程序反映的是肿瘤内在改变,而非来自正常肝组织的污染。为在单细胞分辨率下验证这些观察,我们使用自有的单细胞RNA-seq数据分析了五对配对的原发和肝转移siNET患者样本。整合聚类揭示出截然不同的神经内分泌(NE)和肝细胞样肿瘤群体,反映了转移生态位内的细胞异质性。拟时序和轨迹分析突出显示了从原发肿瘤富集的NE聚类向转移富集的肝细胞样聚类的连续进展,提示肿瘤细胞沿肝脏适应轴发生渐进性重编程。这些肝细胞样状态表现出肝脏功能和外源物代谢等生物学过程的激活。综上,这些提示了一种选择性转录可塑性,使转移肿瘤细胞能够获得肝脏特异性功能特征,从而促进其在肝脏微环境中的存活、代谢整合和持续存在。总之,通过整合bulk和单细胞转录组学数据,本研究揭示siNET肝转移是通过肿瘤细胞状态向肝细胞样身份的转变而产生的,代表了神经内分泌肿瘤转移中肝脏特异性适应的一种新范式,并突出了可作为治疗靶点以破坏siNET转移持续存在的肝脏适应性转录程序。
查看英文原文 English abstract
Liver metastasis is the primary determinant of poor prognosis in small intestinal neuroendocrine tumors (siNETs), yet the molecular mechanisms enabling metastatic adaptation remain largely unknown. Here, we integrate bulk and single-cell transcriptomic analyses to dissect tumor-intrinsic programs underlying this process. Bulk RNA-seq analysis of 44 primary and 37 metastatic siNET patient samples revealed extensive transcriptional remodeling during liver metastasis. Differential expression and pathway enrichment analyses demonstrated upregulation of metabolic and various liver-related pathways, alongside downregulation of intestine-specific programs in metastatic tumors. These results suggest a tumor-intrinsic phenotypic shift toward hepatic functional states. Further, application of the ESTIMATE algorithm, which infers tumor purity from the relative abundance of stromal and immune transcriptional components, demonstrated consistently high purity in metastatic samples, indicating that the observed transcriptional programs reflect tumor-intrinsic alterations rather than contamination from normal liver tissue. To validate these observations at single-cell resolution, we analyzed five matched primary and liver metastatic siNET patient samples using in-house single-cell RNA-seq data. Integrative clustering revealed distinct neuroendocrine (NE) and hepatocyte-like tumor populations, reflecting the cellular heterogeneity within the metastatic niche. Pseudotime and trajectory analyses highlighted a continuous progression from primary tumor-enriched NE clusters toward metastasis-enriched hepatocyte-like clusters, suggesting a gradual reprogramming of tumor cells along a liver-adaptive axis. These hepatocyte-like states exhibited activation of biological processes like hepatic functions and xenobiotic metabolisms. Together, these indicate a selective transcriptional plasticity that enables metastatic tumor cells to acquire liver-specific functional traits, thereby facilitating their survival, metabolic integration, and persistence within the liver microenvironment. In conclusion, by integrating bulk and single-cell transcriptomic data, this study reveals that siNET liver metastasis arises through a tumor cell-state transition toward a hepatocyte-like identity, representing a novel paradigm of liver- specific adaptation in neuroendocrine tumor metastasis and highlights liver-adaptive transcriptional programs that may be therapeutically targeted to disrupt siNET metastatic persistence.
利益披露 Disclosure
S. B. Mukherjee, None.. J. P. Madigan, None.. S. M. Sadowski, None.

← 返回 AACR 2026 检索