PO.TB02.02 · 肿瘤生物学

实体瘤中的神经支配

Nerve innervation in solid tumors

海报缩略图:实体瘤中的神经支配
编号 720 展板 10 时间 4/19 02:00–05:00 区域 Section 29 主讲 Hui Yu, BS;MS
分会场 Molecular Pathology
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作者与单位 Authors & Affiliations

Hui Yu1, Aglaia Schiza1, Victor Ponten1, Viktoria Thurfjell1, Amanda Lindberg1, Julia Sidenius Johansen2, Ulrike Segersten3, Anca Dragomir1, Bengt Glimelius1, Artur Mezheyeuski4, Astrid Børretzen5, Yun-Fan Sun6, Lars A. Akslen5, Patrick Micke1, Carina Strell5

1Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden,2Herlev Hospital, Herlev, Denmark,3Department of Surgical Sciences, Uppsala University, Uppsala, Sweden,4Molecular Oncology Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain,5CCBIO, Department of Clinical Medicine, University of Bergen, Bergen, Norway,6Liver Surgery & Transplantation, Zhongshan Hospital, Shanghai, China

摘要 Abstract

中文摘要
神经支配在癌症进展中的作用仍存在高度争议。虽然各种体外和体内模型提示肿瘤细胞可主动诱导新生神经生成,但基于组织的证据仍然稀少。在本项目中,我们通过寻求关于神经支配和神经芽生与组织病理学肿瘤特征相关的直接的、基于组织的证据,批判性地检验肿瘤相关神经支配的假说。我们的研究队列包括来自六种实体瘤类型的诊断性FFPE全组织样本:乳腺癌(Luminal A/B、HER2+、TNBC)、非小细胞肺癌(腺癌、鳞状细胞癌)、结直肠癌、胰腺癌(PDAC和壶腹周围腺癌)、前列腺癌和膀胱癌。通过多重免疫荧光和多光谱成像识别神经结构,靶向神经丝轻链(NFL,70 kDa)和生长相关蛋白43(GAP43)。为界定肿瘤微环境,额外的标志物包括CD34(神经周围鞘)、CD31(内皮细胞)、CD3(T细胞)和泛细胞角蛋白(肿瘤细胞)。我们开发了一种基于深度学习的算法和一种阈值分割方法来分割神经纤维并表征其空间组织。一个自定义的Python脚本量化神经密度(神经数/肿瘤面积)、神经大小(μm²)以及通过CD34染色评估的神经周围鞘完整性。同时,我们正通过使用定制的Python工具对30张连续的4 μm切片进行对齐和分析来构建3D神经重建。神经周围侵犯(定义为肿瘤细胞与神经的直接接触)最常见于来自高度神经支配器官的肿瘤,如PDAC和前列腺癌,而在其他肿瘤类型中仅偶尔出现。在后续分析中,将系统地把神经特征与组织病理学肿瘤特征、微环境特征和临床数据相关联。最终,本研究旨在生成人类癌症中神经-肿瘤相互作用的全面图谱,并界定神经支配和神经周围侵犯的共有模式及肿瘤类型特异性模式。
查看英文原文 English abstract
The role of nerve innervation in cancer progression remains highly debated. While various in vitro and in vivo models suggest that tumor cells can actively induce neoneurogenesis, tissue-based evidence remains sparse. In this project, we critically examine the hypothesis of tumor-associated nerve innervation by seeking direct, tissue-based evidence of nerve innervation and sprouting in relation to histopathological tumor features.Our study cohort comprises diagnostic FFPE whole-tissue samples from six solid tumor types: breast cancer (Luminal A/B, HER2+, TNBC), non-small cell lung cancer (adenocarcinoma, squamous cell carcinoma), colorectal cancer, pancreatic cancer (PDAC and periampullary adenocarcinoma), prostate cancer, and urinary bladder cancer.Nerve structures are identified via multiplexed immunofluorescence and multispectral imaging, targeting neurofilament light chain (NFL, 70 kDa) and growth-associated protein 43 (GAP43). To delineate the tumor microenvironment, additional markers include CD34 (perineural sheath), CD31 (endothelial cells), CD3 (T-cells), and pan-cytokeratin (tumor cells). We developed both a deep-learning-based algorithm and a thresholding approach to segment nerve fibers and characterize their spatial organization. A custom Python script quantifies nerve density (nerves/tumor area), nerve size (μm²), and the integrity of the perineural sheath via CD34 staining. In parallel, we are constructing 3D nerve reconstructions by aligning and analyzing 30 consecutive 4 μm sections using tailored Python tools.Perineural invasion, as defined as direct contact between tumor cells and nerves, is most frequently observed in tumors from highly innervated organs such as PDAC and prostate cancer, while appearing only sporadically in the other tumor types.In subsequent analyses, nerve features will be systematically correlated with histopathological tumor characteristics, microenvironmental profiles, and clinical data. Ultimately, this study aims to generate a comprehensive atlas of nerve-tumor interactions in human cancer and to delineate both shared and tumor-type-specific patterns of innervation and perineural invasion.
利益披露 Disclosure
H. Yu, None.. A. Schiza, None.. V. Ponten, None.. V. Thurfjell, None.. A. Lindberg, None.. U. Segersten, None.. A. Dragomir, None.. B. Glimelius, None.. A. Mezheyeuski, None.. A. Børretzen, None.. Y. Sun, None.. L. A. Akslen, None.. P. Micke, None.. C. Strell, None.

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