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数字图像分析在神经母细胞瘤 3D 模型中肿瘤细胞增殖、侵袭和迁移相关蛋白合成中的应用。

Digital Image Analysis Applied to Tumor Cell Proliferation, Aggressiveness, and Migration-Related Protein Synthesis in Neuroblastoma 3D Models.

机构信息

Department of Pathology, Medical School, University of Valencia-INCLIVA Biomedical Health Research Institute, 46010 Valencia, Spain.

Low Prevalence Tumors, Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Instituto de Salud Carlos III, 28029 Madrid, Spain.

出版信息

Int J Mol Sci. 2020 Nov 17;21(22):8676. doi: 10.3390/ijms21228676.

DOI:10.3390/ijms21228676
PMID:33212997
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7698558/
Abstract

Patient-derived cancer 3D models are a promising tool that will revolutionize personalized cancer therapy but that require previous knowledge of optimal cell growth conditions and the most advantageous parameters to evaluate biomimetic relevance and monitor therapy efficacy. This study aims to establish general guidelines on 3D model characterization phenomena, focusing on neuroblastoma. We generated gelatin-based scaffolds with different stiffness and performed SK-N-BE(2) and SH-SY5Y aggressive neuroblastoma cell cultures, also performing co-cultures with mouse stromal Schwann cell line (SW10). Model characterization by digital image analysis at different time points revealed that cell proliferation, vitronectin production, and migration-related gene expression depend on growing conditions and are specific to the tumor cell line. Morphometric data show that 3D in vitro models can help generate optimal patient-derived cancer models, by creating, identifying, and choosing patterns of clinically relevant artificial microenvironments to predict patient tumor cell behavior and therapeutic responses.

摘要

患者来源的癌症 3D 模型是一种很有前途的工具，将彻底改变个性化癌症治疗，但需要预先了解最佳细胞生长条件和最有利的参数，以评估仿生相关性并监测治疗效果。本研究旨在建立 3D 模型特征描述的一般指南，重点是神经母细胞瘤。我们生成了具有不同硬度的基于明胶的支架，并进行了 SK-N-BE(2)和 SH-SY5Y 侵袭性神经母细胞瘤细胞培养，还与小鼠基质施万细胞系 (SW10) 进行了共培养。通过在不同时间点进行数字图像分析对模型进行特征描述，结果表明细胞增殖、纤连蛋白产生和与迁移相关的基因表达取决于生长条件，并且是肿瘤细胞系特有的。形态计量学数据表明，3D 体外模型可以通过创建、识别和选择具有临床相关性的人工微环境模式来帮助生成最佳的患者来源的癌症模型，从而预测患者肿瘤细胞的行为和治疗反应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6295/7698558/1736ee985396/ijms-21-08676-g006.jpg
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