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通过延时和无标记的 3D 肿瘤切片培养平台加速精准抗癌疗法。

Accelerating precision anti-cancer therapy by time-lapse and label-free 3D tumor slice culture platform.

机构信息

Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China.

Centre for Precision Medicine Research and Training, Faculty of health Sciences, University of Macau, Macau SAR, China.

出版信息

Theranostics. 2021 Sep 13;11(19):9415-9430. doi: 10.7150/thno.59533. eCollection 2021.

DOI:10.7150/thno.59533
PMID:34646378
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8490519/
Abstract

The feasibility of personalized medicine for cancer treatment is largely hampered by costly, labor-intensive and time-consuming models for drug discovery. Herein, establishing new pre-clinical models to tackle these issues for personalized medicine is urgently demanded. We established a three-dimensional tumor slice culture (3D-TSC) platform incorporating label-free techniques for time-course experiments to predict anti-cancer drug efficacy and validated the 3D-TSC model by multiphoton fluorescence microscopy, RNA sequence analysis, histochemical and histological analysis. : Using time-lapse imaging of the apoptotic reporter sensor C3 (C3), we performed cell-based high-throughput drug screening and shortlisted high-efficacy drugs to screen murine and human 3D-TSCs, which validate effective candidates within 7 days of surgery. Histological and RNA sequence analyses demonstrated that 3D-TSCs accurately preserved immune components of the original tumor, which enables the successful achievement of immune checkpoint blockade assays with antibodies against PD-1 and/or PD-L1. Label-free multiphoton fluorescence imaging revealed that 3D-TSCs exhibit lipofuscin autofluorescence features in the time-course monitoring of drug response and efficacy. : This technology accelerates precision anti-cancer therapy by providing a cheap, fast, and easy platform for anti-cancer drug discovery.

摘要

癌症个体化治疗的可行性在很大程度上受到药物发现成本高、劳动强度大且耗时的模式的阻碍。因此,迫切需要建立新的临床前模型来解决个体化医学中的这些问题。我们建立了一个包含无标记技术的三维肿瘤切片培养(3D-TSC)平台,用于进行时程实验,以预测抗癌药物的疗效,并通过多光子荧光显微镜、RNA 序列分析、组织化学和组织学分析验证了 3D-TSC 模型。使用凋亡报告传感器 C3(C3)的延时成像,我们进行了基于细胞的高通量药物筛选,并对小鼠和人 3D-TSC 进行了高疗效药物的筛选,在手术后 7 天内筛选出有效候选药物。组织学和 RNA 序列分析表明,3D-TSC 准确地保留了原始肿瘤的免疫成分,这使得成功地进行了针对 PD-1 和/或 PD-L1 的免疫检查点阻断抗体的检测。无标记多光子荧光成像显示,3D-TSC 在药物反应和疗效的时程监测中表现出脂褐素自发荧光特征。这项技术通过提供一种廉价、快速和简便的抗癌药物发现平台,加速了精准抗癌治疗。

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