Biobanked Glioblastoma Patient-Derived Organoids as a Precision Medicine Model to Study Inhibition of Invasion.
Biobanked Glioblastoma Patient-Derived Organoids as a Precision Medicine Model to Study Inhibition of Invasion.
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DOI：
10.3390/ijms221910720
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发表时间：
2021-10-03
影响因子：
5.6
通讯作者：
Rodriguez A
中科院分区：
文献类型：
--
作者：
Darrigues E;Zhao EH;De Loose A;Lee MP;Borrelli MJ;Eoff RL;Galileo DS;Penthala NR;Crooks PA;Rodriguez A
胶质母细胞瘤（GBM）对治疗具有高度抗性，并且侵入周围脑是导致尽管手术切除仍会复发的癌症标志。随着精准医学的出现，患者衍生的3D系统被认为是潜在的强大GBM临床前模型。在这项研究中，我们筛选了22种抗侵袭化合物的文库（即，NF-κ B、GSK-3-B、考克斯-2和微管蛋白抑制剂）。我们使用3D Matrigel侵袭测定评估毒性和侵袭抑制。接下来，我们选择了三种抑制侵袭的化合物，并在患者来源的胶质母细胞瘤类器官（GBO）中筛选它们。我们开发了一个平台，使用FIJI/ImageJ的可用宏来量化类器官外缘的入侵。我们的数据表明，高通量侵袭筛选可以使用已建立的细胞系和患者来源的3D模型系统来完成。微管蛋白抑制剂化合物对U-251 MG细胞具有最佳功效，然而，在离体患者类器官中，结果是高度可变的。我们的研究结果表明，化合物的疗效与患者肿瘤内和肿瘤间的异质性高度相关。这些结果表明，这些模型可用于评估个人肿瘤治疗策略。
Glioblastoma (GBM) is highly resistant to treatment and invasion into the surrounding brain is a cancer hallmark that leads to recurrence despite surgical resection. With the emergence of precision medicine, patient-derived 3D systems are considered potentially robust GBM preclinical models. In this study, we screened a library of 22 anti-invasive compounds (i.e., NF-kB, GSK-3-B, COX-2, and tubulin inhibitors) using glioblastoma U-251 MG cell spheroids. We evaluated toxicity and invasion inhibition using a 3D Matrigel invasion assay. We next selected three compounds that inhibited invasion and screened them in patient-derived glioblastoma organoids (GBOs). We developed a platform using available macros for FIJI/ImageJ to quantify invasion from the outer margin of organoids. Our data demonstrated that a high-throughput invasion screening can be done using both an established cell line and patient-derived 3D model systems. Tubulin inhibitor compounds had the best efficacy with U-251 MG cells, however, in ex vivo patient organoids the results were highly variable. Our results indicate that the efficacy of compounds is highly related to patient intra and inter-tumor heterogeneity. These results indicate that such models can be used to evaluate personal oncology therapeutic strategies.
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影响因子：
6.7
作者：
Janganati V;Ponder J;Balasubramaniam M;Bhat-Nakshatri P;Bar EE;Nakshatri H;Jordan CT;Crooks PA
通讯作者：
Crooks PA
影响因子：
4.7
作者：
Darrigues E;Elberson BW;De Loose A;Lee MP;Green E;Benton AM;Sink LG;Scott H;Gokden M;Day JD;Rodriguez A
通讯作者：
Rodriguez A
影响因子：
5.8
作者：
Arganda-Carreras, Ignacio;Kaynig, Verena;Seung, H. Sebastian
通讯作者：
Seung, H. Sebastian
影响因子：
4.6
作者：
Eilenberger, Christoph;Rothbauer, Mario;Kuepcue, Seta
通讯作者：
Kuepcue, Seta
影响因子：
4.6
作者：
Akay M;Hite J;Avci NG;Fan Y;Akay Y;Lu G;Zhu JJ
通讯作者：
Zhu JJ