A high-density 3-dimensional culture model of human glioblastoma for rapid screening of therapeutic resistance
A high-density 3-dimensional culture model of human glioblastoma for rapid screening of therapeutic resistance
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用于快速筛选治疗耐药性的人胶质母细胞瘤高密度三维培养模型
DOI：
10.1101/2022.10.22.22281352
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Brown J
中科院分区：
文献类型：
--
作者：
Brown J
胶质母细胞瘤是最致命的癌症之一，没有已知的治愈方法。许多治疗方法正在开发或临床试验中，但目前没有办法预测哪个患者可能从哪种药物中获益最多。允许预测肿瘤对抗癌药物的反应的测定可以改善临床决策。在这里，我们提出了一个高密度3D原代细胞培养模型，用于手术当天建立的胶质母细胞瘤切除组织的短期测试，在7天内建立，并存活至少3周。高密度3D培养物含有肿瘤和宿主细胞，包括小胶质细胞，并保留其母体肿瘤的关键组织病理学特征，包括增殖活性，标志物GFAP的表达和巨细胞的存在。这提供了3D原代培养物可用于模拟肿瘤异质性的概念验证。重要的是，我们表明高密度3D培养可用于在2-3周的时间范围内测试化疗反应，并可预测患者对替莫唑胺治疗的反应。因此，原代高密度3D培养可能是脑癌研究和预测治疗抗性的有用工具。
Glioblastoma is among the most lethal cancers, with no known cure. A multitude of therapeutics are being developed or in clinical trials, but currently there are no ways to predict which patient may benefit the most from which drug. Assays that allow prediction of the tumor’s response to anti-cancer drugs may improve clinical decision-making. Here, we present a high-density 3D primary cell culture model for short-term testing from resected glioblastoma tissue that is set up on the day of surgery, established within 7 days and viable for at least 3 weeks. High-density 3D cultures contain tumor and host cells, including microglia, and retain key histopathological characteristics of their parent tumors, including proliferative activity, expression of the marker GFAP, and presence of giant cells. This provides a proof-of-concept that 3D primary cultures may be useful to model tumor heterogeneity. Importantly, we show that high-density 3D cultures can be used to test chemotherapy response within a 2–3-week timeframe and are predictive of patient response to Temozolomide therapy. Thus, primary high-density 3D cultures could be a useful tool for brain cancer research and prediction of therapeutic resistance.
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替莫唑胺联合放疗与单独放疗治疗 IDH 野生型胶质母细胞瘤患者：EORTC 随机 3 期 CATNON 试验的事后分析。
影响因子：
11.5
作者：
C. M. S. Tesileanu;M. Sanson;W. Wick;A. Brandes;P. Clement;S. Erridge;M. Vogelbaum;A. Nowak;J. Baurain;W. Mason;H. Wheeler;O. Chinot;S. Gill;M. Griffin;L. Rogers;W. Taal;R. Rudà;M. Weller;C. Mcbain;M. V. van Linde;K. Aldape;R. Jenkins;J. Kros;P. Wesseling;A. von Deimling;Y. Hoogstrate;I. D. de Heer;P. Atmodimedjo;H. Dubbink;R. Brouwer;W. V. van Ijcken;Kin‐Jip Cheung;V. Golfinopoulos;B. Baumert;T. Gorlia;P. French;M. J. van den Bent
通讯作者：
M. J. van den Bent
影响因子：
11.1
作者：
Siebzehnrubl, Florian A.;Silver, Daniel J.;Tugertimur, Bugra;Deleyrolle, Loic P.;Siebzehnrubl, Dorit;Sarkisian, Matthew R.;Devers, Kelly G.;Yachnis, Antony T.;Kupper, Marius D.;Neal, Daniel;Nabilsi, Nancy H.;Kladde, Michael P.;Suslov, Oleg;Brabletz, Simone;Brabletz, Thomas;Reynolds, Brent A.;Steindler, Dennis A.
通讯作者：
Steindler, Dennis A.
影响因子：
50.3
作者：
Verhaak RG;Hoadley KA;Purdom E;Wang V;Qi Y;Wilkerson MD;Miller CR;Ding L;Golub T;Mesirov JP;Alexe G;Lawrence M;O'Kelly M;Tamayo P;Weir BA;Gabriel S;Winckler W;Gupta S;Jakkula L;Feiler HS;Hodgson JG;James CD;Sarkaria JN;Brennan C;Kahn A;Spellman PT;Wilson RK;Speed TP;Gray JW;Meyerson M;Getz G;Perou CM;Hayes DN;Cancer Genome Atlas Research Network
通讯作者：
Cancer Genome Atlas Research Network
影响因子：
64.8
作者：
Bao, Shideng;Wu, Qiulian;Rich, Jeremy N.
通讯作者：
Rich, Jeremy N.