医学
个性化医疗
仿形(计算机编程)
胶质母细胞瘤
替莫唑胺
精密医学
药品
药物发现
肿瘤科
临床试验
生物信息学
计算生物学
内科学
癌症研究
药理学
病理
计算机科学
生物
操作系统
作者
Miriam Ratliff,Hichul Kim,Hao Qi,Minsung Kim,Bosung Ku,Daniel Domínguez Azorín,David Hausmann,Rajiv K. Khajuria,Areeba Patel,Elena Maier,Loic Cousin,Arnaud Ogier,Felix Sahm,Nima Etminan,Lukas Bunse,Frank Winkler,Victoria El‐Khoury,Michael Platten,Yong‐Jun Kwon
摘要
An obstacle to effective uniform treatment of glioblastoma, especially at recurrence, is genetic and cellular intertumoral heterogeneity. Hence, personalized strategies are necessary, as are means to stratify potential targeted therapies in a clinically relevant timeframe. Functional profiling of drug candidates against patient-derived glioblastoma organoids (PD-GBO) holds promise as an empirical method to preclinically discover potentially effective treatments of individual tumors. Here, we describe our establishment of a PD-GBO-based functional profiling platform and the results of its application to four patient tumors. We show that our PD-GBO model system preserves key features of individual patient glioblastomas in vivo. As proof of concept, we tested a panel of 41 FDA-approved drugs and were able to identify potential treatment options for three out of four patients; the turnaround from tumor resection to discovery of treatment option was 13, 14, and 15 days, respectively. These results demonstrate that this approach is a complement and, potentially, an alternative to current molecular profiling efforts in the pursuit of effective personalized treatment discovery in a clinically relevant time period. Furthermore, these results warrant the use of PD-GBO platforms for preclinical identification of new drugs against defined morphological glioblastoma features.
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