A novel integrated system using patient-derived glioma cerebral organoids and xenografts for disease modeling and drug screening
A novel integrated system using patient-derived glioma cerebral organoids and xenografts for disease modeling and drug screening
复制标题
一种使用患者来源的胶质瘤脑类器官和异种移植物进行疾病建模和药物筛选的新型集成系统。
DOI：
10.1016/j.canlet.2020.12.013
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发表时间：
2021-03-01
期刊：
影响因子：
9.7
通讯作者：
Liu, Zhixiong
中科院分区：
文献类型：
--
作者：
Zhang, Liyang;Liu, Fangkun;Liu, Zhixiong
生理学相关的胶质瘤肿瘤模型对于研究疾病进展和筛选候选药物是重要的。然而，目前的临床前胶质瘤模型缺乏脑微环境，建立的肿瘤细胞系不能代表胶质瘤生物学，不能用于评价治疗效果。在这里，我们报告了一个实时集成系统，通过生成3D离体脑类器官和基于胶质瘤患者来源的组织和细胞的体内异种移植肿瘤。我们的系统忠实地概括了组织学特征，对化疗药物的反应，以及相应的亲本肿瘤的临床进展。此外，我们的模型成功地从一名II级星形细胞瘤患者中识别出一例病例，该患者在类器官和异种移植模型中均具有典型的IV级GBM特征，这模拟了该患者的疾病进展。进一步的基因组和转录组学表征与个体临床特征相关。我们已经证明了“GBM-和正常样”特征来预测预后。总之，我们开发了一个来自患者源性胶质瘤脑类器官和异种移植物的并行模型的集成系统，用于了解胶质瘤生物学和预测对化疗药物的反应，这可能会导致这种致命疾病的个性化治疗的新策略。
A physiologically relevant glioma tumor model is important to the study of disease progression and screening drug candidates. However, current preclinical glioma models lack the brain microenvironment, and the established tumor cell lines do not represent glioma biology and cannot be used to evaluate the therapeutic effect. Here, we reported a real-time integrated system by generating 3D ex vivo cerebral organoids and in vivo xenograft tumors based on glioma patient-derived tissues and cells. Our system faithfully recapitulated the histological features, response to chemotherapy drugs, and clinical progression of their corresponding parental tumors. Additionally, our model successfully identified a case from a grade II astrocytoma patient with typical grade IV GBM features in both organoids and xenograft models, which mimicked the disease progression of this patient. Further genomic and transcriptomic characterization was associated with individual clinical features. We have demonstrated the "GBM-&Normal-like" signature to predict prognosis. In conclusion, we developed an integrated system of parallel models from patient-derived glioma cerebral organoids and xenografts for understanding the glioma biology and prediction of response to chemotherapy drugs, which might lead to a new strategy for personalized treatment for this deadly disease.