4.7 Article

A novel integrated system using patient-derived glioma cerebral organoids and xenografts for disease modeling and drug screening

期刊

CANCER LETTERS
卷 500, 期 -, 页码 87-97

出版社

ELSEVIER IRELAND LTD
DOI: 10.1016/j.canlet.2020.12.013

关键词

GBM; Patient-derived glioma model; Cerebral organoid; Disease modeling; Drug screening

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资金

  1. Natural Science Foundation of China (NSFC) [81402249, 81472693, 81702919, 81874030]
  2. Natural Science Foundation of Hunan Province, China [S2019JJQNJJ1625, 2018JJ3809]
  3. China Postdoctoral Science Foundation [2018M643006]

向作者/读者索取更多资源

Researchers developed an integrated system using patient-derived glioma cerebral organoids and xenograft models to understand glioma biology and predict response to chemotherapy drugs, providing a new strategy for personalized treatment of this deadly disease.
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.

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