类有机物
卵巢癌
精密医学
个性化医疗
体内
医学
癌症
癌症研究
内科学
计算生物学
生物
病理
生物信息学
神经科学
遗传学
作者
Oded Kopper,Chris J. de Witte,Kadi Lõhmussaar,Jose Espejo Valle-Inclán,Nizar Hami,Lennart Kester,Anjali Vanita Balgobind,Jeroen Korving,Natalie Proost,Harry Begthel,Lise M. van Wijk,Sonia Aristín Revilla,Rebecca Theeuwsen,Marieke van de Ven,Markus J. van Roosmalen,Bas Ponsioen,Victor W. Ho,Benjamin G. Neel,Tjalling Bosse,Katja N. Gaarenstroom
出处
期刊:Nature Medicine
[Nature Portfolio]
日期:2019-04-22
卷期号:25 (5): 838-849
被引量:645
标识
DOI:10.1038/s41591-019-0422-6
摘要
Ovarian cancer (OC) is a heterogeneous disease usually diagnosed at a late stage. Experimental in vitro models that faithfully capture the hallmarks and tumor heterogeneity of OC are limited and hard to establish. We present a protocol that enables efficient derivation and long-term expansion of OC organoids. Utilizing this protocol, we have established 56 organoid lines from 32 patients, representing all main subtypes of OC. OC organoids recapitulate histological and genomic features of the pertinent lesion from which they were derived, illustrating intra- and interpatient heterogeneity, and can be genetically modified. We show that OC organoids can be used for drug-screening assays and capture different tumor subtype responses to the gold standard platinum-based chemotherapy, including acquisition of chemoresistance in recurrent disease. Finally, OC organoids can be xenografted, enabling in vivo drug-sensitivity assays. Taken together, this demonstrates their potential application for research and personalized medicine. A biobank of ovarian cancer organoids recapitulates the histopathological and molecular hallmarks of patient tumors and provides a resource for preclinical research.
科研通智能强力驱动
Strongly Powered by AbleSci AI