Fusion-negative rhabdomyosarcoma 3D organoids to predict effective drug combinations: A proof-of-concept on cell death inducers

融合阴性横纹肌肉瘤 3D 类器官预测有效药物组合:细胞死亡诱导剂的概念验证

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作者:Clara Savary, Léa Luciana, Paul Huchedé, Arthur Tourbez, Claire Coquet, Maëlle Broustal, Alejandro Lopez Gonzalez, Clémence Deligne, Thomas Diot, Olivier Naret, Mariana Costa, Nina Meynard, Virginie Barbet, Kevin Müller, Laurie Tonon, Nicolas Gadot, Cyril Degletagne, Valéry Attignon, Sophie Léon, Ch

Abstract

Rhabdomyosarcoma (RMS) is the main form of pediatric soft-tissue sarcoma. Its cure rate has not notably improved in the last 20 years following relapse, and the lack of reliable preclinical models has hampered the design of new therapies. This is particularly true for highly heterogeneous fusion-negative RMS (FNRMS). Although methods have been proposed to establish FNRMS organoids, their efficiency remains limited to date, both in terms of derivation rate and ability to accurately mimic the original tumor. Here, we present the development of a next-generation 3D organoid model derived from relapsed adult and pediatric FNRMS. This model preserves the molecular features of the patients' tumors and is expandable for several months in 3D, reinforcing its interest to drug combination screening with longitudinal efficacy monitoring. As a proof-of-concept, we demonstrate its preclinical relevance by reevaluating the therapeutic opportunities of targeting apoptosis in FNRMS from a streamlined approach based on transcriptomic data exploitation.

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