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Modeling the next-generation of rhabdomyosarcoma organoids to predict effective drug combinations

ABSTRACT: Modeling the next-generation of rhabdomyosarcoma organoids to predict effective drug combinations

PROVIDER: PRJNA1041875 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR26866502_1.fastq.gz	Fastqsanger.gz
	SRR26866502_2.fastq.gz	Fastqsanger.gz
	SRR26866503_1.fastq.gz	Fastqsanger.gz
	SRR26866503_2.fastq.gz	Fastqsanger.gz
	SRR26866504_1.fastq.gz	Fastqsanger.gz

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Project description:Rhabdomyosarcoma (RMS) is the main form of pediatric soft-tissue sarcoma. Its cure rate has not improved in the last 20 years notably following relapse, and the lack of reliable preclinical models has so far 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 demonstrated its preclinical relevance by reevaluating the therapeutic opportunities of targeting apoptosis in FNRMS from a streamlined approach based on transcriptomic data exploitation.

2023-12-19 | GSE248178 | GEO