EMBO Molecular MedicineCancer BiologyPeer-reviewed journal article
Repurposing statins and phenothiazines to treat chemoresistant neuroblastoma.
Katarzyna RadkeiD, Kristina E. AaltoneniD, Erick Andrés Muciño-OlmosiD, Javanshir EsfandyariiD, Aleksandra AdamskaiD, Joachim T. SiawiD, Dora AdamičiD, Chiara LagoiD, Adriana MañasiD, Alexandra SegeriD, Karin HanssoniD, Oksana RogovaiD, Sophie LehniD, Daniel J. Mason, Daniel J O’DonovaniD, Ian RobertsiD, Antonia LockiD, Jane BrennaniD, Kristian PietrasiD, Emma Jane DaviesiD, Peter Spégel, Oscar C. Bedoya-ReinaiD, David Brown, Neil T. Thompson, Cesare Spadoni, Daniel BexelliD
Abstract
Relapse and treatment resistance are common in children with high-risk neuroblastoma, and novel therapies are needed. Conventional drug discovery is slow, expensive, often fails in practice, and consequently falls short in addressing pediatric and rare conditions. In such instances, drug repurposing is a promising strategy. Here, we used two independent in silico prediction tools including machine learning to identify approved drugs for repurposing against neuroblastoma. The combination of statins and phenothiazines showed strong synergistic effects in human neuroblastoma organoids, decreased tumor growth and prolonged survival in MYCN-amplified neuroblastoma patient-derived xenografts. The drug combination altered cholesterol metabolism through a dual-hit mechanism and induced a phenotypic switch towards an adrenergic cell state accompanied by increased sensitivity to chemotherapy. Integration of the drug combination into standard-of-care chemotherapy regressed tumors and prolonged survival in chemoresistant patient-derived xenografts. Thus, a combination of safe and approved medications added to standard-of-care chemotherapy outperforms chemotherapy alone in chemoresistant neuroblastoma. TeaserUsing in silico prediction tools we repurposed statins and phenothiazines as a combination with translational potential in high-risk neuroblastoma.
- organism: human
- assay: organoid / primary culture
- research focus: drug response / drug discovery
- approach: machine learning (classical)
- Citations
- 2
- FWCI
- 1.21
- Field percentile
- top 18%
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