Despite advances in precision medicine, 30% of high-risk pediatric cancers lack an actionable molecular target, hindering effective treatment and affecting survival outcomes. Although mouse patient-derived xenograft (PDX) models offer additional insights into clinical drug responses, delivering findings from these models within a clinically actionable time frame remains challenging. This international collaboration between two national precision medicine programs demonstrates proof-of-principle that individualized larval zebrafish PDXs can robustly and rapidly assess clinical responses in high-risk pediatric cancers. Retrospective zebrafish PDX testing was performed on tumor samples from 10 pediatric patients with high-risk cancers. Drug responses in zebrafish models were correlated with clinical responses for each patient and directly compared with responses in cognate mouse PDX models. Responses to conventional and targeted therapies, administered as single agents or in combinations, were assessed. Zebrafish PDXs were successfully established from all 10 patients and provided robust drug response data in every case, including from three patients whose tumor samples could not be engrafted in mice. Remarkably, zebrafish models accurately recapitulated patient responses for 11 of 12 treatment regimens. These findings highlight the potential of larval zebrafish PDX models to provide real-time, clinically relevant drug response data, supporting their potential use in prospective precision medicine studies. SIGNIFICANCE: This proof-of-principle study is the first to compare drug responses in larval zebrafish and mouse PDX models with patient outcomes in pediatric precision oncology, showing high concordance. Results highlight the potential of zebrafish PDX models to predict drug responses in high-risk cancers more accurately, rapidly, and cost-effectively in prospective studies.
Modeling High-Risk Pediatric Cancers in Zebrafish to Inform Precision Therapy.
利用斑马鱼模拟高危儿童癌症,为精准治疗提供信息
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作者:Azzam Nadine, Fletcher Jamie I, Melong Nicole, Lau Loretta M S, Dolman Emmy M, Mao Jie, Tax Gabor, Cadiz Roxanne, Tuzi Lissandra, Kamili Alvin, Dumevska Biljana, Xie Jinhan, Chan Jennifer A, Senger Donna L, Grover Stephanie A, Malkin David, Haber Michelle, Berman Jason N
| 期刊: | Cancer Research Communications | 影响因子: | 3.300 |
| 时间: | 2025 | 起止号: | 2025 Jul 1; 5(7):1215-1227 |
| doi: | 10.1158/2767-9764.CRC-25-0080 | 研究方向: | 肿瘤 |
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