DOI 10.1186/s12967-026-08107-z
Modeling metastasis and predicting drug response with malignant effusion-derived organoids: a systematic review and quantitative assessment.
Аннотация
BACKGROUND
Malignant effusions provide a critical window into metastatic biology. Malignant effusion-derived organoids (ME-PDTOs) hold promise for modeling metastasis and predicting drug response, but the evidence remains fragmented. This systematic review aims to synthesize current evidence on the validity of ME-PDTOs as metastasis models and their concordance with clinical drug responses.
METHODS
We systematically reviewed literature from the past 15 years in strict accordance with PRISMA 2020 guidelines for study identification and selection. Data on genetic, transcriptional, functional, and clinical correlation were extracted. Given sample size limitations and heterogeneity, a descriptive, study-level analysis was performed. Predictive performance (sensitivity, specificity) was calculated with 95% confidence intervals using multiple methods.
RESULTS
Sixteen studies (87 ME-PDTOs from 84 patients) were included. ME-PDTOs retained key driver mutations and displayed transcriptomic enrichment of epithelial-mesenchymal transition and stemness pathways. Functionally, they demonstrated migratory, invasive, and in vivo metastatic capacity. For drug response, six studies provided 77 drug-patient pairs (predominantly lung cancer, 89.6%). In the two largest lung cancer studies, ME-PDTO sensitivity for predicting clinical efficacy ranged from 0.82 to 0.90, and specificity from 0.80 to 1.00, though confidence intervals were wide in smaller studies.
CONCLUSION
Current evidence suggests that ME-PDTOs can recapitulate key metastatic features and show a promising correlative trend with clinical drug responses in lung cancer. However, significant limitations exist: evidence is limited, heterogeneous, and subject to selection and measurement biases. Future standardized, prospective studies are needed to validate their clinical predictive utility and address translational challenges.
TRIAL REGISTRATION REGISTRATION NUMBER (PROSPERO): CRD420251107909.
Предметные рубрики MeSH
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
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