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Zebrafish tumour xenograft models: a prognostic approach to epithelial ovarian cancer
- Publication Year :
- 2024
-
Abstract
- Epithelial ovarian cancer (EOC) is the gynaecological malignancy with highest mortality. Although adjuvant treatment with carboplatin and paclitaxel leads to an objective response in similar to 80% of these patients, a majority will relapse within two years. Better methods for assessing long-term treatment outcomes are needed. To address this, we established safe and efficacious doses of carboplatin and paclitaxel using IGROV-1 zebrafish-CDX models. Then fluorescently-labelled cell suspensions from 83 tumour biopsies collected at exploratory laparotomy of women with suspected EOC were generated and 37 (45%) were successfully implanted in zebrafish larvae. Among these 19 of 27 pathology-confirmed EOC samples (70%) engrafted. These zebrafish patient-derived tumour xenograft (ZTX) models were treated with carboplatin or paclitaxel and tumour growth/regression and metastatic dissemination were recorded. In a subgroup of nine patients, four ZTX models regressed during carboplatin treatment. All four corresponding patients had > 24 months PFS. Furthermore, both ZTX models established from two patients having < 24 months PFS failed to regress during carboplatin treatment. Seven of eight models seeding < 6 metastatic cells were established from patients having > 24 months PFS. In eleven of fourteen patients, FIGO stage I + II or III tumours gave rise to ZTX models seeding < 4 or > 4 metastatic cells, respectively. In conclusion, ZTX models predicted patients having > 24 or < 24 months PFS, based on response/no response to carboplatin. Furthermore, high metastatic dissemination in ZTX models correlated to shorter PFS and more advanced disease at diagnosis. These preliminary results suggest that ZTX models could become a useful prognostic tool in EOC treatment planning.<br />Funding Agencies|VINNOVA (Swedish Governmental Agency for Innovation Systems) [2017-01444, H2020-MSCA-RISE-Crystal3, LIO-934261]; VINNOVA
Details
- Database :
- OAIster
- Notes :
- English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1442970929
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.1038.s41698-024-00550-9