PUBLICATION

Zebrafish Patient-Derived Xenograft Model to Predict Treatment Outcomes of Colorectal Cancer Patients

Authors
Di Franco, G., Usai, A., Piccardi, M., Cateni, P., Palmeri, M., Pollina, L.E., Gaeta, R., Marmorino, F., Cremolini, C., Dente, L., Massolo, A., Raffa, V., Morelli, L.
ID
ZDB-PUB-220728-9
Date
2022
Source
Biomedicines   10(7): (Journal)
Registered Authors
Keywords
chemosensitivity, colorectal cancer, personalized medicine, preclinical model, zebrafish avatar
MeSH Terms
none
PubMed
35884780 Full text @ Biomedicines
Abstract
The use of zebrafish embryos for personalized medicine has become increasingly popular. We present a co-clinical trial aiming to evaluate the use of zPDX (zebrafish Patient-Derived Xenografts) in predicting the response to chemotherapy regimens used for colorectal cancer patients. zPDXs are generated by xenografting tumor tissues in two days post-fertilization zebrafish embryos. zPDXs were exposed to chemotherapy regimens (5-FU, FOLFIRI, FOLFOX, FOLFOXIRI) for 48 h. We used a linear mixed effect model to evaluate the zPDX-specific response to treatments showing for 4/36 zPDXs (11%), a statistically significant reduction of tumor size compared to controls. We used the RECIST criteria to compare the outcome of each patient after chemotherapy with the objective response of its own zPDX model. Of the 36 patients enrolled, 8 metastatic colorectal cancer (mCRC), response rate after first-line therapy, and the zPDX chemosensitivity profile were available. Of eight mCRC patients, five achieved a partial response and three had a stable disease. In 6/8 (75%) we registered a concordance between the response of the patient and the outcomes reported in the corresponding zPDX. Our results provide evidence that the zPDX model can reflect the outcome in mCRC patients, opening a new frontier to personalized medicine.
Genes / Markers
Figures
Show all Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping