zAvatar-test—A functional precision model to personalize ovarian cancer treatments: Results from a co-clinical study

In ovarian cancer, 80% of patients relapse after first-line therapy. In recurrent cases, oncologists lack reliable tests to guide chemotherapy choices, creating an unmet clinical need. Here, we develop the ovarian cancer zebrafish Avatar-test, a functional in vivo model using patient tumor cells implanted in zebrafish embryos to predict treatment responses. We present the largest observational study (32 patients), where the zAvatar-test achieves 91% accuracy in predicting patient outcomes. Patients with a zAvatar-sensitive-test correlate with longer progression-free survival (17 vs. 6 months). Tumors in zAvatars are dynamic, with human-host cell interactions, and higher metastatic potential in poor-prognosis cases. Finally, as a proof of concept, we demonstrate that venetoclax has the potential to sensitize multidrug-resistant tumors. Altogether, this clinical study demonstrates that the zAvatar-test may help clinicians personalize treatments for ovarian cancer patients. We are now conducting a multicentric randomized clinical trial to evaluate the zAvatar-test as a companion tool in clinical oncology.

Authors

Institutions

Publication Details

Journal
Cell Reports Medicine
Published
2025-12-30
DOI
https://doi.org/10.1016/j.xcrm.2025.102530
Primary Topic
Zebrafish Biomedical Research Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

zAvatar-test—A functional precision model to personalize ovarian cancer treatments: Results from a co-clinical study

Ana Marreiros, Cátia Rebelo de Almeida, Audrey LeFormal, Henrique Nabais et al.
Cell Reports Medicine
Zebrafish Biomedical Research Applications
article

zAvatar-test—A functional precision model to personalize ovarian cancer treatments: Results from a co-clinical study

Ana Marreiros, Cátia Rebelo de Almeida, Audrey LeFormal, Henrique Nabais, João Casanova, Heinzelmann-Schwarz Viola, Ricardo Coelho, Maria de Lurdes Batarda, Rita Fior, Rita Canas-Marques, Francis Jacob, Marta F. Estrada, Márcia Fontes, Filipa Amorim, Elisa Yaniz-Galende, Mireia Castillo-Martin, Sónia Ferreira, Filipa Ferreira da Silva, Alexandra Leary
article en

Abstract

In ovarian cancer, 80% of patients relapse after first-line therapy. In recurrent cases, oncologists lack reliable tests to guide chemotherapy choices, creating an unmet clinical need. Here, we develop the ovarian cancer zebrafish Avatar-test, a functional in vivo model using patient tumor cells implanted in zebrafish embryos to predict treatment responses. We present the largest observational study (32 patients), where the zAvatar-test achieves 91% accuracy in predicting patient outcomes. Patients with a zAvatar-sensitive-test correlate with longer progression-free survival (17 vs. 6 months). Tumors in zAvatars are dynamic, with human-host cell interactions, and higher metastatic potential in poor-prognosis cases. Finally, as a proof of concept, we demonstrate that venetoclax has the potential to sensitize multidrug-resistant tumors. Altogether, this clinical study demonstrates that the zAvatar-test may help clinicians personalize treatments for ovarian cancer patients. We are now conducting a multicentric randomized clinical trial to evaluate the zAvatar-test as a companion tool in clinical oncology.

Cell Reports MedicineVol. 7(1)
Champalimaud Foundation (PT), University of Basel (CH), Institut Gustave Roussy (FR), University Hospital of Basel (CH), Algarve Biomedical Center (PT), University of Algarve (PT)
No poverty
Openalex Percentile: Top 33%
Zebrafish Biomedical Research Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.