Back

Zebrafish Avatar-test predicts patient's tumor response to chemotherapy in breast cancer: a co-clinical study towards personalized medicine

Mendes, R. V.; Ribeiro, J.; Gouveia, H.; Almeida, C. R. d.; Castillo-Martin, M.; Brito, M. J.; Canas-Marques, R.; Batista, E.; Alves, C.; Sousa, B.; Gouveia, P.; Ferreira, M. G.; Cardoso, M. J.; Cardoso, F.; Fior, R.

2024-10-07 cancer biology
10.1101/2024.10.03.616349 bioRxiv
Show abstract

Chemotherapy remains the mainstay in most high-risk breast cancer (BC) settings, with several equivalent options of treatment. However, the efficacy of each treatment varies between patients and there is currently no test to determine which option will be the most effective for each individual patient. Here, we developed a fast in-vivo test for BC therapy screening: the zebrafish patient derived xenograft model (zAvatars), where in-vivo results can be obtained in just 10 days. To determine the predictive value of the BC zAvatars we performed a clinical study, where zAvatars were treated with the same therapy as the donor-patient and their response to therapy was compared. Our data shows a 100% correlation between patients clinical response to treatment and its matching zAvatar. Altogether, our results suggest that the zAvatar model constitutes a promising in-vivo assay to optimize cancer treatments in truly personalized manner.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.