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<p>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 preclinical study, where zAvatars were treated with the same therapy as the donor-patient and their response to therapy was compared. Our data show a 100% concordance (18 out of 18) between the zAvatar-test and the corresponding patient’s clinical response to treatment. Altogether, our results suggest that the zAvatar model constitutes a promising in-vivo assay to optimize cancer treatments in a truly personalized manner.</p>

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Zebrafish Avatar testing preclinical study predicts chemotherapy response in breast cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月1日

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  • Raquel V. Mendes,
  • Joana M. Ribeiro,
  • Helena Gouveia,
  • Cátia Rebelo de Almeida,
  • Mireia Castillo-Martin,
  • Maria José Brito,
  • Rita Canas-Marques,
  • Eva Batista,
  • Celeste Alves,
  • Berta Sousa,
  • Pedro Gouveia,
  • Miguel Godinho Ferreira,
  • Maria João Cardoso,
  • Fatima Cardoso,
  • Rita Fior

摘要

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 preclinical study, where zAvatars were treated with the same therapy as the donor-patient and their response to therapy was compared. Our data show a 100% concordance (18 out of 18) between the zAvatar-test and the corresponding patient’s clinical response to treatment. Altogether, our results suggest that the zAvatar model constitutes a promising in-vivo assay to optimize cancer treatments in a truly personalized manner.