Surgery is the primary form of treatment for colorectal cancer, resulting in cure for approximately 50% of patients. However, recurrence after surgery is a major problem and patients can greatly benefit from post-surgical systemic therapies. However, finding the optimal therapy for a patient can be challenging, leading to unnecessary side effects and loss of valuable time. In a new study, Rita Fior’s lab at Champalimaud Foundation used their previously described zebrafish patient-derived xenograft model (zAvatar) to propose a fast predictive platform to guide personalized treatment in colorectal cancer. For this assay, the researchers injected fluorescently labeled patient tumor cells into 2 days post fertilization zebrafish embryos, before treating the fish with the same therapy as their corresponding patient. Three days post injection (two days post treatment), the team assessed zAvatar response to treatment by analyzing several readouts such as apoptosis, tumor size and metastasis potential, which were compared to untreated zAvatar controls. Comparing each zAvatar-test to their corresponding patient’s response to chemotherapy revealed that zAvatars could forecast patient progression with 91% accuracy. This new platform could complement other mouse and cell models to help guide clinical decisions and optimize treatment options for patients with cancer.
Original reference: Costa, B. et al. Nat. Commun. 15, 4771 (2024)
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