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)

手术是结直肠癌的主要治疗方式，大约 50% 的患者可以治愈。然而，手术后复发是一个主要问题，患者可以从术后全身治疗中受益匪浅。然而，为患者找到最佳治疗方法可能具有挑战性，从而导致不必要的副作用并浪费宝贵的时间。在一项新研究中，Champalimaud 基金会的 Rita Fior 实验室使用之前描述的斑马鱼患者异种移植模型 (zAvatar) 提出了一个快速预测平台来指导结直肠癌的个性化治疗。在这项检测中，研究人员将荧光标记的患者肿瘤细胞注射到受精后 2 天的斑马鱼胚胎中，然后用与相应患者相同的疗法治疗斑马鱼。注射三天后（治疗后两天），研究小组通过分析细胞凋亡、肿瘤大小和转移潜力等多项读数来评估 zAvatar 对治疗的反应，并将其与未经治疗的 zAvatar 对照进行比较。将每个 zAvatar 测试与相应患者对化疗的反应进行比较后发现，zAvatar 可以以 91% 的准确度预测患者病情进展。这个新平台可以补充其他小鼠和细胞模型，以帮助指导临床决策并优化癌症患者的治疗选择。

原始参考文献：Costa, B. et al.纳特。交流。 15, 4771 (2024)