zAvatar-test forecasts patient’s treatment outcome in colorectal cancer: a co-clinical study towards personalized medicine

Abstract

Cancer patients often undergo rounds of trial-and-error to find the most effective treatment because there is no test in the clinical practice for predicting therapy response. Here, we conducted a co-clinical study to validate the zebrafish patient-derived xenograft model (zAvatar) as a fast predictive platform for personalized treatment in colorectal cancer. zAvatars were generated with patient tumor cells, treated exactly with the same therapy

as their corresponding patient and analyzed at single-cell resolution. By individually comparing the clinical responses of 55 patients with their zAvatar- test, we developed a tree decision model integrating tumor stage, zAvatar- apoptosis, and zAvatar-metastatic-potential. This model accurately forecasts patient progression with 91% accuracy. Importantly, patients with a sensitive zAvatar-test exhibited longer progression-free survival compared to those with a resistant test. We propose the zAvatar-test as a rapid approach to guide clinical decisions, optimizing treatment options and improving the survival of cancer patients.