Zebrafish Avatar-test forecasts clinical response to chemotherapy in patients with colorectal cancer.

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 conduct a clinical study to validate the zebrafish patient-derived xenograft model (zAvatar) as a fast predictive platform for personalized treatment in colorectal cancer. zAvatars are 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 develop a decision tree 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 exhibit 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.

Textbook Oncological Outcomes for Robotic Colorectal Cancer Resections: An Observational Study of Five Robotic Colorectal Units.

BACKGROUND: The quality of care of patients receiving colorectal resections has conventionally relied on individual metrics. When discussing with patients what these outcomes mean, they often find them confusing or overwhelming. Textbook oncological outcome (TOO) is a composite measure that summarises all the 'desirable' or 'ideal' postoperative clinical and oncological outcomes from both a patient's and doctor's point of view. This study aims to evaluate the incidence of TOO in patients receiving robotic colorectal cancer surgery in five robotic colorectal units and understand the risk factors associated with failure to achieve a TOO in these patients. METHODS: We present a retrospective, multicentric study with data from a prospectively collected database. All consecutive patients receiving robotic colorectal cancer resections from five centres between 2013 and 2022 were included. Patient characteristics and short-term clinical and oncological data were collected. A TOO was achieved when all components were realized-no conversion to open, no complication with a Clavien-Dindo (CD) ≥ 3, length of hospital stay ≤ 14, no 30-day readmission, no 30-day mortality, and R0 resection. The main outcome measure was a composite measure of "ideal" practice called textbook oncological outcomes. RESULTS: A total of 501 patients submitted to robotic colorectal cancer resection were included. Of the 501 patients included, 388 (77.4%) achieved a TOO. Four patients were converted to open (0.8%); 55 (11%) had LOS > 14 days; 46 (9.2%) had a CD ≥ 3 complication; 30-day readmission rate was 6% (30); 30-day mortality was 0.2% (1); and 480 (95.8%) had an R0 resection. Abdominoperineal resection was a risk factor for not achieving a TOO. CONCLUSIONS: Robotic colorectal cancer surgery in robotic centres achieves a high TOO rate. Abdominoperineal resection is a risk factor for failure to achieve a TOO. This measure may be used in future audits and to inform patients clearly on success of treatment.