Using 4D Urinary Proteomics to Predict and Evaluate Treatment Response in Colorectal Cancer

Neoadjuvant therapy is one of the main treatment strategies for patients with locally advanced colorectal cancer (LACC). However, the response to neoadjuvant therapy varies greatly among individuals, presenting a significant clinical challenge in accurately predicting therapeutic efficacy before treatment and dynamically assessing response during therapy. Commonly used clinical methods-such as imaging techniques, tissue biomarkers, and liquid biomarkers-often suffer from low sensitivity and specificity. In our previous research, we applied 4D deep urinary proteomics to analyze pre-treatment urine samples from patients classified as responders and non-responders to neoadjuvant therapy. The results demonstrated that urinary proteomic profiles reflect differences in the tumor microenvironment associated with treatment response and hold promise for predicting therapeutic efficacy. Building on this foundation, the current project aims to optimize the 4D deep urinary proteomics workflow and perform comparative analyses of urine samples collected before and after neoadjuvant therapy. Machine learning algorithms will be employed to identify candidate urinary proteins associated with treatment response, and key proteins will be validated using targeted proteomics and immunological techniques. Additionally, patient-derived organoid (PDO) models will be used to explore the biological functions of candidate proteins and elucidate their roles in mediating sensitivity to neoadjuvant therapy. This study is expected to enable precise stratification of LACC patients and support the implementation of personalized treatment strategies. Furthermore, it may uncover mechanisms of resistance and propose novel therapeutic approaches to improve clinical decision-making and outcomes.