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

Chinese Academy of Medical Sciences & Peking Union Medical College

Status

Enrolling

Conditions

Colorectal Cancer (CRC)

Proteomics

Neoadjuvant Therapy

Study type

Observational

Funder types

Other

Identifiers

NCT06904677

NCC2025C-464

Details and patient eligibility

About

The goal of this observational study is to learn how well urinary proteins can predict treatment response in patients with locally advanced colorectal cancer (LACC) undergoing neoadjuvant therapy. The main question it aims to answer is:

Can urinary protein markers help predict and evaluate how patients with LACC respond to neoadjuvant therapy?

Participants diagnosed with LACC will provide urine samples before and after neoadjuvant therapy. These samples will be analyzed using 4D deep urinary proteomics and machine learning to identify proteins linked to treatment response. Some participants' tumor tissues will also be used to create organoid models for further testing.

Full description

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.

Enrollment

400 estimated patients

Sex

All

Ages

18 to 75 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

Aged 18-75 years;
Pathologically confirmed diagnosis of locally advanced colorectal cancer (cT3-4 and/or N+);
Planned to undergo neoadjuvant therapy followed by surgical resection;
No evidence of distant metastasis (M0) confirmed by imaging (CT and/or PET-CT);
Clinically assessed as being able to tolerate and complete the full course of neoadjuvant treatment;
No prior anti-tumor therapy (e.g., targeted therapy, immunotherapy) before the initiation of treatment;
Willing and able to provide urine samples as required;
Written informed consent obtained.

Exclusion criteria

History of or concurrent diagnosis with other malignancies;
Presence of severe hepatic, renal, cardiovascular, or metabolic diseases that may affect urinary protein metabolism;
Recent use of medications known to affect protein metabolism (e.g., glucocorticoids, high-dose antibiotics);
Urinary tract infection or other diseases known to cause abnormal urinary protein levels (e.g., nephrotic syndrome);
Any other condition deemed unsuitable for enrollment by the investigators.