Clinical trial NCT06904677: Using 4D Urinary Proteomics to Predict and Evaluate Treatment Response in Colorectal Cancer
Official title: Predicting and Evaluating the Efficacy of Neoadjuvant Therapy in Colorectal Cancer Based on 4D Deep Urinary Proteomics Technology
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Colorectal Cancer (CRC)
Neoadjuvant Therapy
Proteomics
Keywords: Colorectal caner
Neoadjuvant therapy
Urinary proteomics
Lead sponsor: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Sponsor class: OTHER
First posted: 2025-04-01
Enrollment: 400
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: This study will enroll patients aged 18 to 75 years with a pathologically confirmed diagnosis of locally advanced colorectal cancer (LACC), defined as clinical stage cT3-4 and/or N+ without distant metastasis (M0) as determined by imaging (CT and/or PET-CT). Eligible participants must be treatment-naïve, with no prior exposure to anti-tumor therapies such as chemotherapy, targeted therapy, or immunotherapy. All patients must be clinically assessed as suitable candidates for neoadjuvant therapy followed by surgical resection and must be able to tolerate and complete the prescribed treatment regimen. In addition, participants must be willing and able to provide urine samples before and after neoadjuvant therapy and must sign written informed consent prior to enrollment.

Patients will be excluded if they have a history of or concurrent diagnosis of other malignancies; suffer from serious hepatic, renal, cardiovascular, or metabolic conditions that may interfere with urinary protein metab

Brief summary: 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

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

Primary outcomes:
measure: Tumor regression grade
timeFrame: through study completion, an average of 3 months
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Cancer Hospital Chinese Academy of Medical Sciences
state: Chaoyang district
Study officials:
References: