Actively Recruiting
Splicing-based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer
Led by City of Hope Medical Center · Updated on 2025-11-10
200
Participants Needed
1
Research Sites
103 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Although adjuvant chemotherapy improves survival after curative resection, its efficacy varies widely among patients. The absence of reliable predictive biomarkers often leads to overtreatment or undertreatment. This study aims to develop a machine learning-based predictive model for adjuvant chemotherapy response using tumor-derived alternative splicing signatures. By integrating RNA-seq data, splicing isoform and clinical outcomes, this study seeks to identify molecular predictors of treatment response and recurrence risk after surgery.
CONDITIONS
Official Title
Splicing-based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Histologically confirmed stage II-III colorectal cancer (TNM classification, 8th edition)
- Received standard adjuvant chemotherapy after curative resection
- Availability of tumor tissue (FFPE or frozen) before chemotherapy
- Sufficient clinical data for outcome analysis (recurrence, survival)
- Age 18-80 years
You will not qualify if you...
- Inflammatory bowel disease
- Inadequate RNA quality or lack of consent
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
City of Hope Medical Center
Duarte, California, United States, 91010
Actively Recruiting
Research Team
A
Ajay Goel, PhD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
4
Not the Right Trial for You?
Explore thousands of other clinical trials that might be a better match.
Sign up to get personalized trial recommendations delivered to your inbox.
Already have an account? Log in here