Splicing-based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer

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.

NCT Number	NCT07226115
Lead Sponsor	City of Hope Medical Center
Conditions	Colorectal Cancer, Colorectal Cancer Recurrent, Colorectal Cancer Stage II, Colorectal Cancer Stage III
Enrollment	200 participants
Start Date	2024-06-21
Primary Completion	2028-06-18 (estimated)
Study Completion	2028-06-18 (estimated)
Updated on ClinicalTrials.gov	2026-07-07