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

Conditions: Colorectal Cancer, Colorectal Cancer Recurrent, Colorectal Cancer Stage II, Colorectal Cancer Stage III

Sex: All

Ages: 18 Years – 80 Years

Healthy volunteers: No

Enrollment: 200

Sponsor: City of Hope Medical Center

Summary

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.

Eligibility Criteria

Inclusion Criteria: * 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 Stage Exclusion Criteria: * Inflammatory bowel disease * Inadequate RNA quality or lack of consent

Interested in this study? View the official listing for contact and enrollment details.

View on ClinicalTrials.gov

Source: ClinicalTrials.gov (NCT07226115). StuddyBuddy aggregates publicly available trial information.