About this trial
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
This trial does not accept healthy volunteersQualifiers
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)
Disqualifiers
Inflammatory bowel disease
Inadequate RNA quality or lack of consent
Trial population
Two independent cohorts of colorectal cancer patients treated with adjuvant chemotherapy after curative-intent surgery.
Trial design
Case-control
Retrospective
Treatments tested in this trial
SPLICE
Other interventionA panel of RNA splicing isoform, whose level is tested in tissue samples derived from the primary tumor.
Treatment groups
Trial outcomes
Primary outcomes
Recurrence Free Survival
Time from disease treatment to development of recurrent colorectal cancer
Secondary outcomes
Overall survival
Time from disease treatment to death from any cause
Sponsors and contacts
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