At a glance
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Splicing-Based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer (SPLICE)
In Brief
An observational study evaluating SPLICE for Colorectal Cancer and 3 related conditions. Currently recruiting, targeting 200 participants across 1 site.
Detailed 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.
Study Details
Timeline
Interventions
A panel of RNA splicing isoform, whose level is tested in tissue samples derived from the primary tumor.