Predicting Chemotherapy Response in Colorectal Cancer Using Tumor RNA Splicing Patterns SPLICE Study

What this Trial Is About

Colorectal cancer CRC is a leading cause of cancer-related death worldwide. While adjuvant chemotherapy after surgery can improve survival, patient responses vary and there is no reliable molecular model to guide treatment choices. This research aims to develop a machine learning-based model using tumor-derived alternative splicing patterns to predict chemotherapy response and recurrence risk in CRC patients. The study will analyze RNA sequencing data to identify alternative splicing signatures linked to chemotherapy outcomes. Researchers will develop an assay called SPLICE, based on RT-qPCR and machine learning, to predict individual responses to adjuvant chemotherapy. The assay will be tested and validated in separate patient cohorts, with follow-up extending up to five years after treatment. Participants will provide tumor tissue samples and clinical data for analysis. The study will measure disease-free survival and overall survival up to 60 months after treatment. This observational study does not involve administering treatments but collects detailed molecular and clinical information to improve future treatment decisions for colorectal cancer.

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