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

NCT07226115 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2025-11-10

No results posted yet for this study

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.

Conditions

  • Colorectal Cancer
  • Colorectal Cancer Recurrent
  • Colorectal Cancer Stage II
  • Colorectal Cancer Stage III

Interventions

OTHER

SPLICE

A panel of RNA splicing isoform, whose level is tested in tissue samples derived from the primary tumor.

Sponsors & Collaborators

  • City of Hope Medical Center

    lead OTHER

Principal Investigators

  • Ajay Goel, PhD · City of Hope Medical Center

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-06-21
Primary Completion
2026-06-18
Completion
2026-06-18

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07226115 on ClinicalTrials.gov