Referral, Uptake, and Outcome of Genetic Counseling and Testing in Patients With Early-Onset Colorectal Cancer.
Hareem Syed, Joshua Sommovilla, Carol A Burke...
https://pubmed.ncbi.nlm.nih.gov/37935108/Actively Recruiting
Led by City of Hope Medical Center · Updated on 2026-07-07
400
Participants Needed
13
Research Sites
N/A
Total Duration
Early-onset colorectal cancer EOCRC, defined as colorectal cancer diagnosed before age 50, is rising sharply among young adults and is a leading cause of cancer-related death in this group. This research aims to develop a non-invasive blood test using microRNA and advanced machine learning to detect EOCRC earlier and more effectively than current screening methods, which are often invasive and have low participation rates in younger populations. The study focuses on analyzing circulating cell-free microRNAs and exosomal microRNAs from blood samples to create a combined diagnostic panel. This approach seeks to improve detection accuracy by leveraging the sensitivity of cell-free microRNAs and the specificity of exosomal microRNAs. The research is conducted in three phases sequencing RNA samples, developing and training detection panels using machine learning, and validating the test in an independent group of participants including EOCRC patients and cancer-free controls. Participants contribute blood samples for molecular testing and data analysis. Researchers will assess the tests sensitivity, specificity, and overall accuracy over approximately one year. The study aims to produce a cost-effective and patient-friendly screening tool that could enhance early detection of EOCRC, improve patient care, and reduce mortality rates.
CONDITIONS
Early Onset Colorectal Cancer Detection
A
Ajay Goel, PhD
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City of Hope Medical Center
Lead Sponsor
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Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 visit (in-person or remote)
Duration - Up to 1 year
Participants provide blood samples for analysis of microRNAs to develop and validate the non-invasive test.
Periodic visits for blood draws
Duration - Up to 1 year
Researchers analyze test results using machine learning models and monitor outcomes to assess sensitivity, specificity, and accuracy.
Follow-up assessments as needed
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
4
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