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A Biomarker Exploration Study for Predicting Localization of Recurrence and Metastasis in Colorectal Cancer (PROBE Study)

A Multicenter, Observational Clinical Study Evaluating the Application of Multi-Omics Tumor-agnostic Technology for Localizing Postoperative Local Recurrence and Distant Metastasis in Colorectal Cancer

Status
Not yet recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT07685236
Acronym
PROBE
Enrollment
586
Registered
2026-07-06
Start date
2026-06-29
Completion date
2027-06-28
Last updated
2026-07-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Metastatic Colorectal Cancer (CRC)

Keywords

Metastatic Colorectal Cancer, Locally recurrent colorectal cancer

Brief summary

With high incidence and mortality rate, the effective therapeutic options of colorectal cancer remain limited. Up to 50% of patients with colorectal cancer will develop metastatic disease. Recurrent lesions can be diagnosed and characterized only when tumors have reached a certain volume by present radiologic imaging techniques such as CT and MRI. The exploration of differentiated clinical applications specifically for local recurrence versus distant metastasis remains an unmet need. The aim of this study is to explore methylation, fragmentomic, and cfRNA markers associated with local recurrence and distant metastasis of colorectal cancer by multi-omics approaches. By constructing a predictive model for the localization of post-treatment recurrence and metastasis, this study will compare the accuracy of different technical approaches in predicting the localization of recurrence and metastasis after colorectal cancer treatment.

Interventions

None listed

Sponsors

The First Affiliated Hospital with Nanjing Medical University
Lead SponsorOTHER

Study design

Observational model
CASE_ONLY
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No

Inclusion criteria

* Age ≥ 18 years, male or female. * Patients with the following tumor types: primary colorectal cancer, locally recurrent colorectal cancer, colorectal cancer liver metastasis, colorectal cancer lung metastasis, colorectal cancer peritoneal metastasis, colorectal cancer bone metastasis, colorectal cancer multi-organ metastasis, primary liver cancer, primary lung cancer, primary peritoneal cancer, primary bone tumor. * No prior surgery or anti-tumor treatment at the time of enrollment assessment. * Subjects voluntarily participate in this study and sign the informed consent form.

Exclusion criteria

* Previous history of malignant tumors other than those within the inclusion criteria. * Pregnant or breastfeeding women.

Design outcomes

Primary

MeasureTime frameDescription
Area Under the Receiver Operating Characteristic Curve (AUC) of the prediction models based on methylation, fragmentomics, and cfRNA data for local recurrence and distant metastasis localization in colorectal cancerAt baseline (at the time of study enrollment, prior to any surgical or anti-tumor treatment)The AUC is calculated from the Receiver Operating Characteristic (ROC) curve to evaluate the discriminatory performance of the prediction model developed by use of methylation, fragmentomics, and cfRNA data. Model performance will be assessed on the validation cohort.

Secondary

MeasureTime frameDescription
Accuracy of the prediction models based on methylation, fragmentomics, and cfRNA data for colorectal cancer recurrence and metastasis localizationBaseline (at enrollment)Accuracy is defined as the proportion of correctly predicted samples (true positives + true negatives) among the total number of samples in the validation cohort.
Sensitivity and specificity of individual omics technologies (methylation, fragmentomics, and cfRNA)Baseline (at enrollment)Comparison of predictive performance of three individual technical approaches for localization of recurrent and metastatic lesions.

Contacts

CONTACTYanhong Gu, PhD
YanhongGu@njmu.edu.cn+025-68307881

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Jul 7, 2026