Clinical trial Recruiting at 1 site Observational Ages 18 to 80

Classifying cancer of unknown primary using DNA methylation data

Official title: PaCIFiC-CUP:Pan-Cancer Integrated Fingerprinting Classifier for Identifying the Origin of Cancer of Unknown Primary: A Multi-Center Prospective Cohort Study

Run by Sun Yat-sen University · NCT06140992 · Data updated 2026-11-12

This study is trying to see if a new computer model can help doctors figure out what type of cancer someone has when they don't know where it started, by looking at patterns in DNA from cancer samples.

Quick facts

Study typeObservational
Ages18 Years to 80 Years
SexAll
Enrollment120 (estimated)
SponsorSun Yat-sen University
Academic / other
Locations1 site (Guangzhou, Guangdong)
StatusRecruiting
StartedJuly 1, 2023
Main results expectedDecember 31, 2026
Study endsDecember 31, 2026
Registry record updatedApril 23, 2026
Trial IDNCT06140992 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Ideal candidates for this study are patients diagnosed with a primary site unknown tumor who have provided consent for their specimens to be used in research.

Probably not a fit

Patients who are pregnant or lactating, or those whose tumors do not meet the specified criteria, may not benefit from this study.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This observational study aims to develop a machine learning model called PaCIFiC-CUP that utilizes DNA methylation data from public databases to classify various types of cancer, particularly those with an unknown primary site. By analyzing the DNA methylation patterns of cancer specimens, the study seeks to improve the accuracy of cancer diagnoses and guide precision treatment strategies. The research will involve patient specimens obtained from the Sun Yat-sen University Cancer Center and affiliated centers, with a focus on integrating pan-cancer data for a comprehensive classification approach.

Why it matters

Potential benefit: If successful, this study could significantly enhance the diagnostic accuracy for patients with unknown primary tumors, leading to more effective and personalized treatment options.

How similar studies have gone: Other studies utilizing machine learning and DNA methylation data have shown promise in cancer classification, suggesting that this approach may yield beneficial results.

Who can join: the full rules

Show full inclusion / exclusion criteria
Inclusion Criteria:

1. The patient specimens were obtained from the Sun Yat-sen University Cancer Center and affiliated cooperating centers, with written consent from the patients authorizing the use of the specimens for research purposes.
2. Following standard assessments (medical history, physical examination, complete blood count, biochemistry, computed tomography scans of the neck, chest, abdomen, and pelvis, targeted evaluations of all symptomatic areas, pathology, and immunohistochemistry), the diagnosis was determined as a primary site unknown tumor (including adenocarcinoma, squamous cell carcinoma, undifferentiated carcinoma, neuroendocrine carcinoma, sarcoma, etc).
3. The diagnosis was confirmed at the participating institution and the patient had received systemic therapy.
4. Complete clinical, pathological, and follow-up data for the patients can be obtained.
5. ECOG performance status score: 0-2 points.

Exclusion Criteria:

1. Pregnant or lactating female patients.
2. Tumor tissue sample size is too small (tumor tissue accounts for \<70% in the biopsy or slice tissue).
3. Organ transplant or history of non-autologous (allogeneic) bone marrow or stem cell transplantation.
4. History of previous tumors, with the current condition being a recurrent tumor.
5. Hematological malignancies (excluding lymphoma).
6. Other diseases that may severely impact patient survival, such as severe cardiovascular or cerebrovascular diseases, sepsis, severe trauma or burns, etc.

Where it's running

  • Sun Yat-sen university cancer center
    Guangzhou, Guangdong, China
    Recruiting

Study contacts

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.