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 type | Observational |
|---|---|
| Ages | 18 Years to 80 Years |
| Sex | All |
| Enrollment | 120 (estimated) |
| Sponsor | Sun Yat-sen University Academic / other |
| Locations | 1 site (Guangzhou, Guangdong) |
| Status | Recruiting |
| Started | July 1, 2023 |
| Main results expected | December 31, 2026 |
| Study ends | December 31, 2026 |
| Registry record updated | April 23, 2026 |
| Trial ID | NCT06140992 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
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Sun Yat-sen university cancer center
Guangzhou, Guangdong, China Recruiting
Study contacts
- Study coordinator: Yiyang Zhang
- Email: zhangyy@sysucc.org.cn
- Phone: +8615652797092
Similar studies
Other studies for the same condition or treatment. Recruiting studies are listed first.
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Methylation markers from saliva to guide lung cancer screening
This study tests whether DNA methylation patterns in saliva can more accurately identify people who should get a low-dose CT scan for lung cancer screening.
-
High-precision detection of leftover cancer using a liquid-biopsy platform (SHERLOCK)
This project will test whether sensitive blood and other fluid tests for circulating tumor DNA can find leftover cancer (minimal residual disease) in adults treated with curative intent for breast, lung, melanoma, gynecologic, or genitourinary cancers.
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Predicting how well chemotherapy will work for esophageal cancer patients
This study is testing if DNA changes in biopsy samples can help doctors predict how well chemotherapy will work for patients with esophageal cancer, so those who might not benefit can avoid unnecessary treatment.
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Predicting early tumor recurrence in esophageal cancer patients
This study is testing if certain DNA patterns from tissue samples can help predict if esophageal cancer will come back early in patients who have had surgery.
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Saliva DNA methylation test for diagnosing oral cavity squamous cell carcinoma
This tests whether a saliva DNA methylation test can detect oral squamous cell carcinoma in adults scheduled for maxillofacial surgery.
-
Developing a diagnostic model for pancreatic cystic tumors using fluid samples
This study is testing if analyzing fluid samples from patients with pancreatic cystic tumors can help doctors tell the difference between harmless and harmful tumors.
How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.