Ovarian CancerNCT05693974
Early Detection of Ovarian Cancer Using Plasma Cell-free DNA Fragmentomics (Retrospective Study)
An observational study of Ovarian Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
Trial summary
The purpose of this study is to enable non-invasive early detection of ovarian cancer in high-risk populations through the establishment of a multimodal machine learning model using plasma cell-free DNA fragmentomics. Read more about this study
- Who is it for?
- Ages 18 years and older, women onlyHealthy volunteers can take part
- Conditions the study is for
- Ovarian Cancer
- Type of treatment
- Injection or infusion
- How many people
- 130 participants
- Where
- 1 siteChina
- Study dates
- 1 Oct 2022to Apr 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Study population: 30 patients with stage I-II ovarian cancer, 30 patients with stage III-IV ovarian cancer, 40 patients with benign ovarian cancer and 30 healthy people were enrolled
Who may be able to join
- Age minimum 18 years
- Patients with I-IV ovarian cancer or benign tumor confirmed by pathological examination.
- Ability to understand and the willingness to sign a written informed consent document
- Non-cancer controls are sex- and age-matched individuals without presence of any tumors or nodules or any other severe chronic diseases through systematic screening
Who cannot join
- Participants must not be pregnant or breastfeeding
- Participants must not have prior cancer histories or a second non-ovarian malignancy
- Participants must not have had any form of cancer treatment before enrollment or plasma collection, including surgery, chemotherapy, radiotherapy, targeted therapy and immunotherapy
- Participants must not present medical conditions of fever or have acute or immunological diseases that required treatment 14 days before plasma collection
- Participants who underwent organ transplant or allogenic bone marrow or hematopoietic stem cell transplantation
- Participants with clinically important abnormalities or conditions unsuitable for blood collection
- Any other disease or clinical condition of participants that the researcher believes may affect the compliance of the protocol, or affect the patient's signing of the informed consent form (ICF), which is not suitable to participate in this clinical trial.
Trial locations and contacts
1 site in 1 country.
China1 site
The Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Guangzhou, Guangdong
About this study
Official title: A Retrospective Study of Early Detection of Ovarian Cancer Using Plasma Cell-free DNA Fragmentomics
The purpose of this study is to enable non-invasive early detection of ovarian cancer in high-risk populations through the establishment of a multimodal machine learning model using plasma cell-free DNA fragmentomics. Plasma cell-free DNA from early stage ovarian cancer patients and healthy individuals will be subjected to whole-genome sequencing. Five diferent feature types, including Fragment Size Coverage (FSC), Fragment Size Distribution (FSD), EnD Motif (EDM), BreakPoint Motif (BPM), and Copy Number Variation (CNV) will be assessed to generate this model.
Read the detailed description
At present, there are many problems in the detection of ovarian cancer in China, such as a large number of high-risk population, lack of effective screening and management methods, and the value of vaginal ultrasound and CA125 in early screening of ovarian cancer is limited. There is an urgent need for a more sensitive screening method for ovarian cancer in clinical practice. In a more advanced window period, a group with higher risk of disease will be screened to enter clinical diagnosis, so as to achieve early prevention and treatment of early patients and win valuable opportunities for effective prevention and treatment of ovarian cancer. Although there are some studies on early screening data of ovarian cancer at home and abroad, most of them use single detection dimension or somatic mutation combined with methylation analysis. At present, the optimization of detection technology, sample accumulation or validation of prospective clinical trials are still under way. In short, the space for early screening of ovarian cancer is vast, and liquid biopsy is non-invasive, convenient and easy to accept. It is an important technical means for early screening research of ovarian cancer, and has great potential to improve the performance of early screening of ovarian cancer. In order to further verify the application value of cfDNA-based fragmentomics in early screening of ovarian cancer and better screen the high-risk population of ovarian cancer in China, this study intends to analyze the characteristics of five cfDNA fragments based on low-depth whole-genome sequencing technology (WGS), and integrate artificial intelligence machine learning technology to establish a prediction model for early screening of ovarian cancer based on cfDNA.
- Conditions
- Ovarian Cancer
- Keywords
- Ovarian Cancer, Plasma Cell-free DNA, Fragmentomic assay, Early detection
- Medical subject headings
- Ovarian Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| stage I-II ovarian cancer | 30 patients with stage I-II ovarian cancer | |
| stage III-IV ovarian cancer | 30 patients with stage III-IV ovarian cancer | |
| benign ovarian cancer | 40 patients with benign ovarian cancer | |
| healthy people | 30 healthy people |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under curve of the model for detecting ovarian cancer
The area under curve of the model for the ultrasensitive early detection of ovarian cancer would be evaluate
Measured: 1 year
Secondary outcomes (2)
Sensitivity of the early detection model
The sensitivity of the model for the ultrasensitive early detection of ovarian cancer would be evaluate
Measured: 1 year
Specificity of the early detection model
The specificity of the model for the ultrasensitive early detection of ovarian cancer would be evaluate
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Jan 2023
- Study start1 Oct 2022
- Primary completion31 Jan 2023 (estimated)
- Study completionApr 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ovarian CancerStudies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 130 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Nanjing Geneseeq Technology Inc.
- Responsible party
- Sponsor
- Investigators
- Bingzhong Zhang, MD, Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05693974
- Sponsor's protocol ID
- SunYatsen2023A
- Status last verified
- Jan 2023
- First submitted
- 12 Jan 2023
- Last updated
- 23 Jan 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 23 Jan 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Retrospective Study of Early Detection of Ovarian Cancer Using Plasma Cell-free DNA Fragmentomics
- ClinicalTrials.gov ID
- NCT05693974
- Acronym / sponsor ID
- SunYatsen2023A
- Conditions
- Ovarian Cancer
- Keywords
- Ovarian Cancer, Plasma Cell-free DNA, Fragmentomic assay, Early detection
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 130 participants (actual)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Collaborators
- Nanjing Geneseeq Technology Inc.
- Study start
- 1 Oct 2022 (actual)
- Primary completion
- 31 Jan 2023 (estimated)
- Study completion
- Apr 2023 (estimated)
- First posted
- 23 Jan 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The purpose of this study is to enable non-invasive early detection of ovarian cancer in high-risk populations through the establishment of a multimodal machine learning model using plasma cell-free DNA fragmentomics. Plasma cell-free DNA from early stage ovarian cancer patients and healthy individuals will be subjected to whole-genome sequencing. Five diferent feature types, including Fragment Size Coverage (FSC), Fragment Size Distribution (FSD), EnD Motif (EDM), BreakPoint Motif (BPM), and Copy Number Variation (CNV) will be assessed to generate this model.
Detailed description
At present, there are many problems in the detection of ovarian cancer in China, such as a large number of high-risk population, lack of effective screening and management methods, and the value of vaginal ultrasound and CA125 in early screening of ovarian cancer is limited. There is an urgent need for a more sensitive screening method for ovarian cancer in clinical practice. In a more advanced window period, a group with higher risk of disease will be screened to enter clinical diagnosis, so as to achieve early prevention and treatment of early patients and win valuable opportunities for effective prevention and treatment of ovarian cancer. Although there are some studies on early screening data of ovarian cancer at home and abroad, most of them use single detection dimension or somatic mutation combined with methylation analysis. At present, the optimization of detection technology, sample accumulation or validation of prospective clinical trials are still under way. In short, the space for early screening of ovarian cancer is vast, and liquid biopsy is non-invasive, convenient and easy to accept. It is an important technical means for early screening research of ovarian cancer, and has great potential to improve the performance of early screening of ovarian cancer. In order to further verify the application value of cfDNA-based fragmentomics in early screening of ovarian cancer and better screen the high-risk population of ovarian cancer in China, this study intends to analyze the characteristics of five cfDNA fragments based on low-depth whole-genome sequencing technology (WGS), and integrate artificial intelligence machine learning technology to establish a prediction model for early screening of ovarian cancer based on cfDNA.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| stage I-II ovarian cancer | – | –30 patients with stage I-II ovarian cancer |
| stage III-IV ovarian cancer | – | –30 patients with stage III-IV ovarian cancer |
| benign ovarian cancer | – | –40 patients with benign ovarian cancer |
| healthy people | – | –30 healthy people |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- 30 patients with stage I-II ovarian cancer, 30 patients with stage III-IV ovarian cancer, 40 patients with benign ovarian cancer and 30 healthy people were enrolled
Key inclusion criteria (4 listed)
- Age minimum 18 years
- Patients with I-IV ovarian cancer or benign tumor confirmed by pathological examination.
- Ability to understand and the willingness to sign a written informed consent document
- Non-cancer controls are sex- and age-matched individuals without presence of any tumors or nodules or any other severe chronic diseases through systematic screening
Key exclusion criteria (7 listed)
- Participants must not be pregnant or breastfeeding
- Participants must not have prior cancer histories or a second non-ovarian malignancy
- Participants must not have had any form of cancer treatment before enrollment or plasma collection, including surgery, chemotherapy, radiotherapy, targeted therapy and immunotherapy
- Participants must not present medical conditions of fever or have acute or immunological diseases that required treatment 14 days before plasma collection
- Participants who underwent organ transplant or allogenic bone marrow or hematopoietic stem cell transplantation
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Area under curve of the model for detecting ovarian cancer
Time frame: 1 year
The area under curve of the model for the ultrasensitive early detection of ovarian cancer would be evaluate
2 secondary outcomes
Sensitivity of the early detection model
Time frame: 1 year
Specificity of the early detection model
Time frame: 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Jan 2023.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Bingzhong Zhang, MD principal investigator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ovarian Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 23 Jan 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under curve of the model for detecting ovarian cancer (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Jan 2023.