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Trial Title:
Early Detection of Ovarian Cancer Using Plasma Cell-free DNA Fragmentomics (Retrospective Study)
NCT ID:
NCT05693974
Condition:
Ovarian Cancer
Conditions: Official terms:
Ovarian Neoplasms
Carcinoma, Ovarian Epithelial
Conditions: Keywords:
Ovarian Cancer
Plasma Cell-free DNA
Fragmentomic assay
Early detection
Study type:
Observational
Overall status:
Active, not recruiting
Study design:
Time perspective:
Retrospective
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. 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.
Criteria for eligibility:
Study pop:
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
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
- 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
Exclusion Criteria:
- 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.
Gender:
Female
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
Accepts Healthy Volunteers
Locations:
Facility:
Name:
The Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Address:
City:
Guangzhou
Country:
China
Start date:
October 1, 2022
Completion date:
April 2023
Lead sponsor:
Agency:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Agency class:
Other
Collaborator:
Agency:
Nanjing Geneseeq Technology Inc.
Agency class:
Industry
Source:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05693974