Early Detection of Ovarian Cancer Using Plasma Cell-free DNA Fragmentomics (Prospective Study)

January 12, 2023 updated by: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

A Prospective 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.

Study Overview

Status

Recruiting

Conditions

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 Type

Observational

Enrollment (Anticipated)

248

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China
        • Recruiting
        • The Sun Yat-sen Memorial Hospital of Sun Yat-sen University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

Female

Sampling Method

Non-Probability Sample

Study Population

54 patients with stage I-II ovarian cancer, 54 patients with stage III-IV ovarian cancer, 40 patients with benign ovarian cancer and 100 healthy people were enrolled.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Case-Control
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
stage I-II ovarian cancer
54 patients with stage I-II ovarian cancer
stage III-IV ovarian cancer
54 patients with stage III-IV ovarian cancer
benign ovarian cancer
40 patients with benign ovarian cancer
healthy people
100 healthy people

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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
1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity of the early detection model
Time Frame: 1 year
The sensitivity of the model for the ultrasensitive early detection of ovarian cancer would be evaluate
1 year
Specificity of the early detection model
Time Frame: 1 year
The specificity of the model for the ultrasensitive early detection of ovarian cancer would be evaluate
1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Collaborators

Nanjing Geneseeq Technology Inc.

Investigators

  • Principal Investigator: Bingzhong Zhang, MD, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 1, 2022

Primary Completion (Anticipated)

February 28, 2023

Study Completion (Anticipated)

April 30, 2023

Study Registration Dates

First Submitted

January 12, 2023

First Submitted That Met QC Criteria

January 12, 2023

First Posted (Actual)

January 23, 2023

Study Record Updates

Last Update Posted (Actual)

January 23, 2023

Last Update Submitted That Met QC Criteria

January 12, 2023

Last Verified

January 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SunYatsen2023B

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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