Early Detection of Endometrial Cancer Using Plasma Cell-free DNA Fragmentomics
Study Details
Study Description
Brief Summary
The purpose of this study is to enable non-invasive early detection of endometrial 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 endometrial cancer patients and healthy individuals will be subjected to whole-genome sequencing. Five different feature types, including Fragment Size Distribution, nucleosome features, SBS Signatures, BreakPoint Motif , and Copy Number Variation will be assessed to generate this model.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Detailed Description
Currently, there is no international consensus on the standard for endometrial cancer screening. The Expert Committee on Endometrial Cancer Screening in China released the "Expert Consensus on Endometrial Cancer Screening and Early Diagnosis (Draft)" in 2017, recommending the use of endometrial brushes for endometrial sampling and the use of endometrial cytology for slide preparation. Transvaginal ultrasound (TVS) can be used as an initial assessment and auxiliary method for endometrial cytology screening for endometrial cancer. For women without clinical symptoms, the routine method of endometrial cancer screening is mainly TVS to monitor endometrial thickness. Although TVS has high sensitivity, its specificity is very low, with a low positive predictive value (PPV) and a high false-positive rate, making it unable to distinguish between benign and malignant endometrial changes. There are also certain operator subjective judgments and instrument-related errors. For women with clinical symptoms, patients need endometrial cytology testing, that is, invasive endometrial sampling with an endometrial brush, followed by cytological slide preparation. Suspicious malignant tumor cells or malignant tumor cells should immediately undergo hysteroscopy and segmental diagnostic curettage to obtain endometrial biopsy tissue, and further clinical treatment should be carried out based on the pathological results. Due to the need to go deep into the uterus, the sampling failure rate for nulliparous women is as high as 20%, and the sampling failure rate for multiparous women is 8%. Whether it is endometrial cytology or hysteroscopic biopsy, which is close to the invasive operation of abortion, it will bring a lot of pain and economic burden to women. Moreover, there are currently no specific and sensitive tumor markers available for the diagnosis and follow-up of endometrial cancer. Therefore, it is urgent to develop a non-invasive, efficient screening detection method.
In short, the space for early screening of endometrial 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 endometrial cancer, and has great potential to improve the performance of early screening of endometrial cancer. In order to further verify the application value of cfDNA-based fragmentomics in early screening of endometrial cancer and better screen the high-risk population of endometrial 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 endometrial cancer based on cfDNA.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Patients with endometrial cancer the 108 patients with early to mid-stage endometrial cancer, more than 50% were in FIGO stages I/II. |
Genetic: low-depth whole-genome sequencing technology
the characteristics of five cfDNA fragments based on low-depth whole-genome sequencing technology (WGS)
|
| healthy people 108 healthy people |
Genetic: low-depth whole-genome sequencing technology
the characteristics of five cfDNA fragments based on low-depth whole-genome sequencing technology (WGS)
|
Outcome Measures
Primary Outcome Measures
- Area under curve of the model for detecting endometrial cancer [1 year]
The area under curve of the model for the ultrasensitive early detection of endometrial cancer would be evaluate
Eligibility Criteria
Criteria
Inclusion Criteria:
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Age minimum 18 years
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Patients diagnosed with early to mid-stage endometrial cancer (more than 50% are in FIGO stages I/II) through histological and/or cytological examination.
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Ability to understand and the willingness to sign a written informed consent document
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Participants can obtain comprehensive clinical and pathological information.
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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:
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Participants must not be pregnant or breastfeeding
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Participants must not have prior cancer histories or a second non-endometrial malignancy
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Participants must not have had any form of cancer treatment before enrollment or plasma collection, including surgery, chemotherapy, radiotherapy, targeted therapy and immunotherapy
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Participants must not present medical conditions of fever or have acute or immunological diseases that required treatment 14 days before plasma collection
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Participants who underwent organ transplant or allogenic bone marrow or hematopoietic stem cell transplantation
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Participants with clinically important abnormalities or conditions unsuitable for blood collection
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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.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | The Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University | Guangzhou | Guangdong | China |
Sponsors and Collaborators
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Nanjing Geneseeq Technology Inc.
Investigators
- Principal Investigator: Bingzhong Zhang, MD, The Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- SYSKY-2023-848-01