Early Detection of Stage I-II Gastric Cancer Using Plasma Cell-free DNA Fragmentomics
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Early Gastric Cancer
- Sponsor
- Zhejiang Cancer Hospital
- Enrollment
- 300
- Locations
- 1
- Primary Endpoint
- Area under curve of the model for detecting stage I/II gastric cancer
- Last Updated
- 3 years ago
Overview
Brief Summary
The purpose of this study is to enable non-invasive early detection of gastric 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 gastric cancer patients and healthy individuals will be subjected to whole-genome sequencing. Features, such as cell-free DNA fragmentation, copy number variations and microbiome, will be assessed to generate this model.
Detailed Description
Improvement in the specificity of early cancer detection reduces financial and mental burdens from unnecessary screenings. Advances in liquid biopsy approaches have expanded the clinical scope of cell-free DNA analysis in cancer early detection, by moving away from cell-free DNA methylome toward an integrative approach that enables the simultaneous assessment of multimodal cell-free DNA features. Integration of liquid biopsy-based cancer early detection into the clinic requires optimization of detection techniques, large-scale studies and prospective clinical validation. In the early detection of gastric cancer, the top research priorities are to identify relevant target features and to improve the sensitivity and specificity of detection. This large-scale early detection study will randomly enroll 200 stage I/II pathologically diagnosed gastric patients and 100 age- and sex-matched healthy individuals upon providing written informed consent. Plasma samples will be collected and extracted cell-free DNA will be subjected to whole genome sequencing. We aimed to incorporate genome-wide copy number variations, cell-free DNA fragmentomics, and microbiome features into the development of a multimodal biomarker-based prediction model.
Investigators
Xiangdong Cheng
MD
Zhejiang Cancer Hospital
Eligibility Criteria
Inclusion Criteria
- •Age minimum 18 years
- •Participants must have histologically and/or cytologically confirmed stage I/II gastric cancer
- •Full access to the patients' clinical and pathological records
- •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-gastric 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 uncontrolled intercurrent illness including, but not limited to, ongoing or active infection, symptomatic congestive heart failure, unstable angina pectoris, cardiac arrhythmia, myocardial infarction, major seizure disorder, unstable spinal cord compression, superior vena cava syndrome, or psychiatric illness/social situations that would limit compliance with study requirements or influence patient signing the written informed consent
Outcomes
Primary Outcomes
Area under curve of the model for detecting stage I/II gastric cancer
Time Frame: 2 years
The area under curve of the model for the ultrasensitive early detection of stage I/II gastric cancer would be evaluate
Secondary Outcomes
- Sensitivity of the early detection model(2 years)
- Specificity of the early detection model(2 years)
- Area under curve of the early detection model at sequencing depth downsampling(2 years)