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| Name | Class |
|---|---|
| Nanjing Geneseeq Technology Inc. | INDUSTRY |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Stage I-II gastric cancer | Cell-free DNA collected from plasma samples of 200 patients with stage I-II gastric cancer will undergo whole-genome sequencing | ||
| Healthy controls | Cell-free DNA collected from plasma samples of 100 non-cancer individuals will serve as controls |
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| Measure | Description | Time Frame |
|---|---|---|
| Area under curve of the model for detecting stage I/II gastric cancer | The area under curve of the model for the ultrasensitive early detection of stage I/II gastric cancer would be evaluate | 2 years |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity of the early detection model | The sensitivity of the model for the ultrasensitive early detection of stage I/II gastric cancer would be evaluate | 2 years |
| Specificity of the early detection model |
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Inclusion Criteria:
Exclusion Criteria:
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Approximately 200 stage I/II gastric cancer patients and 100 non-cancer controls
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Pengfei Yu, MD, PhD | Contact | 86-571-88128031 | ypfzmu@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Xiangdong Cheng, MD, PhD | Zhejiang Cancer Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Zhejiang Cancer Hospital;Cancer hospital of the university of chinese academy of sciences | Recruiting | Hangzhou | Zhejiang | 310022 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38849905 | Derived | Yu P, Chen P, Wu M, Ding G, Bao H, Du Y, Xu Z, Yang L, Fang J, Huang X, Lai Q, Wei J, Yan J, Yang S, He P, Wu X, Shao Y, Su D, Cheng X. Multi-dimensional cell-free DNA-based liquid biopsy for sensitive early detection of gastric cancer. Genome Med. 2024 Jun 7;16(1):79. doi: 10.1186/s13073-024-01352-1. |
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| ID | Term |
|---|---|
| D013274 | Stomach Neoplasms |
| ID | Term |
|---|---|
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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Plasma Cell-free DNA
The specificity of the model for the ultrasensitive early detection of stage I/II gastric cancer would be evaluate
| 2 years |
| Area under curve of the early detection model at sequencing depth downsampling | The area under curve of the model at sequencing depth downsampling for the ultrasensitive early detection of stage I/II gastric cancer would be evaluate | 2 years |
| D004066 |
| Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D013272 | Stomach Diseases |