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The purpose of this study is to enable non-invasive early detection of pancreatic cancer in high-risk populations through the establishment of a machine learning model using plasma cell-free DNA fragmentomics. Plasma cell-free DNA from early stage pancreatic cancer patients and healthy individuals will be subjected to whole-genome sequencing. Features, such as cell-free DNA fragmentation, copy number variations and the status of KRAS gene mutation, will be assessed to generate this model.
The incidence of pancreatic cancer is insidious. Most patients were in advanced stage when diagnosed and could not be cured by surgery. Early diagnosis of pancreatic cancer through screening is so important. Early screening detection projects derived from liquid biopsy technology are not only limited to circulating DNA methylation markers, but have developed into multi-dimensional indicators for joint evaluation. This large-scale early detection study will randomly enroll 260 stage I/II/III pancreatic patients, 80 patients with pancreatic benign diseases and 156 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 status of KRAS gene mutation into the development of a multimodal biomarker-based prediction model.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Stage I-III pancreatic cancer | Cell-free DNA collected from plasma samples of 260 patients with stage I-III pancreatic cancer will undergo whole-genome sequencing |
| |
| Pancreatic disease | Cell-free DNA collected from plasma samples of 80 patients with pancreatic benign disease will undergo whole-genome sequencing |
| |
| Healthy controls | Cell-free DNA collected from plasma samples of 100 non-cancer individuals will serve as controls |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No intervention | Other | No intervention |
|
| Measure | Description | Time Frame |
|---|---|---|
| Area under curve of the model for detecting stage I/II/III pancreatic cancer | The area under curve of the model for the ultrasensitive early detection of stage I/II/III pancreatic cancer would be evaluate | 2 years |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity of the early detection modell | The sensitivity of the model for the ultrasensitive early detection of stage I/II/III pancreatic 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 260 stage I/II/III gastric cancer patients, 70 patients with pancreatic benign diseases and 150 non-cancer controls
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Gao Chuntao, MD | Contact | 15822757365 | gaochuntao@tjmuch.com |
| Name | Affiliation | Role |
|---|---|---|
| Gao Chuntao | Tianjin Medical University Cancer Institute and Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Tianjin Medical University Cancer Institute and Hospital | Recruiting | Tianjin | Tianjin Municipality | China |
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| ID | Term |
|---|---|
| D010190 | Pancreatic Neoplasms |
| ID | Term |
|---|---|
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004701 | Endocrine Gland Neoplasms |
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Plasma Cell-free DNA
The specificity of the model for the ultrasensitive early detection of stage I/II/III pancreatic cancer would be evaluate
| 2 years |
| D004066 |
| Digestive System Diseases |
| D010182 | Pancreatic Diseases |
| D004700 | Endocrine System Diseases |