Gastric Cancer
Conditions
Brief summary
To facilitate the early gastric cancer diagnosis, an assay based on assessing large-scale methylation and fragmentation profiles of the plasma cell free (cfDNA) will be developed and validated.
Detailed description
Cancer-related features in cell-free DNA (cfDNA) fragments have gradually been identified and play essential roles for non-invasive early cancer detection. Integrated analysis of several cfDNA features have proven to achieve enhanced detection sensitivity as compared to single feature. This study aims to develop and validate a novel blood-based whole methylome sequencing followed with a multi-dimensional model to analyze several features of cfDNA for GC early detection. Specifically, blood samples will be prospectively collected before gastroscopy. Cases and controls will be randomly divided into a training and a testing dataset at a ratio of 2:1. Plasma cfDNA will be isolated and extracted, followed with a bisulfite-free low-depth whole methylome sequencing. A multi-dimensional model named THorough Epigenetic Marker Integration Solution (THEMIS) including methylation, fragmentation, and chromosomal copy number alternation will be constructed in the training dataset. The performance of the model in differentiating cancer patients from non-cancer controls will then be evaluated in the testing dataset.
Interventions
GENETICwhole genomic methylation and fragmentation profile analysis of cfDNA
The assay for gastric cancer early detection will be built based on low-depth methylone sequencing followed with a multi-dimensional model construction with analysing several features such as methylation, fragmentation, and chromosomal copy number alternation.
Sponsors
The First Affiliated Hospital of Air Force Medicial University
GeneCast Biotechnology Co., Ltd.
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
1. Complete clinical info; 2. Patients self-agree to join the study and with signed patient consent and good compliance. The specific inclusion criteria for subjects to be included in the malignant group: 1. According to the definition of AJCC's 8th Edition Cancer Staging Manual, patients with gastric adenocarcinoma confirmed by histopathology and with pathological stages of stage I-IV, including patients with esophageal gastric junction adenocarcinoma (EGJ); 2. Has not previously received any local or systematic anti-tumor treatment.
Exclusion criteria
1. Diagnosed previously with any kind of malignant tumor; 2. Have received total or partial gastrectomy; 3. Have received bone marrow or organ transplantation; 4. Have received blood transfusion in the past 6 months; 5. Incomplete clinical info or unqualified to participate in the study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The performance of each single feature and the ensemble model with integrated features for early GC detection | 18 months | The efficacy of each single feature-based model and the ensemble model comparing with pathologic diagnostic results, the gold standard, and gastroscope diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). |
| The performance of each single feature and the ensemble model with integrated features for early GC detection in each clinical stage | 18 months | The efficacy of each single feature-based model and the ensemble model comparing with pathologic diagnostic results, the gold standard, and gastroscope diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The performance of the ensemble model in combination of possible GC related biomarkers such as PG, G17, and/or Hp levels for early GC detection | 18 months | The efficacy of the integrated model comparing with pathologic diagnostic results, the gold standard, and gastroscope diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). |
Countries
China
Contacts
Primary ContactNing Shen
Backup ContactYulong Li
Outcome results
None listed