The Role and Mechanism of MMR/MSI Phenotypes in Evaluating Vaccine Benefit in Glioblastoma
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Huashan Hospital
- Enrollment
- 360
- Locations
- 1
- Primary Endpoint
- Radiomics
Study Overview
Brief Summary
Glioblastoma (GBM) is the most malignant primary intracranial tumor with a median survival of about 18 months, and new therapies are urgently needed. Tumor vaccines has been shown to improve survival of GBM, but not all patients can benefit from vaccine treatment and biomarkers are urgently needed. Deletion of mismatch repair (MMR) protein and microsatellite instability (MSI) state are important features in the biological evolution of GBM, and may be used as markers for tumor vaccine. Therefore, this project will collect samples from GBM patients before and after vaccine treatment respectively, and evaluate the role of MMR/MSI gene phenotype in predicting vaccine efficacy and the potential molecular mechanism. Moreover, MMR/MSI phenotypes will be assessed by deep-learning and radiomics using images to establish noninvasive markers for vaccine.
Study Design
- Study Type
- Observational
- Observational Model
- Other
- Time Perspective
- Other
Eligibility Criteria
- Ages
- 18 Years to 80 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •The patients with glioma in the Department of Neurosurgery of Huashan Hospital Affiliated to Fudan University who meet the following three conditions can be enrolled
- •They were 18-80 years old, male and female;
- •The pathological results of frozen section during operation were gliomas (20 cases of who grade II, II and IV, respectively);
- •Tissue (6 mm * 6 mm) can be used for cell sorting on the basis of not affecting clinical routine diagnosis;
- •Sign informed consent.
Exclusion Criteria
- •Patients who meet any of the following criteria will not be included in this study:
- •Participants in other clinical trials;
- •Pregnant women.
Outcomes
Primary Outcomes
Radiomics
Time Frame: 36 months
The features from images will be extracted using algorithm of Deep-learning or Radiomics
Transcriptomics
Time Frame: 36 months
The issues collected will be used for transcriptome sequencing to measure gene expression level.
Immunomics
Time Frame: 36 months
The issues collected will be used for TCR/BCR sequencing to measure clonality of lymphocytes
Genomics
Time Frame: 36 months
The issues collected will be used for whole genome sequencing or whole exome sequencing to measure gene mutations.
IHC analysis
Time Frame: 36 months
Different expression level of proteins (CD3,CD8,B7-H4, MMR proteins) in Gliomas with different grades and molecular subgroups (300 cases) will be measured using immunohistochemical.
Proteomics
Time Frame: 36 months
The issues collected will be used for TCR/BCR sequencing to measure gene expression level in protein
Secondary Outcomes
No secondary outcomes reported
Investigators
Yu Yao, MD
Professor
Huashan Hospital