The Role of B7-H4 in Tumor Vaccine

Glioma Clinical Trial

Official title:

The Role and Mechanism of B7-H4/ATF3 Axis From Glioma Associated Macrophages in Treatment Resistance of Cancer Vaccine

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06156150
• Other study ID #: KY2023-997
• Secondary ID: 8230162487
• Status: Recruiting
• Start date: November 26, 2023
• Est. completion date: December 31, 2026

Study information

• Verified date: November 2023
• Source: Huashan Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Glioma patients have poor prognosis because of limited choices of treatment. Therapeutic cancer vaccines have been proved to improve survival in glioma, but resistance is a new challenge for vaccine treatment, and the mechanism is unclear. The applicant found in previous papers that glioma cells induced B7-H4 overexpression in macrophages, and the expression level of B7-H4 is highly correlated with vaccine resistance. Preliminary experiments indicated that B7-H4 protein in macrophages inhibited the expression of ATF3, STAT1 and CXCL9/10, which also resulted in decreased T cell infiltration in glioma model of mouse and was a negative factor of vaccine benefits. Therefore, the applicant hypothesize that B7-H4 inhibits STAT1 transcription by reducing expression of ATF3, resulting in decreased phosphorylated-STAT1 in nucleus, which inhibiting expression and secretion of chemokines 9/10. Thereby, reduced infiltration of T cells in microenvironment will be followed, which ultimately promotes resistance of vaccine treatment in glioma. The follow-up plan of this project will be conducted based on the cells, organoid platform and animal experiments to confirm the role and mechanism of macrophage-derived B7-H4 in secretion of chemokines for T cells and treatment resistance of vaccines. Moreover, the DC vaccine produced by team of the applicant will be used to assess the probability of reversing vaccine resistance when intervening B7-H4 axis. Finally, a model for evaluating clinical benefits from vaccine will be established based on data from clinical trials combining with expression of B7-H4 and clinicopathologic features. This study will provide new evidences for the treatment of cancer vaccines in gliomas.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 160
• Est. completion date: December 31, 2026
• Est. primary completion date: December 31, 2026
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

Inclusion Criteria:

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

1. They were 18-80 years old, male and female;

2. The pathological results of frozen section during operation were gliomas;

3. Tissue (6 mm * 6 mm) can be used for cell sorting on the basis of not affecting clinical routine diagnosis;

4. Sign informed consent. Exclusion Criteria:

Patients who meet any of the following criteria will not be included in this study:

1. Participants in other clinical trials;

2. Pregnant women.

Related Conditions & MeSH terms

• Glioma

Intervention

Biological:
• tumor vaccine
DC vaccine produced by the Team

Locations

Country	Name	City	State
China	Di Chen	Shanghai	Shanghai

Sponsors (1)

Lead Sponsor	Collaborator
Huashan Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	IHC analysis	Different expression level of proteins (CD3,CD8,B7-H4 et.ac) in Gliomas with different grades and molecular subgroups (100 cases) will be measured using immunohistochemical.	36 months
Secondary	Transcriptomics	The issues collected will be used for transcriptome sequencing to measure gene expression level.	36 months
Secondary	Immunomics	The issues collected will be used for TCR/BCR sequencing to measure clonality of lymphocytes	36 months
Secondary	Proteomics	The issues collected will be used for proteomic sequencing to measure gene expression level in protein	36 months
Secondary	Radiomics	The features from images will be extracted using algorithm of Deep-learning or Radiomics	36 months
Secondary	Genomics	The issues collected will be used for whole genome sequencing or whole exome sequencing to measure gene mutations.	36 months