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.
Study Type
OBSERVATIONAL
Enrollment
160
DC vaccine produced by the Team
Di Chen
Shanghai, Shanghai Municipality, China
RECRUITINGIHC 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.
Time frame: 36 months
Transcriptomics
The issues collected will be used for transcriptome sequencing to measure gene expression level.
Time frame: 36 months
Immunomics
The issues collected will be used for TCR/BCR sequencing to measure clonality of lymphocytes
Time frame: 36 months
Proteomics
The issues collected will be used for proteomic sequencing to measure gene expression level in protein
Time frame: 36 months
Radiomics
The features from images will be extracted using algorithm of Deep-learning or Radiomics
Time frame: 36 months
Genomics
The issues collected will be used for whole genome sequencing or whole exome sequencing to measure gene mutations.
Time frame: 36 months
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