The AURKA inhibitor alters the immune microenvironment and enhances targeting B7-H3 immunotherapy in glioblastoma
Jinqiu Liu, Yuxuan Deng, Zhuonan Pu, Yazhou Miao, Zhaonian Hao, Herui Wang, Shaodong Zhang, Hanjie Liu, Jiejun Wang, Yifan Lv, Boyi Hu, Hong Wan, Zhengping Zhuang, Tai Sun, Shuyu Hao, Nan Ji, Jie Feng
Jinqiu Liu, Yuxuan Deng, Zhuonan Pu, Yazhou Miao, Zhaonian Hao, Herui Wang, Shaodong Zhang, Hanjie Liu, Jiejun Wang, Yifan Lv, Boyi Hu, Hong Wan, Zhengping Zhuang, Tai Sun, Shuyu Hao, Nan Ji, Jie Feng
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Research Article Oncology Therapeutics

The AURKA inhibitor alters the immune microenvironment and enhances targeting B7-H3 immunotherapy in glioblastoma

Abstract

Glioblastoma (GBM) is one of the most lethal adult brain tumors with limited effective therapeutic options. Immunotherapy targeting B7-H3 (CD276) has shown promising efficacy in the treatment of gliomas. However, the response to this treatment varies among glioma patients due to individual differences. It’s necessary to find an effective strategy to improve the efficacy of targeting B7-H3 immunotherapy for nonresponders. In this study, we demonstrated a strong correlation between aurora kinase A (AURKA) and CD276 expression in glioma tissue samples. Additionally, both AURKA knockdown and overexpression resulted in parallel changes in B7-H3 expression levels in glioma cells. Mechanistically, AURKA elevated B7-H3 expression by promoting epidermal growth factor receptor (EGFR) phosphorylation, which was validated in glioma cell lines and primary GBM cells. What’s more, the combination of AURKA inhibitor (alisertib) and anti–B7-H3 antibody markedly reduced tumor size and promoted CD8+ T cell infiltration and activation in mouse orthotopic syngeneic glioma models. To our knowledge, this study is the first to demonstrate AURKA-mediated B7-H3 upregulation in glioma cells; moreover, it proposes a promising therapeutic strategy combining the AURKA inhibitor alisertib with B7-H3–specific blocking mAbs.

Authors

Jinqiu Liu, Yuxuan Deng, Zhuonan Pu, Yazhou Miao, Zhaonian Hao, Herui Wang, Shaodong Zhang, Hanjie Liu, Jiejun Wang, Yifan Lv, Boyi Hu, Hong Wan, Zhengping Zhuang, Tai Sun, Shuyu Hao, Nan Ji, Jie Feng

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Figure 1

Correlation of AURKA with CD276 in GBM.

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Correlation of AURKA with CD276 in GBM. (A) The filtering process of the...
(A) The filtering process of the DEGs between GBM and LGG in the TCGA and CGGA datasets. (B) Venn diagrams displaying the overlap of gene sets within the cell cycle and immune checkpoint pathways, respectively, on the basis of the DEGs in both the TCGA and CGGA cohorts. (C) Heatmaps displaying the correlations in the CGGA datasets between cell cycle genes among the DEGs in both the TCGA and CGGA cohorts and immune checkpoint genes among the DEGs in both the TCGA and CGGA cohorts. (D and E) AURKA mRNA expression in GBM (n = 190 for CGGA, n = 142 for TCGA) and LGG (n = 135 for CGGA, n = 419 for TCGA) samples from the CGGA dataset (D) and TCGA dataset (E). (F) AURKA mRNA expression in GBM (n = 163) and normal brain tissues (n = 207) from TCGA datasets and the GTEx database. (G and H) CD276 mRNA expression in GBM (n = 190 for CGGA, n = 142 for TCGA) and LGG (n = 135 for CGGA, n = 419 for TCGA) samples from the CGGA dataset (G) and TCGA dataset (H). (I) CD276 mRNA expression in GBM (n = 163) and normal brain tissues (n = 207) from TCGA datasets and the GTEx database. (J and K) Western blot analysis of AURKA and B7-H3 expression in peritumoral, LGG, and GBM samples (n = 4/group) with quantification (K); β-actin was used as the internal control. Statistical significance was assessed via 2-tailed unpaired Student’s t test (D, E, G, and H) and 1-way ANOVA followed by Tukey’s multiple-comparison test (K). The data are presented as the mean ± SD (D, E, G, H, and K). **P < 0.01, ****P < 0.0001.