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VEGF receptor inhibitor brivanib sensitizes chemotherapy by targeting cGAS to boost antitumor immunity

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NIAID Data Ecosystem2026-03-14 收录
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https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190016
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Chemoresistance challenges the clinical application of most widely used platinum-based cancer chemotherapeutics which canonically function through inducing DNA damage. The DNA sensor cyclic GMP–AMP synthase (cGAS) connects genome instability to type I IFN response which confers vulnerability to platinum treatment. Here, by using a high throughput small-molecule-microarray-based screening of cGAS interacting compounds, we identified brivanib, known as an inhibitor of the vascular endothelial growth factor receptor (VEGFR), as a novel cGAS agonist. Brivanib markedly enhanced platinum-induced STING-TBK1-type I IFN response in tumor cells indispensable of cGAS. Importantly, brivanib synergizes the effect of cisplatin in restricting the growth of xenografted Lewis Lung Cancer (LLC) cells by boosting CD8+ T cell response in a cGAS-dependent manner. Mechanistically, brivanib enhances the DNA binding affinity of cGAS by directly targeting leucine 495 of cGAS. Moreover, leucine 495 of cGAS is essential for brivanib-mediated promoting effect on cisplatin-mediated type I IFN response and inhibition of tumor growth. Clinically, higher expression of cGAS in tumor renders a more favorable response to platinum-based chemotherapeutic regimens and better prognosis in lung cancer patient. Taken together, our findings discover cGAS as an unprecedented target of brivanib and provide a rationale for the combination of brivanib with platinum-based chemotherapeutics in cancer treatment. Total RNA was isolated and used for RNA-seq analysis. cDNA library construction and sequencing were performed by Beijing Genomics Institute using BGISEQ-500 platform. High-quality reads were aligned to the Mus musculus reference genome (UCSC_mm10) using Bowtie2. The expression levels for each of the genes were normalized to fragments per kilobase of exon model per million mapped reads (FPKM) using RNA-seq by Expectation Maximization (RSEM).
创建时间:
2022-12-31
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