CD70 as an actionable immunotherapeutic target in recurrent glioblastoma and its microenvironment

JOURNAL FOR IMMUNOTHERAPY OF CANCER(2022)

引用 27|浏览37
暂无评分
摘要
Purpose Glioblastoma (GBM) patients suffer from a dismal prognosis, with standard of care therapy inevitably leading to therapy-resistant recurrent tumors. The presence of cancer stem cells (CSCs) drives the extensive heterogeneity seen in GBM, prompting the need for novel therapies specifically targeting this subset of tumor-driving cells. Here, we identify CD70 as a potential therapeutic target for recurrent GBM CSCs. Experimental design In the current study, we identified the relevance and functional influence of CD70 on primary and recurrent GBM cells, and further define its function using established stem cell assays. We use CD70 knockdown studies, subsequent RNAseq pathway analysis, and in vivo xenotransplantation to validate CD70's role in GBM. Next, we developed and tested an anti-CD70 chimeric antigen receptor (CAR)-T therapy, which we validated in vitro and in vivo using our established preclinical model of human GBM. Lastly, we explored the importance of CD70 in the tumor immune microenvironment (TIME) by assessing the presence of its receptor, CD27, in immune infiltrates derived from freshly resected GBM tumor samples. Results CD70 expression is elevated in recurrent GBM and CD70 knockdown reduces tumorigenicity in vitro and in vivo. CD70 CAR-T therapy significantly improves prognosis in vivo. We also found CD27 to be present on the cell surface of multiple relevant GBM TIME cell populations, notably putative M1 macrophages and CD4 T cells. Conclusion CD70 plays a key role in recurrent GBM cell aggressiveness and maintenance. Immunotherapeutic targeting of CD70 significantly improves survival in animal models and the CD70/CD27 axis may be a viable polytherapeutic avenue to co-target both GBM and its TIME.
更多
查看译文
关键词
antigens, neoplasm, cell engineering, brain neoplasms, immunotherapy, receptors, chimeric antigen
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要