Chemotherapy-induced apoptosis, autophagy and cell cycle arrest are key drivers of synergy in chemo-immunotherapy of epithelial ovarian cancer

Cancer immunology, immunotherapy : CII(2018)

引用 38|浏览16
暂无评分
摘要
Epithelial ovarian cancer (EOC) is the most lethal of all gynecological malignancies in the UK. Recent evidence has shown that there is potential for immunotherapies to be successful in treating this cancer. We have previously shown the effective application of combinations of traditional chemotherapy and CAR (chimeric antigen receptor) T cell immunotherapy in in vitro and in vivo models of EOC. Platinum-based chemotherapy synergizes with ErbB-targeted CAR T cells (named T4), significantly reducing tumor burden in mice. Here, we show that paclitaxel synergizes with T4 as well, and look into the mechanisms behind the effectiveness of chemo-immunotherapy in our system. Impairment of caspase activity using pan-caspase inhibitor Z-VAD reveals this chemotherapy-induced apoptotic pathway as an essential factor in driving synergy. Mannose-6-phosphate receptor-mediated autophagy and the arrest of cell cycle in G2/M are also shown to be induced by chemotherapy and significantly contributing to the synergy. Increased expression of PD-1 on T4 CAR T cells occurred when these were in culture with ovarian tumor cells; on the other hand, EOC cell lines showed increased PD-L1 expression following chemotherapy treatment. These findings provided a rationale to look into testing PD-1 blockade in combination with paclitaxel and T4 immunotherapy. Combination of these three agents in mice resulted in significant reduction of tumor burden, compared to each treatment alone. In conclusion, the mechanism driving synergy in chemo-immunotherapy of EOC is multifactorial. A deeper understanding of such process is needed to better design combination therapies and carefully stratify patients.
更多
查看译文
关键词
CAR T cells,Ovarian cancer,Apoptosis,In vivo models,Combination chemo-immunotherapy,Anti-PD-1
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