TIGIT + NK cells in combination with specific gut microbiota features predict response to checkpoint inhibitor therapy in melanoma patients

BMC cancer(2023)

引用 0|浏览4
暂无评分
摘要
Background Composition of the intestinal microbiota has been correlated to therapeutic efficacy of immune checkpoint inhibitors (ICI) in various cancer entities including melanoma. Prediction of the outcome of such therapy, however, is still unavailable. This prospective, non-interventional study was conducted in order to achieve an integrated assessment of the connection between a specific intestinal microbiota profile and antitumor immune response to immune checkpoint inhibitor therapy (anti-PD-1 and/or anti-CTLA-4) in melanoma patients. Methods We assessed blood and stool samples of 29 cutaneous melanoma patients who received immune checkpoint inhibitor therapy. For functional and phenotypical immune analysis, 12-color flow cytometry and FluoroSpot assays were conducted. Gut microbiome was analyzed with shotgun metagenomics sequencing. To combine clinical, microbiome and immune variables, we applied the Random Forest algorithm. Results A total of 29 patients was analyzed in this study, among whom 51.7% ( n = 15) reached a durable clinical benefit. The Immune receptor TIGIT is significantly upregulated in T cells ( p = 0.0139) and CD56 high NK cells ( p = 0.0037) of responders. Several bacterial taxa were associated with response (e.g. Ruminococcus torques ) or failure (e.g. Barnesiella intestinihominis ) to immune therapy. A combination of two microbiome features ( Barnesiella intestinihominis and the Enterobacteriaceae family) and one immune feature (TIGIT + CD56 high NK cells) was able to predict response to ICI already at baseline (AUC = 0.85; 95% CI: 0.841–0.853). Conclusions Our results reconfirm a link between intestinal microbiota and response to ICI therapy in melanoma patients and furthermore point to TIGIT as a promising target for future immunotherapies.
更多
查看译文
关键词
Microbiome,Melanoma,Immune checkpoint inhibitors,NK cells,TIGIT,Response,Random Forest
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