阿克曼西亚
微生物群
抗生素
免疫学
医学
癌症
队列
人口
肠道菌群
内科学
生物
肿瘤科
生物信息学
拟杆菌
微生物学
遗传学
环境卫生
细菌
作者
Christoph K. Stein‐Thoeringer,Neeraj Saini,Eli Zamir,Viktoria Blumenberg,Maria‐Luisa Schubert,Uria Mor,Matthias Fante,Sabine Schmidt,Eiko Hayase,Tomo Hayase,Roman Rohrbach,Chia‐Chi Chang,Lauren McDaniel,Ivonne I. Flores,Rogier Gaiser,Matthias Edinger,Daniel Wolff,Martin Heidenreich,Paolo Strati,Ranjit Nair
出处
期刊:Nature Medicine
[Nature Portfolio]
日期:2023-03-13
卷期号:29 (4): 906-916
被引量:116
标识
DOI:10.1038/s41591-023-02234-6
摘要
Increasing evidence suggests that the gut microbiome may modulate the efficacy of cancer immunotherapy. In a B cell lymphoma patient cohort from five centers in Germany and the United States (Germany, n = 66; United States, n = 106; total, n = 172), we demonstrate that wide-spectrum antibiotics treatment ('high-risk antibiotics') prior to CD19-targeted chimeric antigen receptor (CAR)-T cell therapy is associated with adverse outcomes, but this effect is likely to be confounded by an increased pretreatment tumor burden and systemic inflammation in patients pretreated with high-risk antibiotics. To resolve this confounding effect and gain insights into antibiotics-masked microbiome signals impacting CAR-T efficacy, we focused on the high-risk antibiotics non-exposed patient population. Indeed, in these patients, significant correlations were noted between pre-CAR-T infusion Bifidobacterium longum and microbiome-encoded peptidoglycan biosynthesis, and CAR-T treatment-associated 6-month survival or lymphoma progression. Furthermore, predictive pre-CAR-T treatment microbiome-based machine learning algorithms trained on the high-risk antibiotics non-exposed German cohort and validated by the respective US cohort robustly segregated long-term responders from non-responders. Bacteroides, Ruminococcus, Eubacterium and Akkermansia were most important in determining CAR-T responsiveness, with Akkermansia also being associated with pre-infusion peripheral T cell levels in these patients. Collectively, we identify conserved microbiome features across clinical and geographical variations, which may enable cross-cohort microbiome-based predictions of outcomes in CAR-T cell immunotherapy.
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