免疫治疗对哪些患者无效?新方法鉴定肠道菌群标志物
创作:大月半 审核:爱的抉择 04月03日
  • 采用综合计算方法,重新分析近期发表的5项免疫检查点抑制剂(ICI)研究(n=303名患者)中16S rRNA扩增子和宏基因组测序数据;
  • 鉴定到一组和有ICI临床反应(R)和无反应(NR)相关的新型细菌信号,R富集人罗斯拜瑞氏菌,NR富集小韦荣球菌等;
  • 基于菌群信号生成整合微生物组预测指数;
  • 通过随机效应模型、敏感性和特异性分析发现,NR相关的综合指数显示其信号强度和一致性最好,并且这一结论在三种癌症人群(n=105)中得到验证。
主编推荐语
爱的抉择
尽管免疫检查点抑制剂(ICI)通过产生持久的抗肿瘤反应而彻底改变了癌症的治疗,但只有10-30%的患者有反应,并且较难预测患者的临床益处。研究表明肠道微生物组可能是一个新的肿瘤反应率的生物标志物,但影响ICI反应的特定细菌或细菌群落在研究人群中是不一致的。Clinical Cancer Research近期发表的文章,通过设计一种新的综合分析方式,揭示出对ICI无反应的患者的肠道菌群生物标志物,或提出一种新的方法来确定哪些患者将受益于肠道菌群干预措施,提高ICI反应率。
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A uniform computational approach improved on existing pipelines to reveal microbiome biomarkers of non-response to immune checkpoint inhibitors

一种在现有pipeline基础上改进的综合计算方法,用于揭示对免疫检查点抑制剂无反应的微生物组生物标志物

10.1158/1078-0432.CCR-20-4834

02-16, Article

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Purpose: While immune checkpoint inhibitors (ICIs) have revolutionized the treatment of cancer by producing durable anti-tumor responses, only 10-30% of treated patients respond and the ability to predict clinical benefit remains elusive. Several studies, small in size and using variable analytical methods, suggest the gut microbiome may be a novel, modifiable biomarker for tumor response rates, but the specific bacteria or bacterial communities putatively impacting ICI responses have been inconsistent across the studied populations. Experimental Design: We have re-analyzed the available raw 16S rRNA amplicon and metagenomic sequencing data across five recently published ICI studies (n=303 unique patients) using a uniform computational approach. Results: Herein, we identify novel bacterial signals associated with clinical response (R) or nonresponse (NR) and develop an Integrated Microbiome Prediction Index. Unexpectedly, the NR-associated Integrated Index shows the strongest and most consistent signal using a random effects model and in a sensitivity and specificity analysis (p < 0.01). We subsequently tested the Integrated Index using validation cohorts across three distinct and diverse cancers (n=105). Conclusions: Our analysis highlights the development of biomarkers for nonresponse, rather than response, in predicting ICI outcomes and suggests a new approach to identify patients who would benefit from microbiome-based interventions to improve response rates.

First Authors:
Fyza Y Shaikh

Correspondence Authors:
Cynthia L Sears

All Authors:
Fyza Y Shaikh,James R White,Joell J Gills,Taiki Hakozaki,Corentin Richard,Bertrand Routy,Yusuke Okuma,Mykhaylo Usyk,Abhishek Pandey,Jeffrey S Weber,Jiyoung Ahn,Evan J Lipson,Jarushka Naidoo,Drew M Pardoll,Cynthia L Sears

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