A uniform computational approach improved on existing pipelines to reveal microbiome biomarkers of non-response to immune checkpoint inhibitors.

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