Meta-analysis improves identification of microbiome associations with antitumor response in melanoma, lung, and kidney cancer patients treated with checkpoint inhibitors.

While immune checkpoint inhibitors (ICIs) have revolutionized the treatment of many cancers by producing durable antitumor responses, only 10-30% of treated patients respond and the ability to predict response to treatment remains elusive. Preliminary studies suggest the gut microbiome may be a novel biomarker for tumor response rates, including high alpha-diversity and a few specific bacterial species that associate with improved tumor responses to ICIs in melanoma, renal cell cancer (RCC), and non-small cell lung cancer (NSCLC). Despite these reports, the specific bacteria or bacterial communities helpful or harmful to ICI responses have been inconsistent across study populations and various malignancies, and further correlation with immune and mutational biomarkers is limited or lacking. We hypothesized that, by use of a larger sample size and a consistent computational approach, we would derive a clearer microbial profile that correlated with immunotherapeutic outcomes in more than one cancer type and in response to ICIs that target different immune checkpoints. To test this hypothesis, we have reanalyzed the available raw 16S rRNA amplicon and metagenomic sequencing data across five recently published studies (n=303) using Resphera Insight v2.2 and MetaPhlAn2, respectively, for taxonomic assignment. We used pathway prediction algorithms (PICRUSt) to examine functional characteristics enriched among responders and nonresponders, as well as effect of antibiotic usage and virulence factors using multiple reference databases. Our results confirm signals reported in each study, though some bacteria reported initially were not statistically significant after correction for false discovery rate. Likely, in part, because our analysis allows for comparison of individual species across cohorts, we were able to identify new bacterial signatures associated with clinical response or nonresponse. Further, these new results enabled us to re-evaluate and develop response and nonresponse indicator indexes. When our composite index was compared to an index assembled from the literature, some improvement occurred in a sensitivity and specificity analysis. Moreover, while lower alpha-diversity has been associated with disease states and higher alpha-diversity with healthy states, we found that alpha-diversity was not consistently predictive of response or nonresponse to ICIs. In summary, this bioinformatics platform improves on existing pipelines by standardizing critical preprocessing and downstream analysis tools, enabling comprehensive evaluations of taxonomic and functional signals across sequencing datasets. These analyses allow for identification of novel bacterial signatures associated with clinical responses that could potentially be used as biomarkers in patients undergoing treatment with checkpoint inhibitors. Results from these analyses will be validated in subsequent analyses of ICI therapy and clinical outcomes in our ongoing prospective cohorts. Citation Format: Fyza T. Shaikh, James R. White, Joell G. Gills, Jarushka Naidoo, Evan Lipson, Drew M. Pardoll, Cynthia L. Sears. Meta-analysis improves identification of microbiome associations with antitumor response in melanoma, lung, and kidney cancer patients treated with checkpoint inhibitors [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr A14.