Exogenous sequences in tumors and immune cells (exotic): a tool for estimating the microbe abundances in tumor RNAseq data

Abstract

The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is unclear. A broad survey of tumor microbiome samples across several independent datasets is needed to identify robust correlations for follow-up testing. We created a tool to carefully identify the tumor microbiome within RNAseq datasets and then applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas (TCGA). We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non-high-throughput sequencing-based approaches. We sought to establish clinical relevance by correlating the microbe abundances with various clinical and tumor measurements, such as age and tumor hypoxia. This process leveraged the two datasets and raised up only the concordant (significant and in the same direction) associations. We identify associations with survival and clinical variables that are highly cancer-specific, and relatively few associations with immune composition. Finally, we explore potential mechanisms by which microbes and tumors may interact using a network approach. Alistipes, a common gut commensal, showed the highest network degree centrality and was associated with genes related to metabolism and inflammation. The exotic tool can support discovery of microbes in tumors in a way that leverages the many existing and growing number of RNAseq datasets.

The tumor microbiome reacts to hypoxia and can influence response to radiation treatment in colorectal cancer

“…Accordingly, we evaluated tumor microbiota composition in ORIEN and TCGA patient samples using the exogenous sequences in tumors and immune cells {exotic} tool for quantifying microbe abundances in tumor RNA-seq data (24). To identify taxa associated with high levels of tumor hypoxia, we stratified patient samples into tertiles of increasing hypoxia scores (23) and quantified log2 abundance fold change between the bottom-and top-ranked tertiles.…”

“…Reads left unclassified after STAR alignment were then aligned to a Kraken 2 database customized to include fungi and archaea, after which Bracken (short for Bayesian reestimation of abundance with Kraken) was used to determine the likely species of all classified reads. The R package {exotic} (24) was then used to decontaminate the results and to normalize the human samples to account for processing at different sequencing centers.…”

Association between Tumor Microbiome and Hypoxia across Anatomic Subsites of Head and Neck Cancers

“…Hypoxia in the tissue samples was quantified using the Buffa hypoxia signature score, which is a validated gene signature and is calculated from the mean expression of a log of 52 well-characterized hypoxia-regulated genes that were consistently expressed in experiments using HNSCCs, breast, and lung cancer cells and were found to be highly prognostic for survival [ 20 ]. The TCGA RNA-seq samples were processed through the exogenous sequencing in tumors and immune cells (exotic) pipeline to identify and count exogenous sequences, filter contaminants, and normalize expression values [ 21 ]. Hypoxia score was calculated as described by Buffa et al, 2010 [ 20 ], implemented in the R package tmesig [ 22 ], following: where the median value is calculated for all i genes and j samples.…”

The tumor microbiome as a predictor of outcomes in patients with metastatic melanoma treated with immune checkpoint inhibitors

“…RNA-seq reads are used to calculate microbe abundances using the {exotic} pipeline, as described previously (22). Briefly, reads are aligned first to the human reference genome, and then unaligned reads are mapped to a database of bacteria, fungi, archaea, viruses, and eukaryotic parasites.…”