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posted on 2023-11-21, 14:20 authored by Rebecca Hoyd, Caroline E. Wheeler, YunZhou Liu, Malvenderjit S. Jagjit Singh, Mitchell Muniak, Ning Jin, Nicholas C. Denko, David P. Carbone, Xiaokui Mo, Daniel J. Spakowicz Summary and validation of the {exotic} tool. A, Schematic of the {exotic} tool, showing the process of aligning raw RNA-seq FASTQs to databases for human and microbial identification, followed by filtering to remove contaminants. Filtering steps include the removal of samples with a high percentage of exogenous reads and samples from small batches that prevent contaminant checks, as well as reads that align to microbes found to be contaminants by statistical or literature review–based filtering. The remaining samples’ microbe counts are normalized by VOOM-SNM. B, The loss of reads at each step of the {exotic} tool. C, Normalization removes batch effects from the sequencing center (all ORIEN samples were sequenced at TCC, and all TCGA at UNC) and preservation method from decontaminated counts. D, Microbe-level filtering removes taxa that align with input RNA concentration and are consistently found in negative controls. Microbes with strong literature precedence as commensals are returned. E, Comparison of the prevalence of Fusobacterium found by {exotic} compared with literature values captured by non–RNA-seq–based approaches, with a much higher prevalence appearing in colorectal samples compared with all cancer types. F, A comparison of the prevalence of various taxa to those reported by Poore and colleagues (9) for TCGA dataset finds mostly lower prevalences of bacteria and viruses but includes the identification of fungi (Malassezia). G, The abundance of microbes found in a 16S-based validation dataset compared with the RNA-seq–based approach. H, Comparison of the distances between microbes identified by 16S and RNA-seq for samples from the same patient and tumor (Paired = TRUE, Tumor) and adjacent normal tissue (Paired = TRUE, Normal) versus samples from different patients (Paired = FALSE).
Funding
American Lung Association (ALA)
HHS | National Institutes of Health (NIH)
HHS | NIH | National Center for Advancing Translational Sciences (NCATS)
HHS | NIH | National Cancer Institute (NCI)
History
ARTICLE 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 in its early stages. 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 called {exotic} for “exogenous sequences in tumors and immune cells” to carefully identify the tumor microbiome within RNA sequencing (RNA-seq) datasets. We applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas. We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non–high-throughput sequencing–based approaches and DNA-amplicon–based measurements of a subset of the same tumors. 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 observed associations with survival and clinical variables that are cancer specific and relatively few associations with immune composition. Finally, we explored potential mechanisms by which microbes and tumors may interact using a network-based 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 the discovery of microbes in tumors in a way that leverages the many existing and growing RNA-seq datasets.
The intrinsic tumor microbiome holds great potential for its ability to predict various aspects of cancer biology and as a target for rational manipulation. Here, we describe a tool to quantify microbes from within tumor RNA-seq and apply it to two independent datasets. We show new associations with clinical variables that justify biomarker uses and more experimentation into the mechanisms by which tumor microbiomes affect cancer outcomes.
