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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 Estimating the clinical relevance of observed tumor microbes through the concordance between two independent datasets. A, Survival analyses by cancer stratified by microbe presence/absence at every taxonomic level. Colors indicate a significant association with survival. The ORIEN and TCGA analyses separately show many significant associations, whereas few are consistent (significant and affecting survival in the same direction) across both datasets. B, Examples of two taxa that show concordant associations between the two datasets. C, Microbes correlate with representative clinical variables and an expression signature of the tumor microenvironment (Buffa hypoxia score). Shown are concordant correlations for ORIEN and TCGA data, except for BMI, which is missing in TCGA for many cancers. D, Correlation of microbes with deconvolved immune cell abundances within each cancer.
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.
