Computational Tools and Resources for Pan-Cancer Analyses of Host-Microbe Interactions

The human microbiome is a dynamic, integrated ecosystem that interacts with the host to influence cancer development and progression, as well as affect response to anti-cancer therapies, suggesting opportunities for diagnostic and therapeutic approaches. Many microbe-microbe and host-microbe interactions relevant to cancer are expected to take place at the tumor site. However, obtaining and sequencing biological samples for the interrogation of these interactions is costly, while the exponential growth of sequencing data for such samples poses analytical and interpretive challenges. Thus, there is a growing need for comprehensive resources, reference databases, and analytical tools for understanding host-microbe interactions relevant to human cancers and other diseases. Herein, I demonstrate that the creation of such resources does not necessitate massive investments into new research programs, and can instead be accomplished by utilizing preexisting, public information. In two trans-kingdom, pan-cancer analyses of sequencing data from The Cancer Genome Atlas (TCGA), it is shown that both bacteria and fungi are involved human tumors samples, and that these signatures are predictive of patient outcomes. In doing so, novel methods for mitigating contamination and false-positive signals in such datasets are described. Lastly, a widely applicable analytical tool and reference database for microbe set enrichment analysis is proposed, which can be used to interpret large microbiome datasets.