Robustness of cancer microbiome signals over a broad range of methodological variation

Abstract In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) 1. Multiple peer-reviewed papers independently verified or extended our findings 2–12. Given this impact, we carefully considered concerns by Gihawi et al. 13 that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR 14), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator 15, we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. 13 was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. 13 pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.