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

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.