Major data analysis errors invalidate cancer microbiome findings

Abstract

Recent reports showing that human cancers have a distinctive microbiome have led to a flurry of papers describing microbial signatures of different cancer types. Many of these reports are based on flawed data that, upon re-analysis, completely overturns the original findings. The re-analysis conducted here shows that most of the microbes originally reported as associated with cancer were not present at all in the samples. The original report of a cancer microbiome and more than a dozen follow-up studies are, therefore, likely to be invalid.

Keywords: bioinformatics; cancer; computational biology; metagenomics; microbiome.

Fig 1

Average number of reads per sample in bladder cancer (BLCA) in the top 20 most-abundant genera reported in Poore et al. (left), averaged across 156 whole-genome sequencing samples. On the right are the counts for the same samples and the same genera, in the same order, as computed in our re-analysis. Note that the y-axis scales are different by a factor of 2,000. The x-axis shows genus names.

Fig 2

Distribution of normalized counts for Hepandensovirus for adrenocortical carcinoma (blue) vs all other samples (orange). The inset shows a zoomed-in view of the distribution for the small values. All raw values were zero.

Fig 3

Distribution of normalized counts for Thiorhodospira reads in KICH cancer (blue) and normal (orange) samples. Nearly all raw values were zero except for seven samples with a raw count of 1.

Fig 4

Distribution of normalized read counts in the APCR data set for Nitrospira reads found in lung squamous cell carcinoma (blue) and all other cancer types (orange). For clarity, the y-axis is truncated at 500, but the peak of the distribution for other cancers is at 1,389.

Fig 5

Distribution of normalized counts for Mulikevirus reads in head and neck squamous cell cancer (orange) and normal (blue) samples. All raw values were zero.

Fig 6

Accuracies for one-vs-all tumor classification models obtained from a selection of samples and genera with zero classified reads prior to normalization. Each row shows the accuracies of a classifier that distinguished one cancer type from all other cancer types in the table. AUC: maximum measured area under the sensitivity-specificity curve. PPV: positive predictive value. NPV: negative predictive value.