Distinguishing potential bacteria-tumor associations from contamination in a secondary data analysis of public cancer genome sequence data

Abstract

BackgroundA variety of bacteria are known to influence carcinogenesis. Therefore, we sought to investigate if publicly available whole genome and whole transcriptome sequencing data generated by large public cancer genome efforts, like The Cancer Genome Atlas (TCGA), could be used to identify bacteria associated with cancer. The Burrows-Wheeler aligner (BWA) was used to align a subset of Illumina paired-end sequencing data from TCGA to the human reference genome and all complete bacterial genomes in the RefSeq database in an effort to identify bacterial read pairs from the microbiome.ResultsThrough careful consideration of all of the bacterial taxa present in the cancer types investigated, their relative abundance, and batch effects, we were able to identify some read pairs from certain taxa as likely resulting from contamination. In particular, the presence of Mycobacterium tuberculosis complex in the ovarian serous cystadenocarcinoma (OV) and glioblastoma multiforme (GBM) samples was correlated with the sequencing center of the samples. Additionally, there was a correlation between the presence of Ralstonia spp. and two specific plates of acute myeloid leukemia (AML) samples. At the end, associations remained between Pseudomonas-like and Acinetobacter-like read pairs in AML, and Pseudomonas-like read pairs in stomach adenocarcinoma (STAD) that could not be explained through batch effects or systematic contamination as seen in other samples.ConclusionsThis approach suggests that it is possible to identify bacteria that may be present in human tumor samples from public genome sequencing data that can be examined further experimentally. More weight should be given to this approach in the future when bacterial associations with diseases are suspected.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-016-0224-8) contains supplementary material, which is available to authorized users.

microDecon: A highly accurate read‐subtraction tool for the post‐sequencing removal of contamination in metabarcoding studies

“…Some researchers have advocated for a log-ratio test for identifying contamination (Robinson, Crabtree, Mattick, Anderson, & Dunning Hotopp, 2017), while others have suggested that contaminants can be identified by looking for negative correlations between prestandardization amplicon concentration and the relative abundance of operational taxonomic units (OTUs) postsequencing (Jervis-Bardy et al, 2015). Some researchers have advocated for a log-ratio test for identifying contamination (Robinson, Crabtree, Mattick, Anderson, & Dunning Hotopp, 2017), while others have suggested that contaminants can be identified by looking for negative correlations between prestandardization amplicon concentration and the relative abundance of operational taxonomic units (OTUs) postsequencing (Jervis-Bardy et al, 2015).…”

“…None of the proposed methods are likely to eliminate contamination in all cases; therefore, there is still a need to identify and deal with contamination postsequencing. Some researchers have advocated for a log-ratio test for identifying contamination (Robinson, Crabtree, Mattick, Anderson, & Dunning Hotopp, 2017), while others have suggested that contaminants can be identified by looking for negative correlations between prestandardization amplicon concentration and the relative abundance of operational taxonomic units (OTUs) postsequencing (Jervis-Bardy et al, 2015). Similarly, Davis, Proctor, Holmes, Relman, and Callahan (2018) proposed the R package decontam for using presequencing quantification data to identify contaminant amplicon sequencing variants (ASVs; for simplicity, we will refer to OTUs hereafter, but all concepts and methods we will discuss also apply to ASVs).…”

An atlas of the tissue and blood metagenome in cancer reveals novel links between bacteria, viruses and cancer

“…in AML and Pseudomonas sp. in both AML and adenocarcinoma of the stomach 20, 35 . Interrogating cancer-specific datasets, Salmonella enterica, Ralstonia pickettii, Escherichia coli and Pseudomonas sp.…”

“…Such clusters or combinations of clusters were then compared to the other clusters by student ttest. 35 Differences in the gender distribution between patient clusters were analyzed by chi-square test for each cancer.…”

AI finds microbial signatures in tumours and blood across cancer types

“…This work has revealed a role for the gut microbiome in several types of cancer that arise in the intestinal lining itself [2][3][4] , and indicated that the gut microbiome might influence cancers at distant sites through its impact on the immune system 1 . In addition, emerging evidence indicates that microbial signatures (such as nucleic acids) can be found in tumours at other sites in the body 5,6 and in the tissues and blood of individuals who don't have cancer 7,8 . On page 567, Poore et al 9 build on this evidence, identifying signatures of microbial DNA and RNA, both in tumours and in the blood, across multiple human cancers.…”