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

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    Author
    Robinson, K.M.
    Crabtree, J.
    Mattick, J.S.
    Date
    2017
    Journal
    Microbiome
    Publisher
    BioMed Central Ltd.
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://www.doi.org/10.1186/s40168-016-0224-8
    Abstract
    Background: A 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. Results: Through 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. Conclusions: This 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. Copyright The Author(s) 2017.
    Sponsors
    This work was funded by the National Institutes of Health through the NIH Director's New Innovator Award Program (1-DP2-OD007372) and an NIH Director's Transformative Research Award (1-R01-CA206188).
    Keyword
    Acinetobacter
    Acute myeloid leukemia
    Batch effects
    Cancer
    Cancer-associated bacteria
    Genome sequencing
    Microbiome
    Pseudomonas
    Stomach adenocarcinoma
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015972930&doi=10.1186%2fs40168-016-0224-8&partnerID=40&md5=b2c1c0c74c413abe2b8ab38900502f32; http://hdl.handle.net/10713/9894
    ae974a485f413a2113503eed53cd6c53
    10.1186/s40168-016-0224-8
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