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Gut. 2018 May;67(5):882-891. doi: 10.1136/gutjnl-2016-313189. Epub 2017 Mar 24.

Leveraging sequence-based faecal microbial community survey data to identify a composite biomarker for colorectal cancer.

Author information

1
Department of Epidemiology, University of Texas School of Public Health, Houston, Texas, USA.
2
Bioinformatics, Second Genome Inc, South San Francisco, California, USA.
3
Department of Pathology, Texas Children's Microbiome Center, Texas Children's Hospital, Houston, Texas, USA.
4
Department of Pathology and Immunology, Baylor College of Medicine, HoustonTexas, USA.
5
Bioinformatics, Whole Biome Inc, San Francisco, California, USA.
6
Diversigen, Inc, Houston, Texas, USA.

Abstract

OBJECTIVE:

Colorectal cancer (CRC) is the second leading cause of cancer-associated mortality in the USA. The faecal microbiome may provide non-invasive biomarkers of CRC and indicate transition in the adenoma-carcinoma sequence. Re-analysing raw sequence and metadata from several studies uniformly, we sought to identify a composite and generalisable microbial marker for CRC.

DESIGN:

Raw 16S rRNA gene sequence data sets from nine studies were processed with two pipelines, (1) QIIME closed reference (QIIME-CR) or (2) a strain-specific method herein termed SS-UP (Strain Select, UPARSE bioinformatics pipeline). A total of 509 samples (79 colorectal adenoma, 195 CRC and 235 controls) were analysed. Differential abundance, meta-analysis random effects regression and machine learning analyses were carried out to determine the consistency and diagnostic capabilities of potential microbial biomarkers.

RESULTS:

Definitive taxa, including Parvimonas micra ATCC 33270, Streptococcus anginosus and yet-to-be-cultured members of Proteobacteria, were frequently and significantly increased in stools from patients with CRC compared with controls across studies and had high discriminatory capacity in diagnostic classification. Microbiome-based CRC versus control classification produced an area under receiver operator characteristic (AUROC) curve of 76.6% in QIIME-CR and 80.3% in SS-UP. Combining clinical and microbiome markers gave a diagnostic AUROC of 83.3% for QIIME-CR and 91.3% for SS-UP.

CONCLUSIONS:

Despite technological differences across studies and methods, key microbial markers emerged as important in classifying CRC cases and such could be used in a universal diagnostic for the disease. The choice of bioinformatics pipeline influenced accuracy of classification. Strain-resolved microbial markers might prove crucial in providing a microbial diagnostic for CRC.

KEYWORDS:

COLORECTAL ADENOMAS; COLORECTAL CANCER; INTESTINAL BACTERIA; META-ANALYSIS

PMID:
28341746
DOI:
10.1136/gutjnl-2016-313189
[Indexed for MEDLINE]

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