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


METHODS
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, includingParvimonas micraATCC 33270,Streptococcus anginosusand 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.
Department of Epidemiology, University of Texas School of Public Health, Houston, Texas, USA.|Bioinformatics, Second Genome Inc, South San Francisco, California, USA.|Department of Pathology, Texas Children's Microbiome Center, Texas Children's Hospital, Houston, Texas, USA.|Department of Pathology and Immunology, Baylor College of Medicine, HoustonTexas, USA.
Manasi S
MS
Shah
Bioinformatics, Second Genome Inc, South San Francisco, California, USA.
Todd Z
TZ
DeSantis
Bioinformatics, Second Genome Inc, South San Francisco, California, USA.
Thomas
T
Weinmaier
Bioinformatics, Second Genome Inc, South San Francisco, California, USA.|Bioinformatics, Whole Biome Inc, San Francisco, California, USA.
Paul J
PJ
McMurdie
Department of Pathology, Texas Children's Microbiome Center, Texas Children's Hospital, Houston, Texas, USA.|Department of Pathology and Immunology, Baylor College of Medicine, HoustonTexas, USA.|Diversigen, Inc, Houston, Texas, USA.
Julia L
JL
Cope
Bioinformatics, Second Genome Inc, South San Francisco, California, USA.
Adam
A
Altrichter
Department of Epidemiology, University of Texas School of Public Health, Houston, Texas, USA.
Jose-Miguel
JM
Yamal
Department of Pathology, Texas Children's Microbiome Center, Texas Children's Hospital, Houston, Texas, USA.|Department of Pathology and Immunology, Baylor College of Medicine, HoustonTexas, USA.
Emily B
EB
Hollister
D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000032:analysis*; D015179:Colorectal Neoplasms / Q000175:diagnosis / Q000382:microbiology*; D004269:DNA, Bacterial / Q000032:analysis; D005243:Feces / Q000382:microbiology*; D000069196:Gastrointestinal Microbiome / Q000235:genetics*; D006801:Humans; D012336:RNA, Ribosomal, 16S; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires
D014408:Biomarkers, Tumor; D004269:DNA, Bacterial; D012336:RNA, Ribosomal, 16S
COLORECTAL ADENOMAS; COLORECTAL CANCER; INTESTINAL BACTERIA; META-ANALYSIS