title: Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions.
abstract: BACKGROUND
  Colorectal cancer (CRC) is the second leading cause of death among cancers in the United States. Although individuals diagnosed early have a greater than 90% chance of survival, more than one-third of individuals do not adhere to screening recommendations partly because the standard diagnostics, colonoscopy and sigmoidoscopy, are expensive and invasive. Thus, there is a great need to improve the sensitivity of non-invasive tests to detect early stage cancers and adenomas. Numerous studies have identified shifts in the composition of the gut microbiota associated with the progression of CRC, suggesting that the gut microbiota may represent a reservoir of biomarkers that would complement existing non-invasive methods such as the widely used fecal immunochemical test (FIT).
  
  
  METHODS
  We sequenced the 16S rRNA genes from the stool samples of 490 patients. We used the relative abundances of the bacterial populations within each sample to develop a random forest classification model that detects colonic lesions using the relative abundance of gut microbiota and the concentration of hemoglobin in stool.
  
  
  RESULTS
  The microbiota-based random forest model detected 91.7% of cancers and 45.5% of adenomas while FIT alone detected 75.0% and 15.7%, respectively. Of the colonic lesions missed by FIT, the model detected 70.0% of cancers and 37.7% of adenomas. We confirmed known associations of Porphyromonas assaccharolytica, Peptostreptococcus stomatis, Parvimonas micra, and Fusobacterium nucleatum with CRC. Yet, we found that the loss of potentially beneficial organisms, such as members of the Lachnospiraceae, was more predictive for identifying patients with adenomas when used in combination with FIT.
  
  
  CONCLUSIONS
  These findings demonstrate the potential for microbiota analysis to complement existing screening methods to improve detection of colonic lesions.
authors.affiliation: Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Nielson T
authors.initials: NT
authors.lastname: Baxter
authors.affiliation: Department of Family Medicine, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Mack T
authors.initials: MT
authors.lastname: Ruffin
authors.affiliation: Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Mary A M
authors.initials: MA
authors.lastname: Rogers
authors.affiliation: Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA. pschloss@umich.edu.
authors.forename: Patrick D
authors.initials: PD
authors.lastname: Schloss
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003108:Colonic Diseases / Q000175:diagnosis* / Q000209:etiology*; D005243:Feces / Q000737:chemistry* / Q000382:microbiology*; D005260:Female; D000069196:Gastrointestinal Microbiome; D006801:Humans; D007120:Immunochemistry / Q000379:methods* / Q000592:standards; D008297:Male; D064307:Microbiota; D008875:Middle Aged; D008954:Models, Biological; D009780:Occult Blood; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA
chemical_list: D012336:RNA, Ribosomal, 16S