Background
Colorectal cancer 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 colorectal cancer, 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 assocaitions 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.
We are in the process of making this analysis workflow fully automated from raw reads to the finished manuscript. In the meantime, the workflow is outlined in the analysis_workflow.md.
project
|- README # the top level description of content
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|- data # raw and primary data, are not changed once created
| |- glne007.final.an.unique_list.0.03.subsample.0.03.filter.shared # table of OTU abundances
| |- metadata.tsv # patient metadata
| |- glne007.0.03.trim.tax # OTU classifications
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|- code/ # any programmatic code
| |- mothur.batch # mothur commands for sequence curation
| |- mothur.pbs # PBS script for running mothur.batch
| |- cluster.batch # mothur commands for OTU clustering
| |- cluster.pbs # PBS script for running cluster.batch
| |- trimTax.py # creates a simplified version of the OTU consenus taxonomy file
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|- results # all output from workflows and analyses
| |- tables/ # text version of tables to be rendered with kable in R
| |- figures/ # graphs, likely designated for manuscript figures
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|- Baxter_glne007_2016_manuscript.Rmd # executable Rmarkdown for this study, if applicable
|- Baxter_glne007_2016_manuscript.docx # docx rendered version of the Rmd file
|- Baxter_GenMed_2016_manuscript.docx # cleaned up version of the final manuscript
|- Baxter_GenMed_2016_CoverLetter.docx # cover letter for submission
|- Baxter_GenMed_2016_revisions1.docx # first set of revisions
|- Baxter_GenMed_2016_revisions2.docx # second set of revisions
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|- rebuttal.docx # rebuttal for first revisions
|- rebuttal2.docx # reubttal for second revisions
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|- bmc.csl # citation style guide for BioMed Central
|- manuscript_format.docx # reference document for formatting docx made from Rmd
|- references.bibtex # bibtex formatted bibliography for the manuscript
|
+- Makefile # executable Makefile for this study, if applicable