Microbiota-based model improves the sensitivity of fecal immunochemical test for
detecting colonic lesions
#MMPMID27056827
Baxter NT
; Ruffin MT 4th
; Rogers MA
; Schloss PD
Genome Med
2016[Apr]; 8
(1
): 37
PMID27056827
show ga
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.
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