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Cited by 302Bacteriophages and microbial interactionsGut microbiota and healthViral-associated cancers and disorders

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Diagnostic Potential and Interactive Dynamics of the Colorectal Cancer Virome

Open access
Hannigan, Geoffrey D., Duhaime, Melissa B., Ruffin, Mack T., Koumpouras, Charlie C., Schloss, Patrick D.·2018·mBio
DOI 10.1128/mbio.02248-18PMC PMC6247079

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Abstract

Colorectal cancer is a leading cause of cancer-related death in the United States and worldwide. Its risk and severity have been linked to colonic bacterial community composition. Although human-specific viruses have been linked to other cancers and diseases, little is known about colorectal cancer virus communities. We addressed this knowledge gap by identifying differences in colonic virus communities in the stool of colorectal cancer patients and how they compared to bacterial community differences. The results suggested an indirect role for the virome in impacting colorectal cancer by modulating the associated bacterial community. These findings both support the idea of a biological role for viruses in colorectal cancer and provide a new understanding of basic colorectal cancer etiology.

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Figures

FIG 1 Cohort and sample processing outline. Thirty subject stool samples were collected from healthy subjects and adenoma (precancer) and carcinoma (cancer) patients. Stool samples were split into two aliquots, the first of which was used for bacterial sequencing and the second of which was used for virus sequencing. Bacterial sequencing was done using both 16S rRNA amplicon and whole-metagenomic shotgun sequencing techniques. Virus samples were purified for viruses using filtration and a combination of chloroform (bacterial lysis) and DNase (exposed genomic DNA degradation). The resulting encapsulated virus DNA was sequenced using whole-metagenomic shotgun sequencing.
FIG 1 Cohort and sample processing outline. Thirty subject stool samples were collected from healthy subjects and adenoma (precancer) and carcinoma (cancer) patients. Stool samples were split into two aliquots, the first of which was used for bacterial sequencing and the second of which was used for virus sequencing. Bacterial sequencing was done using both 16S rRNA amplicon and whole-metagenomic shotgun sequencing techniques. Virus samples were purified for viruses using filtration and a combination of chloroform (bacterial lysis) and DNase (exposed genomic DNA degradation). The resulting encapsulated virus DNA was sequenced using whole-metagenomic shotgun sequencing.CC BY · PubMed Central
FIG 2 Results from healthy versus cancer classification models built using virome signatures, bacterial 16S rRNA gene sequence signatures, whole-metagenomic signatures, and a combination of virome and 16S rRNA gene sequence signatures. (A) An example receiver operating characteristic (ROC) curve for visualizing the performance of each of the models for classifying stool as coming from either an individual with a cancerous colon or an individual with a healthy colon. (B) Quantification of the AUC variation for each model and how it compared to each of the other models based on 15 iterations. A pairwise Wilcoxon test with a false-discovery-rate multiple-hypothesis correction demonstrated that all models are significantly different from each other (P value < 0.01). (C) Mean decrease in accuracy (measurement of importance) of each operational taxonomic unit within the 16S rRNA gene classification model when removed from the classification model. The mean is represented by a point, and bars represent standard errors. ID, identifier. (D) Mean decrease in accuracy of each operational virus unit in the virome classification model. (E) Mean decrease in accuracy of each operational genomic unit and operational taxonomic unit in the model using both 16S rRNA gene and virome features.
FIG 2 Results from healthy versus cancer classification models built using virome signatures, bacterial 16S rRNA gene sequence signatures, whole-metagenomic signatures, and a combination of virome and 16S rRNA gene sequence signatures. (A) An example receiver operating characteristic (ROC) curve for visualizing the performance of each of the models for classifying stool as coming from either an individual with a cancerous colon or an individual with a healthy colon. (B) Quantification of the AUC variation for each model and how it compared to each of the other models based on 15 iterations. A pairwise Wilcoxon test with a false-discovery-rate multiple-hypothesis correction demonstrated that all models are significantly different from each other (P value < 0.01). (C) Mean decrease in accuracy (measurement of importance) of each operational taxonomic unit within the 16S rRNA gene classification model when removed from the classification model. The mean is represented by a point, and bars represent standard errors. ID, identifier. (D) Mean decrease in accuracy of each operational virus unit in the virome classification model. (E) Mean decrease in accuracy of each operational genomic unit and operational taxonomic unit in the model using both 16S rRNA gene and virome features.CC BY · PubMed Central
FIG 3 Lysogenic phage relative abundance in disease states. Phage OVUs were predicted to be either lytic or lysogenic, and the relative abundances of lysogenic phages were quantified and are represented as a box plot. None of the data from the disease groups were statistically significant.
FIG 3 Lysogenic phage relative abundance in disease states. Phage OVUs were predicted to be either lytic or lysogenic, and the relative abundances of lysogenic phages were quantified and are represented as a box plot. None of the data from the disease groups were statistically significant.CC BY · PubMed Central
FIG 4 Relative-abundance correlations between bacterial OTUs and virome OVUs. (A) Pearson correlation coefficient values from comparisons between all bacterial OTUs (x axis) and viral OVUs (y axis), with blue representing positive correlations and red representing negative correlations. Bar plots indicate the levels of viral (left) and bacterial (bottom) operational unit importance in their colorectal cancer classification models, such that the most important units are shown in the top left corner. (B) Magnification of the boxed region in panel A, highlighting the correlation between the most important bacterial OTUs and virome OVUs. The most important operational units are shown in the top left corner of the heat map, and the correlation scale is the same as in panel A. (C) Histogram quantifying the frequencies of Pearson correlation coefficients between all bacterial OTUs and virome OVUs.
FIG 4 Relative-abundance correlations between bacterial OTUs and virome OVUs. (A) Pearson correlation coefficient values from comparisons between all bacterial OTUs (x axis) and viral OVUs (y axis), with blue representing positive correlations and red representing negative correlations. Bar plots indicate the levels of viral (left) and bacterial (bottom) operational unit importance in their colorectal cancer classification models, such that the most important units are shown in the top left corner. (B) Magnification of the boxed region in panel A, highlighting the correlation between the most important bacterial OTUs and virome OVUs. The most important operational units are shown in the top left corner of the heat map, and the correlation scale is the same as in panel A. (C) Histogram quantifying the frequencies of Pearson correlation coefficients between all bacterial OTUs and virome OVUs.CC BY · PubMed Central
FIG 5 Community network analysis utilizing predicted interactions between bacterial and phage operational genomic units. (A) Visualization of the community network for our colorectal cancer cohort. (B) Scatter plot illustrating the correlation between importance (mean decrease in accuracy) and the degree of centrality for each OVU. A linear regression line was fitted to illustrate the correlations (blue) found to be statistically significantly and weakly correlated (P value = 0.00409, R = 0.176).
FIG 5 Community network analysis utilizing predicted interactions between bacterial and phage operational genomic units. (A) Visualization of the community network for our colorectal cancer cohort. (B) Scatter plot illustrating the correlation between importance (mean decrease in accuracy) and the degree of centrality for each OVU. A linear regression line was fitted to illustrate the correlations (blue) found to be statistically significantly and weakly correlated (P value = 0.00409, R = 0.176).CC BY · PubMed Central
FIG 6 Final working hypothesis from this study. These panels summarize our thoughts on our results and represent interesting future directions that we predict will build on the presented work. (A) Basic model illustrating the connections between the virome, bacterial communities, and colorectal cancer. (B) Working hypothesis of how the bacteriophage community is associated with colorectal cancer and the associated bacterial community. ROS, reactive oxygen species.
FIG 6 Final working hypothesis from this study. These panels summarize our thoughts on our results and represent interesting future directions that we predict will build on the presented work. (A) Basic model illustrating the connections between the virome, bacterial communities, and colorectal cancer. (B) Working hypothesis of how the bacteriophage community is associated with colorectal cancer and the associated bacterial community. ROS, reactive oxygen species.CC BY · PubMed Central

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Metadata

Authors
Hannigan, G. D., Duhaime, M. B., Ruffin, M. T., Koumpouras, C. C., & Schloss, P. D.
Affiliations
  • University of Michigan (US)
  • Pennsylvania State University (US)
Corresponding author
Schloss, Patrick D.
ORCID
Venue
mBio
Publisher
American Society for Microbiology
Published
2018-12-21
Volume
9
Issue
6
ISSN
  • 2150-7511 (ISSN-L)
  • 2161-2129
DOI
10.1128/mbio.02248-18
PMC
PMC6247079
OpenAlex
W2950868848
Cited by
298
References
53
Citations by year
2026: 32 · 2025: 40 · 2024: 34 · 2023: 45 · 2022: 48 · 2021: 52 · 2020: 18 · 2019: 25 · 2018: 1
FWCI
18.05
Type
journal article
Language
en
Open access
Licence
Locations
  • mBio (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
HHS | National Institutes of Health (P30DK034933, T32AI007528, 5U01CA86400)
Topics
  • Life Sciences › Immunology and Microbiology › Virology › Bacteriophages and microbial interactions
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Medicine › Oncology › Viral-associated cancers and disorders
Keywords
colorectal cancer, virome, human viruses, colonic microbiota, gut bacterial community, cancer etiology, viral-bacterial interactions, cancer diagnosis
MeSH terms
Bacteria, Bacteriophages, Feces, Humans, RNA, Ribosomal, 16S, Viruses, Colorectal Neoplasms, Cohort Studies, Metagenomics, Microbiota

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