1. Volume 155, Issue 2, Pages 529-541.e5 (August 2018)
Alterations in Enteric Virome Are Associated With Colorectal Cancer and Survival Outcomes 
Geicho Nakatsu, Haokui Zhou, William Ka Kei Wu, Sunny Hei Wong, Olabisi Oluwabukola Coker, Zhenwei Dai, Xiangchun Li, Chun-Ho Szeto, Naoki Sugimura, Thomas Yuen-Tung Lam, Allen Chi-Shing Yu, Xiansong Wang, Zigui Chen, Martin Chi-Sang Wong, Siew Chien Ng, Matthew Tak Vai Chan, Paul Kay Sheung Chan, Francis Ka Leung Chan, Joseph Jao-Yiu Sung, Jun Yu 
Gastroenterology 
Volume 155, Issue 2, Pages e5 (August 2018)
DOI: /j.gastro
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2. Gastroenterology 2018 155, 529-541. e5DOI: (10. 1053/j. gastro. 2018
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3. Figure 1
Variations in CRC-associated enteric viral community. (A) Rarefaction plot showing the richness and sequencing depths of enteric bacteriophage in CRC-associated metagenomes. (B) Comparison of intrasample diversity indicators of phage and bacterial communities in disease state. Plus and minus symbols on integrated boxplots represent mean and median, respectively. (C) Group-specific Spearman rank-order correlation plot showing the relationship between phage and bacterial microbiome diversities. Shaded areas around regression lines represent 80% confidence range.
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4. Figure 2
Performance of virome markers for classification of CRC status. (A) Dotplot (left) showing the average importance score and ranking of most discriminatory viral genus-level markers identified by RF-BFE selection. Error bars represent standard deviations of mean decrease in accuracy from 100 iterations of discovery model fit. Heatmap (right) showing the relative fold change of virome markers against mean normalized abundance of control group. Samples in column (left to right) are sorted in increasing order of average case-class probabilities determined by 10-times–repeated 10-fold model cross-validations. (B) Internal 10-times–repeated 10-fold cross-validations of CRC virome-based metagenomic classifier. Red line represents average true and false positive rates. Greyed-out dots show each individual round of 10-fold model cross-validations summarized by boxplots. (C) Validation of discovery classifier in an independent Chinese cohort (control, n = 112; case, n = 111) and 2 European cohorts for CRC detection, Austrian (control, n = 63; case, n = 46)2 and French/German (control, n = 66; cases, n = 91).5 The performance of discovery classifier for detecting colorectal adenomas was also assessed (Chinese cohort: control, n = 112; case, n = 197; Austrian cohort: control, n = 63; case, n = 47; French/German cohort: control, n = 66; case, n = 42). Shaded areas with gradient represent bootstrapped confidence intervals (n = 10,000) corresponding to approximately 1, 2, and 3 standard deviations of true and false positive rates for externally predicted case-class probabilities. MDA, mean decrease in accuracy.
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5. Figure 3
Clinical stage–associated dysbiosis of the gut virome in CRC. Biplot summarizing Canberra distance–based partial redundancy analysis of virome species-level profiles, controlling for compositional effects of age, sex, obesity, diabetes mellitus, and use of oral prescription drugs. Viral species that correlate with both constrained axes at false discovery rates of 5% (Spearman ρ) or less are shown. Projection axes were assessed individually by Wilcoxon rank-sum tests. P values were adjusted for multiple-hypothesis testing by Benjamini-Hochberg step-up procedures. ∗∗∗∗Q <
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6. Figure 4
Potential of fecal virome taxa for CRC prognosis. (A) Construction and evaluation of RSF classifiers with ensemble permutation importance measure–guided forward selections of survival predictors. C-indexes are computed using OOB-predicted mortality scores from 1000 iterations of RSF modeling. Red line indicates local regression (LOESS) curve fitting. Color-coded taxa (red) represent optimal survival predictors. (B) Kaplan-Meier curve plot of CRC risk groups dichotomized on the basis of OOB-predicted virome mortality scores from final RSF model. Inset: Wilcoxon rank-sum tests, ∗P < .05, ∗∗∗∗P <  B, Betabaculovirus; E, Epsilon15likevirus; M, Mulikevirus; Pu, Punalikevirus. (C) Forest plot summary of multivariate Cox regression analysis of virome-associated risk groups and baseline clinical covariates. Hazard ratios are visualized with 95% CIs. C-index, concordance index; CI, confidence interval.
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7. Figure 5
Altered trans-kingdom interactions between gut-oral bacteria and bacteriophage species in early and late-stage CRC. Heatmap showing strengths of sparse partial correlations adjusted for age, sex, obesity, diabetes mellitus, and use of oral prescription drugs. Taxonomic pairs were clustered within the control group using Ward hierarchical method with Euclidean distances. Differential correlations with FDRs of 5% or less are shown. sp., species.
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8. Supplementary Figure 1
Relative sample composition and breakdown of viral metagenomic sequences classified at family-levels compared with bacterial counterparts. Bar charts depicting taxonomic landscape of viral families and their overall relative proportions compared with bacterial taxa among (A) control (n = 92) and (B) CRC (n = 74) samples.
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9. Supplementary Figure 2
Performance of nested random forests models by the inclusion of viral taxonomic predictors. Delong tests for comparisons of nested area under the receiver operating characteristics curve with (A) metagenomic bacterial species predictors and (B) FIT/gFOBT positivities.
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10. Supplementary Figure 3
Performance of leave-one-out cross-validated Dirichlet multinomial models for classification of CRC virome across 3 independent cohorts. Receiver operating characteristics curves based on (A) virome genera and (B) virome species.
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11. Supplementary Figure 4
Nonparametric Bayesian ordination of virome compositions. Scatterplot showing probabilistic virus community distance among samples based on (A) virome genera and (B) virome species.
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12. Supplementary Figure 5
Constrained ordination analysis of virome genera. Biplot showing consistent separation of β index profiles by CRC clinical stages after adjusting for clinical covariates as described. P values were derived from Wilcoxon rank-sum tests with Benjamini-Hochberg step-up corrections. ∗∗∗∗Q <
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13. Supplementary Figure 6
Differential taxonomic interactions between non–gut-oral bacterial genera and bacteriophage species. Heatmap describing clustered phage-bacteria relationships, controlling for effects of clinical covariates as described. Differential correlations with false discovery rates of 10% or less are shown.
Gastroenterology  , e5DOI: ( /j.gastro )
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