Several studies have investigated links between the gut microbiome and colorectal cancer (CRC), but questions remain about the replicability of biomarkers across cohorts and populations. We performed a meta-analysis of five publicly available datasets and two new cohorts and validated the findings on two additional cohorts, considering in total 969 fecal metagenomes. Unlike microbiome shifts associated with gastrointestinal syndromes, the gut microbiome in CRC showed reproducibly higher richness than controls (P < 0.01), partially due to expansions of species typically derived from the oral cavity. Meta-analysis of the microbiome functional potential identified gluconeogenesis and the putrefaction and fermentation pathways as being associated with CRC, whereas the stachyose and starch degradation pathways were associated with controls. Predictive microbiome signatures for CRC trained on multiple datasets showed consistently high accuracy in datasets not considered for model training and independent validation cohorts (average area under the curve, 0.84). Pooled analysis of raw metagenomes showed that the choline trimethylamine-lyase gene was overabundant in CRC (P = 0.001), identifying a relationship between microbiome choline metabolism and CRC. The combined analysis of heterogeneous CRC cohorts thus identified reproducible microbiome biomarkers and accurate disease-predictive models that can form the basis for clinical prognostic tests and hypothesis-driven mechanistic studies.
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Nucleotide sequences for the two new Italian cohorts are available in the Sequence Read Archive under accession No. SRP136711. MetaPhlAn2 and HUMANn2 profiles for the new cohorts were also added to the curatedMetagenomicData R package27 along with their corresponding metadata. Validation Cohort1 is available in the European Nucleotide Archive under the study identifier PRJEB27928; Validation Cohort2 is available in the DNA data bank of Japan databases under the accession No. DRA006684.
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We thank the members of the Segata, Naccarati and Waldron groups for insightful discussions, all the volunteers enrolled in the study, the NGS facility at the University of Trento for performing the metagenomic sequencing, and the HPC facility at the University of Trento for supporting the computational experiments. This work was primarily supported by Lega Italiana per La Lotta contro i Tumori to N.S., F.C. and A.N., and by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant No. 16/23527-2) to A.M.T. This work was also partially supported by the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, Brazil) to J.C.S. and E.D.-N.; by FAPESP (grant No. 14/26897-0); by Associação Beneficente Alzira Denise Hertzog Silva (ABADHS, Brazil) and PRONON/SIPAR to E.D.-N.; by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) (Finance Code No. 001 to J.C.S.; by the Italian Institute for Genomic Medicine (IIGM) and Compagnia di San Paolo Torino to A.N, A.F., B.P. and S.T.; by Fondazione Umberto Veronesi ‘Post-doctoral Fellowship Years 2014, 2015, 2016, 2017 and 2018’ to B.P. and S.T.; by the Grant Agency of the Czech Republic (grant No. 17-16857S) to A.N.; by Fondazione Umberto Veronesi (grant No. FUV-14-SG-GANDINI) to S.G.; by the European Union H2020 Marie Curie grant (No. 707345) to E.P.; by the European Research Council (ERC-STG project MetaPG) to N.S.; by MIUR ‘Futuro in Ricerca’ (grant No. RBFR13EWWI_001) to N.S.; by the People Programme (Marie Curie Actions) of the European Union FP7 and H2020 to N.S.; and by the National Cancer Institute (grant No. U24CA180996) and National Institute of Allergy and Infectious Diseases (grant No. 1R21AI121784-01) of the National Institutes of Health to L.W. B.P. is the recipient of a Fulbright Research Scholarship (year 2018). We acknowledge funding from EMBL, DKFZ, the Huntsman Cancer Foundation, the Intramural Research Program of the National Cancer Institute, ETH Zürich and the following external sources: the European Research Council (CancerBiome, grant No. ERC-2010-AdG_20100317) to P.B.; Microbios (No. ERC-AdG-669830) to P.B.; the Novo Nordisk Foundation (grant No. NNF10CC1016515) to M.A.; the Danish Diabetes Academy supported by the Novo Nordisk Foundation and TARGET research initiative (Danish Strategic Research Council, grant No. 0603-00484B) to M.A.; the Matthias-Lackas Foundation (to C.M.U.); the National Cancer Institute (grant Nos. R01 CA189184, R01 CA207371, U01 CA206110 and P30 CA042014 ll to C.M.U.); the BMBF (the de.NBI network, grant No. 031A537B) to P.B.; the ERA-NET TRANSCAN project (No. 01KT1503) to C.M.U.; and the Helmut Horten Foundation (to S.S.). For Validation Cohort2, funding was provided by grants from the National Cancer Center Research and Development Fund (grant Nos. 25-A-4, 28-A-4 and 29-A-6); Practical Research Project for Rare/Intractable Diseases from the Japan Agency for Medical Research and Development (AMED, grant No. JP18ek0109187); JST (Japan Science and Technology Agency)-PRESTO (grant No. JPMJPR1507); Japan Society for the Promotion of Science KAKENHI (grant Nos. 16J10135, 142558 and 221S0002); Joint Research Project of the Institute of Medical Science; the University of Tokyo; the Takeda Science Foundation; and the Suzuken Memorial Foundation.
P.B., G.Z., A.Y.V. and S.S. are named inventors on a patent (EP2955232A1: Method for diagnosing colorectal cancer based on analyzing the gut microbiome). All other authors declare no competing interests as defined by Nature Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper.
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a, Boxplots reporting the total number of reads in each dataset. P values between the carcinoma and control groups were calculated by two-tailed Wilcoxon rank-sum tests. b, Boxplots showing the total number of microbial species per dataset. P values were calculated by two-tailed Wilcoxon rank-sum tests. c, Boxplots showing the total number of microbial species per dataset calculated on metagenomes subsampled in each dataset to the number of reads of the tenth percentile. P values were calculated by two-tailed Wilcoxon rank-sum tests. d, Multivariate analysis of species richness using crude and age-, sex- and BMI-adjusted coefficients obtained from linear models. e, Meta-analysis of crude and adjusted multivariate richness coefficients using a random effects model. Bold lines represent the 95% confidence interval for the random effects model estimate.
a, Boxplots reporting the Shannon species diversity in each dataset. P values between the carcinoma and control groups were calculated by two-tailed Wilcoxon rank-sum tests. b, Boxplots reporting the Shannon species diversity calculated on metagenomes subsampled in each dataset to the number of reads of the tenth percentile. P values were calculated by two-tailed Wilcoxon rank-sum tests. c, Multivariate analysis of species diversity using crude and age-, sex- and BMI-adjusted coefficients obtained from linear models. d, Meta-analysis of crude and adjusted multivariate Shannon diversity coefficients using a random effects model. Bold lines represent the 95% confidence interval for the random effects model estimate. e, Boxplots reporting the total number of oral microbial species per dataset. P values were calculated by two-tailed Wilcoxon rank-sum tests comparing values between controls and carcinomas for each dataset. f, Multivariate analysis of putative oral species richness using crude and age-, sex- and BMI-adjusted coefficients obtained from linear models. g, Meta-analysis of crude and adjusted multivariate putative oral species richness coefficients using a random effects model. Bold lines represent the 95% confidence interval for the random effects model estimate.
Extended Data Fig. 3 Two metagenomic cohorts identify clear but only partially overlapping microbiome signatures associated with CRC.
a,b, Relative abundances (log scale) and effect sizes (estimated using the linear discriminant analysis score in LEfSe) for the significantly different microbial species in CRC samples compared to control samples for Cohort1 (significance assessed by the non-parametric test in LEfSe) (a) and Cohort2 (b). c, Alpha-diversities measured as the total number of species and total number of UniProt90 gene families in each sample for the two cohorts. d, Beta-diversities estimated with the Bray–Curtis dissimilarity metric for intra- and inter-condition comparisons in the two cohorts.
a, Percentages of F. nucleatum clade-specific markers (200 in total) in each dataset. P values were obtained by two-tailed Wilcoxon rank-sum tests comparing values between controls and carcinomas for each dataset. b, Multivariate analysis of meta-analysis species-level abundance biomarkers. Crude and age-, sex- and BMI-adjusted coefficients for species associated with disease status in the meta-analysis of standardized mean differences. c, Meta-analysis of CRC datasets using species-level MetaPhlAn2 profiles. Bold lines represent the 95% confidence interval for the random effects model estimate.
Extended Data Fig. 5 Analysis of putative oral species abundances in CRC datasets and gene-family richness across CRC datasets.
a, Effect sizes of the abundances of significant putative oral species identified using a meta-analysis of standardized mean differences and a random effects model. Bold lines represent the 95% confidence interval for the random effects model estimate. b, Total abundance of putative oral species in each gut metagenomic dataset. P values were obtained by two-tailed Wilcoxon rank-sum tests comparing values between controls and carcinomas for each dataset. c, The total number of reads in each sample of each dataset correlates with the total number of gene families identified using HUMANn2. Ellipses represent the 95% confidence level assuming a multivariate t-distribution. d, Distribution of the total number of gene families identified in the samples of each dataset. P values were obtained by two-tailed Wilcoxon rank-sum tests comparing values between controls and carcinomas for each dataset. e, Distribution of the percentages of unmapped reads across datasets for UniProt90 gene families.
Extended Data Fig. 6 Cross-validation, cross-cohort and LODO predictions using pathway abundances, species abundances and species-specific markers.
a, Prediction matrix reporting prediction performances as AUC values obtained using a random forest model on pathway relative abundances. Values on the diagonal refer to 20 times repeated tenfold stratified cross-validations. Off-diagonal values refer to the AUC values obtained by training the classifier on the dataset of the corresponding row and applying it to the dataset of the corresponding column. The LODO row refers to the performances obtained by training the model on pathway abundances using all but the dataset of the corresponding column and applying it to the dataset of the corresponding column. b, Prediction matrix as in a but using MetaPhlAn2 marker presence and absence information. c, Prediction of samples-to-cohort assignments using species-level relative abundances. Only control samples from each dataset are considered. d, Principal coordinate analysis of Bray–Curtis distances computed on MetaPhlAn2 species-level abundances across datasets. Ellipses represent the 95% confidence level assuming a multivariate t-distribution. e, Cross-prediction matrix for the performances of random forest models in predicting adenomas versus CRC conditions. f, Cross-prediction matrix as described in e but on the distinction of adenomas versus controls.
Extended Data Fig. 7 Prediction performances with increasing numbers of external datasets considered in the training model.
a, Prediction performances computed based on MetaPhlAn2 species abundances. The dark yellow line interpolates the median AUC at each number of training datasets considered. b, Prediction performances computed based on HUMANn2 gene-family abundances.
Extended Data Fig. 8 Identification of a minimal number of microbial gene families for CRC detection.
Prediction performances in the LODO settings at increasing numbers of gene families. Each ranking is obtained excluding the testing dataset to avoid overfitting.
a, ShortBRED analysis of yeaW and caiT gene abundances. Points represent the log of RPKM for each sample and crosses represent average values per group/dataset. b, ShortBRED analysis of cutD gene abundances. Boxplots report the RKPM abundances obtained using ShortBRED for the gene of the activating TMA-lyase enzyme cutD. P values were calculated by two-tailed Wilcoxon rank-sum tests comparing values between controls and carcinomas for each dataset. c, Forest plot showing effect sizes calculated using a meta-analysis of standardized mean differences and a random effects model on cutD RPKM abundances between carcinomas and controls. d, Breadth of coverage of cutC gene sequence clusters across CRC datasets. e, Depth of coverage of cutC gene sequence clusters across CRC datasets.
a, Prediction strengths at differing numbers of clusters showing optimum numbers at two and four clusters. b, Tables showing the number of samples for carcinomas, adenomas and controls with breadth of coverage >80% at two different cluster thresholds. P values were calculated using a Fisher t-test, and taxonomy was assigned by BLASTn and the cutC sequence database (criteria of 80% coverage, >97% identity and minimum 2,000 nt alignment length).
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Thomas, A.M., Manghi, P., Asnicar, F. et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat Med 25, 667–678 (2019). https://doi.org/10.1038/s41591-019-0405-7
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