BACKGROUND AND AIMS: The gut microbiota is implicated in the pathogenesis of colorectal cancer (CRC). We aimed to map the CRC mucosal microbiota and metabolome and define the influence of the tumoral microbiota on oncological outcomes. METHODS: A multicentre, prospective observational study was conducted of CRC patients undergoing primary surgical resection in the UK (n = 74) and Czech Republic (n = 61). Analysis was performed using metataxonomics, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), targeted bacterial qPCR and tumour exome sequencing. Hierarchical clustering accounting for clinical and oncological covariates was performed to identify clusters of bacteria and metabolites linked to CRC. Cox proportional hazards regression was used to ascertain clusters associated with disease-free survival over median follow-up of 50 months. RESULTS: Thirteen mucosal microbiota clusters were identified, of which five were significantly different between tumour and paired normal mucosa. Cluster 7, containing the pathobionts Fusobacterium nucleatum and Granulicatella adiacens, was strongly associated with CRC (PFDR = 0.0002). Additionally, tumoral dominance of cluster 7 independently predicted favourable disease-free survival (adjusted p = 0.031). Cluster 1, containing Faecalibacterium prausnitzii and Ruminococcus gnavus, was negatively associated with cancer (PFDR = 0.0009), and abundance was independently predictive of worse disease-free survival (adjusted p = 0.0009). UPLC-MS analysis revealed two major metabolic (Met) clusters. Met 1, composed of medium chain (MCFA), long-chain (LCFA) and very long-chain (VLCFA) fatty acid species, ceramides and lysophospholipids, was negatively associated with CRC (PFDR = 2.61 × 10-11); Met 2, composed of phosphatidylcholine species, nucleosides and amino acids, was strongly associated with CRC (PFDR = 1.30 × 10-12), but metabolite clusters were not associated with disease-free survival (p = 0.358). An association was identified between Met 1 and DNA mismatch-repair deficiency (p = 0.005). FBXW7 mutations were only found in cancers predominant in microbiota cluster 7. CONCLUSIONS: Networks of pathobionts in the tumour mucosal niche are associated with tumour mutation and metabolic subtypes and predict favourable outcome following CRC resection. Video Abstract.

Cancer Biology and Therapeutics Group School of Biomolecular and Biomedical Science UCD Conway Institute University College Dublin Dublin Ireland

Department of Biosciences Nottingham Trent University Nottingham NG11 8NS UK

Department of Gastroenterology Imperial College Healthcare NHS Trust London UK

Department of Life Sciences MRC Centre for Molecular Bacteriology and Infection Imperial College London London UK

Department of Surgery and Cancer Imperial College London London UK

Department of Surgery Faculty Hospital and Faculty of Medicine in Pilsen Charles University Prague Pilsen Czech Republic

Division of Digestive Diseases Department of Metabolism Digestion and Reproduction Imperial College London 10th Floor QEQM Building St Mary's Hospital Praed Street London W2 1NY UK

Division of Systems Medicine Department of Metabolism Digestion and Reproduction National Phenome Centre Imperial College London London UK

Faculty of Medicine in Pilsen Biomedical Centre Charles University Prague Pilsen Czech Republic

GI Cancer Unit Department of Medical Oncology Royal Marsden NHS Foundation Trust London UK

Institute of Global Food Security School of Biosciences Queen's University Belfast Belfast UK

Section of Bioinformatics Division of Systems Medicine Department of Metabolism Digestion and Reproduction Imperial College London London UK

Translational Oncogenomics Laboratory The Institute of Cancer Research 237 Fulham Road London SW3 6JB UK

Zobrazit více v PubMed

Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424. doi: 10.3322/caac.21492. PubMed DOI

Cancer Research UK: Bowel cancer statistics https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/bowel-cancer Accessed August 2020.

Partnership HQI. National Bowel Cancer Audit Annual Report 2020.

Andre T, Boni C, Navarro M, et al. Improved overall survival with oxaliplatin, fluorouracil, and leucovorin as adjuvant treatment in stage II or III colon cancer in the MOSAIC trial. J Clin Oncol. 2009;27:3109–3116. doi: 10.1200/JCO.2008.20.6771. PubMed DOI

Xu W, He Y, Wang Y, et al. Risk factors and risk prediction models for colorectal cancer metastasis and recurrence: an umbrella review of systematic reviews and meta-analyses of observational studies. BMC Med. 2020;18:172. doi: 10.1186/s12916-020-01618-6. PubMed DOI PMC

Roth AD, Tejpar S, Delorenzi M, et al. Prognostic role of KRAS and BRAF in stage II and III resected colon cancer: results of the translational study on the PETACC-3, EORTC 40993, SAKK 60–00 trial. J Clin Oncol. 2010;28:466–474. doi: 10.1200/JCO.2009.23.3452. PubMed DOI

Watanabe T, Kobunai T, Yamamoto Y, et al. Chromosomal instability (CIN) phenotype, CIN high or CIN low, predicts survival for colorectal cancer. J Clin Oncol. 2012;30:2256–2264. doi: 10.1200/JCO.2011.38.6490. PubMed DOI

Bertagnolli MM, Niedzwiecki D, Compton CC, et al. Microsatellite instability predicts improved response to adjuvant therapy with irinotecan, fluorouracil, and leucovorin in stage III colon cancer: Cancer and Leukemia Group B Protocol 89803. J Clin Oncol. 2009;27:1814–1821. doi: 10.1200/JCO.2008.18.2071. PubMed DOI PMC

Tejpar S, Saridaki Z, Delorenzi M, et al. Microsatellite instability, prognosis and drug sensitivity of stage II and III colorectal cancer: more complexity to the puzzle. J Natl Cancer Inst. 2011;103:841–844. doi: 10.1093/jnci/djr170. PubMed DOI

Punt CJ, Koopman M, Vermeulen L. From tumour heterogeneity to advances in precision treatment of colorectal cancer. Nat Rev Clin Oncol. 2017;14:235–246. doi: 10.1038/nrclinonc.2016.171. PubMed DOI

Kostic AD, Chun E, Robertson L, et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe. 2013;14:207–215. doi: 10.1016/j.chom.2013.07.007. PubMed DOI PMC

Prorok-Hamon M, Friswell MK, Alswied A, et al. Colonic mucosa-associated diffusely adherent afaC+ Escherichia coli expressing lpfA and pks are increased in inflammatory bowel disease and colon cancer. Gut. 2014;63:761–770. doi: 10.1136/gutjnl-2013-304739. PubMed DOI PMC

Cougnoux A, Dalmasso G, Martinez R, et al. Bacterial genotoxin colibactin promotes colon tumour growth by inducing a senescence-associated secretory phenotype. Gut. 2014;63:1932–1942. doi: 10.1136/gutjnl-2013-305257. PubMed DOI

Wu S, Rhee KJ, Albesiano E, et al. A human colonic commensal promotes colon tumorigenesis via activation of T helper type 17 T cell responses. Nat Med. 2009;15:1016–1022. doi: 10.1038/nm.2015. PubMed DOI PMC

Long X, Wong CC, Tong L, et al. Peptostreptococcus anaerobius promotes colorectal carcinogenesis and modulates tumour immunity. Nat Microbiol. 2019;4:2319–2330. doi: 10.1038/s41564-019-0541-3. PubMed DOI

Mima K, Sukawa Y, Nishihara R, et al. Fusobacterium nucleatum and T cells in colorectal carcinoma. JAMA Oncol. 2015;1:653–661. doi: 10.1001/jamaoncol.2015.1377. PubMed DOI PMC

Mima K, Nishihara R, Qian ZR, et al. Fusobacterium nucleatum in colorectal carcinoma tissue and patient prognosis. Gut. 2016;65:1973–1980. doi: 10.1136/gutjnl-2015-310101. PubMed DOI PMC

Kosumi K, Hamada T, Koh H, et al. The amount of Bifidobacterium genus in colorectal carcinoma tissue in relation to tumor characteristics and clinical outcome. Am J Pathol. 2018;188:2839–2852. doi: 10.1016/j.ajpath.2018.08.015. PubMed DOI PMC

Scott AJ, Alexander JL, Merrifield CA, et al. International cancer microbiome consortium consensus statement on the role of the human microbiome in carcinogenesis. Gut. 2019;68:1624–1632. doi: 10.1136/gutjnl-2019-318556. PubMed DOI PMC

Vujkovic-Cvijin I, Sklar J, Jiang L, et al. Host variables confound gut microbiota studies of human disease. Nature. 2020;587:448–454. doi: 10.1038/s41586-020-2881-9. PubMed DOI PMC

Tjalsma H, Boleij A, Marchesi JR, et al. A bacterial driver-passenger model for colorectal cancer: beyond the usual suspects. Nat Rev Microbiol. 2012;10:575–582. doi: 10.1038/nrmicro2819. PubMed DOI

Mullish BH, Pechlivanis A, Barker GF, et al. Functional microbiomics: evaluation of gut microbiota-bile acid metabolism interactions in health and disease. Methods. 2018;149:49–58. doi: 10.1016/j.ymeth.2018.04.028. PubMed DOI PMC

Schloss PD, Westcott SL, Ryabin T, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–7541. doi: 10.1128/AEM.01541-09. PubMed DOI PMC

Schloss PD, Gevers D, Westcott SL. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS ONE. 2011;6:e27310. doi: 10.1371/journal.pone.0027310. PubMed DOI PMC

NCBI Resource Coordinators. Database Resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2017;45:D12-d17. PubMed PMC

Anwar MA, Vorkas PA, Li JV, et al. Optimization of metabolite extraction of human vein tissue for ultra performance liquid chromatography-mass spectrometry and nuclear magnetic resonance-based untargeted metabolic profiling. Analyst. 2015;140:7586–7597. doi: 10.1039/C5AN01041A. PubMed DOI

Vorkas PA, Isaac G, Anwar MA, et al. Untargeted UPLC-MS profiling pipeline to expand tissue metabolome coverage: application to cardiovascular disease. Anal Chem. 2015;87:4184–4193. doi: 10.1021/ac503775m. PubMed DOI PMC

Izzi-Engbeaya C, Comninos AN, Clarke SA, et al. The effects of kisspeptin on beta-cell function, serum metabolites and appetite in humans. Diabetes Obes Metab. 2018;20:2800–2810. doi: 10.1111/dom.13460. PubMed DOI PMC

Martens L, Chambers M, Sturm M, et al. mzML–a community standard for mass spectrometry data. Mol Cell Proteomics. 2011;10(R110):000133. PubMed PMC

Chambers MC, Maclean B, Burke R, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918–920. doi: 10.1038/nbt.2377. PubMed DOI PMC

Dunn WB, Broadhurst D, Begley P, et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011;6:1060–1083. doi: 10.1038/nprot.2011.335. PubMed DOI

Sands CJ, Wolfer AM, Correia GDS, et al. The nPYc-Toolbox, a Python module for the pre-processing, quality-control and analysis of metabolic profiling datasets. Bioinformatics. 2019;35:5359–5360. doi: 10.1093/bioinformatics/btz566. PubMed DOI PMC

Lewis MR, Pearce JT, Spagou K, et al. Development and application of ultra-performance liquid chromatography-TOF MS for precision large scale urinary metabolic phenotyping. Anal Chem. 2016;88:9004–9013. doi: 10.1021/acs.analchem.6b01481. PubMed DOI

Wolfer AM, Correia GDS, Sands CJ, et al. peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets. Bioinformatics. 2021;37(24):4886–4888. doi: 10.1093/bioinformatics/btab433. PubMed DOI PMC

Posma JM, Garcia-Perez I, Frost G, et al. Nutriome-metabolome relationships provide insights into dietary intake and metabolism. Nat Food. 2020;1:426–436. doi: 10.1038/s43016-020-0093-y. PubMed DOI PMC

Posma JM, Garcia-Perez I, Ebbels TMD, et al. Optimized phenotypic biomarker discovery and confounder elimination via covariate-adjusted projection to latent structures from metabolic spectroscopy data. J Proteome Res. 2018;17:1586–1595. doi: 10.1021/acs.jproteome.7b00879. PubMed DOI PMC

Nobile S, Deshusses J. Evidence for a role of a vicinal dithiol in the transport of gamma-butyrobetaine in Agrobacterium sp. Biochimie. 1988;70:1411–1416. doi: 10.1016/0300-9084(88)90013-2. PubMed DOI

Hulme H, Meikle LM, Strittmatter N, et al. Microbiome-derived carnitine mimics as previously unknown mediators of gut-brain axis communication. Sci Adv. 2020;6:eaax6328. doi: 10.1126/sciadv.aax6328. PubMed DOI PMC

Nakayama H, Nagafuku M, Suzuki A, et al. The regulatory roles of glycosphingolipid-enriched lipid rafts in immune systems. FEBS Lett. 2018;592:3921–3942. doi: 10.1002/1873-3468.13275. PubMed DOI

Angstrom J, Teneberg S, Milh MA, et al. The lactosylceramide binding specificity of Helicobacter pylori. Glycobiology. 1998;8:297–309. doi: 10.1093/glycob/8.4.297. PubMed DOI

Wang K, Xu R, Snider AJ, et al. Alkaline ceramidase 3 deficiency aggravates colitis and colitis-associated tumorigenesis in mice by hyperactivating the innate immune system. Cell Death Dis. 2016;7:e2124. doi: 10.1038/cddis.2016.36. PubMed DOI PMC

Mills GB, Moolenaar WH. The emerging role of lysophosphatidic acid in cancer. Nat Rev Cancer. 2003;3:582–591. doi: 10.1038/nrc1143. PubMed DOI

Wyss C. Aspartame as a source of essential phenylalanine for the growth of oral anaerobes. FEMS Microbiol Lett. 1993;108:255–258. doi: 10.1111/j.1574-6968.1993.tb06111.x. PubMed DOI

Flemer B, Herlihy M, O'Riordain M, et al. Tumour-associated and non-tumour-associated microbiota: addendum. Gut Microbes. 2018;9:369–373. PubMed PMC

Mlecnik B, Tosolini M, Kirilovsky A, et al. Histopathologic-based prognostic factors of colorectal cancers are associated with the state of the local immune reaction. J Clin Oncol. 2011;29:610–618. doi: 10.1200/JCO.2010.30.5425. PubMed DOI

Pages F, Mlecnik B, Marliot F, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018;391:2128–2139. doi: 10.1016/S0140-6736(18)30789-X. PubMed DOI

Chung L, Thiele Orberg E, Geis AL, et al. Bacteroides fragilis toxin coordinates a pro-carcinogenic inflammatory cascade via targeting of colonic epithelial cells. Cell Host Microbe. 2018;23(203–214):e5. PubMed PMC

Yachida S, Mizutani S, Shiroma H, et al. Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer. Nat Med. 2019;25:968–976. doi: 10.1038/s41591-019-0458-7. PubMed DOI

Brown DG, Rao S, Weir TL, et al. Metabolomics and metabolic pathway networks from human colorectal cancers, adjacent mucosa, and stool. Cancer Metab. 2016;4:11. doi: 10.1186/s40170-016-0151-y. PubMed DOI PMC

Arima K, Lau MC, Zhao M, et al. Metabolic profiling of formalin-fixed paraffin-embedded tissues discriminates normal colon from colorectal cancer. Mol Cancer Res. 2020;18:883–890. doi: 10.1158/1541-7786.MCR-19-1091. PubMed DOI PMC

Kinross J, Mirnezami R, Alexander J, et al. A prospective analysis of mucosal microbiome-metabonome interactions in colorectal cancer using a combined MAS 1HNMR and metataxonomic strategy. Sci Rep. 2017;7:8979. doi: 10.1038/s41598-017-08150-3. PubMed DOI PMC

Cameron ES, Schmidt PJ, Tremblay BJ, et al. Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities. Sci Rep. 2021;11:22302. doi: 10.1038/s41598-021-01636-1. PubMed DOI PMC