Naoyoshi Nagata1, Suguru Nishijima2, Yasushi Kojima3, Yuya Hisada3, Koh Imbe3, Tohru Miyoshi-Akiyama4, Wataru Suda5, Moto Kimura6, Ryo Aoki7, Katsunori Sekine8, Mitsuru Ohsugi9, Kuniko Miki10, Tsuyoshi Osawa11, Kohjiro Ueki12, Shinichi Oka13, Masashi Mizokami14, Ece Kartal15, Thomas S B Schmidt15, Esther Molina-Montes16, Lidia Estudillo16, Nuria Malats16, Jonel Trebicka17, Stephan Kersting18, Melanie Langheinrich18, Peer Bork19, Naomi Uemura20, Takao Itoi21, Takashi Kawai22. 1. Department of Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan; Department of Gastroenterology and Hepatology, National Center for Global Health and Medicine, Tokyo, Japan. Electronic address: nnagata_ncgm@yahoo.co.jp. 2. Computational Bio-Big Data Open Innovation Lab, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan; Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany. Electronic address: nishijima.suguru@gmail.com. 3. Department of Gastroenterology and Hepatology, National Center for Global Health and Medicine, Tokyo, Japan. 4. Pathogenic Microbe Laboratory, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan. 5. Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. 6. Department of Clinical Research Strategic Planning Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan. 7. Institute of Health Sciences, Ezaki Glico Co., Ltd., Osaka, Japan. 8. Department of Gastroenterology and Hepatology, National Center for Global Health and Medicine, Kohnodai Hospital, Tokyo, Japan. 9. Department of Diabetes, Endocrinology, and Metabolism, Center Hospital, National Center for Global Health and Medicine, Tokyo, Japan; Diabetes and Metabolism Information Center, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan. 10. Department of Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan; Department of Gastroenterology and Hepatology, National Center for Global Health and Medicine, Tokyo, Japan. 11. Division of Nutriomics and Oncology, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan. 12. Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan. 13. AIDS Clinical Center, National Center for Global Health and Medicine, Tokyo, Japan. 14. Genome Medical Sciences Project, Research Institute, National Center for Global Health and Medicine, Chiba, Japan. 15. Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany. 16. Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Center (CNIO), Madrid, and CIBERONC, Spain. 17. Section for Translational Hepatology, Department of Internal Medicine I, Goehte University Frankfurt, Frankfurt, Germany; European Foundation for the Study of Chronic Liver Failure, Barcelona, Spain. 18. Department of Surgery, University Hospital of Erlangen, Erlangen, Germany; Department of Surgery, University Clinic Greifswald, Greifswald, Germany. 19. Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany. 20. Department of Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan; Department of Gastroenterology and Hepatology, National Center for Global Health and Medicine, Kohnodai Hospital, Tokyo, Japan. 21. Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan. 22. Department of Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan.
Abstract
BACKGROUND & AIMS: To identify gut and oral metagenomic signatures that accurately predict pancreatic ductal carcinoma (PDAC) and to validate these signatures in independent cohorts. METHODS: We conducted a multinational study and performed shotgun metagenomic analysis of fecal and salivary samples collected from patients with treatment-naïve PDAC and non-PDAC controls in Japan, Spain, and Germany. Taxonomic and functional profiles of the microbiomes were characterized, and metagenomic classifiers to predict PDAC were constructed and validated in external datasets. RESULTS: Comparative metagenomics revealed dysbiosis of both the gut and oral microbiomes and identified 30 gut and 18 oral species significantly associated with PDAC in the Japanese cohort. These microbial signatures achieved high area under the curve values of 0.78 to 0.82. The prediction model trained on the Japanese gut microbiome also had high predictive ability in Spanish and German cohorts, with respective area under the curve values of 0.74 and 0.83, validating its high confidence and versatility for PDAC prediction. Significant enrichments of Streptococcus and Veillonella spp and a depletion of Faecalibacterium prausnitzii were common gut signatures for PDAC in all the 3 cohorts. Prospective follow-up data revealed that patients with certain gut and oral microbial species were at higher risk of PDAC-related mortality. Finally, 58 bacteriophages that could infect microbial species consistently enriched in patients with PDAC across the 3 countries were identified. CONCLUSIONS: Metagenomics targeting the gut and oral microbiomes can provide a powerful source of biomarkers for identifying individuals with PDAC and their prognoses. The identification of shared gut microbial signatures for PDAC in Asian and European cohorts indicates the presence of robust and global gut microbial biomarkers.
BACKGROUND & AIMS: To identify gut and oral metagenomic signatures that accurately predict pancreatic ductal carcinoma (PDAC) and to validate these signatures in independent cohorts. METHODS: We conducted a multinational study and performed shotgun metagenomic analysis of fecal and salivary samples collected from patients with treatment-naïve PDAC and non-PDAC controls in Japan, Spain, and Germany. Taxonomic and functional profiles of the microbiomes were characterized, and metagenomic classifiers to predict PDAC were constructed and validated in external datasets. RESULTS: Comparative metagenomics revealed dysbiosis of both the gut and oral microbiomes and identified 30 gut and 18 oral species significantly associated with PDAC in the Japanese cohort. These microbial signatures achieved high area under the curve values of 0.78 to 0.82. The prediction model trained on the Japanese gut microbiome also had high predictive ability in Spanish and German cohorts, with respective area under the curve values of 0.74 and 0.83, validating its high confidence and versatility for PDAC prediction. Significant enrichments of Streptococcus and Veillonella spp and a depletion of Faecalibacterium prausnitzii were common gut signatures for PDAC in all the 3 cohorts. Prospective follow-up data revealed that patients with certain gut and oral microbial species were at higher risk of PDAC-related mortality. Finally, 58 bacteriophages that could infect microbial species consistently enriched in patients with PDAC across the 3 countries were identified. CONCLUSIONS: Metagenomics targeting the gut and oral microbiomes can provide a powerful source of biomarkers for identifying individuals with PDAC and their prognoses. The identification of shared gut microbial signatures for PDAC in Asian and European cohorts indicates the presence of robust and global gut microbial biomarkers.