A faecal microbiota signature with high specificity for pancreatic cancer
Open accessAbstract
Background: Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression. Objective: To explore the faecal and salivary microbiota as potential diagnostic biomarkers. Methods: We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case–control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case–control study (n=76), in the validation phase. Results: Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19–9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation. Conclusion: Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.
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These studies demonstrated feasibility of developing a global, specific, reproducible predictive model based on non-invasive gut microbiome profiling to screen for early stage I-II PDAC.
“Altogether, these studies demonstrated the feasibility of developing a global, specific and reproducible predictive model based on non-invasive gut microbiome profiling to screen for early stage I–II PDAC54.”
From Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy (2023) · cites this paper as [54]
Faecal microbiome composition in patients with PDAC differed from individuals without cancer across studies accounting for confounders.
“Across various studies taking into account all known possible cofounding factors relevant to pancreatic ductal adenocarcinoma (PDAC), the faecal microbiome composition in patients with this devastating disease differed from that in individuals without cancer45,54.”
From Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy (2023) · cites this paper as [54]
Molecular traits of specific gut microorganisms from Akkermansia, Lactobacillus, Bifidobacterium, Veillonella, Bacteroides, and Streptococcus were detectable in pancreatic tissues using 16S rRNA sequencing and FISH.
“molecular traits of specific gut microorganisms (from Akkermansia spp., Lactobacillus spp., Bifidobacterium spp., Veillonella spp., Bacteroides spp. and Streptococcus spp.) were detectable in pancreatic tissues using 16S ribosomal RNA sequencing and fluorescence in situ hybridization (FISH) assays”
From Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy (2023) · cites this paper as [54]
Comparison of faecal and saliva samples revealed distinct strains of faecal PDAC-associated microorganisms could originate from the oral cavity via oral-intestinal transmission.
“comparison of faecal and saliva samples revealed that distinct strains of faecal PDAC-associated microorganisms could originate from the oral cavity, owing to oral–intestinal transmission.”
From Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy (2023) · cites this paper as [54]
A microbiota-based classification model using 27 microbial species specific for PDAC GOMS achieved AUC up to 0.84, and was highly disease-specific when validated against 25 public metagenomic datasets.
“a microbiota-based classification model with an area under the receiver operating characteristic curve (AUC) of up to 0.84 for a set of 27 microbial species specific for a PDAC GOMS. This signature was highly disease-specific when validated against 25 publicly available metagenomic datasets”
From Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy (2023) · cites this paper as [54]
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- Authors
- Kartal, E., Schmidt, T. S. B., Molina-Montes, E., Rodríguez-Perales, S., Wirbel, J., Maistrenko, O. M., Akanni, W. A., Alashkar Alhamwe, B., Alves, R. J., Carrato, A., Erasmus, H.-P., Estudillo, L., Finkelmeier, F., Fullam, A., Glazek, A. M., Gómez-Rubio, P., Hercog, R., Jung, F., Kandels, S., Kersting, S., Langheinrich, M., Márquez, M., Molero, X., Orakov, A., Van Rossum, T., Torres-Ruiz, R., Telzerow, A., Zych, K., MAGIC Study investigators, PanGenEU Study investigators, Benes, V., Zeller, G., Trebicka, J., Real, F. X., Malats, N., & Bork, P.
- Affiliations
- Heidelberg University (DE)
- European Molecular Biology Laboratory (DE)
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
- Centro de Investigación Biomédica en Red de Oncología (CIBERONC), Madrid, Spain
- Member of the German Center for Lung Research (DZL) and the Universities of Giessen and Marburg Lung School (UGMLC), Philipps University Marburg Faculty of Medicine, Marburg, Germany
- Universidad de Alcalá (ES)
- Goethe University Frankfurt (DE)
- Spanish National Cancer Research Centre (ES)
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Universität Greifswald (DE)
- Universitat Autònoma de Barcelona (ES)
- Genomic Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
- Universitat Pompeu Fabra (ES)
- ORCID
- Kartal, Ece
- Schmidt, Thomas S B
- Molina-Montes, Esther
- Rodríguez-Perales, Sandra
- Wirbel, Jakob
- Maistrenko, Oleksandr M
- Akanni, Wasiu A
- Alashkar Alhamwe, Bilal
- Alves, Renato J
- Carrato, Alfredo
- Estudillo, Lidia
- Finkelmeier, Fabian
- Fullam, Anthony
- Hercog, Rajna
- Jung, Ferris
- Kandels, Stefanie
- Kersting, Stephan
- Langheinrich, Melanie
- Márquez, Mirari
- Molero, Xavier
- Orakov, Askarbek
- Van Rossum, Thea
- Torres-Ruiz, Raul
- Telzerow, Anja
- Zych, Konrad
- Benes, Vladimir
- Zeller, Georg
- Trebicka, Jonel
- Real, Francisco X
- Malats, Nuria
- Bork, Peer
- Venue
- Gut
- Publisher
- BMJ
- Published
- 2022-03-08
- Volume
- 71
- Issue
- 7
- Pages
- 1359-1372
- ISSN
- 0017-5749 (ISSN-L)
- 1468-3288
- PMC
- PMC9185815
- OpenAlex
- W4221001346
- Cited by
- 290
- References
- 89
- Citations by year
- 2026: 49 · 2025: 69 · 2024: 76 · 2023: 67 · 2022: 29
- FWCI
- 19.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Gut (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by)
- Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by-nc-nd)
- Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion, cc-by)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
- Funders
- Red Temática de Investigación Cooperativa en Cáncer (RD12/0036/0034, RD12/0036/0050, RD12/0036/0073)
- BMBF-funded Heidelberg Center for Human Bioinformatics (HD-HuB) within the German Network for Bioinformatics Infrastructure (031A537B)
- Instituto de Salud Carlos III-FEDER (PI17/02303, PI15/01573, PI18/01347)
- EU-6FP Integrated Project (018771-MOLDIAG-PACA)
- H2020 European Research Council (ERC-AdG-669830 MicrobioS)
- World Cancer Research Fund (15-0391)
- EU-FP7-HEALTH (259737-CANCERALIA)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Keywords
- pancreatic ductal adenocarcinoma, fecal microbiota, salivary microbiota, tumor markers, early cancer detection, CA19-9, chronic pancreatitis, non-invasive diagnosis
- MeSH terms
- Humans, Pancreatic Neoplasms, RNA, Ribosomal, 16S, Biomarkers, Tumor, Case-Control Studies, CA-19-9 Antigen, Carcinoma, Pancreatic Ductal, Microbiota
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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