Ece Kartal (@ecekartal.bsky.social)

A faecal microbiota signature with high specificity for pancreatic cancer 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. Data are available in a public, open access repository. All data relevant to the study are included in the article or uploaded as supplementary information. The raw sequencing data for the samples are made available in the European Nucleotide Archive (ENA) under the study identifiers PRJEB38625 and PRJEB42013. Metadata for these samples are available as Supplementary Tables S1 and S2. Filtered taxonomic and functional profiles used as input for the statistical modelling pipeline are available in Supplementary Data S1 and S2. Analysis code and results available under [https://github.com/psecekartal/PDAC.git][1]. [1]: https://github.com/psecekartal/PDAC https://gut.bmj.com/content/71/7/1359