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dc.contributor.authorMasqué-Soler, Neus
dc.contributor.authorGehrung, Marcel
dc.contributor.authorKosmidou, Cassandra
dc.contributor.authorLi, Xiaodun
dc.contributor.authorDiwan, Izzuddin
dc.contributor.authorRafferty, Conor
dc.contributor.authorAtabakhsh, Elnaz
dc.contributor.authorMarkowetz, Florian
dc.contributor.authorFitzgerald, Rebecca C
dc.date.accessioned2022-01-29T00:30:20Z
dc.date.available2022-01-29T00:30:20Z
dc.date.issued2022-02
dc.identifier.issn2352-3964
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333401
dc.description.abstractBACKGROUND: Non-endoscopic cell collection devices combined with biomarkers can detect Barrett's intestinal metaplasia and early oesophageal cancer. However, assays performed on multi-cellular samples lose information about the cell source of the biomarker signal. This cross-sectional study examines whether a bespoke artificial intelligence-based computational pathology tool could ascertain the cellular origin of microRNA biomarkers, to inform interpretation of the disease pathology, and confirm biomarker validity. METHODS: The microRNA expression profiles of 110 targets were assessed with a custom multiplexed panel in a cohort of 117 individuals with reflux that took a Cytosponge test. A computational pathology tool quantified the amount of columnar epithelium present in pathology slides, and results were correlated with microRNA signals. An independent cohort of 139 Cytosponges, each from an individual patient, was used to validate the findings via qPCR. FINDINGS: Seventeen microRNAs are upregulated in BE compared to healthy squamous epithelia, of which 13 remain upregulated in dysplasia. A pathway enrichment analysis confirmed association to neoplastic and cell cycle regulation processes. Ten microRNAs positively correlated with columnar epithelium content, with miRNA-192-5p and -194-5p accurately detecting the presence of gastric cells (AUC 0.97 and 0.95). In contrast, miR-196a-5p is confirmed as a specific BE marker. INTERPRETATION: Computational pathology tools aid accurate cellular attribution of molecular signals. This innovative design with multiplex microRNA coupled with artificial intelligence has led to discovery of a quality control metric suitable for large scale application of the Cytosponge. Similar approaches could aid optimal interpretation of biomarkers for clinical use. FUNDING: Funded by the NIHR Cambridge Biomedical Research Centre, the Medical Research Council, the Rosetrees and Stoneygate Trusts, and CRUK core grants.
dc.format.mediumPrint-Electronic
dc.publisherElsevier BV
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial intelligence
dc.subjectBarrett's oesophagus
dc.subjectComputerized image analysis
dc.subjectDysplasia
dc.subjectOesophageal cancer
dc.subjectScreening
dc.titleComputational pathology aids derivation of microRNA biomarker signals from Cytosponge samples.
dc.typeArticle
dc.publisher.departmentCancer Research Uk Cambridge Institute
dc.date.updated2022-01-27T18:50:09Z
prism.number103814
prism.publicationDate2022
prism.publicationNameEBioMedicine
prism.startingPage103814
prism.volume76
dc.identifier.doi10.17863/CAM.80825
dcterms.dateAccepted2022-01-04
rioxxterms.versionofrecord10.1016/j.ebiom.2022.103814
rioxxterms.versionVoR
dc.contributor.orcidMarkowetz, Florian [0000-0002-2784-5308]
dc.identifier.eissn2352-3964
rioxxterms.typeJournal Article/Review
cam.issuedOnline2022-01-17
cam.depositDate2022-01-27
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International