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Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples

BACKGROUND: 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 exa...

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Autores principales: Masqué-Soler, Neus, Gehrung, Marcel, Kosmidou, Cassandra, Li, Xiaodun, Diwan, Izzuddin, Rafferty, Conor, Atabakhsh, Elnaz, Markowetz, Florian, Fitzgerald, Rebecca C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8883000/
https://www.ncbi.nlm.nih.gov/pubmed/35051729
http://dx.doi.org/10.1016/j.ebiom.2022.103814
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author Masqué-Soler, Neus
Gehrung, Marcel
Kosmidou, Cassandra
Li, Xiaodun
Diwan, Izzuddin
Rafferty, Conor
Atabakhsh, Elnaz
Markowetz, Florian
Fitzgerald, Rebecca C.
author_facet Masqué-Soler, Neus
Gehrung, Marcel
Kosmidou, Cassandra
Li, Xiaodun
Diwan, Izzuddin
Rafferty, Conor
Atabakhsh, Elnaz
Markowetz, Florian
Fitzgerald, Rebecca C.
author_sort Masqué-Soler, Neus
collection PubMed
description BACKGROUND: 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.
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spelling pubmed-88830002022-03-02 Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples Masqué-Soler, Neus Gehrung, Marcel Kosmidou, Cassandra Li, Xiaodun Diwan, Izzuddin Rafferty, Conor Atabakhsh, Elnaz Markowetz, Florian Fitzgerald, Rebecca C. EBioMedicine Articles BACKGROUND: 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. Elsevier 2022-01-17 /pmc/articles/PMC8883000/ /pubmed/35051729 http://dx.doi.org/10.1016/j.ebiom.2022.103814 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Articles
Masqué-Soler, Neus
Gehrung, Marcel
Kosmidou, Cassandra
Li, Xiaodun
Diwan, Izzuddin
Rafferty, Conor
Atabakhsh, Elnaz
Markowetz, Florian
Fitzgerald, Rebecca C.
Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples
title Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples
title_full Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples
title_fullStr Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples
title_full_unstemmed Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples
title_short Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples
title_sort computational pathology aids derivation of microrna biomarker signals from cytosponge samples
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8883000/
https://www.ncbi.nlm.nih.gov/pubmed/35051729
http://dx.doi.org/10.1016/j.ebiom.2022.103814
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