DNA Methylation Profiling Enables Accurate Classification of Nonductal Primary Pancreatic Neoplasms.

Autor:	Verschuur AVD; Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. Electronic address: annaveraverschuur@gmail.com., Hackeng WM; Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands., Westerbeke F; Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands., Benhamida JK; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York., Basturk O; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York., Selenica P; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York., Raicu GM; Department of Pathology, St Antonius Hospital and Pathology DNA, Nieuwegein, The Netherlands., Molenaar IQ; Department of Pathology, St Antonius Hospital and Pathology DNA, Nieuwegein, The Netherlands; Department of Surgery, Regional Academic Cancer Center Utrecht, University Medical Center Utrecht Cancer Center and St. Antonius Hospital, Nieuwegein, The Netherlands., van Santvoort HC; Department of Surgery, Regional Academic Cancer Center Utrecht, University Medical Center Utrecht Cancer Center and St. Antonius Hospital, Nieuwegein, The Netherlands., Daamen LA; Department of Surgery, Regional Academic Cancer Center Utrecht, University Medical Center Utrecht Cancer Center and St. Antonius Hospital, Nieuwegein, The Netherlands., Klimstra DS; Paige.AI, New York, New York., Yachida S; Department of Cancer Genome Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan., Luchini C; Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy., Singhi AD; Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania., Geisenberger C; Institute of Pathology, LMU University, Munich, Germany., Brosens LAA; Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: l.a.a.brosens@umcutrecht.nl.
Jazyk:	angličtina
Zdroj:	Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association [Clin Gastroenterol Hepatol] 2024 Jun; Vol. 22 (6), pp. 1245-1254.e10. Date of Electronic Publication: 2024 Feb 20.
DOI:	10.1016/j.cgh.2024.02.007
Abstrakt:	Background & Aims: Cytologic and histopathologic diagnosis of non-ductal pancreatic neoplasms can be challenging in daily clinical practice, whereas it is crucial for therapy and prognosis. The cancer methylome is successfully used as a diagnostic tool in other cancer entities. Here, we investigate if methylation profiling can improve the diagnostic work-up of pancreatic neoplasms. Methods: DNA methylation data were obtained for 301 primary tumors spanning 6 primary pancreatic neoplasms and 20 normal pancreas controls. Neural Network, Random Forest, and extreme gradient boosting machine learning models were trained to distinguish between tumor types. Methylation data of 29 nonpancreatic neoplasms (n = 3708) were used to develop an algorithm capable of detecting neoplasms of non-pancreatic origin. Results: After benchmarking 3 state-of-the-art machine learning models, the random forest model emerged as the best classifier with 96.9% accuracy. All classifications received a probability score reflecting the confidence of the prediction. Increasing the score threshold improved the random forest classifier performance up to 100% with 87% of samples with scores surpassing the cutoff. Using a logistic regression model, detection of nonpancreatic neoplasms achieved an area under the curve of >0.99. Analysis of biopsy specimens showed concordant classification with their paired resection sample. Conclusions: Pancreatic neoplasms can be classified with high accuracy based on DNA methylation signatures. Additionally, non-pancreatic neoplasms are identified with near perfect precision. In summary, methylation profiling can serve as a valuable adjunct in the diagnosis of pancreatic neoplasms with minimal risk for misdiagnosis, even in the pre-operative setting. (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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