Teodor G Calina, Eilís Perez, Elena Grafenhorst, Jamal Benhamida, Simon Schallenberg, Adrian Popescu, Ines Koch, Tobias Janik, BaoQing Chen, Jana Ihlow, Stephanie Roessler, Benjamin Goeppert, Bruno Sinn, Marcus Bahra, George A Calin, Eliane T Taube, Uwe Pelzer, Christopher C M Neumann, David Horst, Erik Knutsen, David Capper, Mihnea P Dragomir
{"title":"DNA methylation classifier to diagnose pancreatic ductal adenocarcinoma metastases from different anatomical sites.","authors":"Teodor G Calina, Eilís Perez, Elena Grafenhorst, Jamal Benhamida, Simon Schallenberg, Adrian Popescu, Ines Koch, Tobias Janik, BaoQing Chen, Jana Ihlow, Stephanie Roessler, Benjamin Goeppert, Bruno Sinn, Marcus Bahra, George A Calin, Eliane T Taube, Uwe Pelzer, Christopher C M Neumann, David Horst, Erik Knutsen, David Capper, Mihnea P Dragomir","doi":"10.1186/s13148-024-01768-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>We have recently constructed a DNA methylation classifier that can discriminate between pancreatic ductal adenocarcinoma (PAAD) liver metastasis and intrahepatic cholangiocarcinoma (iCCA) with high accuracy (PAAD-iCCA-Classifier). PAAD is one of the leading causes of cancer of unknown primary and diagnosis is based on exclusion of other malignancies. Therefore, our focus was to investigate whether the PAAD-iCCA-Classifier can be used to diagnose PAAD metastases from other sites.</p><p><strong>Methods: </strong>For this scope, the anomaly detection filter of the initial classifier was expanded by 8 additional mimicker carcinomas, amounting to a total of 10 carcinomas in the negative class. We validated the updated version of the classifier on a validation set, which consisted of a biological cohort (n = 3579) and a technical one (n = 15). We then assessed the performance of the classifier on a test set, which included a positive control cohort of 16 PAAD metastases from various sites and a cohort of 124 negative control samples consisting of 96 breast cancer metastases from 18 anatomical sites and 28 carcinoma metastases to the brain.</p><p><strong>Results: </strong>The updated PAAD-iCCA-Classifier achieved 98.21% accuracy on the biological validation samples, and on the technical validation ones it reached 100%. The classifier also correctly identified 15/16 (93.75%) metastases of the positive control as PAAD, and on the negative control, it correctly classified 122/124 samples (98.39%) for a 97.85% overall accuracy on the test set. We used this DNA methylation dataset to explore the organotropism of PAAD metastases and observed that PAAD liver metastases are distinct from PAAD peritoneal carcinomatosis and primary PAAD, and are characterized by specific copy number alterations and hypomethylation of enhancers involved in epithelial-mesenchymal-transition.</p><p><strong>Conclusions: </strong>The updated PAAD-iCCA-Classifier (available at https://classifier.tgc-research.de/ ) can accurately classify PAAD samples from various metastatic sites and it can serve as a diagnostic aid.</p>","PeriodicalId":10366,"journal":{"name":"Clinical Epigenetics","volume":"16 1","pages":"156"},"PeriodicalIF":4.8000,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11550539/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Epigenetics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13148-024-01768-x","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: We have recently constructed a DNA methylation classifier that can discriminate between pancreatic ductal adenocarcinoma (PAAD) liver metastasis and intrahepatic cholangiocarcinoma (iCCA) with high accuracy (PAAD-iCCA-Classifier). PAAD is one of the leading causes of cancer of unknown primary and diagnosis is based on exclusion of other malignancies. Therefore, our focus was to investigate whether the PAAD-iCCA-Classifier can be used to diagnose PAAD metastases from other sites.
Methods: For this scope, the anomaly detection filter of the initial classifier was expanded by 8 additional mimicker carcinomas, amounting to a total of 10 carcinomas in the negative class. We validated the updated version of the classifier on a validation set, which consisted of a biological cohort (n = 3579) and a technical one (n = 15). We then assessed the performance of the classifier on a test set, which included a positive control cohort of 16 PAAD metastases from various sites and a cohort of 124 negative control samples consisting of 96 breast cancer metastases from 18 anatomical sites and 28 carcinoma metastases to the brain.
Results: The updated PAAD-iCCA-Classifier achieved 98.21% accuracy on the biological validation samples, and on the technical validation ones it reached 100%. The classifier also correctly identified 15/16 (93.75%) metastases of the positive control as PAAD, and on the negative control, it correctly classified 122/124 samples (98.39%) for a 97.85% overall accuracy on the test set. We used this DNA methylation dataset to explore the organotropism of PAAD metastases and observed that PAAD liver metastases are distinct from PAAD peritoneal carcinomatosis and primary PAAD, and are characterized by specific copy number alterations and hypomethylation of enhancers involved in epithelial-mesenchymal-transition.
Conclusions: The updated PAAD-iCCA-Classifier (available at https://classifier.tgc-research.de/ ) can accurately classify PAAD samples from various metastatic sites and it can serve as a diagnostic aid.
期刊介绍:
Clinical Epigenetics, the official journal of the Clinical Epigenetics Society, is an open access, peer-reviewed journal that encompasses all aspects of epigenetic principles and mechanisms in relation to human disease, diagnosis and therapy. Clinical trials and research in disease model organisms are particularly welcome.