ArticleClinical epigenetics2024
DNA methylation classifier to diagnose pancreatic ductal adenocarcinoma metastases from different anatomical sites.
Article in Clinical epigenetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- DNA methylation profiling enables subclassification of mucinous ovarian carcinoma and distinguishes it from extraovarian mucinous metastases.Cell reports. Medicine · 2026Article
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Comprehensive molecular analyses for diagnosis and treatment guidance in an adult neuroblastoma patient.The oncologist · 2026Article
- Spatial heterogeneity of antibody-drug conjugate targets in pancreatic ductal adenocarcinoma.The journal of pathology. Clinical research · 2026Article
- DNA methylation profile to aid in the diagnosis of pancreatic ductal adenocarcinoma and its role in disease progression.Epigenomics · 2025Review
- Mucinous cystic neoplasms of the pancreas and liver share a similar DNA methylation profile with mucinous ovarian tumors.The Journal of pathology · 2025Article
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22 authors.
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Abstract
backgroundWe 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.
methodsFor 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.
resultsThe 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.
conclusionsThe 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.
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