TY  - JOUR
AU  - Dragomir, Mihnea-Paul
AU  - Calina, Teodor G
AU  - Perez, Eilís
AU  - Schallenberg, Simon
AU  - Chen, Meng
AU  - Albrecht, Thomas
AU  - Koch, Ines
AU  - Wolkenstein, Peggy
AU  - Goeppert, Benjamin
AU  - Roessler, Stephanie
AU  - Calin, George A
AU  - Sers, Christine
AU  - Horst, David
AU  - Roßner, Florian
AU  - Capper, David
TI  - DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumours.
JO  - EBioMedicine
VL  - 93
SN  - 2352-3964
CY  - Amsterdam [u.a.]
PB  - Elsevier
M1  - DKFZ-2023-01256
SP  - 104657
PY  - 2023
AB  - Differentiating intrahepatic cholangiocarcinomas (iCCA) from hepatic metastases of pancreatic ductal adenocarcinoma (PAAD) is challenging. Both tumours have similar morphological and immunohistochemical pattern and share multiple driver mutations. We hypothesised that DNA methylation-based machine-learning algorithms may help perform this task.We assembled genome-wide DNA methylation data for iCCA (n = 259), PAAD (n = 431), and normal bile duct (n = 70) from publicly available sources. We split this cohort into a reference (n = 399) and a validation set (n = 361). Using the reference cohort, we trained three machine learning models to differentiate between these entities. Furthermore, we validated the classifiers on the technical validation set and used an internal cohort (n = 72) to test our classifier.On the validation cohort, the neural network, support vector machine, and the random forest classifiers reached accuracies of 97.68
KW  - Epigenetic (Other)
KW  - Machine learning (Other)
KW  - Molecular diagnosis (Other)
KW  - Oncology (Other)
KW  - Pathology (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:37348162
DO  - DOI:10.1016/j.ebiom.2023.104657
UR  - https://inrepo02.dkfz.de/record/277072
ER  -