DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumoursResearch in context
Mihnea P. Dragomir,
Teodor G. Calina,
Eilís Perez,
Simon Schallenberg,
Meng Chen,
Thomas Albrecht,
Ines Koch,
Peggy Wolkenstein,
Benjamin Goeppert,
Stephanie Roessler,
George A. Calin,
Christine Sers,
David Horst,
Florian Roßner,
David Capper
Affiliations
Mihnea P. Dragomir
Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Berlin Institute of Health, Berlin, Germany; Corresponding author. Institute of Pathology, Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany.
Teodor G. Calina
TGC Ventures UG, Berlin, Germany
Eilís Perez
Department of Neuropathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany; Berlin School of Integrative Oncology (BSIO), Charite - Universitätsmedizin Berlin (CVK), Berlin, Germany
Simon Schallenberg
Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
Meng Chen
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Thomas Albrecht
Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
Ines Koch
Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
Peggy Wolkenstein
German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
Benjamin Goeppert
Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany; Institute of Pathology and Neuropathology, Hospital RKH Kliniken Ludwigsburg, 71640 Ludwigsburg, Germany
Stephanie Roessler
Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
George A. Calin
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Center for RNA Interference and Non-coding RNAs, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Christine Sers
Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
David Horst
Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
Florian Roßner
Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
David Capper
German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Neuropathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
Summary: Background: 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. Methods: 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. Findings: On the validation cohort, the neural network, support vector machine, and the random forest classifiers reached accuracies of 97.68%, 95.62%, and 96.5%, respectively. Filtering by anomaly detection and thresholds improved the accuracy to 99.07% (37 samples excluded by filtering), 96.22% (17 samples excluded), and 100% (44 samples excluded) for the neural network, support vector machine and random forest, respectively. Because of best balance between accuracy and number of predictable cases we tested the neural network with applied filters on the in-house cohort, obtaining an accuracy of 95.45%. Interpretation: We developed a classifier that can differentiate between iCCAs, intrahepatic metastases of a PAAD, and normal bile duct tissue with high accuracy. This tool can be used for improving the diagnosis of pancreato-biliary cancers of the liver. Funding: This work was supported by Berlin Institute of Health (JCS Program), DKTK Berlin (Young Investigator Grant 2022), German Research Foundation (493697503 and 314905040 – SFB/TRR 209 Liver Cancer B01), and German Cancer Aid (70113922).
