論文掲載：Universitätsklinikum Erlangen & The Fraunhofer Institute for Interfacial Engineering and Biotechnology、非侵襲的リキッドバイオプシーによる膵胆道がんと膵炎患者の鑑別
Molecular Cancer
February 7, 2024
本論文では、非侵襲的リキッドバイオプシーから得られたcfDNAを膵胆道がんと非癌性膵炎患者の鑑別における新規バイオマーカーとして提示しています。Genedata Profiler®と機械学習によって、血液サンプルを用いて患者を鑑別するための診断モデルの開発と評価が行われました。
Abstract
Background. Current diagnostics for the detection of pancreato-biliary cancers (PBCs) need to be optimized. We therefore propose that methylated cell-free DNA (cfDNA) derived from non-invasive liquid biopsies serves as a novel biomarker with the ability to discriminate pancreato-biliary cancers from non-cancer pancreatitis patients.
Methods. Differentially methylated regions (DMRs) from plasma cfDNA between PBCs, pancreatitis and clinical control samples conditions were identified by next-generation sequencing after enrichment using methyl-binding domains and database searches to generate a discriminatory panel for a hybridization and capture assay with subsequent targeted high throughput sequencing.
Results. The hybridization and capture panel, covering around 74 kb in total, was applied to sequence a cohort of 25 PBCs, 25 pancreatitis patients, 25 clinical controls, and seven cases of Intraductal Papillary Mucinous Neoplasia (IPMN). An unbiased machine learning approach identified the 50 most discriminatory methylation markers for the discrimination of PBC from pancreatitis and controls resulting in an AUROC of 0.85 and 0.88 for a training (n=45) and a validation (n=37) data set, respectively. The panel was also able to distinguish high grade from low grade IPMN samples.
Conclusions. We present a proof of concept for a methylation biomarker panel with better performance and improved discriminatory power than the current clinical marker CA19-9 for the discrimination of pancreato-biliary cancers from non-cancerous pancreatitis patients and clinical controls. This workflow might be used in future diagnostics for the detection of precancerous lesions, e.g. the identification of high grade IPMNs vs. low grade IPMNs.
In this project, Genedata Profiler was used to develop and evaluate diagnostic machine learning models to discriminate pancreatic cancer patients from other patient subgroups.