| HRA002648
(Controlled Access)
|
In this study, we profiled plasma-derived cell-free eccDNA from a multi-cancer cohort consisting of 413 cancer patients and 239 healthy individuals. We developed ScanTecc (screening cancer types with cell-free eccDNA), a machine learning-based approach for cancer detection and tissue-of-origin classification. ScanTecc achieved an overall AUC of 0.92 for distinguishing cancer patients from healthy individuals, with consistently high performance across disease stages, including stage I (AUC = 0.92) and stage IV (AUC = 0.93). ScanTecc also enabled accurate tissue-of-origin classification and achieved an overall AUC of 0.79 in identifying specific cancer types, with AUC values ranging from 0.70 for gastric cancer to 0.81 for ovarian cancer. |
