ScanTecc classifies primary cancers via cell-free extrachromosomal circular DNA in peripheral blood
Release date:
2025-12-18
Description:
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.
For each controlled access study, there is a corresponding Data Access Committee(DAC) to determine the access permissions. Access to actual data files is not managed by NGDC.
With the rapid development of sequencing technologies, more and more data is generated in a faster and faster pace, driving the biomedical research into the era of big data and personalized genomics. The goal of our lab is to decipher, interpret and manage the digital life at a single cell resolution. The lab is integrating expertise in Bioinformatics, Immunology, Genomics and Artificial Intelligence (BIGAI) to develop novel experimental and computational methods to predict, dissect and control the large-scale single cell gene regulatory programs, and to provide insights into precise diagnosis and therapeutics for human cancer and autoimmune diseases.
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