Pan-cancer Graph-based Cancer Detection Using the Cell-free DNA Methylome
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Motivation
Cell-free DNA methylation provides a minimally invasive signal for early cancer detection and tissue-of-origin prediction. Most methods represent methylation measurements as independent fixed-window features and therefore do not explicitly model relationships among genomic regions.
Results
We developed PANGEM (Pan-cancer Graph-based Cancer Detection Using the Cell-free DNA Methylome), a graph-learning framework that represents genomic bins as nodes and integrates CpG context, genomic proximity, and sample-specific methylation similarity in the graph topology. Across five repeated stratified train-test splits, PANGEM achieved the highest mean performance among evaluated methods, with an AUROC/AUPR of 0.997±0.003/1.000±0.000 for binary cancer detection and macro-AUROC/AUPR of 0.977±0.027/0.870±0.086 for multiclass tissue-of-origin prediction. In the independent INSPIRE cohort, 72 of 78 cancer cases (92.3%) exceeded the binary classification threshold, and PANGEM correctly classified 9 of 17 head and neck cancer cases (52.9%), the highest accuracy among evaluated methods. Subnetwork analysis further identified recurrent, graph-connected methylation patterns, including a 111-DMR subnetwork with increased methylation in cancer samples.
Availability and Implementation
Source code for this study is available at https://github.com/GenTensor/PANGEM . Contact: ping.luo@algomau.ca
Supplementary information
Supplementary data are available at Bioinformatics online.
