{
  "abstract": "MotivationCell-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.\n\nResultsWe 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.\n\nAvailability and ImplementationSource code for this study is available at https://github.com/GenTensor/PANGEM. Contact: ping.luo@algomau.ca\n\nSupplementary informationSupplementary data are available at Bioinformatics online.",
  "abstract_shown": true,
  "affiliations": [
    "Algoma University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "L. Zhao"
    },
    {
      "name": "Y. Zeng"
    },
    {
      "name": "D. D. Abelman"
    },
    {
      "name": "W. Lin"
    },
    {
      "affs": [
        0
      ],
      "name": "P. Luo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3qcqgdy0ezeqf.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.26.26361432",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26361432v1.full",
  "id": "3qcqgdy0ezeqf",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26361432v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "oncology"
    ],
    [
      "openalex",
      "1306|Cancer Genomics and Diagnostics"
    ]
  ],
  "openalex": "W7204761946",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26361432v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3qcqgdy0ezeqf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.64898/2026.08.26.26361432",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26361432v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Pan-cancer Graph-based Cancer Detection Using the Cell-free DNA Methylome",
  "title_text": "Pan-cancer Graph-based Cancer Detection Using the Cell-free DNA Methylome",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/3qcqgdy0ezeqf",
  "venue": "medRxiv",
  "versions": []
}