{
  "abstract": "Fragmentomics analyzes features of circulating cell-free DNA fragments (e.g., fragment length or end motifs) derived from whole-genome sequencing of liquid biopsy samples (blood or other body fluids), enabling non-invasive early cancer detection. Here we present mFrag, a framework that integrates external methylation reference atlases with fragmentomic features for lung cancer detection. mFrag comprises two complementary methods: mFrag-Weight, which uses DNA methylation information to weight fragmentomic features, and mFrag-Net, which integrates fragmentomic features and external DNA methylation data through neural networks. We average the output probabilities of the two methods to obtain a final prediction score. The mFrag model was trained using 287 individuals at risk for lung cancer. In an independent case-control testing set comprising 385 non-cancer controls and 46 cancers (28 stage I, 12 stage II, and 6 stage III or IV), mFrag outperforms a DELFI-style fragmentomic approach, improving PR-AUC from 0.455 to 0.720 and sensitivity at 99% specificity from 0.043 to 0.478, with ROC-AUC from 0.871 to 0.908. For stage I lung cancer, mFrag raises PR-AUC from 0.314 to 0.558 and ROC-AUC from 0.855 to 0.894. Applied to end 4-mer motif features, mFrag likewise improved over an unweighted baseline, with gains contributed by both mFrag-Weight and mFrag-Net. In summary, mFrag improves the accuracy of fragmentomics-based lung cancer detection through integrating external DNA methylation data and the complementarity of its two component models, without incurring any additional data-generating cost.",
  "abstract_shown": true,
  "affiliations": [
    "Fred Hutchinson Cancer Center"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "S. Cui"
    },
    {
      "name": "N. Swarup"
    },
    {
      "name": "Q. He"
    },
    {
      "name": "W. Hsu"
    },
    {
      "name": "D. Wong"
    },
    {
      "name": "Y. Zheng"
    },
    {
      "name": "R. B. Scharpf"
    },
    {
      "affs": [
        0
      ],
      "name": "W. Sun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0qa0shs6pfyst.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.28.762499",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762499v1.full",
  "id": "0qa0shs6pfyst",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762499v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762499v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0qa0shs6pfyst.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:27:11Z",
      "native_id": "10.64898/2026.10.28.762499",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762499v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "mFrag: Integrating External DNA Methylation References Improves Lung Cancer Early Detection by Fragmentomics",
  "title_text": "mFrag: Integrating External DNA Methylation References Improves Lung Cancer Early Detection by Fragmentomics",
  "type": "preprint",
  "updated_at": "2026-11-10T00:27:11Z",
  "url": "https://research.draconitic.com/paper/0qa0shs6pfyst",
  "venue": "bioRxiv",
  "versions": []
}