{
  "abstract": "Neoantigen immunogenicity prediction is fundamental to personalized cancer vaccines, tumor-infiltrating lymphocyte (TIL) therapy, and TCR-T cell engineering. Existing computational predictors rely primarily on in-vitro correlates of peptide presentation or models trained against assay-based reactivity, and they are typically validated within a single therapeutic setting. We reasoned that the most direct evidence of neoantigen immunogenicity is longitudinal in-vivo elimination: under immune checkpoint blockade (ICB), subclones bearing recognized neoantigens are selectively depleted over time. Here, we present the Neoantigen Elimination Model (NEMo), a two-compartment (CD8 and CD4) machine learning classifier trained on the in-vivo editing (IVE) of neoantigens across serially sequenced, ICB-treated tumors. By using mechanistically inspired NeoPrecis features designed to capture determinants of immunogenicity beyond MHC binding affinity, NEMo recovered assay-confirmed immunogenic neoantigens across four independent, unseen clinical settings -- pre-existing immunogenicity screening, personalized cancer vaccines, TIL therapy, and a radiotherapy +/- ICB ctDNA cohort -- and stratified progression-free survival more strongly than ELISPOT-confirmed reactivity. The editing signal further revealed an immune-evasion architecture in which oncogenic drivers and neoantigens restricted to lost or silenced HLA alleles are systematically spared from editing.",
  "abstract_shown": true,
  "affiliations": [
    "UC San Diego"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "T. J. Sears"
    },
    {
      "name": "K.-h. Lee"
    },
    {
      "name": "M. Munoz Perez"
    },
    {
      "name": "R. Rasmussen"
    },
    {
      "name": "M. S. Pagadala"
    },
    {
      "name": "K. Tanaka"
    },
    {
      "name": "A. Subramanian"
    },
    {
      "name": "E. J. Moding"
    },
    {
      "name": "M. Zanetti"
    },
    {
      "affs": [
        0
      ],
      "name": "H. Carter"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e8gd662215mre.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.17.752425",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752425v1.full",
  "id": "e8gd662215mre",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752425v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "cancer biology"
    ],
    [
      "openalex",
      "2730|Cancer Immunotherapy and Biomarkers"
    ]
  ],
  "openalex": "W7213604889",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752425v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e8gd662215mre.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:09Z",
      "native_id": "10.64898/2026.09.17.752425",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752425v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "biorxiv:cancer biology",
      "subfield": "oncology"
    }
  ],
  "title": "Predicting Neoantigen Immunogenicity from In Vivo Immune Editing",
  "title_text": "Predicting Neoantigen Immunogenicity from In Vivo Immune Editing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:09Z",
  "url": "https://research.draconitic.com/paper/e8gd662215mre",
  "venue": "bioRxiv",
  "versions": []
}