{
  "abstract": "Multiplex immunofluorescence (mIF) assays such as ImmunoProfile provide standardized quantification of prognostic immune markers but are costly and not routinely used in clinical settings. We developed a weakly-supervised AI model, HistoProfile, to estimate intratumoral cell densities of key immune biomarkers (CD8, FOXP3, PD-1, and PD-L1) and the PD-L1 tumor proportion score from routine H&E-stained whole-slide images. Trained on a large pan-cancer cohort with paired H&E and mIF data, HistoProfile discriminated biomarker-high from biomarker-low tumors in held-out samples and tumor-specific subgroups (AUROCs of 0.78-0.89 across biomarkers). Critically, the estimated biomarkers preserved prognostic stratification in downstream survival analyses, and showed cancer-specific survival associations across TCGA cohorts where mIF is unavailable. These findings support scalable H&E-based estimation of key immune biomarker measurements for large-scale immune profiling and risk stratification.",
  "abstract_shown": true,
  "affiliations": [
    "Dana-Farber Cancer Institute"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Y. Li"
    },
    {
      "name": "A. R. Diab"
    },
    {
      "name": "A. Lamane"
    },
    {
      "name": "J. R. Lindsay"
    },
    {
      "name": "S. J. Rodig"
    },
    {
      "name": "R. A. Irizarry"
    },
    {
      "affs": [
        0
      ],
      "name": "W. Lotter"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bw8psjt418x3j.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.06.757083",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757083v1.full",
  "id": "bw8psjt418x3j",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757083v1",
  "language": null,
  "licence": "PDM-1.0",
  "licence_label": "Public domain",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/publicdomain/mark/1.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7221836033",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757083v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bw8psjt418x3j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:38Z",
      "native_id": "10.64898/2026.10.06.757083",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757083v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Pan-cancer estimation of prognostic immune biomarkers from histopathology using artificial intelligence",
  "title_text": "Pan-cancer estimation of prognostic immune biomarkers from histopathology using artificial intelligence",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:38Z",
  "url": "https://research.draconitic.com/paper/bw8psjt418x3j",
  "venue": "bioRxiv",
  "versions": []
}