{
  "abstract": "Accurate treatment selection remains a major challenge in precision oncology because tumors with similar genomic alterations can respond differently to therapy. Most drug-response models evaluate individual drugs or drug-sample pairs, whereas clinical decision-making requires therapies to be prioritized within each tumor context. Here we present DEPICT (Drug Efficacy Prediction via Integrated ConText), a sample-centric deep learning framework that reframes drug-response prediction as the relative prioritization of therapies within an individual tumor context. DEPICT integrates transcriptomic and mutational features with prior alteration-therapy relationship and was trained on 231,880 cell line-drug pairs comprising 941 cancer cell lines and 289 compounds. Without retraining, DEPICT retained predictive performance in independently profiled cell lines, experimentally induced drug-resistant states and matched primary lung tumors and patient-derived organoids. Representations learned exclusively from bulk pharmacogenomic data further captured progressive drug-response states at single-cell resolution and outperformed existing single-cell drug-response inference methods. Applied to spatial transcriptomic data, DEPICT resolved clone-associated therapeutic heterogeneity within individual lung tumors, and predicted regional differences were recapitulated in matched multiregional organoid drug-response assays. These findings establish sample-centric therapeutic prioritization as a transferable strategy for modeling dynamic and spatially heterogeneous drug vulnerabilities across biological scales.",
  "abstract_shown": true,
  "affiliations": [
    "Peking University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "name": "S. Ma"
    },
    {
      "name": "S. Yan"
    },
    {
      "name": "X. Wen"
    },
    {
      "name": "C. Yu"
    },
    {
      "name": "H. Ma"
    },
    {
      "name": "W. Teng"
    },
    {
      "name": "X. Zhou"
    },
    {
      "name": "J. Gao"
    },
    {
      "name": "S. Wang"
    },
    {
      "name": "M. Li"
    },
    {
      "name": "K. Han"
    },
    {
      "name": "Y. Huang"
    },
    {
      "name": "N. Wu"
    },
    {
      "name": "B. Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "C. Zheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2wfyr05xkpxs9.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.07.756157",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.756157v1.full",
  "id": "2wfyr05xkpxs9",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.756157v1",
  "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",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7221320281",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.756157v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2wfyr05xkpxs9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:54Z",
      "native_id": "10.64898/2026.10.07.756157",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.07.756157v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "DEPICT enables sample-centric therapeutic prioritization across evolving and spatially heterogeneous tumor states",
  "title_text": "DEPICT enables sample-centric therapeutic prioritization across evolving and spatially heterogeneous tumor states",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:54Z",
  "url": "https://research.draconitic.com/paper/2wfyr05xkpxs9",
  "venue": "bioRxiv",
  "versions": []
}