{
  "abstract": "Immune checkpoint blockade (ICB) can produce durable responses in cancer, but reliable predictors of benefit are still lacking. CD8+ tumor‐specific T cells (TSTs) are essential for ICB efficacy, yet it remains unclear which functional states of these cells are associated with therapeutic benefit. To address this, we developed TSTScope, an interpretable deep learning framework that integrates single‐cell transcriptomic and T‐cell receptor sequencing data to generate unified representations of CD8+ T‐cell identity. By applying TSTScope to non‐small cell lung cancer (NSCLC) datasets, we characterized the gene programs defining tumor specificity and computationally inferred a population of potential TSTs (pTSTs). Our analyses show that clinical response is associated with the functional state of these cells rather than their abundance alone. We derived the major pathological response (MPR) score, a metric capturing this functional potential. In an independent validation cohort, the MPR score was associated with pathological response and recurrence‐free survival and provided complementary information to selected response‐associated biomarkers. Collectively, TSTScope identifies a distinct functional state of tumor‐specific T cells linked to ICB response, providing an interpretable framework for studying receptor‐linked T‐cell function in immunotherapy cohorts.",
  "abstract_shown": true,
  "affiliations": [
    "School of Life Science and Technology ShanghaiTech University Shanghai China",
    "Guangzhou National Laboratory Guangzhou China",
    "Multimedia Laboratory (MMLab) The Chinese University of Hong Kong Hong Kong China",
    "Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China",
    "School of Intelligent Systems Engineering Sun Yat‐sen University Shenzhen China",
    "Department of Medical Oncology The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China",
    "GMU‐GIBH Joint School of Life Sciences The Guangdong‐Hong Kong‐Macau Joint Laboratory For Cell Fate Regulation and Diseases Guangzhou Medical University Guangzhou China",
    "School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China",
    "Shanghai Institute of Nutrition and Health Chinese Academy of Sciences Shanghai China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Shiwei Cao"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Jinyu Cheng"
    },
    {
      "affs": [
        1,
        3
      ],
      "name": "Feng‐ao Wang"
    },
    {
      "affs": [
        1,
        4
      ],
      "name": "Chenxin Yi"
    },
    {
      "affs": [
        1,
        4
      ],
      "name": "Jiajun Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Keyue Wang"
    },
    {
      "affs": [
        5
      ],
      "name": "Lulu Liu"
    },
    {
      "affs": [
        1,
        6
      ],
      "name": "Junwei Liu",
      "orcid": "0000-0001-8446-4221"
    },
    {
      "affs": [
        1,
        3,
        6,
        7,
        8
      ],
      "name": "Yixue Li",
      "orcid": "0000-0002-1198-7176"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3qxr4r6s8cydf.bib",
  "cited_by": 0,
  "doi": "10.1002/advs.76983",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13440375/",
  "id": "3qxr4r6s8cydf",
  "issns": [
    "2198-3844"
  ],
  "landing_url": "https://doi.org/10.1002/advs.76983",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research Article"
    ],
    [
      "openalex",
      "2730|Cancer Immunotherapy and Biomarkers"
    ]
  ],
  "openalex": "W7172519726",
  "pdf_url": null,
  "pmcid": "PMC13440375",
  "pmid": "42555209",
  "published": "2026-08-05",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/3qxr4r6s8cydf.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:23:42Z",
      "native_id": "PMC13440375",
      "source": "pmc",
      "url": "https://doi.org/10.1002/advs.76983"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2730|Cancer Immunotherapy and Biomarkers",
      "subfield": "oncology"
    }
  ],
  "title": "TSTScope Unifies Single‐Cell Multi‐Omics to Identify Functional T Cell States Predictive of Immunotherapy Response",
  "title_text": "TSTScope Unifies Single‐Cell Multi‐Omics to Identify Functional T Cell States Predictive of Immunotherapy Response",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:20:49Z",
  "url": "https://research.draconitic.com/paper/3qxr4r6s8cydf",
  "venue": "Advanced Science",
  "versions": []
}