{
  "abstract": "Immune checkpoint inhibitor response in non-small cell lung cancer (NSCLC) reflects coordinated tumor and immune states that are not fully captured by single biomarkers. We developed scTIME, an interpretable model that translates single-cell immune programs into bulk transcriptomic scores, and integrated public single-cell, T-cell receptor, transcriptomic, survival, and proteogenomic cohorts. Response modeling used two cohorts with different endpoints (40 patients) and compared six algorithms. ElasticNet was selected as a sparse reporting model, achieving a pooled five-fold out-of-fold ROC-AUC of 0.810; nested model selection yielded 0.772. Matched held-out stratification identified response-enriched scTIME groups in a direct descriptive comparison with CD274, while the fitted score emphasized cytotoxic CD8, natural killer, SPP1 macrophage, interferon-response, and antigen-presentation programs. Single-cell analyses and external projections linked these programs to CXCL10-CXCR3 recruitment, exclusion-associated states, and T-cell receptor ecology across molecular contexts. Exploratory survival and cross-omics analyses extended the framework beyond the response cohorts but were interpreted as projection rather than independent validation. Together, the results show how cell-resolved immune biology can support transparent, multi-omic biomarker development without reducing response to a single pathway. scTIME remains exploratory because model choice was post hoc, cohorts were small and clinically heterogeneous, and endpoint-matched external validation was unavailable.",
  "abstract_shown": true,
  "affiliations": [
    "YongJia People's Hospital, Wenzhou, China.",
    "Shanxi Acupuncture Hospital, Taiyuan, China.",
    "Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Guozhi Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Chenqiu You"
    },
    {
      "affs": [
        1
      ],
      "name": "Hong Gao"
    },
    {
      "affs": [
        2
      ],
      "name": "Chengjie Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d2x13e9gx0z10.bib",
  "cited_by": 0,
  "doi": "10.3389/frai.2026.1918104",
  "fulltext_url": "https://europepmc.org/articles/PMC13680457",
  "id": "d2x13e9gx0z10",
  "issns": [
    "2624-8212"
  ],
  "landing_url": "https://europepmc.org/article/MED/42903172",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2730|Cancer Immunotherapy and Biomarkers"
    ]
  ],
  "openalex": "W7220744723",
  "pdf_url": "https://europepmc.org/articles/PMC13680457?pdf=render",
  "pmcid": "PMC13680457",
  "pmid": "42903172",
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d2x13e9gx0z10.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:57Z",
      "native_id": "MED:42903172",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42903172"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2730|Cancer Immunotherapy and Biomarkers",
      "subfield": "oncology"
    }
  ],
  "title": "scTIME: single-cell-informed interpretable modeling of immunotherapy-associated immune states in non-small cell lung cancer.",
  "title_text": "scTIME: single-cell-informed interpretable modeling of immunotherapy-associated immune states in non-small cell lung cancer.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:57Z",
  "url": "https://research.draconitic.com/paper/d2x13e9gx0z10",
  "venue": "Frontiers in artificial intelligence",
  "versions": []
}