{
  "abstract": "T-cell clonal organization is central to anti-tumor immunity, yet its functional characterization typically requires paired single-cell RNA and TCR sequencing (scRNA/TCR-seq). Existing multimodal methods integrate transcriptome and TCR information but are limited when TCR data are unavailable. Here we present CloneTrast, a supervised contrastive-learning framework that uses paired scRNA/TCR-seq during training to learn clonal-functional states from gene expression, while requiring only scRNA-seq at inference. Using paired scRNA/TCR-seq profiles from 2.6 million human T cells across 16 cancer types, CloneTrast identified distinct T-cell clonal niches not explained solely by TCR sequence similarity or conventional cell-state annotations. Among these, an NK-like T-cell niche enriched for tumor-blood shared TCRs was consistently associated with poor immunotherapy response at the pan-cancer level. Analysis of antigen-specific T-cell clones revealed that tumor-resident and blood-borne antigen-specific T cells were preferentially associated with distinct clonal niches. Overall, CloneTrast enables scalable and interpretable inference of clonal organization from single-cell transcriptomes.",
  "abstract_shown": true,
  "affiliations": [
    "Technion - Israel Institute of Technology"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "O. Shorer"
    },
    {
      "name": "A. Pinhasi"
    },
    {
      "name": "R. Amit"
    },
    {
      "affs": [
        0
      ],
      "name": "K. Yizhak"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1qfedj6ept4ab.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.06.757206",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757206v1.full",
  "id": "1qfedj6ept4ab",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757206v1",
  "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",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7221789146",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757206v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1qfedj6ept4ab.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:51Z",
      "native_id": "10.64898/2026.10.06.757206",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757206v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Revealing pan-cancer clonal niches of T cells from single-cell RNA sequencing using contrastive learning",
  "title_text": "Revealing pan-cancer clonal niches of T cells from single-cell RNA sequencing using contrastive learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:51Z",
  "url": "https://research.draconitic.com/paper/1qfedj6ept4ab",
  "venue": "bioRxiv",
  "versions": []
}