{
  "abstract": "Lung neuroendocrine tumours (lung NETs) are rare neoplasms comprising approximately 2% of lung cancers. Recent studies have identified distinct molecular groups based on transcriptome and methylome data, but genomic and morphological features remain underexplored due to limited whole-genome and imaging data. We have generated the largest multi-omic dataset of lung NETs to date (201 participants, for a total of n = 294 tumours), including RNA sequencing, EPIC 850K methylation arrays, and whole-genome sequencing. This multiomic dataset also include multi-regional whole-genome sequencing for 41 participants, allowing for the quantification of intra-tumoural heterogeneity. We additionally generated spatial proteomics (64 participants), spatial transcriptomics (4 participants) and whole-slide histopathology images for 212 cases. This dataset enables a comprehensive characterization of lung NET molecular groups and the identification of group-specific morphological features using deep learning algorithms. All quality control analyses, processed data, and scripts are provided to ensure reproducibility. This dataset is available as a basis for further molecular and morphological analysis of lung NETs, and for future research on multi-scale integration.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "name": "Lipika Kalson"
    },
    {
      "name": "Alexandra Sexton-Oates"
    },
    {
      "name": "Emilie Mathian"
    },
    {
      "name": "Catherine Voegele"
    },
    {
      "name": "Alex Di Genova"
    },
    {
      "name": "Zhaozhi Li"
    },
    {
      "name": "Jaehee Kim"
    },
    {
      "name": "Leigh M. Marsh"
    },
    {
      "name": "Luka Brcic"
    },
    {
      "name": "Lynnette Fernandez-Cuesta"
    },
    {
      "name": "Matthieu Foll"
    },
    {
      "name": "Nicolas Alcala"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/37tfwvrye83vw.bib",
  "cited_by": 2,
  "doi": "10.64898/2026.05.12.724489",
  "fulltext_url": null,
  "id": "37tfwvrye83vw",
  "issns": [],
  "landing_url": "https://hal.science/hal-05842840v1",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "openalex",
      "2713|Neuroendocrine Tumor Research Advances"
    ]
  ],
  "openalex": "W7161233642",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/37tfwvrye83vw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:03Z",
      "native_id": "hal-05842840",
      "source": "hal",
      "url": "https://hal.science/hal-05842840v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2713|Neuroendocrine Tumor Research Advances",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort",
  "title_text": "A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:03Z",
  "url": "https://research.draconitic.com/paper/37tfwvrye83vw",
  "venue": "HAL",
  "versions": []
}