A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort
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.
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BibTeX
@misc{kalson2026multiomic,
title = {{A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort}},
author = {Lipika Kalson and Alexandra Sexton-Oates and Emilie Mathian and Catherine Voegele and Alex Di Genova and Zhaozhi Li and Jaehee Kim and Leigh M. Marsh and Luka Brcic and Lynnette Fernandez-Cuesta and Matthieu Foll and Nicolas Alcala},
howpublished = {HAL},
year = {2026},
month = oct,
doi = {10.64898/2026.05.12.724489},
url = {https://hal.science/hal-05842840v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort
AU - Lipika Kalson
AU - Alexandra Sexton-Oates
AU - Emilie Mathian
AU - Catherine Voegele
AU - Alex Di Genova
AU - Zhaozhi Li
AU - Jaehee Kim
AU - Leigh M. Marsh
AU - Luka Brcic
AU - Lynnette Fernandez-Cuesta
AU - Matthieu Foll
AU - Nicolas Alcala
PY - 2026
DA - 2026/10/28
PB - HAL
DO - 10.64898/2026.05.12.724489
UR - https://hal.science/hal-05842840v1
AB - 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.
ER -