A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort

Kalson, L.; Sexton-Oates, A.; Mathian, E.; Voegele, C.; Di Genova, A.; Li, Z.; Kim, J.; Marsh, L. M.; Brcic, L.; Fernandez-Cuesta, L.; Foll, M.; Alcala, N.

doi:10.64898/2026.05.12.724489

bioRxiv · 10.64898/2026.05.12.724489

A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort

Kalson, L.·Sexton-Oates, A.·Mathian, E.·Voegele, C.·Di Genova, A.·Li, Z.·Kim, J.·Marsh, L. M.·Brcic, L.·Fernandez-Cuesta, L.·Foll, M.·Alcala, N.

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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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Kalson, L., Sexton-Oates, A., Mathian, E., Voegele, C., Di Genova, A., Li, Z., Kim, J., Marsh, L. M., Brcic, L., Fernandez-Cuesta, L., Foll, M., Alcala, N.. 2026-05-14. A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort. https://doi.org/10.64898/2026.05.12.724489

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