A molecular map of lung neuroendocrine neoplasms
Abstract
BACKGROUND: Lung neuroendocrine neoplasms (LNENs) are rare solid cancers, with most genomic studies including a limited number of samples. Recently, generating the first multi-omic dataset for atypical pulmonary carcinoids and the first methylation dataset for large-cell neuroendocrine carcinomas led us to the discovery of clinically relevant molecular groups, as well as a new entity of pulmonary carcinoids (supra-carcinoids). RESULTS: To promote the integration of LNENs molecular data, we provide here detailed information on data generation and quality control for whole-genome/exome sequencing, RNA sequencing, and EPIC 850K methylation arrays for a total of 84 patients with LNENs. We integrate the transcriptomic data with other previously published data and generate the first comprehensive molecular map of LNENs using the Uniform Manifold Approximation and Projection (UMAP) dimension reduction technique. We show that this map captures the main biological findings of previous studies and can be used as reference to integrate datasets for which RNA sequencing is available. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_lungNENomics/LNEN). The data, source code, and compute environments used to generate and evaluate the map as well as the raw data are available, respectively, in a Nextjournal interactive notebook (https://nextjournal.com/rarecancersgenomics/a-molecular-map-of-lung-neuroendocrine-neoplasms/) and at the EMBL-EBI European Genome-phenome Archive and Gene Expression Omnibus data repositories. CONCLUSIONS: We provide data and all resources needed to integrate them with future LNENs transcriptomic studies, allowing meaningful conclusions to be drawn that will eventually lead to a better understanding of this rare understudied disease.
Related
- Molecular studies of lung neuroendocrine neoplasms uncover new concepts and entities.
- Spotlight on Small-Cell Lung Cancer and Other Lung Neuroendocrine Neoplasms.
- Druggable growth dependencies and tumor evolution analysis in patient-derived organoids of neuroendocrine neoplasms from multiple body sites
- Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms
- Diagnostic pitfalls and unmet needs in neuroendocrine neoplasms: The added value of collegial expert pathology review through the national TENpath network
BibTeX
@article{gabriel2026molecular,
title = {{A molecular map of lung neuroendocrine neoplasms}},
author = {Aurélie a G Gabriel and Emilie Mathian and Lise Mangiante and Catherine Voegele and Vincent Cahais and Akram Ghantous and James D Mckay and Nicolas Alcala and Lynnette Fernandez-Cuesta and Matthieu Foll},
journal = {GigaScience},
year = {2026},
month = oct,
doi = {10.1093/gigascience/giaa112},
url = {https://hal.science/hal-05842045v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - A molecular map of lung neuroendocrine neoplasms
AU - Aurélie a G Gabriel
AU - Emilie Mathian
AU - Lise Mangiante
AU - Catherine Voegele
AU - Vincent Cahais
AU - Akram Ghantous
AU - James D Mckay
AU - Nicolas Alcala
AU - Lynnette Fernandez-Cuesta
AU - Matthieu Foll
PY - 2026
DA - 2026/10/28
JO - GigaScience
DO - 10.1093/gigascience/giaa112
UR - https://hal.science/hal-05842045v1
AB - BACKGROUND: Lung neuroendocrine neoplasms (LNENs) are rare solid cancers, with most genomic studies including a limited number of samples. Recently, generating the first multi-omic dataset for atypical pulmonary carcinoids and the first methylation dataset for large-cell neuroendocrine carcinomas led us to the discovery of clinically relevant molecular groups, as well as a new entity of pulmonary carcinoids (supra-carcinoids). RESULTS: To promote the integration of LNENs molecular data, we provide here detailed information on data generation and quality control for whole-genome/exome sequencing, RNA sequencing, and EPIC 850K methylation arrays for a total of 84 patients with LNENs. We integrate the transcriptomic data with other previously published data and generate the first comprehensive molecular map of LNENs using the Uniform Manifold Approximation and Projection (UMAP) dimension reduction technique. We show that this map captures the main biological findings of previous studies and can be used as reference to integrate datasets for which RNA sequencing is available. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_lungNENomics/LNEN). The data, source code, and compute environments used to generate and evaluate the map as well as the raw data are available, respectively, in a Nextjournal interactive notebook (https://nextjournal.com/rarecancersgenomics/a-molecular-map-of-lung-neuroendocrine-neoplasms/) and at the EMBL-EBI European Genome-phenome Archive and Gene Expression Omnibus data repositories. CONCLUSIONS: We provide data and all resources needed to integrate them with future LNENs transcriptomic studies, allowing meaningful conclusions to be drawn that will eventually lead to a better understanding of this rare understudied disease.
SN - 2047-217X
ER -