Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms
Abstract
BACKGROUND: Organoids are 3-dimensional experimental models that summarize the anatomical and functional structure of an organ. Although a promising experimental model for precision medicine, patient-derived tumor organoids (PDTOs) have currently been developed only for a fraction of tumor types. RESULTS: We have generated the first multi-omic dataset (whole-genome sequencing [WGS] and RNA-sequencing [RNA-seq]) of PDTOs from the rare and understudied pulmonary neuroendocrine tumors (n = 12; 6 grade 1, 6 grade 2) and provide data from other rare neuroendocrine neoplasms: small intestine (ileal) neuroendocrine tumors (n = 6; 2 grade 1 and 4 grade 2) and large-cell neuroendocrine carcinoma (n = 5; 1 pancreatic and 4 pulmonary). This dataset includes a matched sample from the parental sample (primary tumor or metastasis) for a majority of samples (21/23) and longitudinal sampling of the PDTOs (1 to 2 time points), for a total of n = 47 RNA-seq and n = 33 WGS. We here provide quality control for each technique and the raw and processed data as well as all scripts for genomic analyses to ensure an optimal reuse of the data. In addition, we report gene expression data and somatic small variant calls and describe how they were generated, in particular how we used WGS somatic calls to train a random forest classifier to detect variants in tumor-only RNA-seq. We also report all histopathological images used for medical diagnosis: hematoxylin and eosin-stained slides, brightfield images, and immunohistochemistry images of protein markers of clinical relevance. CONCLUSIONS: This dataset will be critical to future studies relying on this PDTO biobank, such as drug screens for novel therapies and experiments investigating the mechanisms of carcinogenesis in these understudied diseases.
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BibTeX
@article{alcala2026multiomic,
title = {{Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms}},
author = {Nicolas Alcala and Catherine Voegele and Lise Mangiante and Alexandra Sexton-Oates and Hans Clevers and Lynnette Fernandez-Cuesta and Talya L Dayton and Matthieu Foll},
journal = {GigaScience},
year = {2026},
month = oct,
doi = {10.1093/gigascience/giae008},
url = {https://hal.science/hal-05842056v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms
AU - Nicolas Alcala
AU - Catherine Voegele
AU - Lise Mangiante
AU - Alexandra Sexton-Oates
AU - Hans Clevers
AU - Lynnette Fernandez-Cuesta
AU - Talya L Dayton
AU - Matthieu Foll
PY - 2026
DA - 2026/10/28
JO - GigaScience
DO - 10.1093/gigascience/giae008
UR - https://hal.science/hal-05842056v1
AB - BACKGROUND: Organoids are 3-dimensional experimental models that summarize the anatomical and functional structure of an organ. Although a promising experimental model for precision medicine, patient-derived tumor organoids (PDTOs) have currently been developed only for a fraction of tumor types. RESULTS: We have generated the first multi-omic dataset (whole-genome sequencing [WGS] and RNA-sequencing [RNA-seq]) of PDTOs from the rare and understudied pulmonary neuroendocrine tumors (n = 12; 6 grade 1, 6 grade 2) and provide data from other rare neuroendocrine neoplasms: small intestine (ileal) neuroendocrine tumors (n = 6; 2 grade 1 and 4 grade 2) and large-cell neuroendocrine carcinoma (n = 5; 1 pancreatic and 4 pulmonary). This dataset includes a matched sample from the parental sample (primary tumor or metastasis) for a majority of samples (21/23) and longitudinal sampling of the PDTOs (1 to 2 time points), for a total of n = 47 RNA-seq and n = 33 WGS. We here provide quality control for each technique and the raw and processed data as well as all scripts for genomic analyses to ensure an optimal reuse of the data. In addition, we report gene expression data and somatic small variant calls and describe how they were generated, in particular how we used WGS somatic calls to train a random forest classifier to detect variants in tumor-only RNA-seq. We also report all histopathological images used for medical diagnosis: hematoxylin and eosin-stained slides, brightfield images, and immunohistochemistry images of protein markers of clinical relevance. CONCLUSIONS: This dataset will be critical to future studies relying on this PDTO biobank, such as drug screens for novel therapies and experiments investigating the mechanisms of carcinogenesis in these understudied diseases.
SN - 2047-217X
ER -