Supporting data for "Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms"

Dataset type: Genomic, Transcriptomic
Data released on February 22, 2024

Alcala N; Voegele C; Mangiante L; Sexton-Oates A; Clevers H; Fernandez-Cuesta L; Dayton TL; Foll M (2024): Supporting data for "Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms" GigaScience Database. http://dx.doi.org/10.5524/102494

DOI10.5524/102494

Organoids are three-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.
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 provide the raw and processed data as well as all scripts for genomic analyses to ensure an optimal re-use 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.
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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Data Access AgreementDOC25.14 KB2024-01-28
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Funding body Awardee Award ID Comments
Worldwide Cancer Research L Fernandez-Cuesta
Ligue Contre le Cancer L Mangiante
H2020 T L Dayton 797966 Marie Skłodowska-Curie Actions
Neuroendocrine Tumor Research Foundation L Fernandez-Cuesta 2019 Investigator award
Neuroendocrine Tumor Research Foundation H Clevers 2017 Accelerator award
European Molecular Biology Organization T L Dayton ALTF-21-2017
The French National Cancer Institute M Foll & L Fernandez-Cuesta PRT-K 2017
Date Action
February 22, 2024 Dataset publish
April 10, 2024 Manuscript Link added : 10.1093/gigascience/giae008