Investigating pulmonary neuroendocrine cells in human respiratory diseases with airway models.

Despite accounting for only ∼0.5% of the lung epithelium, pulmonary neuroendocrine cells (PNECs) appear to play an outsized role in respiratory health and disease. Increased PNEC numbers have been reported in a variety of respiratory diseases, including chronic obstructive pulmonary disease and asthma. Moreover, PNECs are the primary cell of origin for lung neuroendocrine cancers, which account for 25% of aggressive lung cancers. Recent research has highlighted the crucial roles of PNECs in lung physiology, including in chemosensing, regeneration and immune regulation. Yet, little is known about the direct impact of PNECs on respiratory diseases. In this Review, we summarise the current associations of PNECs with lung pathologies, focusing on how new experimental disease models, such as organoids derived from human pluripotent stem cells or tissue stem cells, can help us to better understand the contribution of PNECs to respiratory diseases.

Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms.

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.