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

Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers

Neuroendocrine tumors (NETs) occur sporadically or as part of rare endocrine tumor syndromes (RETSs) such as multiple endocrine neoplasia 1 and von Hippel-Lindau syndromes. Due to their relative rarity and lack of model systems, NETs and RETSs are difficult to study, hindering advancements in therapeutic development. Causal or mechanistic mathematical modeling is widely deployed in disease areas such as breast and prostate cancers, aiding the understanding of observations and streamlining in vitro and in vivo modeling efforts. Mathematical modeling, while not yet widely utilized in NET research, offers an opportunity to accelerate NET research and therapy development. To illustrate this, we highlight examples of how mathematical modeling associated with more common endocrine cancers has been successfully used in the preclinical, translational and clinical settings. We also provide a scope of the limited work that has been done in NETs and map how these techniques can be utilized in NET research to address specific outstanding challenges in the field. Finally, we include practical details such as hardware and data requirements, present advantages and disadvantages of various mathematical modeling approaches and discuss challenges of using mathematical modeling. Through a cross-disciplinary approach, we believe that many currently difficult problems can be made more tractable by applying mathematical modeling and that the field of rare diseases in endocrine oncology is well poised to take advantage of these techniques.

Elevated sortilin expression discriminates functional from non-functional neuroendocrine tumors and enables therapeutic targeting

A subset of neuroendocrine tumors (NETs) can cause an excessive secretion of hormones, neuropeptides, and biogenic amines into the bloodstream. These so-called functional NETs evoke a hormone-related disease and lead to several different syndromes, depending on the factors released. One of the most common functional syndromes, carcinoid syndrome, is characterized mainly by over-secretion of serotonin. However, what distinguishes functional from non-functional tumors on a molecular level remains unknown. Here, we demonstrate that the expression of sortilin, a widely expressed transmembrane receptor involved in intracellular protein sorting, is significantly increased in functional compared to non-functional NETs and thus can be used as a biomarker for functional NETs. Furthermore, using a cell line model of functional NETs, as well as organoids, we demonstrate that inhibition of sortilin reduces cellular serotonin concentrations and may therefore serve as a novel therapeutic target to treat patients with carcinoid syndrome.

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.