Multi-omic Characterization of Patient-Derived Tumor Organoids from Rare Neuroendocrine Neoplasms

Core Concepts

This dataset provides the first multi-omic (whole-genome sequencing and RNA-sequencing) characterization of patient-derived tumor organoids from rare neuroendocrine neoplasms, including lung, pancreatic, and small intestine tumors. The data enables investigation of the molecular features and oncogenic processes underlying these understudied diseases and supports future personalized treatment studies using these organoid models.

Abstract

This study generated the first multi-omic dataset (whole-genome sequencing and RNA-sequencing) of patient-derived tumor organoids (PDTOs) from rare neuroendocrine neoplasms, including:

Lung neuroendocrine tumors (NETs) (n=12; 6 grade 1, 6 grade 2)
Small intestine (ileal) NETs (n=6; 2 grade 1, 4 grade 2)
Large-cell neuroendocrine carcinomas (LCNECs) of the lung (n=4) and pancreas (n=1)
The dataset includes matched samples from the parental tumors (primary or metastatic) for most samples, as well as longitudinal sampling of the PDTOs over multiple passages.
The authors performed extensive quality control and data processing to ensure the high quality and reusability of the dataset. This includes:

Validating sample matching and sex using multi-omic data
Developing a random forest classifier to accurately distinguish somatic and germline variants from RNA-seq data
Providing all raw and processed data, as well as analysis scripts, to enable future studies
This unique multi-omic dataset of rare neuroendocrine neoplasm PDTOs will be critical for future studies investigating the molecular mechanisms of these understudied diseases and enabling personalized treatment testing using these organoid models.

Stats

Whole-genome sequencing data showed that all normal and normal-derived organoid samples had a mean coverage of at least 30X, while most tumor and tumor-derived organoid samples had a coverage of at least 60X.
RNA-sequencing data showed good alignment quality, with the number of known junctions saturating at 75-100% of the reads, indicating sufficient sequencing depth.

Quotes

"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."
"Given the rarity of neuroendocrine tumors from the lung, pancreas, and small intestine, past genomic studies each only reported data for a handful of samples, limiting the potential discoveries."

Key Insights Distilled From

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

by Alcala,N., V... at www.biorxiv.org 09-03-2023

https://www.biorxiv.org/content/10.1101/2023.08.31.555732v1

Deeper Inquiries

How can this multi-omic dataset be integrated with other publicly available datasets on neuroendocrine neoplasms to gain deeper insights into the molecular drivers and heterogeneity of these rare cancers

To gain deeper insights into the molecular drivers and heterogeneity of neuroendocrine neoplasms, integrating this multi-omic dataset with other publicly available datasets is crucial. By combining data from different sources, researchers can enhance the statistical power of their analyses and uncover patterns that may not be apparent in individual datasets.
One approach is to perform integrative analyses that merge the data from this dataset with other omics datasets, such as genomics, transcriptomics, proteomics, and epigenomics, from similar or related studies on neuroendocrine neoplasms. By comparing and contrasting the molecular profiles across different datasets, researchers can identify commonalities, unique features, and potential biomarkers associated with these rare cancers.
Furthermore, integrating clinical data, treatment outcomes, and patient characteristics from diverse cohorts can provide a comprehensive understanding of the disease landscape, treatment responses, and patient outcomes. This holistic approach can help in stratifying patients based on molecular subtypes, predicting treatment responses, and identifying novel therapeutic targets for personalized medicine approaches in neuroendocrine neoplasms.

What are the potential limitations of using patient-derived tumor organoids as models for neuroendocrine neoplasms, and how can these be addressed to improve their translational relevance

Patient-derived tumor organoids offer a valuable experimental model for studying neuroendocrine neoplasms, but they also come with certain limitations that need to be addressed to improve their translational relevance.

Genetic Stability: Organoids can undergo genetic changes during long-term culture, potentially altering their molecular characteristics. Regular monitoring of genetic stability and comparison with the original tumor samples is essential to ensure the fidelity of the organoid model.

Microenvironment Representation: Organoids lack the complexity of the tumor microenvironment present in vivo, which can influence drug responses and disease progression. Incorporating components of the tumor microenvironment, such as immune cells or stromal cells, into the organoid culture can better mimic the in vivo conditions.

Heterogeneity: Tumors are inherently heterogeneous, and organoids derived from a single tumor sample may not fully capture this heterogeneity. Utilizing multiple organoid lines from different regions of the same tumor or from different patients can help address this issue and provide a more comprehensive representation of tumor diversity.

Drug Metabolism and Pharmacokinetics: Organoids may not fully recapitulate the drug metabolism and pharmacokinetics observed in the human body. Incorporating organ-on-a-chip technologies or co-culturing organoids with liver organoids can enhance the predictive value of drug response studies.

By addressing these limitations through advanced culture techniques, co-culturing strategies, genetic monitoring, and incorporation of relevant microenvironment components, patient-derived tumor organoids can better mimic the complexity of neuroendocrine neoplasms and improve their translational relevance for drug discovery and personalized medicine.

What novel therapeutic targets or combination strategies could be identified by systematically profiling drug responses across this panel of neuroendocrine tumor organoids

Systematically profiling drug responses across this panel of neuroendocrine tumor organoids can reveal novel therapeutic targets and combination strategies for treating these rare cancers. Some potential findings and strategies include:

Identification of Sensitivity Patterns: Analyzing drug responses across different neuroendocrine tumor organoids can identify common sensitivity patterns to specific drugs or drug classes. This information can guide the selection of targeted therapies for patients based on their tumor's molecular profile.

Drug Synergy and Combination Therapies: By testing drug combinations across the organoid panel, researchers can identify synergistic drug interactions that enhance treatment efficacy. Combinations of targeted therapies, immunotherapies, and conventional chemotherapies can be optimized for improved outcomes.

Resistance Mechanisms: Studying drug resistance mechanisms in organoids can uncover novel targets to overcome resistance. By elucidating the molecular pathways involved in resistance, researchers can develop strategies to enhance treatment responses and prevent relapse.

Personalized Treatment Approaches: Using organoid drug response data in conjunction with patient-specific molecular profiles can enable the development of personalized treatment regimens. Tailoring therapies based on individual tumor characteristics and drug sensitivities can improve patient outcomes and reduce adverse effects.

Overall, the comprehensive drug screening and profiling of neuroendocrine tumor organoids can lead to the discovery of novel therapeutic targets, innovative combination strategies, and personalized treatment approaches for patients with these rare cancers.