Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms
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Abstract
Background
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.
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 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 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.
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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AbstractBackground 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.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 …
AbstractBackground 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.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 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 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.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.
A version of this preprint has been published in the Open Access journal GigaScience (see https://doi.org/10.1093/gigascience/giae008 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
Reviewer Qiuyue Yuan
The authors conducted a study where they generated multi-omics datasets, including whole-genome sequencing and RNA sequencing , for rare neuroendocrine tumors in the lungs, small intestine, and large cells.
They used patient-derived tumor organoids and performed quality control analysis on the datasets. Additionally, they developed a random forest classifier specifically for detecting mutations in the RNA-seq data.
The pipeline used in this study is well-organized, but I have a few queries that I would like to clarify before recommending it for publication.Major concerns:The data processing and quality control procedures would be valuable for other researchers working with similar datasets. It would be beneficial to add these procedures to the GitHub repository (https://github.com/IARCbioinfo/MS_panNEN_organoids).
Furthermore, it would be helpful to provide insights into what constitutes good quality reads, such as the number of unique reads and the ratio of duplicate reads.Regarding the random forest (RF) model, it is mentioned that there are 10 features. Could you clarify if these features are from the public information, or are all the features extracted solely from the RNA-seq data?
Also, does the RF model work for WGS data as well?Was there any specific design implemented to address the issue of imbalanced positive and negative samples?RNA-seq are not used to generate the gene expression here, which would waste important information.Minor concerns:In Figure 6C, what does "Mean minimum depth" refer to?Is the most important feature identified by the RF model a good predictor?
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Background 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.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 …
Background 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.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 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 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.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.
A version of this preprint has been published in the Open Access journal GigaScience (see https://doi.org/10.1093/gigascience/giae008 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
Reviewer Saurabh V Laddha
Alcala et al., did an excellent work on rare cancer type by creating PDTOs molecular fingerprint which has a direct impact for researcher working on these rare cancer type. As a data note, this is excellent resource and covering huge gap in this rare cancer field.These PDTOs holds high impact specially for such cancers which are slow growing and not easy culture in lab. Authors covered details regarding each technique used in this study and figures are clear to understand with exceptional writing.Minor comments:- Did authors compare the PDTOs to tumor molecular dataset ? This will be the key to understand how closely and qualitatively PTDOs are related to actual tumor datasets molecular profile. It is not clear in the current version and it will be helpful to readers to decide whether PTDOs molecular fingerprint system are valuable to them. This is not required for this manuscript to address but a note will be helpful to make valulabe decision to use such resources and with what limitations.- Authors covered longitudinal samples in this system for 1 to 2 timepoints. What changes did they observe (molecularly) looking at this data from a longitudinal timepoints view will be helpful for readers. Also, based on author's experience for longitudinal sampling, do authors have key suggestions for researcher ? a brief discussion will be helpful.- Authors did comprehensive small variant analysis from WGS and RNAseq. Did you authors find known somatic variations for these samples ? mainly comparing against the known published mutational landscape. A note of this will be helpful.- A comment on limitations of PTDOs and molecular fingerprint created from such PDTOs will be valuable.- Authors briefly comment on using such molecular datasets from PDTOs and combining with other datasets to improve on power statistics to discover informative molecular features of these cancers. This points towards my first point on how similar PDTOs are to tumor molecular profile.
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Background 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.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 …
Background 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.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 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 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.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.
A version of this preprint has been published in the Open Access journal GigaScience (see https://doi.org/10.1093/gigascience/giae008 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
Reviewer Masashi Fujita
In this manuscript, Alcala et al. have reported on the whole genome sequencing (WGS) and RNA sequencing (RNA-seq) of 23 patient-derived tumor organoids of neuroendocrine neoplasms.
This is a detailed report on the quality control of WGS, RNA-seq, and sample swap. The methods are solid and well-described. The raw sequencing data have been deposited in a public repository. This dataset could be a valuable resource for exploring the biology and treatment of this rare type of tumor.
Here are my comments to the authors:
Could you please clarify whether the organoids described in this manuscript will be distributed? If so, could you provide the contact address and any restrictions, such as a material transfer agreement?You have deposited the RNA-seq gene expression matrix in the public repository European Genome-phenome Archive (dataset ID: EGAD00001009994).
However, the file is under controlled access. This limits the availability of data, especially for scientists who just want a quick glance at the data. Since the gene expression matrix does not contain personally identifiable information, I wonder if you could make the file open access.
You have reported how you detected somatic mutations in the organoids. However, you did not share the list of detected mutations. Sharing this list would help scientists who do not have a computational background. Open access is preferable in this case, but controlled access is also acceptable because germline variants could be misclassified as somatic.
The primary site of mLCNEC23 is unknown. Could you infer its primary site based on gene expression patterns or driver mutations?I have concerns about the generalizability of your random forest model because it was trained using only 22 somatic mutations. Could you assess your prediction model using publicly available datasets of cancer genomes (e.g., TCGA)?
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