Mesenchymal tumor organoid models recapitulate rhabdomyosarcoma subtypes
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Reply to the reviewers
1. General Statements
*The reviewers are enthusiastic. They agree with the claims made and comment favorably with regard to the impact as well on the short- and long-term potential for translation. All three go out of their way to emphasize positive aspects. A variety of questions were raised and we submit a complete revision with point-by-point replies that addresses all of these. This includes addressing tumor organoid (tumoroid) plasticity (reviewer #1) and composition/heterogeneity (reviewer #3) by incorporating single cell data as well as other analyses. We thank the reviewers for the thorough feedback. The additional data, analyses and clarifications …
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Reply to the reviewers
1. General Statements
*The reviewers are enthusiastic. They agree with the claims made and comment favorably with regard to the impact as well on the short- and long-term potential for translation. All three go out of their way to emphasize positive aspects. A variety of questions were raised and we submit a complete revision with point-by-point replies that addresses all of these. This includes addressing tumor organoid (tumoroid) plasticity (reviewer #1) and composition/heterogeneity (reviewer #3) by incorporating single cell data as well as other analyses. We thank the reviewers for the thorough feedback. The additional data, analyses and clarifications strengthen the study. *
To keep the rebuttal as short as possible we have only copied the reviewers’ concerns/questions, not the favorable comments. The copied remarks are in highlighted. Our replies are in italics. Each question is accompanied by a reply and a brief description of changes made in response.
2. Point-by-point description of the revisions
__Reviewer #1, Major Comment #1: “__The authors provided a foundational validation of their organoids through various methods, and their protocol stands to impact the field of RMS biology. To validate the organoids as recapitulating the primary human tumors, the authors perform analysis on the bulk organoid and bulk human primary tumors. The authors showed through sequencing efforts that the bulk mutational profile and transcriptional profiles do not dramatically change between the parent tumor and organoids. This analysis was done well; however, the authors fail to rigorously illustrate that the organoids maintain tumor cell heterogeneity of the primary human tumors. To rigorously validate the organoid system, the authors should illustrate the organoid culture conditions do not alter the heterogeneity of cells (cell plasticity) compared to that of the primary tumor. A formal assessment of the cellular plasticity in the organoids to the primary tumor would determine how the organoid system either maintains or shifts the cancer cell plasticity because of changes in microenvironment (Oncogene, 2020, 39: 2055-2068). The addition scRNA-seq would illustrate whether the organoids maintain the same populations as the primary tumor or bias for the propagation of specific cell populations at a single cell level and provide more rigorous information about every cell type present.”
Reply:* The reviewer’s question is whether the tumor cells in the tumoroid culture have the same degree of plasticity and are therefore as heterogeneous in culture as they are in the tumors that they are derived from. We agree that evaluating the heterogeneity of tumor cells in the tumoroid culture is desirable. This would ensure that the procedure has not simply selected for a single type of tumor cell. We have therefore generated single-cell RNA sequencing (scRNA-seq) data of tumoroid cells as suggested. It is important to point out that a complete inventory of RMS tumor cell heterogeneity by scRNA-seq has not been published as yet. Such an undertaking, i.e., scRNA-seq of a large cohort of RMS tumors, is an entire study in itself and lies outside the scope of this study. It would also not be feasible due to limited sample material for many of the tumors used here. Nevertheless, as is being alluded to by the reviewer, there is ample evidence of tumor cell heterogeneity in primary RMS tumors based on previous studies using immunohistochemistry (for example the well-known heterogeneity in expression of RMS marker proteins such as Myogenin, MyoD1 and Desmin). As shown in new Fig. 2D, when cultured as tumoroid models, examples from both of the main tumor types (FP-RMS sample RMS127 and FN-RMS sample RMS444) show a large degree of heterogeneity in expression of the known, heterogeneously expressed tumor cell markers Myogenin (MYOG gene), MyoD1 (MYOD1 gene) and Desmin (DES gene). Comparison with the cell cycle marker Ki-67 (MKI67 gene) shows that this heterogeneity is not due the cells being present in different cell cycle phases. *Tumor cell heterogeneity in the tumoroid culture is further indicated by the heterogeneous CNV patterns derived from the tumoroid scRNA-seq data (new Suppl. Fig. 1B).
Both the CNV analysis and the scRNA-seq marker gene expression indicate that the tumoroid culture conditions neither stringently select for a single type of tumor cell, nor drive the tumor cells into a uniform expression pattern phenotype, consistent with maintaining plasticity, even after the 7 (RMS127) and 5 (RMS444) passages. These are good indications of retained plasticity/heterogeneity. Additionally, we make it clear in the revised version that a more exhaustive answer would benefit from having a complete cohort of tumor scRNA-seq data to first determine the degree of heterogeneity exhibited by RMS tumors for all genes.
The related question of tumoroid cellular composition, with regard to the presence of non-tumor cells, is addressed in response to reviewer #3, major comment #1.
Changes:* Addition of a new Fig. 2D and a new Suppl. Fig. 1B with figure captions. Additional text in the Results and the Discussion sections. Additions to the Methods for the generation and analysis of the scRNA-seq data.*
Reviewer #1, Major Comment #2: “The authors took great strides to show that the organoids respond to therapeutics similarly to primary tumors. However, Figure 5A could be more transparent with more data labelled in the graph instead of just in the app and the implications of the variable responses could have been explored in the discussion section. Furthermore, for this model to be clinically relevant for pharmacokinetic studies, propagation in mice needs to be shown.”
Reply:
- We have made Fig. 5A more transparent by adding the drug names.
- The different response between FN-RMS and FP-RMS subtypes for certain drugs is known and the implication that the models reflect this is discussed more thoroughly now as suggested.
- We agree that animal experiments are imperative for pharmacokinetic studies of new drugs. However, most of the drugs that were included here, especially the ones highlighted, have already been evaluated in early phase clinical trials in adults and/or children. The pharmacokinetic data for humans is therefore already available for these drugs, making additional animal studies for pharmacokinetics of these drugs redundant. For future studies, various types of animal studies are likely to be required and we make this clear in the Discussion, also emphasizing that in general, tumoroid models do propagate in mice. To address this specifically for RMS, we have started a collaboration to generate PDX mouse models derived from RMS tumor and tumoroid samples in parallel. Anecdotally we can state here that at least 50% propagate. However, since we wish to investigate this systematically and with a complete set of tumoroid models, it is not prudent to wait for these results before publishing the current study. This would delay making the protocols, findings and tumoroid models available to the scientific community and as our (and many other groups’) work exemplifies, tumoroid models can yield important findings on their own. Changes:* Drug names added to rows in Fig. 5A. The Discussion has been expanded to include the differential response of tumor subtypes and tumoroids to different drugs and to include the uses (including pharmacokinetics) of different types of models has been expanded.*
Reviewer #1, Major Comment #3: “Figure 1 is well put together to graphically demonstrate the process by which organoids were obtained and manipulated. Figure 1B, however, as a graphical summary is a little confusing, and the information would be greatly enhanced by the addition of a comprehensive table. Furthermore, additional information could be added to the table to make it a more inclusive and impactful addition to the paper.”
Reply:* We agree.*
Changes:* A new Table 1 has been added as a separate file with a corresponding revised legend in the main document.*
Reviewer #1, Major Comment #4: “It is quite impactful that the authors were able to actively engineer the organoids with CRISPR/Cas9 and accurately delete TRP53, but controls were not represented in the figure. The experiment should have included a sgRNA targeting a pan-essential gene as a positive control and a non-targeting sgRNA as a negative control. We recommend addition of both controls to the experiment outlined in Figure 6 to increase the validity and rigor of the data presented.”
Reply:* We respectfully note that all appropriate controls were done. This included a non-targeting sgRNA as negative control (see Methods lines 1137 to 1140). As also explained in Fig. 6A, the strategy for generating a P53 knock-out involved selection through nutlin-3 exposure, whereby cells wildtype for P53 are selected against. As described (Methods lines 1144 to 1146), cells transfected with the non-targeting sgRNA plasmid indeed died upon nutlin-3 exposure. A sgRNA against a pan-essential gene was not included in this strategy since the nutlin-3 already kills all cells with a wildtype P53. Finally, we draw attention to the fact that the success of the approach was assessed by Western Blotting (Fig. 6B) and Sanger sequencing (Suppl. Fig. 6A). *
Changes:* None.*
Reviewer #1, Major Comment #5: “Although the authors provide an insight into a useful preclinical RMS model, the paper lacks mechanistic insight besides cursory description of the model.”
Reply:* Insight into a wide variety of different molecular and cellular mechanisms will be exciting to explore in future studies. This publication is indeed focused on describing an approach that works for RMS, and therefore showing for the first time that this works systematically for mesenchymal-derived tumors. In addition, the study describes key characteristics of the tumoroid models that are important to establish their validity as models and that are essential to demonstrate before making the tumoroid models available to the wider scientific community in order to perform the further mechanistic analyses. The word cursory is in contrast to the many positive comments made by this reviewer and the other two reviewers with regard to the extensive characterization.*
Changes: None.
Reviewer #1, Minor Comment #1: “Figure 3C and 4B are not transparent in their labels and could be altered so that every line has an associated gene in the publication. Furthermore, there are sample specific differences that could be explored in the discussion.”
Reply:* We agree.*
Changes:* Gene names have been added for every row in both figures. The Discussion now incorporates the observed differences.*
Reviewer #1, Minor Comment #2: “In Supplementary Figure 1, higher magnification inserts are needed to get a closer look at the IHC. Furthermore, the white balance is not the same in all the images and needs to be corrected prior to publication. The difference in white balance can clearly be seen in the last panels depicting IHC for RMS335, where the MYOD1 staining has a yellow background whereas the H&E staining has a white background.”
Reply:* We agree.*
Changes:* Higher magnification inserts have now been provided throughout Suppl. Fig. 1A. The white balance has been corrected.*
Reviewer #1, Minor Comment #3____: “The authors mentioned in line 202 that some of their organoids contain the novel fusion of PAX3 and WWTR1, but this fusion is not indeed novel as it has previously been seen in biphenotypic sinonasal sarcoma (Am J Surg Pathol 2019, 43:747-754).”
Reply:* We rephrased this to clarify that this is the first report of such a fusion in RMS, rather than in general.*
Changes:* The corresponding sentence has been rephrased.*
Reviewer #1, Comment within the Significance Statement: “The authors state that this is the first system to use organoids but should recognize the advances demonstrated by Manzella et al. (Nat Commun, 2020, 11:4629). Additionally, the authors state that this is the first demonstration of pre-clinical models harboring FGFR4V550L mutations; this fails to recognize the prior reported work by several groups (Chen et al., Cancer Cell, 2013, 24:710-24; Manzella et al., Nat Commun, 2020, 11:4629; McKinnon et al., Oncogene, 2018, 37:2630-2644).”
Reply:* We had in fact already recognized the advances described by Manzella et al. which was referenced in two places in the original submission (current lines 100 and 388). We thank the reviewer for pointing out the previous work done on an RMS cell line that harbored an FGFR4 p.V550L mutation. *
Changes:* We rephrased the corresponding passages concerning the FGFR4 mutation.*
We thank reviewer #1 for all the comments. This has resulted in many improvements.
Reviewer #2: W____e thank reviewer #2 for the positive comments.* There are no major/minor queries to address.*
Reviewer #3, Major Comment #1: “The authors describe the models derived as organoids/tumoroids implying that multiple cell types are represented potentially recreating the tumor microenvironment. Can the authors comment more specifically and demonstrate the extent to which cell types in addition to the tumor cells are represented, viable and are organized through analyses of the original and tumoroid sections (extend fig 2C/supplementary fig) and via analyses of the RNAseq data?”
Reply:* We use the term tumor organoid or tumoroids as coined by the field in general. This indeed indicates a degree of self-organization such as the three-dimensional growth in spheres and the propagation of a heterogeneous population of tumor cells (see comment #1, reviewer 1) for example. In general, however, tumoroids do not include growth of a non-tumor cell microenvironment inter-woven with the (different types of) tumor cells. Exceptions to this are very early passage tumoroids that are not yet stable and which may still contain non-tumor cells, or specialized co-culture conditions that are currently being actively sought to allow for co-culture of tumor cells within a non tumor cell microenvironment. It is therefore not anticipated that late passage tumoroid models will have non-tumor cells. The basis of the technology is that the defined set of growth factors in the medium mimics the tumor stimulating conditions of the non-tumor cell microenvironment. Since the mixed presence of tumor and non-tumor cells generally gives rise to one (frequently the non-tumour cell) outgrowing the other, it is often considered the hallmark of an unsuccessful tumoroid.*
The reviewer therefore raises an important point that we have failed to make clear. We have addressed this in two ways. We emphasize that the scRNA-seq data that are now included in response to reviewer #1, comment #1 do not indicate the presence of any non-tumor cells (as expected). In addition, this aspect of tumor organoid technology is explained better in the Introduction.
Changes:* The results section has been expanded with the description of the scRNA-seq data emphasizing the expected lack of non-tumor cells and the introductory section on tumor organoid technology has been improved to make it clear that currently this generally involves growth of different types of tumor cells only.*
Reviewer #3, Major Comment #2: “Does the quantification from the RT-qPCR analyses for the MYOD1, MYOG and Desmin of the models match that in the samples from which they were derived? Does the RNAseq that was performed on tumor and the culture at the time of the drug screen tie in with this?”
Reply:* The answer is yes. The figure below shows tumor and tumoroid bulk RNA seq of those genes also analyzed by RT-qPCR (i.e., DES, MYOG, and MYOD1). Note that this is also the same stage as for the drug screening. As can be appreciated, the expression of these markers is generally very comparable between tumors and the derived tumoroid models. Note that this also constitutes a nice independent (albeit indirect) verification of the similar degree of heterogeneity issue raised by Reviewer #1 (comment #1). Expression of the markers was lower in the tumoroid models of RMS000HQC and RMS000ETY compared to the primary tumor. In line with this, expression of these genes was also already lower in the early passages of the culture as determined by RT-qPCR (Fig. 2A). Nevertheless, copy-number analysis inferred from whole-genome sequencing showed that the resulting tumoroid models are indeed tumor cells (Suppl. Fig. 2A top panel and Suppl. Fig. 2B lower panel).*
We therefore conclude that the expression of probed marker genes is generally comparable between tumor and tumoroid and that early passage RT-qPCR based expression analysis of these markers can be reflective of the expression in the fully established model.
*- Rebuttal letter includes corresponding figure here - *
Changes:* None. The expression data are already available within the interactive browser-based Shiny App.*
Reviewer #3, Major Comment #3: “How do the frequencies of SNVs compare with recent studies? Or are the numbers in the risk groups not appropriately represented?”
Reply:* The SNV frequencies are quite comparable to recent studies, with similar differences between risk groups, all as depicted in the new Suppl. Fig 2E. The SNV frequency was calculated from our WGS data following the procedure from the most recent report in pediatric cancer (https://www.biorxiv.org/content/10.1101/2021.09.28.462210v1). Across tumor and tumoroid models we found a somatic mutation frequency of SNVs with a VAF of >0.3 ranging from 0.03 to 1.92 mut/MB (median 0.70 mut/MB) which is comparable to the reported somatic mutation frequency in the afore-mentioned study (median 0.9 mut/MB in RMS). Concerning the risk groups, a recent study (https://pubmed.ncbi.nlm.nih.gov/31699828/*) found a significant difference in the tumor mutational burden between fusion-negative (FN) and fusion-positive (FP) RMS (2.6 mut/MB vs. 1.0 mut/MB, respectively) with a higher mutational burden associated with poorer outcome. In our study, the FN-RMS tumoroid models also show a higher mutation frequency compared to the FP-RMS tumoroid models (FN 4 vs. FP 15, p = 0.02, Wilcoxon). Such a difference is also found between the original tumors but without statistical difference (FN 4 vs. FP 15, p = 0.15, Wilcoxon) likely related to the small sample sizes. This underscores the representative nature of the tumoroid models and is of obvious interest to include. We have made the appropriate changes.
Changes:* To include these analyses in the manuscript, we added a new Suppl. Fig. 2E with corresponding Suppl. Fig. legend and a new paragraph in the main text.*
Reviewer #3, Minor Comment #1: “The number of models and success rates would be useful to indicate in the abstract.”
Reply: We agree.
Changes: We added this information to the abstract.
Reviewer #3, Minor Comment #2: “It would be helpful to define the SBS1, 5,and 18 in the figure legends. Do the age related signatures in any way correlate with patient age or aggressivness of tumors?”
Reply:
- Agreed. The definitions of SBS1, 5, and 18 have now been included the legends of Fig. 3B and 4A.
- The age-related signatures SBS1 (but not SBS5) shows a weak albeit significant correlation with patient age only in RMS tumoroid models but not in RMS tumors. Furthermore, concerning aggressiveness, FP-RMS tumors and tumoroid models show a significantly higher contribution of SBS1 (but not SBS5) to their overall somatic mutation frequency compared to FN-RMS tumors and tumoroid models. However, since FP-RMS tumor samples were obtained from older patients (median 14 years versus median 6 years in FN-RMS tumor samples), this observation could also be related to the patient-age and not primarily to the fusion-status. The heterogeneity of samples (e.g., primary therapy-naïve samples versus relapse and therefore pre-treated samples) and the relatively low sample number could be explanations for the lack of a stronger correlation in general. Changes:* Added definitions of SBS1, 5, and 18 in the legends of Fig. 3B and 4A. Added text in the Results section to indicate the observed correlations.*
Reviewer #3, Minor Comment #3: “Page 13 line 300 just because the RH30 cell line has TP53 mutation doesn't mean that it was acquired in culture - unless there is specific evidence that supports this.”
Reply:* We thank the reviewer for this rectification. To our knowledge, there is indeed no specific evidence that this cell line acquired the TP53 mutation during culturing or whether the mutation was already present in the primary tumor the cell line was derived from. *
Changes:* The corresponding statement has been removed.*
We thank reviewer #3 for all the comments. This has resulted in many improvements.
Besides the changes described above, additional minor changes were made:
*We have moved the interactive, browser-based Shiny app to a server that is managed by our institute instead of having it hosted on shinyapps.io. We include the new URL in line 556. *
The data upload to the European Genome-Phenome Archive (EGA) of the data from the initial submission has been completed and the raw sequencing data can now be accessed. The data upload of the scRNA-seq data generated for the revision is currently ongoing. We have therefore adapted and renamed the “Bulk sequencing data availability” section in the Methods in the manuscript (lines 1043 to 1050).
We updated the code available at https://github.com/teresouza/rms2018-009* following the additional analyses performed for the revision. *
Supplementary Table 1: The values for row “RMS000FLV” for columns “sample_body_site” and “primary_site_specific” were corrected as this tumor was located in the upper leg and not the upper arm of the patient. Furthermore, we added patient numbers as in the new Table 1 and corrected spelling errors. This does not change any of the conclusions in the manuscript.
Figure 6A: The protein “P53” was spelled without capital “P” in the initial version. We corrected this.
We included the recently described Zebrafish RMS PDX models (https://pubmed.ncbi.nlm.nih.gov/31031007/) in the Discussion of RMS models. See lines 507 to 510.
With the addition of Fig. 2D, the figure legends of Fig. 2A and 2B were moved to the side (Fig. 2A) or below (Fig. 2B) the figure. With the addition of the single-cell copy-number plots, Suppl. Fig. 1 was divided in Suppl. Fig. 1A and 1B.
Some of the original scale bars in Fig. 2C and Suppl. Fig. 1A were incorrectly labelled and this has now been corrected. This does not change any of the conclusions.
Minor corrections in the sections Affiliations, Financial support, Author contributions and Conflict of Interests.
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Referee #3
Evidence, reproducibility and clarity
Meister et al., describe their methodology in establishing what they term organoid or tumoroid 2D/3D cultures derived from samples of rhabdomyosarcoma (RMS) patient tumours. They have success to varying degrees across the subtypes with greater success in those more clinically aggressive. Their analyses of markers, the somatic genetics and gene expression profiles suggest that they are largely representative of RMS and the tumor samples from which they were derived. Their utility in drug screening and manipulation by knocking out TP53 by CRISP/Cas9 is also demonstrated. The conclusion is that this represents a useful approach for …
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Referee #3
Evidence, reproducibility and clarity
Meister et al., describe their methodology in establishing what they term organoid or tumoroid 2D/3D cultures derived from samples of rhabdomyosarcoma (RMS) patient tumours. They have success to varying degrees across the subtypes with greater success in those more clinically aggressive. Their analyses of markers, the somatic genetics and gene expression profiles suggest that they are largely representative of RMS and the tumor samples from which they were derived. Their utility in drug screening and manipulation by knocking out TP53 by CRISP/Cas9 is also demonstrated. The conclusion is that this represents a useful approach for generating patient derived models and a unique resource for preclinical analyses and other research into RMS.
This is a major piece of work that is well written and presented. The link to interrogate the data worked. I have only a few comments.
Major comments
The authors describe the models derived as organoids/tumoroids implying that multiple cell types are represented potentially recreating the tumor microenvironment. Can the authors comment more specifically and demonstrate the extent to which cell types in addition to the tumor cells are represented, viable and are organized through analyses of the original and tumoroid sections (extend fig 2C/supplementary fig) and via analyses of the RNAseq data?
Does the quantification from the RT-qPCR analyses for the MYOD1, MYOG and Desmin of the models match that in the samples from which they were derived? Does the RNAseq that was performed on tumour and the culture at the time of the drug screen tie in with this?
How do the frequencies of SNVs compare with recent studies? Or are the numbers in the risk groups not appropriately represented?
Minor comments
The number of models and success rates would be useful to indicate in the abstract.
It would be helpful to define the SBS1, 5,and 18 in the figure legends. Do the age related signatures in any way correlate with patient age or aggressivness of tumors?
Page 13 line 300 just because the RH30 cell line has TP53 mutation doesn't mean that it was acquired in culture - unless there is specific evidence that supports this.
Significance
The significance of this study is in describing how a relatively large number of models of RMS were established plus increasing awareness of the biobank resource and associated data that has been created. The approach, although used in more ad hoc reports of smaller numbers of RMS, represents a useful development for mesenchymal tumors versus the more established development of such models in epithelial cancers. Although a lower success rate than xenografts, it is a useful and practical cost-effective alternative for preclinical testing and research. Likely interest to a speciaclist audience for those involved in the RMS, sarcoma and pediatric cancer field.
Referees cross-commenting
OK with the balance of comments for the authors to address. I think the extent to which they are prepared to address the heterogeneity issue, and the results of this for the models, is likely to affect the impact of their study.
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Referee #2
Evidence, reproducibility and clarity
This manuscript describes the possibility to generate a collection of pediatric rhabdomyosarcoma (RMS) tumor organoid models comprising broad spectrum subtypes from highly aggressive to extremely rare. The authors were able very successfully establish 19 RMS models from 46 pediatric RMS patient samples with 41 % efficiency. All RMS tumoroid models were thoroughly characterized and retained the molecular characteristics of the tumor they are derived from as well as they displayed genetic stability over time. Most of the tested tumors showed long-term propagation potential, reaching passage 40 and remaining stable. Though, …
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Referee #2
Evidence, reproducibility and clarity
This manuscript describes the possibility to generate a collection of pediatric rhabdomyosarcoma (RMS) tumor organoid models comprising broad spectrum subtypes from highly aggressive to extremely rare. The authors were able very successfully establish 19 RMS models from 46 pediatric RMS patient samples with 41 % efficiency. All RMS tumoroid models were thoroughly characterized and retained the molecular characteristics of the tumor they are derived from as well as they displayed genetic stability over time. Most of the tested tumors showed long-term propagation potential, reaching passage 40 and remaining stable. Though, establishing time for RMS tumoroid models varied with a median time from acquisition of the tumor sample to successful drug screening being 81 days, highly aggressive tumors were established in as little as 27 days. Also, authors shown us in elegant manner the suitability of RMS tumoroid models for research in two specific ways: via drug screening and CRISPER/Cas9 genome editing.
Significance
In summary, the author's work made significant progress in 3D culture and tumor organoid models of mesenchymal origin, being the first collection of tumoroid models from mesenchymal malignant tumors and the second thoroughly characterized tumoroid collection specific for pediatric cancers. Without doubt, biobanked collection of RMS tumoroids will be useful for drug screening as well as molecular editing. Also, these models will be a useful resource for future research and in preclinical and clinical testing therapeutics for RMS. In the future, organoids generated from patients with RMS may lead to precise and personalized treatment.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Meister et al. set out to develop a new organoid preclinical model of rhabdomyosarcoma (RMS). The authors comment that this system would be beneficial for preclinical modeling because it has the ability to maintain the tumor's molecular characteristics. The authors then proved that organoids derived from multiple RMS subtypes resembled their parent tumors using RT-qPCR for characteristic markers, histopathology, copy number profiles, mutational signature analyses, and transcriptional profiling. Importantly, the authors performed long term studies to show that the organoids remain stable over multiple passages and do not …
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Referee #1
Evidence, reproducibility and clarity
Summary:
Meister et al. set out to develop a new organoid preclinical model of rhabdomyosarcoma (RMS). The authors comment that this system would be beneficial for preclinical modeling because it has the ability to maintain the tumor's molecular characteristics. The authors then proved that organoids derived from multiple RMS subtypes resembled their parent tumors using RT-qPCR for characteristic markers, histopathology, copy number profiles, mutational signature analyses, and transcriptional profiling. Importantly, the authors performed long term studies to show that the organoids remain stable over multiple passages and do not change their mutational landscape dramatically. Finally, the authors tested their organoids with known RMS therapeutics and for their ability to be engineered with the CRISPR/Cas9 system. Not surprisingly, the authors found their organoids sensitive to known RMS therapeutics and were successfully able to generate TP53-/- organoids with CRISPR/Cas9, underscoring this organoid system in translatable use. This report nicely describes a method for the establishment of human RMS organoid culture systems that can be leveraged for preclinical testing.
Major Comments:
- The authors provided a foundational validation of their organoids through various methods, and their protocol stands to impact the field of RMS biology. To validate the organoids as recapitulating the primary human tumors, the authors perform analysis on the bulk organoid and bulk human primary tumors. The authors showed through sequencing efforts that the bulk mutational profile and transcriptional profiles do not dramatically change between the parent tumor and organoids. This analysis was done well; however, the authors fail to rigorously illustrate that the organoids maintain tumor cell heterogeneity of the primary human tumors. To rigorously validate the organoid system, the authors should illustrate the organoid culture conditions do not alter the heterogeneity of cells (cell plasticity) compared to that of the primary tumor. A formal assessment of the cellular plasticity in the organoids to the primary tumor would determine how the organoid system either maintains or shifts the cancer cell plasticity because of changes in microenvironment (Oncogene, 2020, 39: 2055-2068). The addition scRNA-seq would illustrate whether the organoids maintain the same populations as the primary tumor or bias for the propagation of specific cell populations at a single cell level and provide more rigorous information about every cell type present.
- The authors took great strides to show that the organoids respond to therapeutics similarly to primary tumors. However, Figure 5A could be more transparent with more data labelled in the graph instead of just in the app and the implications of the variable responses could have been explored in the discussion section. Furthermore, for this model to be clinically relevant for pharmacokinetic studies, propagation in mice needs to be shown.
- Figure 1 is well put together to graphically demonstrate the process by which organoids were obtained and manipulated. Figure 1B, however, as a graphical summary is a little confusing, and the information would be greatly enhanced by the addition of a comprehensive table. Furthermore, additional information could be added to the table to make it a more inclusive and impactful addition to the paper.
- It is quite impactful that the authors were able to actively engineer the organoids with CRISPR/Cas9 and accurately delete TRP53, but controls were not represented in the figure. The experiment should have included a sgRNA targeting a pan-essential gene as a positive control and a non-targeting sgRNA as a negative control. We recommend addition of both controls to the experiment outlined in Figure 6 to increase the validity and rigor of the data presented.
- Although the authors provide an insight into a useful preclinical RMS model, the paper lacks mechanistic insight besides cursory description of the model.
Minor Comments
- Figure 3C and 4B are not transparent in their labels and could be altered so that every line has an associated gene in the publication. Furthermore, there are sample specific differences that could be explored in the discussion.
- In Supplementary Figure 1, higher magnification inserts are needed to get a closer look at the IHC. Furthermore, the white balance is not the same in all the images and needs to be corrected prior to publication. The difference in white balance can clearly be seen in the last panels depicting IHC for RMS335, where the MYOD1 staining has a yellow background whereas the H&E staining has a white background.
- The authors mentioned in line 202 that some of their organoids contain the novel fusion of PAX3 and WWTR1, but this fusion is not indeed novel as it has previously been seen in biphenotypic sinonasal sarcoma (Am J Surg Pathol 2019, 43:747-754).
Significance
As has been mentioned previously, this research is impactful to the field of RMS biology because the authors were successfully able to use organoid technology, which has not previously been reported. The authors do a great job of listing current RMS modelling techniques and explaining how their system addresses the pitfalls of the others. Furthermore, this protocol could be expanded to the development of other organoid systems for other sarcomas. The rhabdomyosarcoma field and larger sarcoma community would be keenly interested in this work. It is clear that this system has the potential for use in pre-clinical settings as well as in high-throughput screens, but further validation and increased rigor is required on both fronts.
It is astounding and the authors should be complimented that they were able to show a median time from patient to drug screen was 81 days! This has enormous potential such as rapid translation of therapies and personalized medicine. That said, the authors must first refine the heterogeneity of the organoids and demonstrate how the organoids reflect the phenotypic and cellular plasticity of the parent tumors. Furthermore, the authors ought to be careful when making priority claims. The authors state that this is the first system to use organoids but should recognize the advances demonstrated by Manzella et al. (Nat Commun, 2020, 11:4629). Additionally, the authors state that this is the first demonstration of pre-clinical models harboring FGFR4V550L mutations; this fails to recognize the prior reported work by several groups (Chen et al., Cancer Cell, 2013, 24:710-24; Manzella et al., Nat Commun, 2020, 11:4629; McKinnon et al., Oncogene, 2018, 37:2630-2644).
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