Deciphering the CD73⁺ Regulatory γδ T Cell ecosystem associated with poor survival in Ovarian cancer

  1. Ghita Chabab
  2. Henri-Alexandre Michaud
  3. Cécile Dejou
  4. Laure-Agnès Chépeaux
  5. Yaël Glasson
  6. Florence Boissière
  7. Marion Lenain
  8. Anne-Sophie Dumé
  9. Pauline Sarrant
  10. Gabriel Chemin
  11. Pauline Wajda
  12. Bertrand Dubois
  13. Anna MacManus
  14. Pierre-Emmanuel Colombo
  15. Michel Fabbro
  16. Nathalie Bonnefoy
  17. Maeva Chauvin
  18. Virginie Lafont

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Abstract

The ability of tumor cells to overcome immune surveillance is an essential step in tumor development and progression. Among the immune cells playing a role in tumor control, γδ T cells contribute to the immune response against many tumor types through their direct cytotoxic activity against cancer cells and their capacity to regulate the functions of other immune cells. However, their presence in the tumor microenvironment is also associated with poor prognosis, suggesting that γδ T cells may also have pro-tumor activities. We previously described a regulatory γδ T-cell subset that expresses CD73 and produces IL-10, IL-8 and adenosine. Here, we report a higher CD73+ γδ T cell density in the tumor microenvironment of ovarian cancer samples from patients with short-term than long-term survival. Starting from this original observation, we investigated their neighborhood and described a specific ecosystem according to their pro-tumor functions.

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    Reply to the reviewers

    Reviewer #1 (Evidence, reproducibility and clarity (Required)):

    The authors provide a detailed characterization of the tumor microenvironment (TME) of 91 ovarian cancer patients, broken down in long and short-term survivors (post 5 years). The focus on the role of a subgroup of T cells, gamma/delta γδ) T cells with reported anti but also pro tumorigenic properties, Prior work of the lab has established a link between a subgroup of γδ T cells expressing CD73 and poor prognosis, due to the ability of these cells to produce immunosuppressive cytokines, such as IL10 or IL8 and the production of adenosine, by CD73, in the micromilieu. The data is further backed up by the analysis of fresh tumor specimens and tissue culture work.

    Here they continue this story by investigating the TME using tumor microarrays (91 samples), single cell RNA seq (12 patients), imaging mass cytometry (> 30 samples) and flow cytometry (form confirmatory purposes) to define cellular neighborhoods of CD73+ and CD73- γδ T cells. This revealed differences in cellular composition and spatial transcriptome analysis further helped to define the transcriptomes in γδ T cells, cancer cells and cancer associated fibroblasts.

    The authors conclude the in ovarian cancer γδ T cells expressing CD73 dampen anti-tumor immunity and propose detection and evaluation of CD73+ γδ T cells as prognostic marker.

    The manuscript is well written, and despite its descriptive nature, easy to follow. Data is presented in a clear and easy to read fashion.

    Reviewer #1 (Significance (Required)):

    Using a well characterized cohort of ovarian cancer patients with detailed clinical follow up the authors report on the predictive power of a subset of γδ T cells expressing CD73, with immune suppressive / regulatory capacity, reading out patient survival in high grade serous ovarian cancer, a still deadly disease. As such the identification of reliable markers predicting survival is a clear medical need. These findings contrast others made in different solid cancers, suggesting tumor type specific differences, which are only starting to emerge, but are of clear clinical relevance.

    What is unclear to me and needs to be addressed, is if these patient specimens were taken before or after initial therapy, whether the samples have been stratified according the treatment that they got, assuming it will be mostly platinum compounds (but maybe not), and that the p53 status of the tumors are (if genetics are available this would help to add some granularity to the study that, as it stands is largely descriptive, even though with extremely high resolution. This data should be available and could be integrated.

    We thank the reviewer for this insightful and constructive comment. We agree that clinical context and treatment stratification are essential to strengthen the interpretation and translational value of our findings.

    We confirm that all tumor samples used in this study were obtained prior to any systemic treatment, i.e., before first-line chemotherapy, during the Biopsy realized for the diagnosis. This information has now been clearly stated in the Methods and Results section (page 4, line 103) and also in Table S1.

    Although our primary aim was not to evaluate correlations with mutational status, we recognize the critical role that tumor genetics play in shaping the immune microenvironment. Using available clinical genomics data, we found that the TP53 mutational status of our cohort aligns with that of previous analyses. As expected for high-grade serous ovarian cancer (HGSOC), nearly all tumors exhibited TP53 mutations (present in 95% of patients). Due to the lack of variability in TP53 status, no meaningful stratification was observed based on this factor. This information has been added in the Materials and methods part (page 4 lines 104 to 106)

    Some minor issues

    • I would stick to CD73, and not mix it with NT5E, which is confusing at first (Fig 2).

    We appreciate this suggestion. To clarify the nomenclature and avoid confusion, we have consistently indicated throughout the text and figure legends that NT5E refers to the CD73 gene.

    • I would ask to compare the overall survival of CD73+ between densities - is it still significantly different in fig 1 - meaning is it about density, CD73 expression, or both. Comparing survival of tumors with a low density of CD73+ γδ T cells does not seem to be different from those having a low density of CD73- γδ T cells, which could be considered in data interpretation. Same for high density tumors.

    In the manuscript, the term “density” specifically refers to the density of γδ____ T cells and not the density of CD73 molecules expressed by these cells. Additionally, it is not feasible to conduct a density analysis of molecules using the data obtained from immunofluorescence (IF) staining of sample sections.

    Kaplan-Meier analyses were performed to assess patient survival based on the density of total γδ____ T cells, as well as the subsets of CD73⁺ and CD73⁻ γδ T cells. The results indicate that a higher density of γδ____ T cells is associated with poorer patient survival, with a more pronounced effect seen in those with a high density of CD73⁺ γδ T cells compared to those with CD73⁻ γδ T cells.

    As the reviewer pointed out, patients with a low density of CD73⁺ γδ T cells do not show significantly different survival outcomes compared to those with a low density of CD73⁻ γδ T cells (IC50 for low CD73⁺ = 6.0 years vs. IC50 for low CD73⁻ = 6.2 years). In response, we have revised the corresponding sentence in the text and included the IC50 values for greater clarity and informativeness (page 9).

    • figure 1, caption should include the word "patients" at the end, I guess.

    The modification has been done.

    • labelling and font can be improved in many panels, eg. the dot plots in Fig 2, panel B, right, same for panel C and D

    We appreciate the feedback on figure presentation. We have now updated Figure 2 with improved labeling, consistent font size, and enhanced resolution to ensure better readability across all panels, particularly panels B–D. The revised figure has been updated in the main manuscript.

    Reviewer #2 (Evidence, reproducibility and clarity (Required)):

    In this manuscript ("Deciphering the tumor-infiltrating CD73+ regulatory γδ T cell ecosystem associated with poor survival of patients with ovarian cancer"), Chabab et al. report on the phenotype and location of CD73+ γδ T cells in ovarian cancer. CD73+ γδ T cells can be immunosuppressive via the production of cytokines (IL-8, IL-10) and the expression of PD-L1. Here, the authors investigated the phenotype and location of CD73+ and -neg γδ T cells in ovarian cancers with a particular focus on the cells surrounding the γδ T cells in the tumour.

    Overall, the study is informative and well-performed. However, the way some of the data are presented does not allow to fully evaluate them. Besides this, this reviewer only has some minor comments.

    General comments:

    • The data provided in this manuscript are descriptive/correlative, and as such, causation cannot be inferred. Therefore, the language needs to reflect this; statements like "we investigated the impact of CD73+ regulatory γδ T cells in ovarian cancer" (L89) and "CD73+ γδ T cells were in close contact with more aggressive tumor cells" (L426), among others, are incorrect without functional data. The authors are advised to adjust the text throughout.

    We thank the reviewer for this thoughtful point. We have amended the text to make it consistent with the data.

    • Please make the figure legends self-explanatory without the need to search for the information in the M&M. For example, the graphs in fig1 and 3 contain many dots, but it is not explained what these dots represent. Please also add n for each experiment shown and state how often the experiment was performed independently.

    As requested by the reviewer, we have revised the figure legends to make them more explicit. We have indicated the number of biological replicates (n) and how many times each experiment was performed independently. This information has been added to each legend where consistent and relevant, to ensure clarity and reproducibility.

    • It would be helpful for the reader if abbreviations introduced in the M&M were also explained the first time they appeared in the results section.

    This point has been addressed as requested by the reviewer.

    • Please explain all abbreviations, e.g. FIGO, CST, NT5E, etc.
    • L235: typo 'that' instead of 'than'; L258 'reduced'; L259 'fig1d-f'; L451f twice 'CD73+'; 'naive' instead of 'naïve' throughout; SF2 legend: '2f' instead of '3f', SF9 legend: '1.105'.
    • L280ff: "Tumor cells ... were the most important cell type" - it may be clearer to use 'most frequent';

    All these points have been addressed.

    M&M

    • Please be consistent, if you provide catalogue numbers or dilutions (antibody, reagents) [which is good, maybe even adding the RRID number], do so for all items.

    This point has been addressed as requested by the reviewer.

    • The M&M does not state for the CAFs how long they were cultured before the supernatant was taken for the cytokine measurements.

    This point has been added in M&M section.

    • For the IL-6 ELISA, it is stated that the "cells were harvested"; what happened to them, and how do you get any SN from these cells?

    We have amended the protocol of IL-6 Elisa in M&M section for clarification.

    Figures Fig.1:

    • The authors used the word 'predict' in the heading, which seems not appropriate for a retrospective study; something like 'correlate' seems better.

    The word “predict” has been replaced by “correlate” as suggested by the reviewer.

    • Similarly, the title of the figure legends claims that the 'impact of γδ T cells' is shown, while only a correlation is presented.

    The title of the figure has been modified

    • For Fig1a-c, only summary data are presented. Please add exemplary pictures as well.

    Pictures of IF have been added as Supplementary Fig 1.

    • For Fig1d-f, the label for the x-axis is missing.

    The figure has been corrected.

    Fig.2

    • It seems funny to call the patients 'naïve', maybe 'untreated' is clearer.

    We appreciate this suggestion and agree that ‘untreated’ is a clearer and more appropriate term in this context. We have replaced all instances of ‘naïve’ with ‘untreated’ throughout the manuscript to avoid ambiguity.

    • The graph in Fig2e does not allow comparing the cell frequencies properly. This would require either bar graphs or a table. Furthermore, the statistical analysis is missing. Without that, a statement like "associated with higher proportion of CAFs" (L265) is not supported.

    We thank the reviewer for this valuable observation. In response, we have replaced the original visualization in Figure 2E with grouped bar graphs showing the mean ± SEM of the relative proportions of each major cell type in the NT5E_low and NT5E_high groups, based on the median split. This format allows for clearer visual comparison of cell frequencies across conditions.

    Furthermore, we performed statistical comparisons using a t-test (a parametric test) on each population to evaluate differences in cell type proportions between the two groups. The results indicate a significantly higher proportion of CAFs and γδ T cells in the NT5E_high tumor profile. The corresponding p-values are provided in the figure legend. We hope this revised analysis and clearer presentation address the reviewer’s concerns.

    Fig.3

    • For Fig3b+c, the IMC are derived from 4 patients (not clear for the flow data)

    As stated in both the figure legend and the text, the IMC analysis was conducted on 38 ROIs from four patient samples, while the flow cytometry analysis was performed on tumor samples from seven ovarian cancer patients.

    • did the authors noticed differences between patients?

    "As shown in new Figures 3b and 3c, no significant differences were observed between patients. Each individual patient is represented by a different color."

    • For Fig3e, the description in the text does not reflect the figure, e.g. cluster 1 does not show LAG3 expression, but this is claimed in the text (other descriptions are off as well).

    The text describing Fig. 3e has been amended in the new version of the manuscript.

    • In Fig3h, the authors stain cytokines in γδ T cells purified from ovarian cancer samples. The text seems to imply that the cytokine staining was performed directly ex vivo, without an in vitro stimulation of the cells, e.g. with PMA/ionomycin (if so, the description is missing). In any case, the values appear surprisingly high. Exemplary data are needed to clarify how the gating was done (for γδ T cells and the cytokines) and what the primary data looked like.

    The protocol has been amended in the “Materials and Methods” section. A gating strategy and primary data analysis from one representative patient are included in a supplementary Figure 4c.

    We agree with the reviewer’s comments that it is surprising that γδ T cell stimulation is not required for IL-8, IL10 and IFNγ production. However, one possible explanation is the high reactivity of γδ T cells compared to other T cell subsets, as well as their localization in the tumor microenvironment rather than in healthy tissue or blood.

    • In Fig3h, it is not clear what is meant with "IL-8 / IL-10", please explain.

    This analysis shows the percentage of cells that are positive for both IL-8 and IL-10.

    The figure and its legend have been amended for clarity.

    Fig.4

    • Please provide the values and the statistical analyses for all cell populations.

    We performed statistical analyses (Wilcoxon signed-rank test) for all cell populations and provide the data in the Supplementary Fig. 5A. However, due to the heterogeneity of ROIs, a significant difference was observed for tumor cells, which were more prevalent more in the neighborhood of CD73- than CD73+ γδ T cells (p

    Fig.5/6

    • In Fig5, the authors state that 8 cell populations were differentially enriched around CD73+ or -neg γδ T cells. However, in Fig4, only 4 of these populations are mentioned. Please add the remaining 4 to fig4 and name the 8 clusters in fig5 in line with the gating strategy used in fig4.

    We thank the reviewer for highlighting that the description of Figure 5 in our text was unclear. We have revised the text for clarification and specify that based on Supplementary Figure 7, which shows the number of cells for each cell type found in the neighborhood of all γδ T cell subsets (CD73- and CD73+) in all ROIs. We decided to perform phenotypic analysis on only four cell types (those with a sufficient cell counts), setting the cutoff at 700 cells.

    The four cell types are analyzed in Figures 5 and 6. Figure 5A shows tumor cells, with eight clusters identified, while Figure 5B represents fibroblasts, with seven clusters identified. Figure 6A shows CD4 T cells, with eight clusters, and Figure 6B CD8 T cells, with ten clusters.

    • Furthermore, the authors want to show in fig5 how the phenotype of these 8 cell populations differs depending on whether they are close to CD73+/- γδT cells. tSNE plots do not allow illustrating this (BTW: the plots lack the colour code). The frequencies of the cell types/phenotypes in the vicinity of CD73+/- γδ T cells need to be depicted differently (e.g. bar graphs). Furthermore, the claim that differences are observed, needs to be supported by showing the statistical values obtained. The same argument applies to Fig6 and SF8.

    We have added the code color of tSNE plots in Figures 5, 6, and SF9. The tables in Supplementary Figure 8 show the percentage of cells in each cluster within the vicinity of CD73+/- γδ T cells, allowing for an investigation of the neighborhood of each γδ T cell subset.

    • Fig6: This reviewer disagrees with the notion that the expression of HLA-DR or CD279 is enough to imply a functional state of the cell.

    As requested by the reviewer, we have amended the text to clarify that: “Cluster analysis revealed that CD4+ T cells in contact with effector γδ T cells (i.e., the CD73- subset) express HLA-DR and/or PD-1, both activation markers.”

    Supplements

    • SF2a: please check the labels; how can CD8+ CD4+ cells be labelled 'CD8 T cells' and why do the authors exclude the possibility that e.g. B cells could express HLA-DR?

    We thank the reviewer for pointing out the error in Figure 2a, which has now been corrected. The CD8+ cells have been relabeled as 'CD8 T cells,' and the B cells are now shown expressing HLA-DR.

    • SF7 is not clear to this reviewer. If the clusters represent different cell types, how can e.g. tumours be found in all of them?

    We believe the reviewer is referring to SF9 rather than SF7 in this comment. SF9 analyzes γδ T cells in proximity to CD73+ and CD73- γδ T cells. As in Figures 5 and 6, γδ T cell neighbors of CD73+ and CD73- γδ T cells were identified, and a clustering analysis revealed five distinct clusters. Tumor cells was not analyzed in this figure. We have clarified the text to prevent confusion

    • SF9b lacks a negative control and a statistical analysis, and SF9c lacks the summary data and statistical analysis.

    As requested by the reviewer, we have performed statistical analysis for SF9b and added a negative control. Additionally, we have included summary data with a statistical analysis in SF9c.

    • In the text, the authors state, "We and others reported that in ovarian tumors, IL-6 is mainly produced by CAFs and induces CD73 expression by γδ T cells (Extended Data Fig. 9 and 15)." The data in SF9b are not enough to make this claim and reference 15 is a review article that does not even mention 'IL-6'. This needs to be corrected.

    We have updated Supplementary Figure 9B to provide more robust data. We thank the reviewer for pointing out our error. The publication we intend to cite is a research article, not a review.” Hu G, Cheng P, Pan J, Wang S, Ding Q, Jiang Z, et al. An IL6-Adenosine Positive Feedback Loop between CD73+ γδ Tregs and CAFs Promotes Tumor Progression in Human Breast Cancer. Cancer Immunol Res. 2020;8:1273–86.” we made the correction in the manuscript.

    Reviewer #2 (Significance (Required)):

    In this manuscript ("Deciphering the tumor-infiltrating CD73+ regulatory γδ T cell ecosystem associated with poor survival of patients with ovarian cancer"), Chabab et al. report on the phenotype and location of CD73+ γδ T cells in ovarian cancer.

    CD73+ γδ T cells can be immunosuppressive via the production of cytokines (IL-8, IL-10) and the expression of PD-L1. Here, the authors investigated the phenotype and location of CD73+ and -neg γδ T cells in ovarian cancers with a particular focus on the cells surrounding the γδ T cells in the tumour.

    Overall, the study is informative and well-performed. However, the way some of the data are presented does not allow to fully evaluate them. Besides this, this reviewer only has some minor comments.

    To enable a full evaluation of the data, we have added new figures, amended others, and clarified certain points in the text, hoping that the reviewer will find these modifications sufficient to consider our manuscript for publication.

    Reviewer #3 (Evidence, reproducibility and clarity (Required)):

    In this article, Chabab et al. analyze sample from ovarian cancer patients, with a specific focus on gamma-delta T cells (Tγδ). The authors claim that CD73+ cells are associated with poor prognosis in ovarian cancer, and that CD73 expression is correlated with the composition and polarization of the microenvironment. Using imaging mass cytometry data, they also claim that the neighborhoods of CD73+ and CD73- Tγδ cells differs in composition.

    Major comments:

    • The prognostic value of CD73/NT5E is analyzed in TCGA-Ovarian RNAseq data. In the context of this article, it is implied that this should reflect CD73 expression by Tγδ but it is likely that other cell types are contributing to bulk CD73 expression.

    We appreciate the reviewer’s insightful comment. In fact, due to low proportion of Tγδ in TME we have stratified on NT5E total expression. We agree that this signal likely includes contributions from multiple cell types beyond γδ T cells, such as cancer-associated fibroblasts and endothelial cells, which are also known to express CD73 (NT5E gene).

    The stratification of patient based on NT5E total expression showed an association between high NT5E expression and poorer overall survival and increase in Tγδ gene markers (TRDC, TRGC1/2) and percentage of cells (Fig2E) in the patient cohort (Fig2C). To clarify this point, we have revised the Results and Discussion sections to explicitly state that the TCGA-based survival analysis reflects total intratumoral NT5E enrichment and cannot be attributed specifically to γδ T cells. We now refer to this analysis as an independent validation of the clinical relevance of CD73, while noting that its cell-type-specific contribution remains to be resolved in future studies using spatial transcriptomics or deconvolution approaches.

    • In the analysis of scRNAseq data, multiple public datasets are aggregated and the overall level of CD73 is used for stratification. Is this stratification confounded by dataset of origin?

    We thank the reviewer for raising this critical point regarding potential batch effects and dataset-driven bias in our stratification strategy. To address this, we performed additional analyses to assess whether NT5E (CD73) expression is confounded by dataset of origin.

    First, we verified that all single-cell datasets (GSE147082, GSE241221, and GSE235931) were processed using a harmonized integration workflow, including SCTransform normalization and integration using Seurat’s reciprocal PCA approach, which effectively minimizes batch-related variability.

    • The last part of the results discusses the role of IL6 produced by CAFs on Tγδ, but very little data is shown to support the proposed mechanisms. The authors report expression of CD73 by flow cytometry on blood-sorted Tγδ following culture with IL2, IL6, IL21. The data shown however only represents one donor and should therefore be repeated on multiple donors.

    We appreciate the reviewer’s insightful comment. We have added data and updated Supplementary Figure 9 to provide more robust findings. Regarding the role of IL-6, our data in ovarian cancer are consistent with the study by Hu et al. in breast cancer, which reports an IL-6-Adenosine Positive Feedback Loop between CD73+ γδ Tregs and CAFs that promotes tumor progression in human breast cancer."

    Minor comments:

    • The authors stratify their cohort by Tγδ density but I could not find the threshold used for stratification

    The threshold has been added in figure and text.

    • Labels for CD8+ and CD4+CD8+ T cells are swapped in Extended Data Fig 2A

    The correction of figure has been made.

    • The legend of graphs shown in multiple panels (for instance: Fig 3F) are not very clear: is each dot representing the average expression of one cluster in one patient?
    • In figure 3G there is no color scale, the authors need to add it with appropriate units so that readers can interpret the data shown

    These points have all been amended and corrected in the next version of the manuscript.

    Reviewer #3 (Significance (Required)):

    This paper shows interesting imaging mass cytometry data of ovarian cancer specimens. The focus on CD73 expression by Tγδ is fairly specific, although the exonucleotidases pathway involving CD73 is currently extensively studied for its immunosuppressive role.

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    Referee #3

    Evidence, reproducibility and clarity

    In this article, Chabab et al. analyze sample from ovarian cancer patients, with a specific focus on gamma-delta T cells (Tgd). The authors claim that CD73+ cells are associated with poor prognosis in ovarian cancer, and that CD73 expression is correlated with the composition and polarization of the microenvironment. Using imaging mass cytometry data, they also claim that the neighborhoods of CD73+ and CD73- Tgd cells differs in composition.

    Major comments:

    • The prognostic value of CD73/NT5E is analyzed in TCGA-Ovarian RNAseq data. In the context of this article, it is implied that this should reflect CD73 expression by Tgd but it is likely that other cell types are contributing to bulk CD73 expression.
    • In the analysis of scRNAseq data, multiple public datasets are aggregated and the overall level of CD73 is used for stratification. Is this stratification confounded by dataset of origin ?
    • The last part of the results discuss the role of IL6 produced by CAFs on Tgd, but very little data is shown to support the proposed mechanisms. The authors report expression of CD73 by flow cytometry on blood-sorted Tgd following culture with IL2, IL6, IL21. The data shown however only represents one donor and should therefore be repeated on multiple donors.

    Minor comments:

    • The authors stratify their cohort by Tgd density but I could not find the threshold used for stratification
    • Labels for CD8+ and CD4+CD8+ T cells are swapped in Extended Data Fig 2A
    • The legend of graphs shown in multiple panels (for instance : Fig 3F) are not very clear : is each dot representing the average expression of one cluster in one patient ?
    • In figure 3G there is no color scale, the authors need to add it with appropriate units so that readers can interpret the data shown

    Significance

    This paper shows interesting imaging mass cytometry data of ovarian cancer specimens. The focus on CD73 expression by Tgd is fairly specific, although the exonucleotidases pathway involving CD73 is currently extensively studied for its immunosuppressive role.

  3. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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    Referee #2

    Evidence, reproducibility and clarity

    In this manuscript ("Deciphering the tumor-infiltrating CD73+ regulatory γδ T cell ecosystem associated with poor survival of patients with ovarian cancer"), Chabab et al. report on the phenotype and location of CD73+ gd T cells in ovarian cancer. CD73+ gd T cells can be immunosuppressive via the production of cytokines (IL-8, IL-10) and the expression of PD-L1. Here, the authors investigated the phenotype and location of CD73+ and -neg gd T cells in ovarian cancers with a particular focus on the cells surrounding the gd T cells in the tumour. Overall, the study is informative and well-performed. However, the way some of the data are presented does not allow to fully evaluate them. Besides this, this reviewer only has some minor comments.

    General comments:

    • The data provided in this manuscript are descriptive/correlative, and as such, causation cannot be inferred. Therefore, the language needs to reflect this; statements like "we investigated the impact of CD73+ regulatory γδ T cells in ovarian cancer" (L89) and "CD73+ γδ T cells were in close contact with more aggressive tumor cells" (L426), among others, are incorrect without functional data. The authors are advised to adjust the text throughout.
    • Please make the figure legends self-explanatory without the need to search for the information in the M&M. For example, the graphs in fig1 and 3 contain many dots, but it is not explained what these dots represent. Please also add n for each experiment shown and state how often the experiment was performed independently.
    • It would be helpful for the reader if abbreviations introduced in the M&M were also explained the first time they appeared in the results section.
    • Please explain all abbreviations, e.g. FIGO, CST, NT5E, etc.
    • L235: typo 'that' instead of 'than'; L258 'reduced'; L259 'fig1d-f'; L451f twice 'CD73+'; 'naive' instead of 'naïve' throughout; SF2 legend: '2f' instead of '3f', SF9 legend: '1.105'.
    • L280ff: "Tumor cells ... were the most important cell type" - it may be clearer to use 'most frequent';

    M&M

    • Please be consistent, if you provide catalogue numbers or dilutions (antibody, reagents) [which is good, maybe even adding the RRID number], do so for all items.
    • The M&M does not state for the CAFs how long they were cultured before the supernatant was taken for the cytokine measurements.
    • For the IL-6 ELISA, it is stated that the "cells were harvested"; what happened to them, and how do you get any SN from these cells?

    Figures

    Fig.1:

    • The authors used the word 'predict' in the heading, which seems not appropriate for a retrospective study; something like 'correlate' seems better.
    • Similarly, the title of the figure legends claims that the 'impact of gd T cells' is shown, while only a correlation is presented.
    • For Fig1a-c, only summary data are presented. Please add exemplary pictures as well.
    • For Fig1d-f, the label for the x-axis is missing.

    Fig.2

    • It seems funny to call the patients 'naïve', maybe 'untreated' is clearer.
    • The graph in Fig2e does not allow comparing the cell frequencies properly. This would require either bar graphs or a table. Furthermore, the statistical analysis is missing. Without that, a statement like "associated with higher proportion of CAFs" (L265) is not supported.

    Fig.3

    • For Fig3b+c, the IMC are derived from 4 patients (not clear for the flow data) - did the authors noticed differences between patients?
    • For Fig3e, the description in the text does not reflect the figure, e.g. cluster 1 does not show LAG3 expression, but this is claimed in the text (other descriptions are off as well).
    • In Fig3h, the authors stain cytokines in gd T cells purified from ovarian cancer samples. The text seems to imply that the cytokine staining was performed directly ex vivo, without an in vitro stimulation of the cells, e.g. with PMA/ionomycin (if so, the description is missing). In any case, the values appear surprisingly high. Exemplary data are needed to clarify how the gating was done (for gd T cells and the cytokines) and what the primary data looked like.
    • In Fig3h, it is not clear what is meant with "IL-8 / IL-10", please explain.

    Fig.4

    • Please provide the values and the statistical analyses for all cell populations.

    Fig.5/6

    • In Fig5, the authors state that 8 cell populations were differentially enriched around CD73+ or -neg gd T cells. However, in Fig4, only 4 of these populations are mentioned. Please add the remaining 4 to fig4 and name the 8 clusters in fig5 in line with the gating strategy used in fig4.
    • Furthermore, the authors want to show in fig5 how the phenotype of these 8 cell populations differs depending on whether they are close to CD73+/- gdT cells. tSNE plots do not allow illustrating this (BTW: the plots lack the colour code). The frequencies of the cell types/phenotypes in the vicinity of CD73+/- gd T cells need to be depicted differently (e.g. bar graphs). Furthermore, the claim that differences are observed, needs to be supported by showing the statistical values obtained. The same argument applies to Fig6 and SF8.
    • Fig6: This reviewer disagrees with the notion that the expression of HLA-DR or CD279 is enough to imply a functional state of the cell.

    Supplements

    • SF2a: please check the labels; how can CD8+ CD4+ cells be labelled 'CD8 T cells' and why do the authors exclude the possibility that e.g. B cells could express HLA-DR?
    • SF7 is not clear to this reviewer. If the clusters represent different cell types, how can e.g. tumours be found in all of them?
    • SF9b lacks a negative control and a statistical analysis, and SF9c lacks the summary data and statistical analysis.
    • In the text, the authors state, "We and others reported that in ovarian tumors, IL-6 is mainly produced by CAFs and induces CD73 expression by γδ T cells (Extended Data Fig. 9 and 15)." The data in SF9b are not enough to make this claim and reference 15 is a review article that does not even mention 'IL-6'. This needs to be corrected.

    Significance

    In this manuscript ("Deciphering the tumor-infiltrating CD73+ regulatory γδ T cell ecosystem associated with poor survival of patients with ovarian cancer"), Chabab et al. report on the phenotype and location of CD73+ gd T cells in ovarian cancer. CD73+ gd T cells can be immunosuppressive via the production of cytokines (IL-8, IL-10) and the expression of PD-L1. Here, the authors investigated the phenotype and location of CD73+ and -neg gd T cells in ovarian cancers with a particular focus on the cells surrounding the gd T cells in the tumour. Overall, the study is informative and well-performed. However, the way some of the data are presented does not allow to fully evaluate them. Besides this, this reviewer only has some minor comments.

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    Referee #1

    Evidence, reproducibility and clarity

    The authors provide a detailed characterizsation of the tumor microenvironment (TME) of 91 ovarian cancer patients, brokend down in long and short term survivors (post 5 years). The focus on the role of a subgroup of T cells, gamma/delta (gd) T cells with reported anti but also pro tumorigenic properties, Prior work of the lab has established a link between a subgroup of gd T cells expressing CD73 and poor prognosis, due to the ability of these cells to produce immunosuppressive cytokines, such as IL10 or IL8 and the production of adenosine, by CD73, in the micromilieu. The data is further backed up by the analysis of fresh tumor specimens and tissue culture work.

    Here they continue this story by investigating the TME using tumor microarrays (91 samples), single cell RNA seq (12 patients), imaging mass cytometry (> 30 samples) and flow cytometry (form confirmatory purposes) to define cellular neighborhoods of CD73+ and CD73- gd T cells. THis revealed differences in cellular composition and spatial transcriptome analysis further helped to define the ttranscriptomes in gd T cells, cancer cells and cancer associated fibroblasts.

    The authors conclude the in ovarian cancer gd T cells expressing CD73 dampen anti-tumor immun ity and propose detection and evaluation of CD73+ gd T cells as prognostic marker.

    The manuscript is well written, and despite its descriptive nature, easy to follow. Data is presented in a clear and easy to read fashion.

    Significance

    Using a well characterized cohort of ovarian cancer patients with detailed clinical follow up the authors report on the predictive power of a subset of gd T cells expressing CD73, with immune suppressive / regulatory capacity, reading out patient survival in high grade serous ovarian cancer, a still deadly disease. As such the identificaiton of reliable markers predicting survival is a clear medical need. These findings contrast others made in different solid cancers, suggesting tumor typ specific differences, which are only starting to emerge, but are of clear clinical relevance.

    What is unclear to me and needs to be addressed, is if these patient specimens were taken before or after initial therapy, whether the samples have been stratified according the treatment that they got, assuming it will be mostly platinum compounds (but maybe not), and that the p53 status of the tumors are (if genetics are available this would help to add some granularity to the study that, as as it stands is largely descriptive, even though with extremely high resolution. This data should be available and could be integrated.

    Some minor issues

    • I would stick to CD73, and not mix it with NT5E, which is confusing at first (Fig 2).
    • I would ask to compare the overall survival of CD73+ between densities - is it still significantly different in fig 1 - meaning is it about density, CD73 expression, or both. Comparing survival of tumors with a low density of CD73+ gd T cells does not seem to be different from those having a low density of CD73- gd T cells, which could be considered in data interpretation. Same for high density tumors.
    • figure 1, caption should include the word "patients" at the end, I guess.
    • labelling and font can be improved in many panels, eg. the dot plots in Fig 2, panel B, right, same for panel C and D
  5. Version published to 10.1101/2024.12.09.627460 on bioRxiv