Predicting developmental relationships of tumor resident and circulating T cells in ovarian cancer

  1. Mayra S. Carneiro
  2. Yacine Bareche
  3. Cheng Zhao
  4. Pamela Thébault
  5. Kurosh Rahimi
  6. Diane Provencher
  7. Vanessa Samouélian
  8. Béatrice Cormier
  9. Jean-François Cailhier
  10. Anne-Marie Mes-Masson
  11. Sophie Petropoulos
  12. John Stagg
  13. Réjean Lapointe

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Abstract

Characterizing T cell populations and understanding their developmental relationships may help design more effective cancer immunotherapies. We coupled single-cell transcriptomics and T cell receptor (TCR) αβ profiling of intratumoral and peripheral T cells in ovarian cancer patients to identify transcriptional programs and infer their relationship by trajectory and TCR overlap analyses. We proposed a model of differentiation pathway from an intermediate GZMH-expressing CD8 T cell subset found in the blood and tumor that progressively reinforces the exhaustion and tissue residency programs from a CCL4 -expressing cluster towards XCL1 - and CXCL13 -expressing terminally exhausted cells. Inferred cell communication analysis suggests that interaction with CXCL13 -expressing CD4 T cells, which we refer to as Tfh-like cells, sustains the effector function of this intermediate GZMH-expressing CD8 T cell subset. Moreover, our results suggest that Tfh-like cells attract cells expressing GPR183 through the production of its ligand 7α,25 dihydroxycholesterol (7α,25-HC). Finally, we demonstrated that GPR183 is highly expressed in a subset of pre-effector GZMK -expressing CD8 T cells and plasmacytoid dendritic cells. Collectively, our results suggest that Tfh-like cells expressing IL-21 help promote antitumor immunity against ovarian tumors by coordinating the action of immune cells responsive to 7α,25-HC.

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    Reply to the reviewers

    Reviewer #1*. This is a good paper dealing with gap of our knowledge in understanding reason of ICB failures. Subject being difficult it is expected that the design and content of such experiment will be complex.But the authors forget practicality of readers attention and making paper apear interesting. They need to organise and may be classify the varied information in such a way that reader can find a rhythm in excavating data more easily. It appears confusing at time, so they may try to make it more simple. In this way they may concentrate more on methods and classify results too. A thorough revision is suggested, to make it consize. *

    __Authors’ answer: __We thank the Reviewer for his positive evaluation and constructive feedback. We appreciate the complexity of single-cell RNA-sequencing analyses. In order to simplify our manuscript, our revised manuscript now focuses on the transitional states of tumor-resident and circulating T cells found in ovarian cancer patients. Our study is timely as it is the first to report the developmental relationship of TILs in ovarian cancer. We substantially edited our manuscript to make it clear that our findings suggest a gradual acquisition of the exhaustion program initiated by effector-like cells (cluster CD8_GZMH) that eventually gives rise to more terminal states with features of tissue residency and chemotaxis (clusters CD8_CCL4, CD8_XCL1, and CD8_CXCL13). We also include new analyses revealing the presence and proportion of these T cell states in different cancer patients (New Fig. 4A-B), and how these T cell states associate with clinical responses to immune checkpoint blockade (ICB). We hope the Reviewer will find our revised manuscript easier to read.

    Reviewer #2. I think the first half of the article, in which the GZMH-CD8 cluster is considered to be in an intermediate state of transition to exhaustion, is interesting, and I feel that the single-cell seq and TCR data are well analyzed to make the point. On the other hand, I feel that the latter part of the paper may not be anything more than a hypothesis. In particular, the part claiming that it is related to prognosis or applicable to the prediction of the effect of ICB is insufficient, since their gene signature is not described in detail and the contents of the Figure are not mentioned in the manuscript. In the latter part, the effects of GPR184 and 25-HC, or the effects of IL21, would require experiments to verify (to verify whether the addition of chemokine or the inhibition of the receptor changes the specific CD8 population).

    Author’s answer: Thank you for discussing the limitation of the signature employed. We agree with the reviewer’s comment. Old Figure 5 has been removed from the revised manuscript.

    Reviewer #2. Minor point: In particular, there is little mention of Figure 5 in the text, making it difficult to understand.

    Author’s answer: Thank you for your comment. As we previously discussed, we have removed Figure 5 from the revised manuscript. The method used to generate the signature was found to be inappropriate.

    Reviewer #2. The latter part is difficult to understand. To begin with, it is already known that ovarian cancer does not contribute much to ICB, so what does it mean to analyze the CD8 population, which is known as a marker of ICB response in other carcinomas, as an indicator? Especially for clinicians like us, it is hard to imagine that the results will lead to clinical trials that will attempt to sort out the population that ICB is favored in.

    Author’s answer: Although immune checkpoint blockade has demonstrated limited effectiveness against ovarian cancer, subset analyses suggest superior efficacy for some patients and according to subtype. Combination anti-PD-1/CTLA-4 therapy for instance achieved response rates up to 31% (Zamarin et al., 2020), and superior benefit for single agent PD-1 blockade has been reported in clear cell ovarian cancer. Moreover, encouraging clinical results have recently been reported in studies exploring combinations with PARP and VEGF inhibitors. As example, interim analysis of the phase 3 DUO-O trial (NCT03737643) showed a statistically significant and clinically meaningful improvement in PFS in patients with newly diagnosed advanced ovarian cancer without a BRCA1/2 mutation (Harter et al., 2023).

    Our study aimed to better understand how ovarian tumor-infiltrating T cells acquire their exhaustion program after migrating from the periphery and whether these mechanisms are unique or shared amongst cancer types. Recent studies in other cancer types had shown the dynamics of T cells and demonstrated the clonal replacement of intratumoral T cells after ICB and emphasized the role of peripheral clones in this process (Wu et al., 2020; Yost et al., 2019). In lung cancer, it has been proposed a transitional state between precursor and terminally differentially cells (Gueguen et al., 2021). Our study demonstrates, for the first time in ovarian cancer, the presence of similar transitional states of CD8 T cells. Our revised manuscript also now includes new data revealing that pre-effector *GZMK- *and intermediary GZMH-expressing CD8 cells are better biomarkers of ICB response than terminally differentiated *XCL1 *and *CXCL13 *expressing CD8 T cells (New Figure 4). Altogether, our study provides important and novel insights on the development of tumor-infiltrating T cells in ovarian cancer patients, which may serve to better select ovarian cancer patients for ICB therapy.

    Reviewer #2*. *Since the first half of the study is very interesting, we feel that it is more important to confirm the mechanism of exhaustion from the blood via the intermediate (GZMH_CD8), including functional experiments. Also, as a clinician, we are very interested in the perspective of whether some of the fractions identified in this study are different in proportion in different patients and whether they correlate with the clinical course of the disease since the study only analyzed a sample of 5 patients.

    Author’s answer: We thank the reviewer for proposing to extend our analysis. As suggested, our revised manuscript now includes new analyses which reveals the different proportions of our identified T cells states in different cancer patients (New Figure 4). We further investigated whether these T cell states associate with clinical responses and observed that pre-effector *GZMK- *and intermediary GZMH-expressing CD8 T cells are better biomarkers of ICB response than terminally exhausted *XCL1- *and CXCL13-expressing CD8 T cells (New Figure 4).

    Reviewer #3. Question 1:* Whether the distribution patterns of CD4+ and CD8+ T cell clusters in Figure 1B were comparable among the 5 patient samples? Whether the proportion of five types of clones in Figure 3C are comparable among the 5 patient samples?*

    Author’s answer: Thank you for the question. We included the results to answer these questions in the supplementary material (fig. S1C-D). For each patient, we calculated the proportion of a cluster among T cells in the blood or tumor. As observed in the boxplot (fig. S1C), the proportion of some subsets were higher in certain patients, such as the higher proportion of CD8_GZMK in the tumor of patient p09454. A recent study classified patients’ tumors based on the spatial distribution of CD8 T cells and performed scRNA-seq to identified cell subsets enriched in the groups inflamed/infiltrated (characterized by the distribution of CD8 T cells within the tumor epithelium), excluded (infiltrating CD8 T cells are restricted to the tumor stroma) or desert (T cells are not present or have low frequency) (Hornburg et al., 2021). Interestingly, this subset of CD8_GZMK cells were enriched in desert tumors, suggesting that the difference we observed in our dataset might reflect the spatial distribution of CD8 T cells in patient p09454. Regarding the TCR-seq data, the frequency of the five types of clones was different among patients. To show this data, we included a barplot (fig. S2D), showing for example, a higher proportion of tumor-expanded clones in patient p10329.

    Reviewer #3. Question 2: In Figure S2C, only a very small number of cells in the CD8-GZMK K-22 population. Are these cells representative? Do they generally exist in multiple samples or only in one sample?

    Author’s answer: Thank you for your comment. The subcluster k_22 indeed has a smaller number of cells compared to other subclusters. Nevertheless, the K_22 cluster was found in every patient and in every healthy donor. To clarify, we edited our revised manuscript to include a statement that cluster k_22 was composed of fewer cells compared to other clusters.

    Reviewer #3. Question 3:* In the Fig.S6 legend, the authors stated "Our results suggest the differentiation of cluster CD8-GZMK into the effector-like subset CD8-GZMH." However, there seems to be no corresponding analysis in the main text to support this conclusion.*

    Author’s answer: We appreciate your attention to this statement. We agree the results of our study doesn’t sustain this statement and so we have excluded it in the revised manuscript.

    Reviewer #3____.* Question 4: Is there more detailed clinical information that can be provided for the 5 patients included in the study? Per the methods all patients were receiving debulking surgery and were treatment naïve, but did they differ in stage, age, comorbidities, etc.?*

    Author’s answer: Thank you for your comment on this. We have included a table with clinical information on the stage, age, and menopause status of the five patients.

    Reviewer #3. Question 5:* Were any cells included for sequencing from adjacent 'normal' tissue uninvolved with tumor (these samples are from surgical debulking of primary tumors, which may include such areas of non-involved tissue.) While shared TCR clonotypes between blood and intratumoral T cells strongly suggests the tumor-resident populations are recruited from the blood, the degree of sharing with normal tissue-resident T cells would be of interest as well.*

    Author’s answer: Thank you for your comment. Samples were provided for sc-RNA-seq after pathology review and validation of tumor histology. We did not perform sc-RNA-seq on normal adjacent tissue (NAT) We agree this would be interesting as a follow up study, since in other cancer types (renal, colon and lung) it has been demonstrated that T clones expanded in the tumor and NAT are also present in peripheral blood (Wu et al., 2020).

    Reviewer #3. Question 6:* Very little is discussed about HGSOC itself in the main text (eg clinical background, prior literature on the composition of infiltrating immune populations and potential reasons for at best modest poor responses to IO) until the first sentence of the discussion. As the entirety of the new data produced in this study is from HGSOC tumors there should be more focus on this tumor type and conversation with the prior literature on it (mainly from prior studies on the immune environment of HGSOC). Further, how distinct do the authors suspect the cell populations found in their study to be to ovarian as opposed to other epithelial tumor types?*

    Author’s answer: Thank you for the suggestion. We now included more background information on immunotherapy of HGSOC. Specifically, we added the following paragraph in our introduction: “In ovarian cancer, the presence of both T and B cells improves patients' survival (Nelson, 2015; Nielsen et al, 2012). They are usually organized in lymphoid aggregates ranging from a small group of cells to a well-organized TLS (Kroeger et al, 2016). Organized TLSs correlate with better survival, such as observed in patients treated with ICB. Although immunotherapy has demonstrated limited effectiveness against ovarian cancer, subsets of patients may thus benefit from ICB. In support of this, combination anti-PD-1/CTLA-4 therapy can achieve response rates above 30% (Zamarin et al., 2020), and encouraging clinical results have recently been reported when combining ICB with with PARP and VEGF inhibitors (Harter et al., 2023)”.

    Reviewer #3. Question 7:* Were the signature genes used for analysis in figure 5 remove chosen in a formal, unbiased manner, or simply hand-picked as representative of the respective cell types? This information is not provided in the supplement.*

    Author’s answer: Another reviewer has also expressed similar concerns. The genes selected to represent cell types were chosen manually, which we acknowledge is not the best method for defining a signature. As a result, we have decided to exclude Figure 5 from the manuscript under review. We believe an unbiased approach is more suitable for characterizing the cell network proposed in our study.

    Reviewer #3. Question 8:* While the NicheNet analysis of potential interactions among lymphocyte populations raises some strong hypotheses, it would be interesting to extend the interaction analysis to all CD45+ populations, given the sequencing was done on CD45+ immune cells.*

    Author’s answer: Thank you for suggesting analysis. We have included the results of cell interaction including all CD45+ cells (fig. S3). We observed CD40L as one of the top predicted ligands highly expressed in CD4_CXCL13 subset mediating a response in subsets of antigen-presenting cells, such as B cells (cluster B), plasma cells (cluster PC_2), and plasmacytoid dendritic cells (cluster pDC). Interestingly, this result also support the hypothesis of Tfh-like cells (cluster CD4_CXCL13) coordinating the action of intratumoral immune cells involved in the antitumor immune response.

    Reviewer #3. Question 9:* A sample size of 5 patients is relatively small for current single cell RNAseq studies of human tumor patients.*

    Author’s answer: We agree with the reviewer that a sample size of 5 patients is relatively small. Thus, to validate our results in other patients, we included in the reviewed manuscript the analysis of scRNA-seq of 47 patients across10 cancer types (dataset from (Zheng et al., 2021). As demonstrated in figure 3 and figure 5, we could identify subsets of CD8 and CD4 T cells from our ovarian cancer patients in those 10 cancer types dataset.

    Reviewer #3.____* Minor*

    *1. In lines 96-97, "CD8-GZMB" was mentioned twice in the description. *

    2. In line 126, this section did not discuss residency markers, yet a conclusion about residency was made in this sentence.

    Author’s answer: We appreciate you bringing these errors to our attention. We fixed them in the updated version of the manuscript.

    References:

    Gueguen, P., Metoikidou, C., Dupic, T., Lawand, M., Goudot, C., Baulande, S., … Amigorena, S. (2021). Contribution of resident and circulating precursors to tumor-infiltrating CD8 T cell populations in lung cancer. Science Immunology, Vol. 6, p. eabd5778. doi:10.1126/sciimmunol.abd5778

    Harter, P., Trillsch, F., Okamoto, A., Reuss, A., Kim, J.-W., Rubio-Pérez, M. J., … Aghajanian, C. (2023). Durvalumab with paclitaxel/carboplatin (PC) and bevacizumab (bev), followed by maintenance durvalumab, bev, and olaparib in patients (pts) with newly diagnosed advanced ovarian cancer (AOC) without a tumor BRCA1/2 mutation (non-tBRCAm): Results from the randomized, placebo (pbo)-controlled phase III DUO-O trial. Journal of Clinical Orthodontics: JCO, 41(17_suppl), LBA5506–LBA5506.

    Hornburg, M., Desbois, M., Lu, S., Guan, Y., Lo, A. A., Kaufman, S., … Wang, Y. (2021). Single-cell dissection of cellular components and interactions shaping the tumor immune phenotypes in ovarian cancer. Cancer Cell. doi:10.1016/j.ccell.2021.04.004

    Wu, T. D., Madireddi, S., de Almeida, P. E., Banchereau, R., Chen, Y.-J. J., Chitre, A. S., … Grogan, J. L. (2020). Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature. doi:10.1038/s41586-020-2056-8

    Yost, K. E., Satpathy, A. T., Wells, D. K., Qi, Y., Wang, C., Kageyama, R., … Chang, H. Y. (2019). Clonal replacement of tumor-specific T cells following PD-1 blockade. Nature Medicine. doi:10.1038/s41591-019-0522-3

    Zamarin, D., Burger, R. A., Sill, M. W., Powell, D. J., Jr, Lankes, H. A., Feldman, M. D., … Aghajanian, C. (2020). Randomized Phase II Trial of Nivolumab Versus Nivolumab and Ipilimumab for Recurrent or Persistent Ovarian Cancer: An NRG Oncology Study. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 38(16), 1814–1823.

    Zheng, L., Qin, S., Si, W., Wang, A., Xing, B., Gao, R., … Zhang, Z. (2021). Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science, 374(6574), abe6474.

  2. Review Commons

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

    Learn more at Review Commons

    Referee #3

    Evidence, reproducibility and clarity

    Below are some questions as well as suggestions for revision and to strengthen the manuscript.

    Major:

    1. Whether the distribution patterns of CD4+ and CD8+ T cell clusters in Figure 1B were comparable among the 5 patient samples? Whether the proportion of five types of clones in Figure 3C are comparable among the 5 patient samples?
    2. In Figure S2C, only a very small number of cells in the CD8-GZMK K-22 population. Are these cells representative? Do they generally exist in multiple samples or only in one sample?
    3. In the Fig.S6 legend, the authors stated "Our results suggest the differentiation of cluster CD8-GZMK into the effector-like subset CD8-GZMH." However, there seems to be no corresponding analysis in the main text to support this conclusion.
    4. Is there more detailed clinical information that can be provided for the 5 patients included in the study? Per the methods all patients were receiving debulking surgery and were treatment naïve, but did they differ in stage, age, comorbidities, etc.?
    5. Were any cells included for sequencing from adjacent 'normal' tissue uninvolved with tumor (these samples are from surgical debulking of primary tumors, which may include such areas of non-involved tissue.) While shared TCR clonotypes between blood and intratumoral T cells strongly suggests the tumor-resident populations are recruited from the blood, the degree of sharing with normal tissue-resident T cells would be of interest as well.
    6. Very little is discussed about HGSOC itself in the main text (eg clinical background, prior literature on the composition of infiltrating immune populations and potential reasons for at best modest poor responses to IO) until the first sentence of the discussion. As the entirety of the new data produced in this study is from HGSOC tumors there should be more focus on this tumor type and conversation with the prior literature on it (mainly from prior studies on the immune environment of HGSOC). Further, how distinct do the authors suspect the cell populations found in their study to be to ovarian as opposed to other epithelial tumor types?
    7. Were the signature genes used for analysis in figure 5 chosen in a formal, unbiased manner, or simply hand-picked as representative of the respective cell types? This information is not provided in the supplement.
    8. While the NicheNet analysis of potential interactions among lymphocyte populations raises some strong hypotheses, it would be interesting to extend the interaction analysis to all CD45+ populations, given the sequencing was done on CD45+ immune cells.
    9. A sample size of 5 patients is relatively small for current single cell RNAseq studies of human tumor patients.

    Minor

    1. In lines 96-97, "CD8-GZMB" was mentioned twice in the description.
    2. In line 126, this section did not discuss residency markers, yet a conclusion about residency was made in this sentence.

    Significance

    In this manuscript titled "Predicting Developmental Relationships of Tumor-Resident and Circulating T Cells in Ovarian Cancer," Carneiro and colleagues employed single-cell transcriptomics and T cell receptor profiling of immune cells sorted from paired peripheral blood and tumor tissue in a small cohort of ovarian cancer patients to investigate the developmental relationships of T cell populations and their potential interactions. They identified a possible differentiation pathway involving GZMH-expressing CD8+ T cells that progresses towards tissue residency and exhaustion. The researchers suggested the effector function of intermediate GZMH-expressing CD8+ T cells could be sustained through interaction with CXCL13-expressing CD4+ Tfh-like cells. Moreover, they proposed that CD4+ Tfh-like cells could attract GPR183-expressing pre-effector GZMK-expressing CD8+ T cells and plasmacytoid dendritic cells via the production of 7α,25 dihydroxycholesterol (7α,25-HC). Ultimately, the study hypothesized that CD4+ Tfh-like cells expressing IL-21 among other molecules might enhance antitumor immunity against ovarian tumors by coordinating the actions of multiple immune populations. Strengths of the study include detailed, combined analysis of inferred developmental trajectories via shared TCR clonotypes across tissue as well as potential crosstalk between cellular populations, as well as association of signature genes with clinical outcomes. Weaknesses include a small number of patients and the dataset being limited only to single cell RNAseq and thus providing descriptive findings without functional validation or perturbation.

  3. Review Commons

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    Referee #2

    Evidence, reproducibility and clarity

    Summary: This study used single-cell transcriptomics and T cell receptor profiling to identify the developmental relationships of T cell populations in ovarian cancer patients. The researchers proposed a model of differentiation pathway that showed how an intermediate GZMH-expressing CD8 T cell subset progressively reinforces exhaustion and tissue residency programs towards terminally exhausted cells. Then they also focus on the nature of TPEX, dual-expanded clone, which is considered an important indicator for the efficacy of ICB, and argue that it is strongly related to GPR183, 25-OHC, and IL21. Based on the analysis of clinical samples, they argue that their proposed gene signature may also be prognostically relevant and predictive of ICB efficacy.

    Major comment: I think the first half of the article, in which the GZMH-CD8 cluster is considered to be in an intermediate state of transition to exhaustion, is interesting, and I feel that the single-cell seq and TCR data are well analyzed to make the point. On the other hand, I feel that the latter part of the paper may not be anything more than a hypothesis. In particular, the part claiming that it is related to prognosis or applicable to the prediction of the effect of ICB is insufficient, since their gene signature is not described in detail and the contents of the Figure are not mentioned in the manuscript. In the latter part, the effects of GPR184 and 25-HC, or the effects of IL21, would require experiments to verify (to verify whether the addition of chemokine or the inhibition of the receptor changes the specific CD8 population).

    Minor point: In particular, there is little mention of Figure 5 in the text, making it difficult to understand.

    Significance

    It is interesting to note that the authors simultaneously analyze immune cells in the blood and in the tumor, and examine in detail what is characteristic of the blood, what is characteristic of the tumor, and what is seen in both. And it is very interesting that they specifically proposes an intermediate group that is recruited from the blood to the tumor and is in the process of becoming exhausted. I am sure there are many studies on TILs and TLSs, but this study would be helpful to understand how they are concentrated locally (near the tumor) in comparison with immune cells in the blood as well.

    However, the latter part is difficult to understand. To begin with, it is already known that ovarian cancer does not contribute much to ICB, so what does it mean to analyze the CD8 population, which is known as a marker of ICB response in other carcinomas, as an indicator? Especially for clinicians like us, it is hard to imagine that the results will lead to clinical trials that will attempt to sort out the population that ICB is favored in.

    Since the first half of the study is very interesting, we feel that it is more important to confirm the mechanism of exhaustion from the blood via the intermediate state, including functional experiments. Also, as a clinician, we are very interested in the perspective of whether some of the fractions identified in this study are different in proportion in different patients and whether they correlate with the clinical course of the disease, since the study only analyzed a sample of 5 patients.

  4. Review Commons

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

    Learn more at Review Commons

    Referee #1

    Evidence, reproducibility and clarity

    This is a good paper dealing with gap of our knowledge in understanding reason of ICB failures. Subject being difficult it is expected that the design and content of such experiment will be complex.But the authors forget practicality of readers attention and making paper apear interesting.

    They need to organise and may be classify the varied information in such a way that reader can find a rhythm in excavating data more easily.

    It appears confusing at time, so they may try to make it more simple.

    In this way they may concentrate more on methods and classify results too.

    A thorough revision is suggested, to make it consize.

    Significance

    This is a good paper dealing with gap of our knowledge in understanding reason of ICB failures. Subject being difficult it is expected that the design and content of such experiment will be complex.But the authors forget practicality of readers attention and making paper apear interesting.

    They need to organise and may be classify the varied information in such a way that reader can find a rhythm in excavating data more easily.

    It appears confusing at time, so they may try to make it more simple.

    In this way they may concentrate more on methods and classify results too.

    A thorough revision is suggested, to make it consize.

  5. Version published to 10.1101/2023.02.28.530488v1 on bioRxiv