Author response:
The following is the authors’ response to the original reviews.
Reviewer #1 (Public Review):
Summary:
Sha K et al aimed at identifying the mechanism of response and resistance to castration in the Pten knockout GEM model. They found elevated levels of TNF overexpressed in castrated tumors associated with an expansion of basal-like stem cells during recurrence, which they show occurring in prostate cancer cells in culture upon enzalutamide treatment. Further, the authors carry on a timed dependent analysis of the role of TNF in regression and recurrence to show that TNF regulates both processes. Similarly, CCL2, which the authors had proposed as a chemokine secreted upon TNF induction following enzalutamide treatment, is also shown to be elevated during recurrence and associated with the remodeling of an immunosuppressive microenvironment through depletion of T cells and recruitment of TAMs.
Strengths:
The paper exploits a well-established GEM model to interrogate mechanisms of response to standard-of-care treatment. This is of utmost importance since prostate cancer recurrence after ADT or ARSi marks the onset of an incurable disease stage for which limited treatments exist. The work is relevant in the confirmation that recurrent prostate cancer is mostly an immunologically "cold" tumor with an immunosuppressive immune microenvironment
Weaknesses:
While the data is consistent and the conclusions are mostly supported and justified, the findings overall are incremental and of limited novelty. The role of TNF and NF-kB signaling in tumor progression and the role of the CCL2-CCR2 in shaping the immunosuppressive microenvironment are well established.
We contend there is novelty in: the experimental design; our finding of a TNF signaling ‘switch’ and the role of androgen-deprivation induced immunosuppression.
On the other hand, it is unclear why the authors decided to focus on the basal compartment when there is a wealth of literature suggesting that luminal cells are if not exclusively, surely one of the cells of origin of prostate cancer and responsible for recurrence upon antiandrogen treatment. As a result, most of the later shown data has to be taken with caution as it is not known if the same phenomena occur in the luminal compartment.
While we appreciate the reviewer’s interest in the cancer stem cell biology occurring in the tumor in response to androgen deprivation, our focus in this report is identifying mechanisms that account for a switch in TNF signaling. Specifically, our previous studies showed a rapid increase in TNF mRNA following castration (in the normal murine prostate) but in the current report we also observe an increase in TNF at late times post-castration (in a murine prostate cancer model). We propose that the increase in TNF at late times is due to plasticity (increased stemness) in the tumor cell population, rather than - for example - a change in signal-driven TNF mRNA transcription. While a possible mechanism is expansion of a recurrent tumor stem-cell population, a careful investigation is beyond the scope of this report. Therefore, in the revised manuscript, we have altered the text in multiple places to indicate a suggestive, rather than definitive, role for tumor stem cells. Indeed, we did include caveats regarding the role of tumor stem cells in the original discussion (lines 425-429 in the revised manuscript), and this is now made more explicit in the revised manuscript.
Reviewer #2 (Public Review):
Summary:
In this study, Sha and Zhang et al. reported that androgen deprivation therapy (ADT) induces a switch to a basal-stemness status, driven by the TNF-CCL2-CCR2 axis. Their results also reveal that enhanced CCL2 coincides with increased macrophages and decreased CD8 T cells, suggesting that ADT resistance may be related to the TNF/CCL2/CCR2-dependent immunosuppressive tumor microenvironment (TME). Overall, this is a very interesting study with a significant amount of data.
Strengths:
The strengths of the study include various clinically relevant models, cutting-edge technology (such as single-cell RNA-seq), translational potential (TNF and CCR2 inhibitors), and novel insights connecting stemness lineage switch to an immunosuppressive TME. Thus, I believe this work would be of significant interest to the field of prostate cancer and journal readership.
Weaknesses:
(1) One of the key conclusions/findings of this study is the ADT-induced basal-stemness lineage switch driving ADT resistance. However, most of the presented evidence supporting this conclusion only selects a couple of marker genes. What exacerbates this issue is that different basal-stemness markers were often selected with different results. For example, Figure S1A uses CD166/EZH2 as markers, while Figure S1B uses ITGb1/EZH2. In contrast, Figure 1D uses Sca1/CD49, and Figure 2B-C uses CD49/CD166. Since many basal-stemness lineage gene signatures have been previously established, the study should examine various basal-stemness gene signatures rather than a couple of selected markers. Moreover, why were none of the stemness/basal-gene signatures significantly changed in the GO enrichment analysis in Figure 6A/B?
Mice and human cells express similar but also partially distinct prostate stem cell markers. For example, Sca1 is predominantly used as a stem cell marker in mice but not in human prostate epithelial cells. CD166 and CD49f are expressed in both human and murine prostate epithelium and therefore we used these in both sets of studies. Also see the response to R1-2.
(2) A related weakness is the lack of functional results supporting the stemness lineage switch. Although the authors present colony formation assay results, these could be influenced simply by promoted cell proliferation, which is not a convincing indicator of stemness. To support this key conclusion, widely accepted stemness assays, such as the prostasphere formation assay (in vitro) and Extreme Limiting Dilution Analysis (ELDA) xenograft assay (in vivo), should be carried out.
See the response to R1-2 and R2-1, above.
(3) Another significant concern is that this study uses concurrency to demonstrate a causal relationship in many key results, which is entirely different. For example, Figure S4A and S4B only show increased CCL2 and TNF secretion simultaneously, which cannot support that CCL2 is dependent on TNF. Similarly, Figure 5A only shows that CCL2 increased coincidently with a rise in TNF, which cannot support a causal relationship. To support the causal relationship of this conclusion, it is necessary to show that TNF-KO/KD would abolish the increased CCL2 secretion.
Regarding Fig. S4A and S4B: We previously demonstrated (Sha et al, 2015; reference 10) that CCL2 secretion is dependent on TNF, in the same cell lines. We have added additional data (new Fig. S4B) in this report to confirm this dependency.
Regarding Fig 5: In Fig 5B we demonstrated that the increase in CCL2-staining cells in recurrent tumors from castrated animals (the equivalent of human CRPC in our model) was significantly inhibited in animals receiving etanercept, demonstrating TNF dependency for CCL2 in this context.
While the use of TNF KO cell lines and animals could provide additional insights, the creation of such cell lines and tumor models is arduous. Moreover, we previously demonstrated that administration of anti-TNF drugs such as etanercept are as effective as the KO phenotypes (Davis et al 2011; ref. 11).
(4) Some of the selective data presentations are not explained and are difficult to understand. For example, why does CD49 staining in Figure S3A have data for all four time points, while CD166 in Figure S3D only has data for the last time point (day 21)? Similarly, although several TNF_UP gene signatures were highlighted in Figure 4B, several TNF_DN signatures were also enriched in the same table, such as RUAN_RESPONSE_TO_TNF_DN. What is the explanation for these contrasting results?
Regarding Fig. S3A and S3D: The cell-staining studies in Fig. S3 are confirmatory of the FACS studies in Figs. 2 and 3. We were not able to stain all of the CD166 time-points for technical reasons (difficulty optimizing the automated staining protocol) but we were able to successfully stain key late time-points, so we have included this data in the supplementary figure. There was no attempt to selectively present data; this was just a practical limitation of the time and funds that we could devote to confirmatory studies.
Regarding Fig 4B: The highlighting identifies a common (i.e., identical) group of gene sets in the two GSEA analyses, demonstrating that these very same gene sets are all up-regulated in one instance, and down-regulated in the other. The ‘TNF DN’ genes were not identical in the two GSEA analyses and so we cannot draw any conclusions about these. Note that we are scoring the TNF-related genes sets with the 10 largest (positive or negative) normalized enrichment scores (NES), and are not relying on DN or UP designations in the gene set name (identifier). In this analysis up- and down-regulation refers to the sign and magnitude of the NES, not the gene set names.
Reviewer #3 (Public Review):
Summary:
The current manuscript evaluates the role of TNF in promoting AR targeted therapy regression and subsequent resistance through CCL2 and TAMs. The current evidence supports a correlative role for TNF in promoting cancer cell progression following AR inhibition. Weaknesses include a lack of descriptive methodology of the pre-clinical GEM model experiments and it is not well defined which cell types are impacted in this pre-clinical model which will be quite heterogenous with regards to cancer, normal, and microenvironment cells.
Strengths:
(1) Appropriate use of pre-clinical models and GEM models to address the scientific questions.
(2) Novel finding of TNF and interplay of TAMs in promoting cancer cell progression following AR inhibition.
(3) Potential for developing novel therapeutic strategies to overcome resistance to AR blockade.
Weaknesses:
(1) There is a lack of description regarding the GEM model experiments - the age at which mice experiments are started.
Table S1 in the supplementary data summarizes the salient characteristics of the GEM models. Note that as described in the M&M, we selected animals for experimental groups based on the tumor volume (determined by HFUS) and not based on the age of the mouse, since there is some variability in the kinetics of tumor growth in genetically identical mice, as shown by our HFUS observations of hundreds of mice harboring the genetic changes (PTEN loss, MYC gain) in the models we have studied most extensively. Although admittedly an imperfect criteria, we reasoned that tumor volume would be the best surrogate criteria for tumor biology.
(2) Tumor volume measurements are provided but in this context, there is no discussion on how the mixed cancer and normal epithelial and microenvironment is impacted by AR therapy which could lead to the subtle changes in tumor volume.
The reviewer’s criticism is well-founded - most of our studies involved bulk analysis, which makes it difficult to probe the cellular interactions within the TME. Future studies - beyond the scope of this report - using single cell technical approaches - are needed to investigate these subtle changes. We have added a statement to this effect to the manuscript (lines 464-468).
(3) There are no readouts for target inhibition across the therapeutic pre-clinical trials or dosing time courses.
The reviewer’s criticism is well-founded, since we cannot be 100% certain of drug delivery in the TNF and CCL2 blockade experiments. Two points in this regard. First, with the assistance of institutional veterinarian staff, we have had good success in training multiple scientists (PhD student, technicians) to deliver both biological and small molecule drugs i.p. Second, the observation that the drugs did ‘work’ in most animals in well-defined experimental protocols strongly suggests that the delivery methodology is reliable. If sporadic delivery failures do occur, this would tend to underestimate the magnitude of the ‘positive’ (i.e., blocking) effects rather than leading to false negatives.
(4) The terminology of regression and resistance appears arbitrary. The data seems to demonstrate a persistence of significant disease that progresses, rather than a robust response with minimal residual disease that recurs within the primary tumor.
We explain our rationale for the criteria defining regression and recurrence in the M&M and in the legend to Table S2. In the revised version of the manuscript, we now explicitly reference these descriptions in the relevant RESULTS section (lines 222-223). Note that we use the term ‘recurrence’ rather than ‘resistance’ as the former does not necessarily imply a particular biological mechanism.
(5) It is unclear if the increase in basal-like stem cells is from normal basal cells or cancer cells with a basal stem-like property.
See the response to R1-2 and R2-1.
(6) In the Hi-MYC model, MYC expression is regulated by AR inhibition and is profoundly ARi responsive at early time points.
We agree that this is the likely mechanism of castration-induced regression (so-called ‘MYC addiction’) but it is unclear what the reviewer’s concern is vis-a-vis our manuscript.
Reviewer #4 (Public Review):
In this manuscript by Sha et al. the authors test the role of TNFa in modulating tumor regression/recurrence under therapeutic pressure from castration (or enzalutamide) in both in vitro and in vivo models of prostate cancer. Using the PTEN-null genetic mouse model, they compare the effect of a TNFα ligand trap, etanercept, at various points pre- and post-castration. Their most interesting findings from this experiment were that etanercept given 3 days prior to castration prevented tumor regression, which is a common phenotype seen in these models after castration, but etanercept given 1 day prior to castration prevented prostate cancer recurrence after castration. They go on to perform RNA sequencing on tumors isolated from either sham or castrate mice from two time points post-castration to study acute and delayed transcriptional responses to androgen deprivation. They found enrichment of gene sets containing TNF-targets which initially decrease post-castration but are elevated by 35 days, the time at which tumors recur. The authors conduct a similar set of experiments using human prostate cancer cell lines treated with the androgen receptor inhibitor enzalutamide and observe that drug treatment leads to cells with basal stem-like features that express high levels of TNF. They noticed that CCL2 levels correlate with changes in TNF levels raising the possibility that CCL2 might be a critical downstream effector for disease recurrence. To this end, they treated PTEN-null and hi-MYC castrated mice with a CCR2-antagonist (CCR2a) because CCR2 is one receptor of CCL2 and monitors tumor growth dynamics. Interestingly, upon treatment with CCR2a, tumors did not recur according to their measurements. They go on to demonstrate that the tumors pre-treated with CCR2a had reduced levels of putative TAMs and increased CTLs in the context of TNF or CCR2 inhibition providing a cellular context associated with disease regression. Lastly, they perform single-cell RNA sequencing to further characterize the tumor microenvironment post-castration and report that the ratio of CTLs to TAMs is lower in a recurrent tumor.
While the concepts behind the study have merit, the data are incomplete and do not fully support the authors' conclusions. The author's definition of recurrence is subjective given that the amount of disease regression after castration is both variable (Figure 8) and relatively limited
See the response to R3-4, above.
particularly in the PTEN loss model. Critical controls are missing. For example, both drug experiments were completed without treating non-castrate plus drug controls
In these experiments, we are investigating the effect of anti-TNF or anti-CCL2 therapy on the response to the castration. The appropriate controls are castrated mice which received vehicle or no treatment. The response of intact animals (with tumors still increasing in size) is not only irrelevant to the question we are asking, but also impractical, as the tumor size would be too large for mouse viability.
which raises the question of how specific these findings are to castration resistance. No validation was performed to ensure that either the TNF ligand trap or the CCR2 agonist was acting on target.
See the response to R3-3, above.
The single-cell sequencing experiments were done without replicates which raises concern about its interpretation.
The goal in these experiments is to address a relatively narrow question concerning changes in a few key TAM-associated transcripts versus changes in a few CTL-associated transcripts. This is not meant to provide rigorous single cell transcriptomic analysis that is required - for example - to definitely assess the levels of various cell populations. As noted in R3-2 (and in the DISCUSSION , lines 467-468) future single cell analysis is ongoing, but beyond the scope of this manuscript.
At a conceptual level, the authors say that a major cause of disease recurrence in the immunosuppressive TME, but provide little functional data that macrophages and T cells are directly responsible for this phenotype.
The requirement for CCL2-CCR2 signaling for recurrence suggests that TAMs drive recurrence, presumably due to immunosuppression in the TME. However, CCR2 is expressed by other cell types. Therefore, in future studies we will need to examine the response to additional inhibitors and also employ single cell ‘omics to more thoroughly characterize the changes in the cellular components of the tumor immune microenvironment. Functional analysis of T-cell subsets is an even more formidable experimental challenge.
Statistical analyses were performed on only select experiments.
See the response to R1-3, below.
In summary, further work is recommended to support the conclusions of this story.
Reviewer #1 (Recommendations For The Authors):
I suggest the authors address the following:
(1) Throughout the figures, statistical analysis needs to be made clear including n numbers, replicates, and whether or not differences shown are statistically significant. These includes Figure 1c, and d,; Figure 2 A and B, Figure 3A; Figure 4A; Figure 5A, C and D; Figure 7B.
We thank the reviewer for identifying these issues and we have inserted statistical analyses into the text as follows:
Figure 1C-D: Statistical analysis added to the legend of Fig. 1.
FIgure 2A: Statistical analysis added to the legend of Fig. 2.
Figures 2B: These are representative FACS scatter plots – the corresponding statistical analysis is shown in Fig. 2C (left panel).
Figure 3A: Statistical comparisons are not relevant to this figure – the data is presented to document the cell sorting enrichment process.
Figure 4A and Figure 5C-D: For the small n, categorical data sets related to the studies using GEM prostate cancer models shown in Figures 4A, 5C and 5D, we employed the exact binomial test to determine the Clopper-Pearson confidence interval for the proportion and Fisher’s exact test to determine the p-values and now present these analyses in a new Supplementary Table 3. We have included this information in the M&M section and edited the Figure legends to direct the reader to the new Supplementary Table.
We would like to emphasize that the reported p-values are exact probabilities from Fisher’s exact test. Given the small sample sizes and the discrete nature of the distribution, these values should not be interpreted as if they strictly conform to conventional thresholds such as p<0.05. Instead, they represent the exact probability of observing data as extreme as (or more extreme than) what we obtained under the null hypothesis.
Figure 5A: The legend of Fig. 5A was edited to clarify the statistical analysis.
Figure 7B: The differences in CD8+ T cells and F4/80 macrophages due to CCR2a-35d treatment were not statistically different (p>0.05) - we have now stated this explicitly in the figure legend.
(2) Several experiments either lack appropriate controls or the choice of data presentation is confusing. In Figure 4A vehicle controls should
We have not observed any effect of IP administration of vehicle in any experiments across multiple published studies employing these GEMMs, and so we conclude that the injection of vehicle is very unlikely to modify the outcome of these experiments.
be included in the graphs and for ease of interpretation perhaps average tumor growth should be shown with individual tumor growth can be shown in the supplement. In Figure 5 the vehicle control is missing and in Figure 5D 4 out of 5 CX+vehicle tumors are said to have recurred but the trend line in the graph shows otherwise.
We thank the reviewer for noting this issue - the color designations were inadvertently reversed in the legend text. This error has been corrected in the revised version of the manuscript.
In Figure 8B flow cytometry would actually be more convincing than scRNAseq. If scRNAseq is chosen, a higher quality UMAP or t_SNE plot is needed with a broader color palette.
We did consider the FACS approach suggested by the reviewer, but decided against it as we could not readily identify and validate a TAM-specific antibody to allow such measurements.
Reviewer #3 (Recommendations For The Authors):
(1) A clear description of the GEM model experiments will be helpful in interpreting the data as it is unclear what age the PTEN or MYC mice were when therapy was started. PTEN are generally intrinsically resistant to ARi whereas MYC are robustly sensitive.
(2) Prostate organoid technology of the GEM prostate cell, and normal prostate cells may allow for a better evaluation of which basal stem-like cells are expressing TNF - dissecting out normal basal from cancer with basal-like properties.
(3) Experiments to demonstrate targeting inhibition should be performed for AR and TNF inhibition. Especially across the spectrum of TNF blockade timing given the differences in proposed responsiveness over an acute change in dosing schedule.
(4) Detailed histology and pathologic evaluation should be provided to characterize the impact on cancer and TME as well as normal prostate mixed in these tumors.
(5) Prostate organoid development with genetic manipulation (PTEN ko) and transplant back into immunocompetent mice may provide experiments to prove causality and address the impact on the immune microenvironment.
(6) The descriptive of regression and recurrence need to be defined as based on the kinetics and presented data this seems to be associated with minimal responsiveness and progression from a substantial volume of persistent cells.
(7) The authors should also explore the impact of TNF inhibition on the cancer cell directly and evaluate downstream PI3K signaling.
Responding to this set of recommendations: A number of these recommendations (R3-7, -9, -12) are similar or identical to those already noted in Reviewer 3’s public review and have been addressed above. The remaining recommendations (R3-8, -10, -11; organoids, histological approaches to the TME, etc.) are potentially interesting experimental approaches but beyond the scope of the current manuscript.
Reviewer #4 (Recommendations For The Authors):
Major comments:
(1) Figure 1A-B: While the decrease in tumor growth post-castration is apparent, the increase in tumor growth that has been designated as the point of androgen-independence is a mild increase from the 28 measurements and would benefit from statistical support. Further time points demonstrating that the tumors continue to increase in size would better support the claim that these tumors appropriately model disease recurrence.
This data meets our criteria for recurrence (outlined in the M&M and in the legend to Table S2).
(2) Figure 2A: Statistical analysis should be performed and why is this figure shown twice (also in the S2A right panel)?
We added statistical analysis to the legend of Fig. 2A. The data from Fig 2 (C4-2 cell line) is replicated in Supplementary Fig S2 to allow the reader to directly compare the response of the C4-2 cell line with the response of the LNCaP cell line.
(3) Figure 4A: Non-castrate + etan control is needed here. Also, the data should be statistically assessed.
Regarding non-castrate controls, see our response to R4-2. Statistical analysis has been added - see Supplementary Table S3.
(4) It appears that at least two of the mice shown in Figure 5C have the same level of disease recurrence as was demonstrated in Figure 1B, yet the analysis defines recurrence in 0/6 mice.
Again, similar to R4-7, None of the mice in Figure 5C meet our criteria for recurrence (outlined in the M&M and in the legend to Table S2).
(5) The text for Figure 5D states that vehicle-treated tumors (red) regress then recur while mice pre-treated with a CCR2 antagonist (blue) don't recur, but in the figure, these groups appear to be reversed. In addition, it would be good to have noncastrate + CCR2a control for Figure 5C and 5D.
We corrected the labeling error in the legend to Figure 5.
(6) It would be good to validate major RNAseq findings using orthogonal approaches.
We agree that it is valuable to validate our findings but these experiments are beyond the scope of the manuscript
(7) Figure 7B is quite puzzling. It appears to show the opposite of what was written.
We thank the reviewer for bringing this error to our attention. Our internal review of previous versions of the manuscript showed that the corresponding author (JJK) inadvertently mis-edited this figure when preparing the BioRxiv submission. Figure 7B has been corrected and now aligns with the Results text. We have also appended a PDF documenting the editing error/ mistake.
(8) Figure 8: This experiment appears to have been done without replicates making the current interpretation questionable.
A more detailed scRNAseq analysis of the GEMM response to castration (with replicated) is already underway. The analysis in Fig. 8 includes 1000’s of cells, capturing the variation in mRNA levels. However, it does not capture animal-to-animal variation. Given the supporting role of this data in this manuscript, we believe that the single animal approach is adequate in this case.
(9) The level of detail included in the mechanism described in Figure S8 is not supported by the work shown.
Fig. S8 is not presented as a summary of our findings but as a model that is consistent with our data - since it is by definition somewhat speculative, we present it in the supplementary data.
Minor Comments:
(1) Figure 6S title is written incorrectly.
We thank the reviewer for noticing this - we have corrected this in the revised manuscript.
(2) Images shown in Figure S7C need scale bars.
These images are at 40X magnification - this has been added to the legend.
