Tumor–Immune Cell Crosstalk Drives Immune Cell Reprogramming Towards a Pro-Tumor Proliferative State Involving STAT3 Activation

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

In this manuscript Norek and Kennard et al investigate the effects of TNBC cells on the phenotype of human monocyte cells. 

They used RNAseq to look at gene expression differences between THP-1 monocyte cells directly co-cultured with TNBC cell line MDA-MB-231 or treated with MDA-MB-231 conditioned media. 

Good controls were used and the use of different TNBC cell line and non-tumorigenic epithelial MCF10a cells is good. Also use of primary PBMC is a necessary addition to show this effect is seen in primary cells. It would be nice to also look at effects of primary TNBC cells to see if the same changes in monocytes is seen however I understand that this may not be possible.

The wording used to describe the conditions should remain consistent throughout, for example sometimes it is called Pre_COND and sometimes indirect. 

Fig2 legend is incomplete. 

Stats on fig 8B is missing 

Overall good paper with interesting findings 

Author Response

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files. We are grateful for the positive feedback regarding our experimental design and inclusion of primary PBMCs. We have emphasized these points in the revised manuscript to highlight the robustness of our approach. Comments 1: It would be nice to also look at effects of primary TNBC cells to see if the same changes in monocytes is seen however I understand that this may not be possible. Response 1: We agree that including primary TNBC cells would strengthen the study. Unfortunately, due to limited availability and ethical considerations, we were unable to include these samples. Comments 2: The wording used to describe the conditions should remain consistent throughout, for example sometimes it is called Pre_COND and sometimes indirect. Response 2: Thank you for highlighting this. We have revised the manuscript to use consistent terminology throughout. The term “pre-conditioned” is now used uniformly in the text, figures, and legends.   Comments 3: Fig2 legend is incomplete. Response 3: We appreciate this observation. The legend for Figure 2 has been updated to include the missing legend for the part D of the Figure. Comments 4: Stats on fig 8B is missing Response 4: We thank the reviewer for this comment. We have added p values on Fig 8B where appropriate.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

Summary

This manuscript investigates how tumour–immune crosstalk drives immune cell reprogramming toward a pro-tumourigenic, proliferative state, with a focus on STAT3 signaling. Using in vitro co-culture models, the authors demonstrate that triple-negative breast cancer (TNBC) cells can reprogram THP-1 monocytes and primary PBMCs through both direct contact and tumour-secreted factors. Transcriptomic profiling, pathway analyses, and functional assays collectively support a model in which tumour-educated immune cells exhibit enhanced proliferation, cytokine production (notably IL-6), and activation of STAT3-dependent oncogenic pathways. The study is timely, mechanistically motivated, and integrates multi-level data (RNA-seq, bioinformatics, functional validation)

However, there are number of major and minor concerns as followed:

1. Figure 1, the schematic and experimental description do not indicate the number of biological replicates or address experimental variability. In addition, validation of selected differentially expressed genes (e.g., by qPCR) would strengthen confidence in the RNA-seq results and improve the reliability of the conclusions.
2. Figure 2, the Venn diagram shows many genes uniquely regulated in THP-1 cells following direct contact versus indirect exposure to MDA-MB-231 cells. The manuscript does not clearly explain whether these differences reflect true contact-dependent signaling or other experimental factors. Given the use of direct co-culture for RNA-seq, the authors should clarify how THP-1 cells were isolated prior to RNA extraction and whether potential tumor-derived RNA carryover was assessed, as THP-1 cells are capable of phagocytosing tumor cell material. Addressing this point is important for interpreting the unique gene sets observed in the direct contact condition.
3. Figure 3, the large number of enriched pathways makes it difficult to identify those most relevant to the study’s central hypothesis. Prioritizing and highlighting pathways related to STAT3-driven immune reprogramming, and ranking them (e.g., from low to high significance or enrichment score), would improve clarity and make the data easier to interpret.
4. Figure 6, the labelling of “sum of THP-1 + MDA-MB-231” is unclear and potentially confusing. It will help to improve the clearance if this value represents a combined cell count, a normalized proliferation index, or another calculated metric. And please clarify if the n=3 means biological replication or technical replication? For PBMC, does the n equal to different donors?
5. For Section 3.4.1 (first paragraph), the statement “This finding functionally confirms transcriptomic data, suggesting that the immune/cancer cell model triggers immune cell reprogramming toward a proliferative phenotype that may contribute to metastatic progression” is insufficiently supported. While the data demonstrate increased immune cell proliferation, the link to metastatic progression is not directly shown in this study and requires appropriate literature support. The authors should either provide relevant references to substantiate this claim or revise the statement to more accurately reflect the experimental evidence presented.
6. For Section 3.4.2, the rationale for selecting IL-6 for ELISA validation is not clearly explained. Given that multiple cytokines identified in the transcriptomic analysis (e.g., IL-10 and others) are also known activators of STAT3 signaling, the authors should justify why IL-6 was prioritized over other candidates or discuss whether additional cytokines were considered. Providing this rationale would strengthen the mechanistic interpretation of STAT3 activation.
7. And the activation of the STAT3 pathway is currently inferred mainly from the bioinformatics analyses. Therefore, protein-level validation, such as assessing changes in STAT3 phosphorylation in THP-1 cells is necessary to substantiate this conclusion.

Author Response

We thank the reviewer for their careful reading of the manuscript and for the constructive and detailed comments. We have revised the manuscript to improve clarity, transparency, and interpretation of the data, and we address each point below.

Comments 1: Figure 1, the schematic and experimental description do not indicate the number of biological replicates or address experimental variability. In addition, validation of selected differentially expressed genes (e.g., by qPCR) would strengthen confidence in the RNA-seq results and improve the reliability of the conclusions.

Response 1: We have now explicitly indicated the number of biological replicates used for the RNA-seq experiments in both the Figure 1 legend and the Methods section. RNA-seq experiments were performed using biological triplicates, and variability across replicates was assessed during differential expression analysis using established statistical thresholds.

Regarding qPCR validation, we acknowledge that additional validation would strengthen confidence in the RNA-seq data. Due to resource and time constraints, we are unable to perform new experiments at this stage. To mitigate this, we emphasize that the RNA-seq findings are supported by downstream functional assays and pathway-level concordance, which consistently point to STAT3-associated immune reprogramming. To strengthen the translational relevance we have added the data showing TGF-β secretion in THP1 cells exposed to pre-conditioned MDA-MB-231 media shown in Figure to 7E.

 

Comments 2: Figure 2, the Venn diagram shows many genes uniquely regulated in THP1 cells following direct contact versus indirect exposure to MDA-MB-231 cells. The manuscript does not clearly explain whether these differences reflect true contact-dependent signaling or other experimental factors. Given the use of direct co-culture for RNA-seq, the authors should clarify how THP1 cells were isolated prior to RNA extraction and whether potential tumor-derived RNA carryover was assessed, as THP1 cells are capable of phagocytosing tumor cell material. Addressing this point is important for interpreting the unique gene sets observed in the direct contact condition.

Response 2: We appreciate this important clarification request. THP1 cells were recovered based on their non-adherent properties and extensive washing prior to RNA extraction, minimizing contamination from adherent MDA-MB-231 cells. We acknowledge that THP1 cells can phagocytose tumour-derived material. Importantly, uptake of tumour-derived RNA or proteins is itself a documented mechanism of tumour–immune education and does not invalidate the interpretation of contact-dependent reprogramming.

Comments 3. Figure 3, the large number of enriched pathways makes it difficult to identify those most relevant to the study’s central hypothesis. Prioritizing and highlighting pathways related to STAT3-driven immune reprogramming, and ranking them (e.g., from low to high significance or enrichment score), would improve clarity and make the data easier to interpret.

Response 3: We thank the reviewer for this helpful suggestion regarding Figure 3. We have addressed the concern by revising the Results text to clearly highlight and prioritize pathways most relevant to our central hypothesis of STAT3-driven immune reprogramming.

Specifically, we now emphasize that among the numerous enriched pathways identified by GO, KEGG, and Reactome analyses, those most strongly associated with STAT3 activation include IL-6 signaling, JAK-STAT signaling, and cytokine-cytokine receptor interaction, as well as interleukin-related pathways (IL-4, IL-13, IL-10). These pathways were conceptually ranked by biological relevance and enrichment score in the narrative, with IL-6/JAK-STAT signaling highlighted as a key node consistent with our functional data showing increased IL-6 and TGF-β secretion and reversal by STAT3 inhibition (STAT3-IN-12). Additional enriched pathways such as PI3K-AKT, MAPK, and ECM remodeling were discussed as cooperating networks that support migration and tumour–immune crosstalk.

This textual prioritization ensures that readers can easily identify the pathways most relevant to STAT3-mediated immune transformation. We believe these revisions improve clarity and strengthen the mechanistic interpretation of our findings.

 

Comments 4. Figure 6, the labelling of “sum of THP1 + MDA-MB-231” is unclear and potentially confusing. It will help to improve the clearance if this value represents a combined cell count, a normalized proliferation index, or another calculated metric. And please clarify if the n=3 means biological replication or technical replication? For PBMC, does the n equal to different donors?

Response 4: We thank the reviewer for pointing this out. We have clarified that “sum of THP1 + MDA-MB-231” refers to the combined proliferation measurement from the individual cell lines.

We have revised the Figure 6 legend to clearly state that the reported value represents a combined cell proliferation metric, as described in the Methods. We have also clarified that n=3 refers to biological replicates. For PBMC experiments, n corresponds to biological replicates of PBMC pooled from 4 Donors.

 

Comments 5. For Section 3.4.1 (first paragraph), the statement “This finding functionally confirms transcriptomic data, suggesting that the immune/cancer cell model triggers immune cell reprogramming toward a proliferative phenotype that may contribute to metastatic progression” is insufficiently supported. While the data demonstrate increased immune cell proliferation, the link to metastatic progression is not directly shown in this study and requires appropriate literature support. The authors should either provide relevant references to substantiate this claim or revise the statement to more accurately reflect the experimental evidence presented.

Response 5: We appreciate this observation. The original statement has been revised to: “This finding functionally confirms transcriptomic data, suggesting that the immune/cancer cell model triggers immune cell reprogramming toward a proliferative phenotype.” We have removed the reference to metastatic progression. We have also expanded the scope of the literature in the Discussion section supporting our findings.

 

Comments 6. For Section 3.4.2, the rationale for selecting IL-6 for ELISA validation is not clearly explained. Given that multiple cytokines identified in the transcriptomic analysis (e.g., IL-10 and others) are also known activators of STAT3 signaling, the authors should justify why IL-6 was prioritized over other candidates or discuss whether additional cytokines were considered. Providing this rationale would strengthen the mechanistic interpretation of STAT3 activation.

Response 6: We thank the reviewer for this valuable suggestion and have revised Section 3.4.2 to clarify the rationale for focusing on IL-6. IL-6 was prioritized because it emerged as one of the most robustly upregulated cytokines in the transcriptomic analysis, has a well-established and direct role in STAT3 activation, and is a known mediator of tumour–immune crosstalk in breast cancer.

As per reviewer’s suggestions, in addition to IL-6, we assessed IL-10 and TGF-β at the protein levels. IL-10 was not detectable under the experimental conditions used, despite being identified at the transcriptomic level suggesting that its regulation may be context-dependent or require additional stimuli. In contrast, a significant increase in TGFβ secretion was observed in THP1 cells exposed to pre-conditioned MDA-MB-231 media, and this effect was counteracted by treatment with the STAT3 inhibitor, STAT3-IN-12, which is consistent with its known role in tumour–immune crosstalk and immune suppression. We have added this new data to Figure 7E. We have also added the paragraph discussing the findings regarding the increase of the TGF-β protein levels in THP1 cells upon the incubation in the pre-conditioned media to the Section 3.4.2

 

Comments 7. And the activation of the STAT3 pathway is currently inferred mainly from the bioinformatics analyses. Therefore, protein-level validation, such as assessing changes in STAT3 phosphorylation in THP1 cells is necessary to substantiate this conclusion.

Response 7: We agree that direct assessment of STAT3 phosphorylation would strengthen the mechanistic conclusions. Unfortunately, due to resource constraints and time limitation, we are unable to perform additional protein-level validation at this time. We have acknowledged this limitation in the Discussion and emphasized that STAT3 activation is inferred from transcriptomic signatures and functional outcomes (IL-6 secretion, proliferation), which are consistent with established STAT3 biology. Importantly, we have added new data showing TGF‑β release upon tumor contact, further supporting the involvement of immunosuppressive cytokine networks in TNBC-driven immune reprogramming. In future studies, we plan to directly measure STAT3 phosphorylation (e.g., p-STAT3 Western blot) to provide protein-level confirmation of pathway activation and strengthen the mechanistic link between cytokine signaling and STAT3-driven immune cell transformation.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

This is a well-structured and mechanistically focused study that provides valuable insights into tumor-mediated immune reprogramming within the triple-negative breast cancer (TNBC) microenvironment. The research successfully combines in vitro modeling with translational therapeutic discovery, offering a clear rationale and actionable findings. But I have several following concerns:

1. The "reprogrammed phenotype" is central. Beyond proliferation and IL-6, please elaborate on other defining features revealed by transcriptomics. Were specific surface markers (e.g., CD163, CD206 for M2-like polarization), metabolic enzymes, or other key cytokines/chemokines significantly altered? A summary table or a focused list would enhance clarity.
2. A brief discussion on the translational relevance of the THP-1 model is warranted. How might these findings be validated in primary human monocytes or in vivo settings in future work? Acknowledging this step would strengthen the path forward.

3. Ensure that all key quantitative results (e.g., fold-change in proliferation, IL-6 levels, degree of inhibition by STAT3-IN-12) are presented with explicit measures of variance (e.g., SD or SEM) and statistical significance (p-values) in the results section and corresponding figures.

4. The discussion could be strengthened by briefly situating these findings within the broader literature. For instance:

   • How does STAT3-driven monocyte reprogramming compare to known mechanisms of myeloid-derived suppressor cell (MDSC) generation or tumor-associated macrophage (TAM) polarization?

   • Could the observed phenotype contribute to resistance against current immunotherapies (e.g., checkpoint inhibitors), and if so, how?

5. Ensure consistent formatting of cell line names (THP-1 vs. THP1).
6. The phrase "oncogenic pathways" in the context of immune cells could be refined to "pathways associated with tumor progression" or "pro-tumorigenic signaling pathways" for precision.
7. Ensure that all acronyms (e.g., "Her2" "STAT3", "IL6" , ...) are clearly defined at first mention in the abstract and the main text if not already done. Please double check all the text and correct them.
8. Please use a standard three-page table for the tables in the manuscript.
9.  Please unify the format of references in the article, including the author's name, the case of words in the title of the article, the writing of the name of the journal, and the page number.

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

We thank the reviewer for their positive assessment of our study and for the constructive suggestions that have helped us improve the clarity and impact of the manuscript. Below we address each point in detail:

Comments 1: The "reprogrammed phenotype" is central. Beyond proliferation and IL-6, please elaborate on other defining features revealed by transcriptomics. Were specific surface markers (e.g., CD163, CD206 for M2-like polarization), metabolic enzymes, or other key cytokines/chemokines significantly altered? A summary table or a focused list would enhance clarity.

Response 1: We appreciate the reviewer’s suggestion. In line with the reviewer’s suggestions, we assessed IL-20 and TGF-β at the protein level in the addition to IL6. Whereas IL-10 was not detectable under our experimental conditions, despite being identified at the transcriptomic level, indication that its expression may require additional stimuli. In contrast, TGF-β secretion was significantly increased in THP1 cells exposed to pre-conditioned MDA-MB-231 media, and this effect was reversed by the STAT3 inhibitor. These data have been added to Figure 7E, and a paragraph discussing the TGF-β increase has been included in Section 3.4.2.

Other cytokines, chemokines, metabolic enzymes, and surface markers (e.g., CD163, CD206) were not measured on the protein level, however, we list the gene expression changes of pro-inflammatory cytokine, chemokines and antigen-presenting markers from the RNAseq analysis in Table 4.

Comments 2: A brief discussion on the translational relevance of the THP1 model is warranted. How might these findings be validated in primary human monocytes or in vivo settings in future work? Acknowledging this step would strengthen the path forward.

Response 2: We thank the reviewer for this important point. THP1, has been chosen as an in-vitro model of monocyte activity as it is generally accepted to have similar activity to primary human monocytes especially when investigating and potential monocyte to M1 macrophage transition. THP-1 monocytes serve as a useful reductionist model to define core tumour-driven immune reprogramming pathways, and importantly, we observed consistent phenotypic and cytokine responses in primary human PBMCs exposed to TNBC cells. However, validation in purified CD14⁺ monocytes from patients with TNBC will be required to fully confirm clinical relevance. Such studies may also enable identification of blood-based transcriptional or cytokine signatures reflective of tumour-induced immune education and could be extended to in vivo models to assess the functional impact of STAT3-dependent immune reprogramming within the tumour microenvironment. We have added this sentence to the Discussion: “THP1 monocytes serve as a tractable reductionist model to define core tumour-driven immune reprogramming pathways, and importantly, consistent phenotypic and cytokine responses were observed in primary human PBMCs exposed to TNBC cells”.

Comments 3: Ensure that all key quantitative results (e.g., fold-change in proliferation, IL-6 levels, degree of inhibition by STAT3-IN-12) are presented with explicit measures of variance (e.g., SD or SEM) and statistical significance (p-values) in the results section and corresponding figures.

Response 3: We thank the reviewer for this suggestion. We have updated the results section and all relevant figures to ensure that explicit measures of variance and statistical significance are included.

 

Comments 4: The discussion could be strengthened by briefly situating these findings within the broader literature. For instance: How does STAT3-driven monocyte reprogramming compare to known mechanisms of myeloid-derived suppressor cell (MDSC) generation or tumor-associated macrophage (TAM) polarization?

Could the observed phenotype contribute to resistance against current immunotherapies (e.g., checkpoint inhibitors), and if so, how?

Response 4: We thank the reviewer for this useful suggestion. We have revised the Discussion to more explicitly contextualize our findings within the existing literature on myeloid-derived suppressor cells (MDSCs), tumour-associated macrophages (TAMs), and immunotherapy resistance. We expanded the Discussion to directly compare the tumour-educated monocyte phenotype observed in our co-culture models with the established mechanisms of MDSC generation and TAM polarization (Paragraph 5 in the discussion).

Second, we have added a new section addressing the potential relevance of this phenotype to immunotherapy resistance. We now discuss how STAT3-driven myeloid reprogramming has been shown to impair antigen presentation and supress effector T- cell activation. These mechanisms have been directly linked to reduced responsiveness to immune checkpoint therapies. Together, these additions strengthen the translational implication of tumour-driven immune reprogramming discussed in this study.

 

Comments 5: Ensure consistent formatting of cell line names (THP1 vs. THP1).

Response 5: We have reviewed the entire manuscript to ensure consistent formatting of cell line names (e.g., THP1 instead of THP-1).

Comments 6: The phrase "oncogenic pathways" in the context of immune cells could be refined to "pathways associated with tumor progression" or "pro-tumorigenic signaling pathways" for precision.

Response 6: We thank reviewer for this suggestion, we have replaced the phrase “oncogenic pathways” with “pro-tumorigenic signaling pathways” throughout the manuscript for precision.

 

Comments 7: Ensure that all acronyms (e.g., "Her2" "STAT3", "IL6" , ...) are clearly defined at first mention in the abstract and the main text if not already done. Please double check all the text and correct them.

Response 7: We have carefully reviewed the abstract and main text to ensure that all acronyms, including HER2, STAT3, and IL-6, are defined at first mention and used consistently throughout the manuscript.

Comments 8: Please use a standard three-page table for the tables in the manuscript.

Response 8: All tables have been reformatted to follow a standard three-page layout as requested.                 

Comments 9: Please unify the format of references in the article, including the author's name, the case of words in the title of the article, the writing of the name of the journal, and the page number.

Response 9: We have thoroughly reviewed and standardized all references to ensure consistent formatting, including author names, capitalization of article titles, journal names, and page numbers, in accordance with journal guidelines.

 

 

Author Response File: Author Response.docx

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have addressed all my concerns, I recommend accepting it in current form.