3D pentaculture model unveils malignant cell-driven macrophage polarization in high-grade serous ovarian cancer

Domain: biology
Published: 2026-03-25
Editorial outcome: minor_revision

Abstract
High-grade serous ovarian cancer (HGSOC) is characterized by a complex, immunosuppressive tumor microenvironment (TME) that contributes to poor clinical outcomes and resistance to therapy. To replicate the human TME in vitro, we develop a 3D pentaculture model incorporating five human cell types: malignant HGSOC cells and primary fibroblasts, mesothelial cells, adipocytes, monocytes. Monocytes differentiate into macrophages without exogenous cytokines in the pentacultures. Bulk RNA sequencing and deconvolution with single-cell RNA data from patient biopsies reveal that macrophage clusters within the pentacultures replicate those found in human HGSOC metastases, and proportions of individual clusters vary according to the malignant cell line. The pentacultures enabled detailed analysis of malignant cell–macrophage interactions and highlight the influence of malignant cell genomic, transcriptomic and proteomic heterogeneity on the TME. Furthermore, targeting of “do-not-eat-me” signals with anti-CD47 and anti-CD24 monoclonal antibodies demonstrated differential effects on macrophage activity and cancer cell viability, again depending on the individual malignant cell line. Real-time microscopic monitoring of the pentacultures confirms dynamic modulation of macrophage behavior. We conclude that this pentaculture model offers a platform to study malignant cell control of TME elements and in particular their interactions with myeloid cells.

Introduction
High-grade serous ovarian cancer (HGSOC) is the most diagnosed and deadliest type of ovarian cancer. The lack of specific symptoms accounts for a late diagnosis, when the disease is already in an advanced state, rendering it challenging to treat1,2. Although most of the patients respond well to first line platinum-taxane based chemotherapy, more than 80% of patients with advanced disease will relapse and eventually develop chemoresistance3,4. In recent years, the addition of anti-angiogenic and targeted therapies has improved outcomes for some patients, although the five-year survival rate remains low5–7. Genomic and transcriptomic analysis of HGSOC tumors has shed light on the heterogeneity of this disease and how tumor intrinsic characteristics can affect response to treatment. Several studies have identified distinct molecular subtypes by clustering tumors with similar transcriptomic profiles8–11. The HGSOC subtypes identified correlate with different clinical and/or pathological characteristics10, and reflect variations in immune and stromal content of the tumor microenvironment (TME)11. Moreover, single-cell and spatial transcriptomics studies have further highlighted the intratumoral heterogeneity of HGSOC tumors. These analyses have also revealed heterogeneity in the ovarian tumor microenvironment which can be influenced by subclone-specific signaling to stromal and immune cells12.

Until recently, only few in vitro or in vivo models of HGSOC reflected its complex TME and malignant cell genetics13–16. This may have contributed to the limited success of recent clinical trials e.g., with immune checkpoint blockade or chimeric antigen receptor (CAR) T cell technology17. Apart from therapies that aim to stimulate T cell cytotoxicity, macrophage targeted therapies are under investigation for the treatment of many different malignancies18. Current clinical trials in various malignancies aim to inhibit the recruitment of immunosuppressive macrophages by targeting chemokine pathways such as the C-C motif chemokine ligand 2 (CCL2) axis or colony stimulating factor 1 receptor (CSF1R). Alternatively, they focus on reprogramming myeloid cells from a pro-tumor to an anti-tumor state by modulating specific receptors or signaling pathways19,20. Macrophages are highly abundant in the TME of HGSOC, and they are known to contribute to the immunosuppressive microenvironment9,21–23. Macrophages exhibit an innate ability to kill cancer cells through mechanisms such as phagocytosis and the activation of adaptive immune responses. However, in advanced tumors, macrophages often support cancer progression.

Macrophage-targeting agents can be studied in mouse models24,25, but many of the potential agents are humanized antibodies for which mouse phenocopies might not be available or may not reflect the mechanism of action on human cells. Furthermore, establishing mouse models that replicate the TME of advanced disease for the evaluation of novel treatments can be time-consuming compared to in vitro models. Pre-clinical experiments that test new therapies in more relevant mouse models can take many months e.g.24. In vitro experiments can be more efficient, but plastic can polarize macrophages, and this may lead to biased results. Thus, to better study the potential of myeloid-targeted drugs, researchers need complex in vitro TME models that move away from 2D culture14,26–28.

In this work, building on our previous work developing 3D in vitro human cell models of HGSOC29,30, we incorporate human monocytes into our established tetraculture model, creating a pentaculture (i.e., five cell types) which aims to mimic later stage of metastasis dissemination. In this 3D in vitro model, monocytes rapidly differentiate to macrophages. We use this model to investigate how malignant HGSOC cells influence the TME, particularly in driving macrophage polarization and activity. Monocytes differentiate to macrophage subsets previously identified by single cell RNAseq (scRNAseq) in human HGSOC omental metastases24, and these subsets vary according to the malignant cell used. Genomic, transcriptomic, and proteomic differences between three HGSOC malignant cell lines shape the TME and modulate macrophage polarization. Cancer cells can coerce macrophages to immunosuppressive phenotypes and inhibit phagocytosis through different signaling pathways, such as the “do-not-eat-me” signals31,32. Using the pentaculture models, we study therapeutic drugs targeting the “do-not-eat-me” signals and investigate characteristics of malignant cells that affect these processes. We conclude that the 3D in vitro pentaculture models described in this article provide insights into mechanisms by which malignant cells can drive myeloid cell differentiation that are relevant to the HGSOC TME in patient samples and may provide a medium-throughput platform for assessing cancer therapies that target malignant cell-macrophage interactions.

Results
3D pentaculture models reveal monocyte-to-macrophage differentiation and malignant cell-driven TME architecture
In an attempt to reconstruct the TME of HGSOC in vitro, we isolated primary fibroblasts, mesothelial cells and adipocytes from macroscopically normal omental tissue from patients undergoing gynaecological surgery either chemo naïve or post three/six cycle of chemotherapy, following a previously established protocol33.

To explore the role of macrophages in the TME of HGSOC, we introduced CD14+ peripheral blood monocytes from healthy donors into our previously published tetraculture model30 which also included one of three HGSOC malignant cell lines, AOCS1, G164 and OvCAR334. Briefly, adipocyte gels were layered with primary fibroblasts and mesothelial cells to recreate a ‘normal’ omental environment. Malignant cells and monocytes were subsequently added, and the resulting model was maintained in culture for 7 days (Fig. 1A). After 7 days of culture, we observed a significantly different proportion of cancer cells between the pentacultures, whereas the proportions of fibroblasts, mesothelial cells, and macrophages were similar (Supplementary Fig. 1A). Overall cell viability remained high (Supplementary Fig. 1B). While cancer and stromal cells remained highly viable, macrophage viability was lower at day 7. However, no significant differences were observed between the different pentacultures in the viability of the primary cells (Supplementary Fig. 1C).

The pentaculture self-organized in a manner that resembled the micro-metastases seen in HGSOC metastases in human omentum (Fig. 1B and Supplementary Fig. 1D). However, the morphology of the pentacultures varied, depending on the malignant cell line that was added (Fig. 1B). AOCS1 and OvCAR3 cancer cells were located close to the stromal cells in the pentacultures, while G164 cancer cells generally formed clusters away from the stromal cells (Fig. 1B, Supplementary Fig. 1E and 1F). Immunohistochemistry (IHC) and flow cytometry analyses showed that AOCS1, G164, and OvCAR3 pentacultures retained a substantial population of CD45+ cells (Fig. 1B, C). To evaluate the potential monocyte to macrophage differentiation, we measured the expression of macrophage surface markers CD163, CD206, and HLA-DR after 7 days of culture (Fig. 1B, D and Supplementary Fig. 1G). We also examined macrophage polarization dynamics by assessing CD163+, CD206+, and HLA-DR+ macrophage markers at day 0, which represents the monocytic state, (Fig. 1E), day 3, and day 7 of culture (Fig. 1F). We observed a progressive increase in marker expression as the culture developed, as visualized by representative overlaid flow cytometry contour plots in Fig. 1F. These profiles show a clear shift toward higher membrane marker expression at day 7 (dark lines) compared to day 3 (light lines).

Thus, our 3D HGSOC in vitro model incorporated five human cell types, four of which were primary cells, including monocytes which differentiated to macrophages within the culture, without the addition of exogenous cytokines or any growth factors to the culture medium. Furthermore, the results suggested that malignant cells might have a role in influencing the morphology of these models.

Bulk RNA-sequencing analysis reveals malignant cells as main drivers of the TME in the pentaculture models
Next, we investigated the role of malignant cells in modulating the TME in a bulk RNA sequencing (RNAseq) experiment. For this experiment, we constructed three omental microenvironments by incorporating primary adipocytes, fibroblasts, and mesothelial cells isolated from three different chemo naïve donors used as biological replicates. Monocytes from a single donor were used in all experiments. This setup ensured that the only variable across the different models was the malignant cells (Fig. 2A). Additionally, we extracted RNA from 2D monocultures of fibroblasts, mesothelial cells, monocytes, malignant cells and from omental biopsies free of malignant tissue. Unsupervised clustering revealed that each pentaculture model segregated according to the malignant cell line rather than the donor of normal cells (Fig. 2B). Monocultures of malignant cells in 2D clustered close to each other and separately from the corresponding cell line pentaculture, highlighting the effect of the microenvironment on the pentaculture transcriptomes. Fibroblasts, mesothelial cells and monocyte monoculture RNA profiles were indistinct clusters as was the adjacent non-malignant omental tissue. We also observed that subclustering within the pentaculture models highlighted transcriptome variations that occurred within the context of the multicellular system, extending beyond the baseline differences observed in the malignant cell line controls (Fig. 2C, Supplementary Data 1). Together these data suggest that the malignant cells played a pivotal role in orchestrating the TME of the pentacultures.

Consistent with the monocyte-to-macrophage differentiation shown in Fig. 1, we found that pathways for macrophage chemotaxis, migration, and differentiation were upregulated in the pentacultures and non-malignant omental tissue samples when compared to pathways in monocytes, primary cell monocultures (fibroblasts and mesothelial cells) and malignant cell line monocultures (Fig. 2D and Supplementary Fig. 2A). Moreover, hallmark pathways related to cell cycle regulation were highly represented in the 2D malignant cell cultures, whereas pathways resembling a more tissue-like environment appeared in the pentacultures, but not in the 2D cell cultures (Fig. 2E). To further investigate the relevance of our models, we performed a direct correlation of GSVA enrichment scores of Hallmark pathways of the AOCS1 pentacultures to publicly available RNAseq data from the original AOCS1 tumor35. This analysis showed that each of the three AOCS1 pentacultures positively correlated with the original tumor’s Hallmark signatures, whereas the AOCS1 cell line cultured alone did not significantly correlate (Supplementary Fig. 2B). This finding suggested that our pentaculture system induces elements of the TME that resemble the original human tumor from which the malignant cell was derived.

Taken together, these results suggested malignant cells were the primary drivers of the TME in our models. Notably, some macrophage-related pathways were only found in the 3D models and tissue samples, underscoring the differentiation of monocytes to macrophages in these conditions.

We next assayed the cytokine and chemokine secretome. The pentacultures secreted a broad spectrum of cytokines and chemokines, with patterns influenced by the presence of macrophages and malignant cells in the model (Fig. 3I). Notably, these cytokine and chemokine levels were elevated compared to those released from the 2D cancer cell cultures (Fig. 3J) and the other components, such as fibroblasts, mesothelial cells, and adipocytes grown as monocultures (Supplementary Fig. 3E). Interestingly, stromal cells, such fibroblasts and mesothelial cells, were found to secrete the highest levels of CCL2 and M-CSF (Supplementary Fig. 3E and 3F). Since these cytokines are required to promote macrophage differentiation and viability, this finding confirms that the stromal cells alone are sufficient to drive the initial monocyte-to-macrophage differentiation (Supplementary Fig. 3B) and shows why external supplementation of these cytokines was not necessary in the culture medium.

Lastly, we investigated whether malignant cells could also influence fibroblast phenotypes in our pentaculture models. Our results indicated that malignant cells might also drive fibroblast activation and differentiation transcriptional programmes (Supplementary Fig. 3G). We looked at two different fibroblasts markers, αSMA (alpha smooth muscle actin) and FAP (fibroblast activation protein), using either immunofluorescence or flow cytometry. We found that malignant cells might influence the fibroblast phenotype, though this effect was less pronounced than the effects observed on macrophages (Supplementary Fig. 3H and 3I). Specifically, a significant increase in αSMA expression was observed in the AOCS1 pentaculture fibroblasts compared to their OvCAR3 counterparts, with a non-significant trend toward an increase also noted in the G164 comparison to OvCAR3 (Supplementary Fig. 3G).

In conclusion, we observed distinct proportions of malignant cell line populations in the pentacultures models at the RNA level, which were corroborated by measuring the amount of viable cancer cells in the models after 7 days of culture. We found that macrophage clusters identified by scRNAseq in HGSOC omental diseased tissues were also present in our pentacultures in varying proportions, depending on the malignant cell line used. This finding further suggests that malignant cells influence the TME and drive macrophage polarization in the pentacultures in a similar manner to that seen in human biopsies.

Transcriptomic and genomic diversity among HGSOC malignant cells
To understand the mechanisms by which malignant cells influence the TME of the pentacultures, we explored the genome and transcriptome of AOCS1, G164 and OvCAR3 cells prior to their incorporation in the 3D pentaculture. At genomic level, we confirmed TP53 mutations consistent with HGSOC in all three cell lines (Fig. 4A), along with mutations in other genes commonly associated with HGSOC (Fig. 4B, Supplementary Data 2). No somatic BRCA1/2 or homologous recombination deficiency (HRD)-related gene mutations were detected but there was copy number loss of BRCA2, RAD51D, FANCA, CDK12 and RB1 in the G164 cell line (Fig. 4C). In line with the copy number loss of HRD genes, there was low activity of homologous recombination and mismatch repair functionality in G164 cells at transcriptome level (Supplementary Fig. 4A). All cell lines had extensive copy number alterations which reflect the chromosomal instability observed in HGSOC driven by loss of TP53 function (Supplementary Data 3)36. AOCS1 exhibited a tetraploid karyotype, G164 a diploid state, while OvCAR3 was triploid (Fig. 4D).

We then explored previously developed HGSOC copy number (CN) signatures that represent different putative causes of CIN (chromosomal instability)37, and found distinct profiles of these CN signatures across our cell lines (Fig. 4E). G164 presented high activity of signature 3 (s3), in line with HRD-related genomic lesions observed in these cells; AOCS1 and OvCAR3 presented high activity of signature 4 confirming whole genome duplication events and in line with a higher amplification of CCNE1. OvCAR3 exhibited activity of signature 2, a signature of replication stress-induced tandem duplication. Taken together these data indicate that our cell lines capture a diversity of chromosomal instability features that are also observed in HGSOC tumors and in HGSOC cell organoids37. Furthermore, in addition to our analysis of HGSOC CN signatures on the three cell lines, we have conducted an additional exploration using a pan-cancer study defining 17 CN signatures38. Again, we observed that the cell lines exhibit profiles of signature activities capturing a diversity of CIN features that are also observed in human tumors. (Supplementary Fig. 4B). We also observed the expression of several genes, including CCL2 and IL6, that may influence the behavior of these cells and the pentacultures (Fig. 4F and Supplementary Fig. 4C).

We then focused on differentially expressed genes that maximally segregated the three cell lines and observed that G164 was highly enriched in wound healing factors and inflammatory response mediators including IL6, TNF and IL8 (Fig. 4G, Supplementary Data 4 and 5). AOCS1 had the highest expression of the monocyte/macrophage chemoattractant CCL2 along with high expression of CXCL12, IL33 and extracellular matrix organization genes ITGB3 COL14A1, COL27A1, MMP10, VCAN. OvCAR3 cells had the lowest expression of inflammatory mediators and had elevated expression of the metabolic regulators AKT2, ALDH1A1 (Fig. 4G, H and Supplementary Data 4 and 5). As shown in Fig. 3J, the levels of secreted CCL2, IL6, IL1β, TNFα, IL8 cytokines matched their transcript abundance in the respective cell lines. Moreover, consistent with other published studies demonstrating how tumor-intrinsic genomic and epigenetic alterations can define a specific tumor microenvironment (TME)39, we found that the G164 cell line had increased cGAS-STING pathway expression compared to the other two malignant cell lines (Supplementary Fig. 4E). This profile is associated with a more inflammatory TME, which is consistent with the profiles of both the G164 malignant cell line and its corresponding pentacultures, as shown previously in Fig. 3I, J.

In conclusion, the malignant cell lines used in our pentacultures share genomic, transcriptomic and proteomic features with HGSOC patient biopsies and organoids. HGSOC malignant cells exhibited significant genomic and transcriptomic heterogeneity. Despite sharing TP53 mutations and common CN alterations typical of HGSOC tumors, the malignant cells displayed considerable differences in CN alterations and transcriptomic profiles that are consistent with distinct proteomic characteristics and might explain their different influence in our models.

Pentaculture models display distinct phagocytosis mechanisms through the expression of ‘do-not-eat-me’ signals CD47 and CD24
Our results so far showed that the different histology and macrophage subtypes generated by the three HGSOC malignant cell lines may be influenced by transcriptomic and genomic differences that alter cytokine profiles. We also found that the number of malignant cells varied in the pentacultures. This could have been due to variation in their proliferation, but we wondered whether this was influenced by malignant cell interactions with the macrophages. To investigate potential mechanisms underlying the higher proportion of G164 cancer cells in pentacultures compared to AOCS1 and OvCAR3, as shown in Fig. 3B and previously in Supplementary Fig. 1A, we examined the expression of phagocytosis pathways. We observed that phagocytosis pathways were enriched in the pentaculture transcriptomes but were absent in the 2D malignant cell lines (Fig. 5A). Of note, some of these pathways were expressed at higher levels in the AOCS1 and OvCAR3 pentacultures compared to the G164 model. To investigate this further, we used red-labeled malignant cells and examined the proportion of macrophages positive for red cell fragments using flow cytometry. The results showed that OvCAR3 and AOCS1 cells were significantly more phagocytosed than G164 cells in the pentaculture models after seven days of culture (Fig. 5B). Of note, in models without macrophages (i.e. tetracultures) the number of viable malignant cells was similar for all three cell lines after 7 days culture (Supplementary Fig. 5A).

To visualize and quantify the spatial relationship between macrophages and malignant cells in our models, we used a technique developed by us on mouse and human ex vivo tumor slices40. Spinning disk fluorescence imaging of entire pentacultures showed that significantly more macrophages, stained in green for CD45+/CD14+, were located less than 20 µm from malignant cells in the AOCS1 and OvCAR3 pentacultures compared to G164 pentacultures (Fig. 5C, D). Time-lapse recordings of live cultures also enabled the assessment of trajectory length, speed, and macrophage behavior based on movement metrics such as maximum displacement and trajectory length (Supplementary videos 1–3). Using these parameters, we were previously able to identify four different types of immune cell behavior: static, wobbling (defined as visually moving cells that present either with non-directional migration or dynamic cytoplasmic extensions over time), migrating and long migrating40. Analysis of macrophage trajectory length, speed, and straightness (Fig. 5E, F and Supplementary Fig. 5B), as well as the different proportions of macrophages classified by behavior (Supplementary Fig. 5C), revealed similar types of cell behavior in the pentacultures. Moreover, macrophages in the pentacultures had similar behavior characteristics to macrophages in HGSOC patient biopsies and mouse HGSOC models40. This suggests that macrophage behavior is consistent across the models, but their functions may be influenced by spatial localization within the microenvironment.

Given the role of “do-not-eat-me” signals, such as CD47 and CD2432, in modulating phagocytosis, we examined their membrane expression on the malignant cells. We found that CD47 protein (Fig. 5G) and RNA (Fig. 5H) were significantly higher in OvCAR3 and G164 cells grown in 2D compared to AOCS1, consistent with CD47 copy number alterations (Fig. 5I). Similar results were observed for CD24 expression in 2D malignant cell lines (Supplementary Fig. 5D). We then looked at the expression of these markers in the pentacultures and found similar results: CD47 and CD24 remained highly expressed at both RNA and protein levels in OvCAR3 and G164 pentacultures compared to AOCS1 (Fig. 5J and Supplementary Fig. 5E). As phagocytosis inhibition by CD47 and CD24 requires interaction with their respective macrophage receptors, signal regulatory protein α (SIRPα) and sialic acid-binding Ig-like lectin 10 (SIGLEC10), we looked at the expression of both macrophage membrane markers in our models. We found that SIRPα was highly expressed at both RNA and protein levels across all the pentacultures (Fig. 5K), while SIGLEC10 was significantly expressed only by the OvCAR3 associated macrophages (Supplementary Fig. 5F). The low CD47 levels in AOCS1 explained the high phagocytosis observed, while the elevated CD47 expression in G164 might account for less phagocytosis by macrophages. However, the high CD47 levels and increased phagocytosis in the OvCAR3 pentaculture were unexpected. We looked back at CD47 expression in OvCAR3 cells using flow cytometry and found that these cells exhibit heterogeneous CD47 expression with 33.5% low and 68.8% high expressing cells (Supplementary Fig. 5G). We hypothesized that low-CD47 OvCAR3 cells were preferentially phagocytosed in the model, while the cancer cells remaining at the end of the culture have higher level of CD47.

CD47 expression in human tumors has been widely studied, but we wanted to investigate if the patterns of expression in omental biopsies of HGSOC were also variable. We performed CD47 staining on omental biopsies from 43 patients and observed variable staining patterns. Tumor cells in the biopsies exhibited either uniform CD47 positivity, complete absence of staining, or a heterogeneous (patchy) pattern characterized by regions of both high and low CD47 expression (Supplementary Fig. 5H), suggesting that similar mechanisms may be at play in patients. Moreover, to investigate potential clinical outcomes when macrophage activity is engaged, we analyzed a previously published ovarian cancer dataset of matched pre- and post-chemotherapy samples41. We found a significant increase in phagocytic activity in post-chemotherapy samples from patients who had a good response to chemotherapy (late relapse patients) compared with samples from patients who relapsed within six months from the end of treatment (Fig. 5L). This observation suggests that successful treatment or post-treatment engagement of macrophage phagocytic capacity might provide more sustained response to therapy.

Overall, we observed significantly higher macrophage uptake of red-labeled cancer cells as an indicator of phagocytosis in OvCAR3 and AOCS1 pentacultures compared to G164. These findings, supported by analyses of macrophage-cancer cell interactions and localization, suggest that the CD47-SIRPα and CD24-SIGLEC10 pathways may contribute to these differences.

Anti-CD47 and anti-CD24 mAbs enhance macrophage phagocytosis and reduce cancer cell number in OvCAR3 but not G164 models
Given that targeting “do-not-eat-me” signals can effectively modulate macrophage function42, we next tested monoclonal antibodies (mAbs) against CD47 and CD24 in our pentaculture models. Treatments with an anti-CD47 mAb or anti-CD24 mAb did not affect the overall percentage of viable cells in the pentacultures (Supplementary Fig. 6A). As expected, in AOCS1 pentacultures, which express very low levels of CD47 and CD24, treatment with either mAb had no effect on cancer cell proportion and did not increase phagocytosis (Fig. 6A, B and Supplementary Fig. 6B). The G164 pentaculture models showed no significant changes in cancer cell amount or phagocytosis following mAb treatment (Fig. 6C and Supplementary Fig. 6C), although we observed a reduction in CD47 and CD24 staining following the treatments indicating that the antibodies have successfully bound to the target (Supplementary Fig. 6D). To investigate this further, we examined macrophage localization in the G164 pentaculture models using immunofluorescence. We found that in both control and anti-CD47 treated G164 pentacultures, macrophages resided primarily in stromal areas (Fig. 6D). We hypothesized that the absence of phagocytosis in this model might reflect the localization of the macrophages or additional mechanisms of phagocytosis resistance in G164 malignant cells that prevent macrophage recruitment to, or recognition of, the cancer cells.

In contrast, treatment of OvCAR3 pentacultures with either anti-CD47 or anti-CD24 antibodies led to a significant reduction in numbers of cancer cells, accompanied by a marked increase in phagocytosis (Fig. 6E and Supplementary Fig. 6E). Live imaging analysis further revealed an increased number of macrophages in proximity to cancer cells following anti-CD47 mAb treatment compared to control (Fig. 6F, G and Supplementary Fig. 6F, Supplementary Videos 4, 5). Additionally, together with the increase in phagocytosis, we observed an increase in ‘wobbling’ macrophages following treatment, accompanied by a decrease in their trajectory straightness and speed (Fig. 6H, I and J), suggesting that the wobbling behavior might be indicative of an increased activity of the macrophages.

One hypothesized mechanism of resistance to anti-CD47 treatment in our G164 pentaculture model is the failure of macrophages to move from the stromal areas. As the inhibition of TGFβ has been shown to enhance immune cell motility in in vitro and in vivo43 and TGFβ pathway expression is elevated in our pentaculture models and in biopsies from HGSOC patients44, we utilized a TGFβ receptor inhibitor (TGFβRi), previously tested in our models with promising results in terms of enhanced CAR-T cell motility and reduction of poor prognosis-associated extracellular matrix (ECM) components30,45. Treatment of G164 pentacultures with TGFβRi led to a significant reduction in the number of viable cancer cells (Fig. 6K) without affecting the overall viability of the cultures (Supplementary Fig. 6G). However, the reduction in cancer cell number did not correspond to an increase in macrophage-mediated phagocytosis (Fig. 6K). To determine whether this effect was due to direct toxicity of the TGFβRi on G164 cancer cells, we treated a G164 tetraculture (lacking macrophages) with the TGFβRi and observed no significant change in viable cancer cell number (Fig. 6L). This suggests that the observed reduction in cancer cells in the pentaculture requires the presence of macrophages, which are not actively phagocytosing cancer cells, but could contribute to cancer cell death through alternative mechanisms, such as the release of cytokines or chemokines. Indeed, there was an increase TNFα release in G164 pentacultures following TGFβRi treatments suggesting that TNFα could be one of the cytokines mediating this effect (Fig. 6M).

In summary, our results demonstrated that the pentaculture model may be useful for studying macrophage-targeted therapies and macrophage-malignant cell interactions. We found that anti-CD47 and anti-CD24 mAb treatments significantly reduced cancer cell numbers and enhanced phagocytosis in the OvCAR3 model. However, no such effects were observed in the G164 model, possibly due to an alternative resistance mechanism and/or macrophage localization, or in the AOCS1 model because of its low expression of both CD47 and CD24. However, treatment with TGFβRi reduced the number of viable cancer cells in the G164 pentaculture but not in the G164 tetraculture, suggesting that macrophages may contribute to cancer cell death through alternative mechanisms in this model.

Methods
Patients samples and study approval
Human samples used in this research were obtained from Royal London Hospital under SIGNPOST (Systematic GeNetic Testing for Personalised Ovarian Cancer Therapy) study ethics (REC17/LO/0405) and Royal Marsden Hospital (REC20/LO/0529). All ethics used for this study were approved by a UK review board. Experiments were conducted in accordance with the Declaration of Helsinki and International Ethical Guidelines for Biomedical Research Involving Human Subjects. Omental tissues deemed by a lead surgeon to be surplus to diagnostic requirement were collected after the provision of written informed consent. Macroscopically normal omental tissues were kindly donated by patients undergoing gynecological debulking surgery, either primary debulking surgery (chemo naïve) or interval debulking surgery (after three to six cycles of chemotherapy). For the RNAseq experiment of Fig. 2, three chemo naïve samples have been used to avoid any bias.

Primary cell line isolation
Human omental mesothelial cells, fibroblasts and adipocytes were isolated from macroscopic normal omentum through a series of digestion and centrifugation following the protocol described previously33. Briefly, 10 cm3 of the normal omentum was incubated in a solution of DPBS and Trypsin-EDTA 10X (Gibco, Cat. 15400054) 1:1 for 20 minutes at 37C. After the incubation, tissue was removed and equivalent amount of DMEM:F12 Glutamax with 1% pen/strep, 5% FBS was added to the solution to neutralize the trypsin. The solution was spun down at 300g for 5 min. The pellet, recognized as mesothelial cells, was then plated in mesothelial cell medium. The tissue was then minced in small pieces and digested using Liberase TL 1mg/mL (Roche, Cat. 5401020001) in 20ml of DMEM:F12 Glutamax with 1% pen/strep, 5% FBS for 30-45 min at 37C with gentle shaking. Digested tissue was then filtered using 250 µm filters (Thermo Fisher, Cat. 87791). The filtered tissue was spun down at 300g for 3 min to separate the adipocyte content (top layer) from the pellet, stromal vascular fraction, which represents the fibroblasts. Isolated mesothelial cells and fibroblasts were cultured in DMEM:F12 Glutamax medium, supplemented either with 20% FBS for the mesothelial cells or 10% FBS for the fibroblasts, and 1% pen/strep until use. Mesothelial cells and fibroblasts were previously characterized using different markers, such as calretinin, α smooth muscle actin (αSMA) and/or fibroblast activation protein (FAP)29. Adipocytes were cast immediately in a collagen I based gel (Thermo Fisher, Cat. A1048301) following the protocol previously described33 and kept in M199 medium (Gibco, Cat. 22350029) supplemented with 10% FBS and 1% pen/strep until further use.

Cell culture
AOCS1 cells were kindly gifted from Prof Bowtell from the Australian Ovarian Cancer Study. G164 cells were established in our laboratory from omental HGSOC tumors and are well characterized34. HGSOC cell line, OvCAR3 (ATCC, Cat. HTB-161), was obtained from ATCC. AOCS1 and OvCAR3 cell lines were maintained in RPMI 1640 (Gibco, Cat. 21875034) supplemented with 10% fetal bovine serum (FBS, Gibco, Cat.), 1% L-glutamine (Sigma, G7513-100ML) and 1% penicillin/streptomycin (pen/strep) (Sigma, Cat. P4333). G164 cell line was maintained in DMEM:F12 medium (Gibco, Cat. 31331093) with the addition of 4% human serum (Sigma, Cat. H4522) and 1% pen/strep. For all the experiments, 2D and pentaculture, all the cells and pentaculture were maintained in the G164 medium to standardize the culture conditions. To better visualize the malignant cells, we have also used m-Cherry lentiviral transfected AOCS1, G164 and OvCAR3 (Sartorius, Incucyte® Nuclight Red Lentivirus (EF1a, Puro), Cat. 4476; Cambridge Bioscience, Cat. BPS-78932-P; Puromycin, Gibco, Cat. A1113802). STR sequencing (ATCC, 135-XV) and mycoplasma testing (InvivoGen) were carried out routinely.

Isolation of CD14+ monocytes from peripheral blood samples
Blood was collected from healthy donors and purchased in the form of leukocyte cones. CD14+ monocytes were positively selected using CD14 MicroBeads (Milteny Biotec, Cat. 130-050-201) and LS columns (Milteny Biotec, Cat. 130-042-401) following the manufacturer protocol. CD14+ isolated monocytes were then subjected to purity check using flow cytometry. All the monocyte donors used throughout this work (n=8) were 93-98% pure.

Three-dimensional models setup
For the pentaculture model, adipocyte gels were anchored to the bottom of the well of a 96-well plate using a layer of activated collagen as described before33. 40,000 human primary fibroblasts were plated on the adipocyte gels, followed after 2 hours by 200,000 mesothelial cells per gel. After 24 hours, 40,000 malignant cells and 400,000 CD14+ monocytes per gel were added to the model. After 24 hours, gels were scooped out and placed in G164 culture medium in a 24-well plate, 3 gels per well.

Enzyme-linked immunosorbent assay (ELISA) and Mesoscale discovery (MSD)
Supernatant subjected to ELISA and MSD analysis were collected at the selected time point. Media were spun down 8000g for 5 minutes to remove cellular debris and subsequently stored at -80C until use. ELISA was used to quantify CXCL10 (Bio-Techne, Cat. DIP100) and CCL4 (Bio-Techne, Cat. DMB00) according to manufacturer’s protocol. ELISA plates were read using FLUOstar optima microplate reader (BMG Labtech). MSD was performed using the V-PLEX Proinflammatory Panel 1 human kit, the V-PLEX Cytokine Panel 1 human kit and the V-PLEX Chemokine Panel 1 Human Kit (MSD, Cat. K15054D-1) following the manufacturer’s instructions. Plates were read on the MSD sector 600 imager (MSD).

Flow cytometry
Pentaculture gels were digested using 1mg/ml Liberase TL (Merck, Cat. 05401020001) for 10 min and then spun down at 5,000g for 5 minutes. Cells were then resuspended and incubated in 1X PBS with 1:100 Human TruStain FcX (BioLegend, Cat. 422301) and 1:1000 fix viability dye eFlour 780 (Thermo Fisher, Cat. 65-0865-14). Cells were spun down and resuspended in FACS buffer (1X DPBS + 2mM EDTA + 2.5% BSA) with the fluorescent antibody mix. Each fluorescent antibody was diluted 1:200 in FACS buffer. Cells were then fixed using a solution 1:1 of 10% formalin buffer solution and 1X PBS. Data was the acquired on LSR fortessa cell analyzer (BD Bioscience) and analyzed using FlowJo v10.1.5 (BD Bioscience). A table with the antibodies used, catalogue numbers and dilution is provided in Supplementary Data 6.

Bulk RNA-sequencing (RNAseq)
RNA isolation was performed using RNeasy microkit (Qiagen, Cat. 74004) according to manufacturer’s instructions. RNA sequencing was performed by the Wellcome Trust Centre for Human Genetic (Oxford, UK) to an average of 62 million reads per sample. Library preparation was performed using ribosomal depletion and a strand-specific protocol and sequencing was carried out generating 150 base pairs (bp) paired-end reads on the NovaSeq600 platform. FastQC v0.11.5 was performed and quality trimming was applied with trim galore v0.6.5. Alignment to the reference genome GRCh37 was performed using STAR v2.7.9a (PMID: 26334920). Counting of the reads was performed with RSEM v1.3.1 using the Ensembl annotation GRCh37.87. Only genes that achieved at least one count per million (CPM) in at least 25% of the samples were kept and a log2 RPKM normalized gene expression matrix was generated. To identify clusters of differentially expressed genes the sam function from R package siggenes was used followed by kmeans clustering. Gene set enrichment analysis for Gene Ontology Biological processes on gene clusters was performed using R package ClusterProfiler. Gene set variation analysis (GSVA) was performed with R package gsva. Gene set enrichment analysis for phagocytosis on the Adzibolosu et al. dataset, GSE227100, was performed using R package fgsea on the ranked log fold-changes using the GOBP_PHAGOCYTOSIS.v2024.1.Hs.gmt gene-list. RNA-seq data have been deposited in Gene Expression Omnibus under the accession number GSE293887.

Whole Genome Sequencing
Genomic DNA was isolated and sequenced at Novogene. Whole genome sequencing was performed on the NovaSeq X Plus Series, with 150bp, paired-end reads, yielding 90G of raw data per sample (~300 million sequencing reads per sample, 30x coverage). After initial quality check using fastqc v0.11.5, reads were quality trimmed using trim galore v0.6.5 and aligned to the human reference genome GRCh37 (hg19) using bwa v0.7.17. Picard v2.25.7 was used to mark read duplicates and assess insert size distributions. Base quality score recalibration was performed on known sites of variation using gatk v4.2.1.0 and Homo_sapiens_assembly19.dbsnp138.vcf, Homo_sapiens_assembly19.known_indels.vcf Mills_and_1000G_gold_standard.indels.b37.vcf, downloaded from the GATK bundle. Variant calling was performed using mutect275 on tumor only mode using gnomad.exomes.r2.1.1.sites.vcf.bgz as a germline resource. VCF files were further filtered using gatk FilterMutectCalls followed by filtering out annovar of variants from 1000 genomes (1000g2015aug_all and the exome sequencing project esp6500siv2_all with maf < 0.01. Annotation with refgene, avsnp150, cosmic92_coding and cytoband was performed in annovar. Further filtering was subsequently applied to obtain variants with sequencing depth DP > 10 and AF > 0.1. Non synonymous mutations with cosmic ID were used for further downstream analysis and graphical representations. CNV analysis was performed with R package ACE. Ploidy of OvCAR3 is reported in ATCC, ploidy of AOCS1 and G164 cells has been determined previously34. Activity of the ovarian CIN signatures was calculated using britroc.cnsignatures functions from37. Activity of the 17 pan-cancer CIV signatures was calculated using R package CINSignatureQuantification (ref. 38).

Immunohistochemistry (IHC)
Pentaculture gels were fixed in 10% formalin buffer solution (Sigma, Cat. HT501128) and subsequently embedded in 2% agarose (Thermo Fisher, Cat. 10264544) in PBS prior to paraffin embedding and sectioning. Sections were then deparaffinized by incubating the sections at 60C for 30 minutes followed by two washes in xylene. Rehydration was performed using a sequence of washing in 100%, 90%, 70% and 50% ethanol solutions. Antigen unmasking was performed using a 1x antigen retrieval Tris-EDTA buffer, pH 9.0 (abcam, Cat. ab93684) depending on the antibody used. Peroxidase enzymes were blocked with 0.6% hydrogen peroxide (H2O2; Fisher Scientific, Cat. H/1800/15) in methanol. Sections were then blocked using 2.5% goat serum (Life Technologies, Cat. 16210064) + 2.5% BSA in 1X PBS and subsequently incubated with primary antibody for 1 hour. Impress goat anti-rabbit or anti-mouse kit (Vector Laboratories, Cat. MP2737451-50 or MP-7452-15) followed by DAB (Dako, Cat. K346811-2) chromogen staining was used for antibody detection. Sections were counterstained in Gill’s I hematoxylin (Merck, Cat. CI75290) and dehydrated with washes in 50%, 70%, 90% and 100% ethanol solutions, followed by xylene. Sections were then mounted in DPX resin (Sigma-Aldrich, Cat. 06522) and imaged using a Pannoramic 250 high throughput scanner (3D Histech). A table with the antibodies used, catalogue numbers and dilution is provided at the end of the manuscript (Supplementary Data 6).

Treatments
At day 3 and 5, cells or gels were treated with 10 µg/ml anti-CD47 mAb, magrolimab (Cambridge Bioscience, Cat. HY-P99029) or 10 µg/ml anti-CD24, mAb, ATG-031 (Cambridge Bioscience, Cat. HY-P99176) diluted in G164 culture medium. Human IgG4 κ isotype control was used as control in these experiments (Cambridge Bioscience, Cat. HY-P99003). G164 pentaculture were also treated with 20 μmol/L of TGFβ receptor inhibitor SB431542 (Sigma-Aldrich, Cat. S417). Conditioned media were collected at 7 days, unless otherwise indicated and stored at -80C until further analysis.

Western blot (WB)
Cells were lysed with RIPA buffer (Sigma, Cat. R0278) containing 1:100 protease inhibitors (Roche, Cat. 11836153001) and 1:1000 phosphatase inhibitors (Sigma, Cat. P0044). 40 μg of protein were loaded into a 4-12% NuPAGE Bis-Tris gels (Invitrogen, Cat. NP0321BOX). Samples were run in 1x NuPAGE running buffer (Invitrogen, Cat. NP0001) and transferred onto a nitrocellulose membrane (Immobilon, Cat. IPVH00010) in 1x NuPAGE transfer buffer (Invitrogen, NP0006-1). The membrane was blocked in 5% BSA in TBS-T (0,05%) for 1 hour at room temperature. The membrane was incubated with primary antibodies antibody at 4°C overnight, followed by incubation with secondary horseradish peroxide (HPR) conjugated antibodies (Dako, Cat. P0160) for 1 hour at room temperature. HRP activity was visualized with Amersham Imager 600 (GE Healthcare).

Pentaculture live imaging
At day 7, pentacultures were washed in 1X PBS and placed in a 96-well plate in 100 µl of Human TruStain FcX (BioLegend, Cat. 422301) for 15 min at 37°C. After blocking non-specific binding, antibodies diluted 1:10 in phenol red–free RPMI 1640 medium were incubated with the pentacultures for 15 minutes at 37°C and subsequently washed twice with phenol red-free RPMI 1640 for 2 min at 37°C. Gels were then transferred to a glass bottom dish, covered with phenol red–free RPMI 1640 medium and secured with a tissue slice anchor.

Gels were imaged using Nikon TE Eclipse confocal microscope equipped with a spinning disk and a temperature-controlled chamber set at 37°C, with 5% CO2. Slices were imaged every 15 seconds for 30 minutes. Images were analyzed using Imaris v9.9 (Oxford Instruments) and migration metrics were aggregated in R v4.2.2 and RStudio 2022.12.0.353, using a custom built R script, as previously described71.

Immunofluorescence
At day 7, pentaculture gels were washed with 1X PBS and fixed in 10% neutral-buffered formalin for 30 min at room temperature (RT). Samples were then permeabilized and blocked for 2 h at RT in blocking buffer consisting of 1% bovine serum albumin (BSA), 0.5% Triton X-100, 0.05% Tween-20, 2% goat serum, and 0.01% sodium deoxycholate in PBS. Unconjugated primary antibodies were diluted in blocking buffer and incubated with the gels overnight (o/n) at 4 C on a shaker. The following day, gels were washed three times for 10 min in PBS containing 0.05% Tween-20 (PBS-T). Fluorescent secondary antibodies were diluted 1:500 in blocking buffer and incubated with the gels for 4 h at RT on a shaker, followed by three 10-min washes in PBS-T. Where applicable, fluorophore-conjugated primary antibodies were subsequently added (diluted in blocking buffer) and incubated o/n at 4°C. On day three, gels were washed three times for 10 min in PBS-T. Nuclei were stained with DAPI (1:5000 in PBS) for 10 min at RT, rinsed with PBS, and imaged without mounting using glass-bottom dishes. Images were acquired using an Evident FV4000 confocal microscope. Image analysis was performed using Imaris v9.9 (Oxford Instruments) and Fiji/ImageJ v2.17.0.

Real-time qPCR (RT-qPCR)
RNA isolation was performed using RNeasy microkit (Qiagen, Cat. 74004) according to manufacturer’s instructions. RNA purity and quality was checked evaluating the 260/280 nm ratio. cDNA was then synthesized from 800 ng of total RNA using the Vilo cDNA kit (Invitrogen, Cat. 11754-050).

Applied Biosystems® QuantStudio™ 7 Flex Real-Time PCR was used to analyze CD24 gene expression. Taqman Gene Expression Assay Kits (Hs02379687_s1, Invitrogen) were purchased to examine CD24 gene expression and HRPT1 and GADPH as housekeeping genes.

Statistical analysis
All statistical analysis were performed with the software Graphpad Prism (San Diego, CA, version 9.0.0). Kolmogorov–Smirnov test was used to determine whether the data was normally distributed. Differences between two groups were analyzed using an unpaired student t-test. Mann–Whitney test was used for nonparametric data. Paired samples were analyzed using the Wilcoxon test. Statistical differences between multiple groups were evaluated using multiple unpaired t-test, one-way or two-way analysis of variance (ANOVA). p values, along with n numbers are displayed in the figures.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Malacrida et al. present a pentaculture model incorporating HGSOC cancer cell lines alongside primary environmental cells, including donor-derived monocytes, to analyze their impact. The robustness of the study is a concern, as key experimental controls are often missing and statistical support is inconsistently applied. Several claims, particularly those based on bulk RNA sequencing, appear overinterpreted given the available data. Furthermore, it is unclear how the presented model meaningfully advances beyond the authors’ previous work or compares to more advanced organoid-based systems that already incorporate multicellular microenvironments (e.g., Polak et al., Nature Reviews Cancer, 2024). The investigation into the influence of cancer cells on macrophages is not well executed and lacks sufficient contextualization within the existing literature or in comparison to other in vitro models. Similarly, the exploration of CD47 and CD24 is not new and lacks sufficient referencing. While the proposed alternative resistance mechanisms could represent a more compelling direction, they remain preliminary and are not supported by clear mechanistic insight. Overall, the structure of the manuscript could be revisited and streamlined, particularly in how the model is characterized and contextualized relative to existing in vitro systems. See further comments below.\n- Although the pentaculture is maintained for 14 days, most analyses are conducted at day 7. If day 14 is not a relevant time point due to suboptimal culture conditions, it should be omitted; if it is relevant, it warrants proper analysis. Overall, the choice of the most appropriate time point should be clearly justified and supported by data. For example, what are the proportions of the different cell types, and how do these change over the course of the culture? The authors should include a brightfield or other microscopy-based time course over the 14-day period, along with measurements of cell viability, proliferation, and cell death.\n- Why did the authors choose to use cancer cell lines rather than primary tumor cells? Using primary material could provide greater physiological relevance.\n-The authors conclude from bulk RNA sequencing that unsupervised clustering segregates samples according to malignant cell types rather than the donor of the normal cells, suggesting that malignant cells are the primary drivers (Figure 2B). However, this conclusion is not supported by this presented analysis. Bulk RNA-seq inherently masks cellular heterogeneity, making it insufficient to attribute clustering solely to malignant cell types. Moreover, what stands out more clearly is that the Pentacultures cluster together as a group, and not with their corresponding cancer cell types. If the authors aim to explore intercellular influences or the contribution of specific cell types within these cultures, single-cell RNA sequencing would be a more appropriate and informative approach.\nIn Figure 2C, how can the authors conclude that differentiation into macrophages occurred in their culture based solely on GO term analysis? These GO terms are also enriched in monocytes, so this does not provide definitive evidence for macrophage differentiation.\nIn Figure 2D, it is unclear which GO terms specifically support the conclusion of a tissue-like environment. The figure is poorly presented and does not convincingly support the authors' claims. Simply listing GO terms feels more like a supplementary figure. Higher-resolution data, with single-cell RNA sequencing, along with validation of key marker genes at the protein level, with appropriate controls, would be necessary to robustly substantiate the findings.\n- It is unclear whether the genomic and transcriptomic profiling of the cancer cell lines was performed on the isolated cell lines or within the context of the Pentaculture system. This distinction is important, as the microenvironment can significantly influence cancer gene expression. Additionally, this characterization of the cancer cell lines feels disconnected from the main narrative of the manuscript. The conclusions drawn regarding their relevance to macrophage states are underdeveloped and lack clear integration into the broader context of the study. Also, it is broadly known in many cancer and litterature on this topics should be thoroughly reviewed by the authors (e.g. Weverwijk, De visser, nature review cancer 2023)\n- Statistical analyses are missing from many figure panels, but their inclusion would be important to appropriately support and nuance the authors' claims.\n-Legends need more information\n- Fig 1 D axis labeling and legend not clear.\n- The graphical presentation in Fig. 1F is somewhat unclear. Were multiple donors analyzed? Additionally, could you include data for day 0 (monocytes) and day 14?\n- In Fig3, I-J, I am missing the monocytes as control.\n- Although the video quality is high, the objective and key observations are not clearly communicated. Adding annotations and labels would greatly enhance clarity by guiding the viewer to the relevant events. Specifically, it is not evident that CD47 blockade results in increased phagocytosis. If this is a key conclusion, more direct visual evidence of phagocytosis should be shown. Additionally, the significance of the observed wobbling motion is unclear, why should this be interpreted as an indicator of phagocytic activity? Clarification or supporting rationale would be helpful.\n-The conclusion that increased phagocytic activity in post-chemotherapy samples from responders, compared to those from relapsed patients, implies that enhancing macrophage phagocytic capacity may improve chemotherapy response is not well supported. An alternative explanation could be that effective chemotherapy leads to greater tumor cell death and debris accumulation, which in turn triggers macrophage activation and phagocytic activity as a secondary effect, rather than a cause of therapeutic success. Could the authors clarify whether their data distinguish between these possibilities, and comment on the directionality of this relationship?", "reviewer_2": "Malacrida et al. report the development and initial characterization of high grade serous ovarian cancer (HGSOC) “pentacultures”. This organotypic cell system includes cancer cells, mesothelial cells, fibroblasts, adipocytes and monocytes, the latter of which differentiate into macrophages in the culture. The adipocytes, mesothelial cells, and fibroblasts are obtained from omental biopsies and cultured in vitro. Three separate donors are used for this study, although the authors imply that they have studied many more (see below). The monocytes are purified from peripheral blood of a single donor. Using these pentacultures, the authors report several key findings. First, tumor cells seem to drive most of the macrophage polarization in the cultures, as shown by differences in transcriptomic and proteomic data, which had more effect than varying the origin (i.e. donors) of the other cell types. Second, each HGSOC cell line drives different degrees of macrophage polarization and different spatial architecture. Third, the authors report intriguing macrophage-directed effects on tumour cell viability, which could, at least for one cell line, be modulated with monoclonal antibodies targeting “do not eat me” signals.\nOverall, this is an interesting, well written, and technically well-performed study that should be of general interest to the readership of Nature Communications. There are, however, some conceptual and technical issues that should be addressed in a revision.\nMajor Issues:\n(1) A penta-culture (actually, a sexta-culture, as it includes endothelium) of the ovarian omentum, incorporating a macrophage cell line, has been described previously (Estermann et al., Biomaterials, 2023), albeit that work was more focused on dissemination of HGSC within the omentum than on the issues discussed here. In some ways, the authors of the current paper have also reported a 5-cell type culture (see Malacrida et al. 2021, Figure 4F), although that work focused on platelets, not macrophages, The authors should discuss the similarities and differences between their system and the previous reports. Also, they should emphasize the novelty of this paper, which is the demonstration that tumour cells drive the TME (at least those components of the TME that are present in this culture), and the “don’t eat me signal” aspects. The utility of their system in enabling the study of monocyte-> macrophage differentiation should also be discussed.\n(2) Surprisingly, the authors do not comment on whether the tumor cells also drive fibroblast differentiation to CAFs (or induce additional fibroblast/CAF differentiation from mesothelium). Alternatively, does culturing the primary omental fibroblasts in vitro prior to their incorporation into pentacultures induce activation similar to that of CAFs? Several reports in other tumor systems have emphasized the role of CAFs in directing macrophage polarization, so this point would seem to be important.\n(3) The authors state that they used 3 donors for the omentum-derived cells for this study, but in the Discussion (line 366), they say there are >50 donors for the first three cell types and 5 for the monocytes. To what extent have these other donors been characterized in this system? Also, what types of surgeries were the patients who donated ometum undergoing (i.e., what were their underlying disorders)?\n(4) In Figure 1, the authors indicate that monocytes spontaneously differentiate into macrophages when introduced into the quad-culture model. An important missing control would seem to be incubating the monocytes without any cancer cells; have the done this? Also, even if the cancer cells are necessary to start the monocyte differentiation process, are they sufficient? Or is there cross talk with other cell types (e.g., CAFs, as per point 2)?\n(5) The authors perform the studies in this paper over 7 days. How long do the cultures last beyond that? Also, they comment (and show data that) the viability of the cancer cells is preserved over the 7-day period, but what about the other cell types?\n(6) They note that a major limitation of the current work is the absence of adaptive immune cells (I would include other innate immune cells, e.g., gMDSC/neutrophils, NK cells). A priori, it would seem that at least some of these could be added. Could the authors comment on whether they have tried to do so in the Discussion. Also, is there a reason why they chose to use immortalized HGSOC lines instead of primary tumor cells or organoids?\n(7) Line 151-154: the authors state that CIBERSORTx ..revealed that fibroblast and mesothelial cells were present at similar abundance..”, but to my eyes, it looks like there are significantly more of these cells in AOCS1 cultures—am I wrong?\n(8) Lines 159-60, the authors correctly note that the percentage of G164 cells is significantly higher by flow, as predicted by CIBERSORTx. But why are AOCS1 and OVCAR1 identical when they are significantly different in the CIBERSORTx analysis?\n(9) Line 197, Why don’t the authors also comment on CDK12 loss in this line? And what about CCNE1 amplification in OVCAR3?\n(10) Lines 311-313, referring to the G164 pentacultures, the authors state that macrophages resided mainly in the stroma. But from the micrograph in 6D, it looks mainly like there are far fewer macrophages, period. Could they comment?\n(11) The quantification in Fig. 5D does not agree with the photo in 5C, at least to my eyes.\nMinor issues:\n1) Line 39, HGSOC is misspelled; Line 306 “the” should be capitalized.\n2) In Fig 1A, I found it difficult to see the fibroblasts in the cartoon. I suggest using a different color for more contrast.\n3) Lines 104-106; the authors state that “…pentacultures retained a substantial population of monocytes, as indicated by CD45+ staining…” But macrophages are also CD45+? How can this statement be used to support the retention of MONOCYTES?\n4) Figs. 2C-E are too small to see without magnification (i.e., when printed out at 1x). So are Fig. 4D and 6A and Supp Figs 2 and 3 (especially those numbers in blue boxes).\n5) Figure 2F. To facilitate reader interpretation, I recommend adding arrows in the graph that indicate the change of the center of mass of each population between days 3 and 7\n6) Why aren’t all the chemokines and cytokines presented in the same order in Figure 3I for pentaculture and 2D cultures?\n7) Line 205, CIN should be defined as chromosomal instability when used for the first time.\n8) Lines 212-214 is worded awkwardly. Also, flow cytometry does not measure “expression”; it measures “surface expression” (e.g., Fig 1 legend, and elsewhere in the paper”", "reviewer_3": "The authors present an interesting preclinical model for high-grade serous ovarian cancer, incorporating tumor cells, primary fibroblasts, mesothelial cells, adipocytes, and monocytes using three previously established cell lines.\nThis manuscript builds on their previous work on “tetracultures”, further incorporating monocytes from healthy donors to their TME model. They show that the monocytes differentiated into macrophages, without exogenous cytokines, in the pentacultures. The authors then describe the model using IHC, flow cytometry RNAsequencing and whole genome sequencing. They highlighted the potential influence of malignant cell genomic, transcriptomic and proteomic heterogeneity on the TME. The findings on the functional characterization are interesting. The work is highly relevant and timely; however, the strength of the conclusions and translational impact would be improved with more detailed characterisation of the models and source tumors.\n1. For the representativeness of the models: Fig 1 B and S Fig 1b: is the human biopsy from the same patient? It is difficult to assess the cell morphology. To conclude how well the models match the human tumors, it would be important to should show 1) matching human tumor and model H&E stainings 2) higher magnification of H&E to allow for assessing the cell morphology 3) It is difficult to see any tumor cells in the models - the authors should show also tumor marker stainings e.g. CK7, PA8 in the models. Based on Fig1 B and SFig 1B, HLA-DR is widely expressed and most of the cells seem to be macrophages and tumor cells cannot be identified from the figures. A4) are fibroblasts and mesothelial cells present in the tumors and in the cultures? Would be important to see stainings for these cell types as well in both the tumors and in the models.\n2. Row 101: “AOCS1 and OvCAR3 cancer cells were located close to the stromal cells in the pentacultures, while G164 cancer cells generally formed clusters away from the stromal cells.” Where is this shown? Is this quantified? Deeper characterisation of the full composition (see point 1) would also better support the conclusion that can be made on the influence of the malignant cells on the morphology and composition of the models.\n3. For the RNAseq and analysis: Fig 2 a - it is difficult to assess which conditions were sequenced and compared. This figure could be improved to better show the different conditions and comparisons. Figure 2 b is not really informative as part of the results, but rather shows sample-specific clustering. Was there a batch effect? Was that corrected for?\n4. Tissue composition: bulkRNAseq deconvolution has known biases for decomposing the signal - the authors should validate these findings using another technology, for example, immunofluorescence or flow cytometry.\n5. Fig 3: the tumor cell fractions were quite low after - could this be corrected using higher numbers of cancer cells when establishing the cultures? Did the authors test different ratios of the cells in the setup of the pentacultures?\n6. How much of the transcriptional differences of the pentacultures stem from the significantly different gene expression profiles of the tumor cells? To show that the differences in the immune/stromal cell phenotypes are not just reflections of the (often poorly tumor signal - scRNAseq (or bulk from flow-sorted populations) from the pentacultures would significantly strengthen the conclusions.\n7. Colour annotations are missing in Figure 4 B. The value of WGS exploration in only three tumor samples remains limited. Is G164 a functionally HRD tumor by e.g. functional RAD51 staining? Exploring more models with HRD vs HRP tumors would be really interesting!\n8. In the end, it remains unclear how well do the observed models match the clinical tumors in terms of composition and phenotype. It would strengthen the conclusion if the authors had the original tumor samples, and were able to better show that they recapitulate the source tumors. Further, the conclusion that the tumor genotypes shape the TME would need significantly more models to be established to reach statistical conclusions. E.g. the clinical relevance of these phenotypes could then be expanded to other public clinical datasets for increased impact.\n9. Figures 6 G-J only show two datapoints per condition and the statistical comparisons - please add biological replicates to these experiments."}, "author_response": "REVIEWERS COMMENTS\nReviewer #1 (Remarks to the Author):\nMalacrida et al. present a pentaculture model incorporating HGSOC cancer cell lines\nalongside primary environmental cells, including donor-derived monocytes, to analyze their\nimpact. The robustness of the study is a concern, as key experimental controls are often\nmissing and statistical support is inconsistently applied (see Reviewer 1 point 4, for detailed\nresponse) Several claims, particularly those based on bulk RNA sequencing, appear\noverinterpreted given the available data (see Reviewer 1, point 3). Furthermore, it is unclear\nhow the presented model meaningfully advances beyond the authors’ previous work or\ncompares to more advanced organoid-based systems that already incorporate multicellular\nmicroenvironments (e.g., Polak et al., Nature Reviews Cancer, 2024) (see Reviewer 2, point\n1). The investigation into the influence of cancer cells on macrophages is not well executed\nand lacks sufficient contextualization within the existing literature or in comparison to other in\nvitro models (see Reviewer 2, point 1). Similarly, the exploration of CD47 and CD24 is not\nnew and lacks sufficient referencing. While the proposed alternative resistance mechanisms\ncould represent a more compelling direction, they remain preliminary and are not supported\nby clear mechanistic insight. Overall, the structure of the manuscript could be revisited and\nstreamlined, particularly in how the model is characterized and contextualized relative to\nexisting in vitro systems. See further comments below.\n1. Although the pentaculture is maintained for 14 days, most analyses are conducted at day\n7. If day 14 is not a relevant time point due to suboptimal culture conditions, it should be\nomitted; if it is relevant, it warrants proper analysis. Overall, the choice of the most\nappropriate time point should be clearly justified and supported by data. For example, what\nare the proportions of the different cell types, and how do these change over the course of\nthe culture? The authors should include a brightfield or other microscopy-based time course\nover the 14-day period, along with measurements of cell viability, proliferation, and cell\ndeath.\nWe appreciate the reviewer's comment regarding the choice of time points. To improve the\nclarity of our manuscript, we have removed all reference to the 14-day time point. Our\nanalyses focus on day 7 because, as demonstrated by our characterisation experiments, the\nmacrophages within the pentaculture have differentiated by this time point. We have also\nnow included the proportion of the different cell compartments at day 0 and day 7 which\nreflect the viable cells present in the culture at these time points (Reviewer Figure 1 and\nnew Supplementary Figure 1A).\nslle\nc\ne100\nlb\na\niv\nfo\nn\nM\nFi\na\nb\nc\nro\nro\na\np\nn\nh\nd\na g\nM\ne\neso\no itro Cancer cells\np 50\no\rp\nn\no\nita\nlu\np\no\nP\n%\nPenta Penta Penta\nDay 0 Day 7\nReviewer Figure 1 (now Supplementary Figure 1A) - Abundance of the different viable cell populations on the\nmodels at day 0 and day 7 using flow cytometry (n=3). Holm-Sidak multiple unpaired t-tests between the different\nmodels at day 7 indicated that the models differed significantly in their cancer cell percentages: AOCS1 penta vs.\nmanuscript.\n2. Why did the authors choose to use cancer cell lines rather than primary tumor cells?\nUsing primary material could provide greater physiological relevance.\nWe agree with the reviewer that incorporating primary tumour cells could increase the\nrelevance of our model. However, AOCS1 and G164 were derived and established as\nprimary malignant cell lines, without genetic modification or introduction of viral oncogenes.\nAlthough the OvCAR3 cell line was purchased from ATCC, in the manuscript we have\nextensively characterised all three cell lines at the proteomic, transcriptomic and genomic\nlevel. We show that our malignant cells are relevant to the human disease in a similar\nmanner to, for instance, HGSOC organoids. As shown in Figure 4E, the three cell lines we\nhave used exhibit genomic and mutational profiles similar to those of HGSOC patient-\nderived organoids. Furthermore, in addition to our analysis of HGSOC copy number (CN)\nsignatures on the three cell lines, we have conducted an additional exploration (Reviewer\nFigure 2, new Supplementary Figure 4B) using a pan-cancer study defining 17 CN\nsignatures1. Again, we observed that the cell lines exhibit profiles of signature activities\ncapturing a diversity of CIN features that are also observed in human tumours. For these\nreasons, we think that our chosen cell lines, when added into the pentaculture model,\nrepresent genetic and biological characteristics of HGSOC (Figure 4E) and human\nmalignant cells in general (Supplementary Figure 4B) whilst offering an accessible and\npractical in-vitro model platform (lines 254-259).\nReviewer Figure 2 (now Supplementary Figure 4B) – Stacked bar plot showing the abundance of seventeen copy\nnumber signatures across the three cell lines. Copy number signatures were defined according to 1.\n3. The authors conclude from bulk RNA sequencing that unsupervised clustering segregates\nsamples according to malignant cell types rather than the donor of the normal cells, suggesting that malignant cells are the primary drivers (Figure 2B). However, this conclusion\nis not supported by this presented analysis. Bulk RNA-seq inherently masks cellular\nheterogeneity, making it insufficient to attribute clustering solely to malignant cell types.\nMoreover, what stands out more clearly is that the Pentacultures cluster together as a group,\nand not with their corresponding cancer cell types. If the authors aim to explore intercellular\ninfluences or the contribution of specific cell types within these cultures, single-cell RNA\nsequencing would be a more appropriate and informative approach.\nWe acknowledge that bulk RNAseq can mask cellular heterogeneity, making direct\nattribution of clustering solely to malignant cell types challenging without further\ndeconvolution. In the present study, our primary focus was to introduce the pentaculture\n2\nsystem as a model incorporating macrophages – a novelty for in vitro multi-cellular systems.\nIn our RNAseq experiment, by including individual cell types (malignant cells, fibroblasts,\nmesothelial cells and monocytes) alongside the pentaculture samples we were able to\nobserve the enrichment of macrophage and microenvironmental transcriptional programs in\nthe pentacultures compared to the individual cell type controls (Figures 2C-D) which we\nstrengthened with further in vitro and in silico analysis in following figures. In Figures 3 A-D,\nwe used our scRNAseq data from human HGSOC omental metastasis tissue biopsies where\nwe had performed an in-depth characterisation of the macrophage population2 to\ndeconvolute and interpret the bulk RNAseq profiles of our models. These analyses revealed\nthe presence of key macrophage subpopulations consistent with those identified in patient\nmetastases within our pentacultures and the complexity of the macrophage population\nvaried according to the malignant cell line used (as detailed in Figure 3A and 3C). We\nbelieve this validation supports our bulk RNAseq findings albeit the effect of the individual\nstromal cells with a detailed intercellular communication analysis would need further\nexploration in a separate study. We include below a PCA analysis to enhance the\ninformation of the unsupervised clustering and illustrate the complexity of the variance in the\ndata beyond the differences between the malignant cells used in the study (Reviewer\nFigure 3).\nReviewer Figure 3 - PCA plot illustrating sample clustering on the first two principal components and scree plot of\nthe variance captured over ten principal components.\n4. In Figure 2C, how can the authors conclude that differentiation into macrophages\noccurred in their culture based solely on GO term analysis? These GO terms are also\nenriched in monocytes, so this does not provide definitive evidence for macrophage\ndifferentiation.\nWe observed increased enrichment of several GO terms relating to macrophage\ndifferentiation compared to the individual cell-type samples as well as to monocytes. We\nhave refined Figure 2C (now Figure 2D in the revised manuscript) to illustrate only GO\nterms with a significant change between pentacultures vs single cell controls (Welch t-test,\nBenjamini-Hochberg (BH) adjusted p <= 0.05) and included a Supplementary Figure 2A\n(Reviewer Figure 4) identifying the pathways with increased expression compared to the\nmonocytes control. Our intention with Figure 2D was not for the pathway analysis alone to\nserve as proof of differentiation. The pathway analysis in Figure 2D was designed to\nhighlight pathways upregulated between malignant and primary cells in 2D and the\npentacultures, complementing our evidence of macrophage differentiation shown in Figures\n1B, 1D, and 1F. The enriched GOBP pathways in Figure 2D, particularly those directly\nassociated with macrophage chemotaxis, migration, and differentiation, indicate an activated\n3\nand differentiated macrophage status within the pentacultures that is significantly distinct\nfrom isolated monocytes. We have now revised the manuscript to clarify the use of the\nGOBP pathway analysis in Figure 2D (lines 142-146).\nReviewer Figure 4 (new Supplementary Figure 2A) - Heatmap illustrating GSVA enrichment scores of\nmacrophage-related Gene Ontology Biological processes (GOBPs). Welch t-test, BH adjusted p <= 0.05 for\npenta-cultures vs single cell-types and mean enrichment scores greater than monocytes.\n5. In Figure 2D, it is unclear which GO terms specifically support the conclusion of a tissue-\nlike environment. The figure is poorly presented and does not convincingly support the\nauthors' claims. Simply listing GO terms feels more like a supplementary figure. Higher-\nresolution data, with single-cell RNA sequencing, along with validation of key marker genes\nat the protein level, with appropriate controls, would be necessary to robustly substantiate\nthe findings.\nWe appreciate the reviewer's critique of Figure 2D and the need for stronger evidence of a\ntissue-like environment. Although original G164 and OvCAR3 tumour sequencing is not\navailable, we performed a direct correlation of GSVA enrichment scores for Hallmark\npathways of interest using publicly available RNAseq data from the original AOCS1 tumour\nfrom which the cell line was derived. This analysis showed that the transcriptomes of each of\nthe three AOCS1 pentacultures positively correlated with the original tumour’s gene\nexpression signature, whereas the AOCS1 cell line cultured alone exhibited a negative\ncorrelation (Reviewer Figure 5, new Supplementary Figure 2B). This finding further\nsuggests that our pentaculture system induces a tumour microenvironment state that mimics\nthe original human tumour. We have revised the manuscript to improve the clarity of these\npoints (lines 148-154).\nReviewer Figure 5 (new Supplementary Figure 2B) - Correlation plots of GSVA enrichment scores of Hallmark\npathways in AOCS1 tissue vs AOCS1 penta or vs the AOCS1 cell line in monoculture. Pearson’s correlation\ncoefficient and p-value are illustrated on the plots.\n4\n6. It is unclear whether the genomic and transcriptomic profiling of the cancer cell lines was\nperformed on the isolated cell lines or within the context of the Pentaculture system. This\ndistinction is important, as the microenvironment can significantly influence cancer gene\nexpression. Additionally, this characterization of the cancer cell lines feels disconnected from\nthe main narrative of the manuscript. The conclusions drawn regarding their relevance to\nmacrophage states are underdeveloped and lack clear integration into the broader context of\nthe study. Also, it is broadly known in many cancer and litterature on this topics should be\nthoroughly reviewed by the authors (e.g. Weverwijk, De visser, nature review cancer 2023)\nWe appreciate the reviewer's comments on the profiling of our cancer cell lines and their\nintegration into the manuscript. To clarify, the genomic and transcriptomic analyses were\nperformed on the isolated malignant cell lines, i.e. as a single-cell type not incorporated into\nthe pentacultures. This was done because our data (Figures 2 and 3) suggest that the\nintrinsic properties of the malignant cell lines are major drivers of the distinct\nmicroenvironments observed. We acknowledge the extensive literature on the role of cancer\ncells in shaping the TME, as highlighted by Weverwijk and De Visser. Our work's novelty lies\nin applying this established knowledge within a complex in vitro pentaculture model. For\nexample, the above review described how higher STING pathway expression correlated with\nan inflammatory cytokine tumour microenvironment. In agreement with this, we found that\nthe HRD-deficient G164 cell line has high STING pathway expression compared to the other\ntwo cell lines, and the highest level of inflammatory cytokine secretion Figure 3J. (see below\nReviewer Figure 6 and new Supplementary Figure 4E).\nWe have revised the manuscript text to better integrate this rationale and provide additional\ncontext regarding the established literature (lines 234-236 and 268-274).\nReviewer Figure 6 (new Supplementary Figure 4E) – Heatmap illustrating upregulation of STING pathways in\nG164 malignant cell compared to AOCS1 and OvCAR3 (one-way anova p = 0.0005 and p = 0.003 for\nREACTOME_STING_MEDIATED_INDUCTION_OF_HOST_IMMUNE_RESPONSES and\nISHIKAWA_STING_SIGNALING respectively).\n7. Statistical analyses are missing from many figure panels, but their inclusion would be\nimportant to appropriately support and nuance the authors' claims.\nThe information about the statistical analyses has now been added to each figure legend.\n8. Legends need more information\nWe have added more information to the figure legends e.g. Figure 1 legend now contains all\nthe statistical tests used to calculate significance and it has been rephrased. We hope this\nmakes it clearer and more comprehensive.\n9. Fig 1 D axis labeling and legend not clear.\nWe hope that the changes are an improvement.\n5\n10. The graphical presentation in Fig. 1F is somewhat unclear. Were multiple donors\nanalyzed? Additionally, could you include data for day 0 (monocytes) and day 14?\nWe appreciate the reviewer's feedback regarding Figure 1F. To clarify, the data for Figure\n1E shows the D0 time point with three different monocyte donors. Figure 1F shows a\nrepresentative plot of macrophage differentiation at day 3 and day 7 of one of the previous\ndonor in a pentaculture experiment. Figure 1D summarises day 7 macrophage\ndifferentiation from five different monocyte donors in the pentacultures. Hence Figure 1F is\nan example of one of the experiments shown in Figure 1D. (see lines 111-117).\n11. In Fig3, I-J, I am missing the monocytes as control.\nThe data in Figure 3I-J represent the secretome collected from the pentacultures or the\nmalignant cell lines in monoculture, i.e., the active secretion of molecules into the\nconditioned medium over time. However, a direct secretome control of monocytes only is not\ntechnically feasible as monocytes in 2D monoculture don’t survive without addition of\nexogenous growth factors. Hence, the monocytes are immediately introduced into the\npentaculture system upon thawing, without in vitro culture. While we could quantify cytokine\nand chemokine levels in monocyte cell lysates, these intracellular measurements are distinct\nfrom secretome analysis.\n12. Although the video quality is high, the objective and key observations are not clearly\ncommunicated. Adding annotations and labels would greatly enhance clarity by guiding the\nviewer to the relevant events. Specifically, it is not evident that CD47 blockade results in\nincreased phagocytosis. If this is a key conclusion, more direct visual evidence of\nphagocytosis should be shown. Additionally, the significance of the observed wobbling\nmotion is unclear, why should this be interpreted as an indicator of phagocytic activity?\nClarification or supporting rationale would be helpful.\nWe appreciate the feedback regarding the clarity of our videos. To enhance clarity and guide\nthe viewer, we have added annotations and labels to the videos. Specifically, we've included\narrow pointing to effective interactions (readout of phagocytosis), providing more direct\nvisual evidence of this process. However, our original manuscript provided quantitative\nmeasurement data to support our findings on phagocytosis. For both Figure 5D and 6G, we\nmeasured the percentage of macrophages positive for red cancer cells and the effective\ndistance between macrophages and the nearest cancer cells. We graphed macrophages\nlocated at less than 20µm from cancer cells, under the rationale that such close proximity or\ndirect overlap indicates effective interaction and potential phagocytosis.\nWe also understand the request for clarification on the wobbling macrophages and its\ninterpretation as an indicator of phagocytic activity. In our previous work3, we observed\ndiverse movement patterns in both CD8 T cells and macrophages in ex vivo human and\nmouse tissues, though we couldn't provide a biological explanation for these variations at\nthat time. In this work, we applied the same analysis to macrophage movement within our\npentaculture model. We observed that the wobbling population was the only population\nwhose proportion significantly increased together with the increase in phagocytosis. Based\non this correlation, we hypothesise that this specific movement pattern might reflect a more\nphagocytic state. However, we want to be clear that we cannot definitively conclude that all\nthe wobbling macrophages are phagocytic macrophages. Their wobbling nature could also\nbe influenced by other factors within the microenvironment, such as the density of the\nextracellular matrix (ECM), the presence of other cell types, or localised depots of cytokines\nand chemokines that might restrict their movement.\nrevised manuscript (lines 375-378 and 443-446)\n13. The conclusion that increased phagocytic activity in post-chemotherapy samples from\nresponders, compared to those from relapsed patients, implies that enhancing macrophage\nphagocytic capacity may improve chemotherapy response is not well supported. An\nalternative explanation could be that effective chemotherapy leads to greater tumor cell\ndeath and debris accumulation, which in turn triggers macrophage activation and phagocytic\nactivity as a secondary effect, rather than a cause of therapeutic success. Could the authors\nclarify whether their data distinguish between these possibilities, and comment on the\ndirectionality of this relationship?\nWe thank the reviewer for this comment. We agree that both explanations are possible. Our\ncurrent data cannot definitively distinguish between these two possibilities. We suggest that\nenhancing macrophage phagocytic activity is a potential mechanism that contributes to\nimproved therapeutic responses, as it could potentially happen after chemotherapy. We have\ncommented on this in the revised manuscript (lines 342-348).\nMalacrida et al. report the development and initial characterization of high grade serous\novarian cancer (HGSOC) “pentacultures”. This organotypic cell system includes cancer cells,\nmesothelial cells, fibroblasts, adipocytes and monocytes, the latter of which differentiate into\nmacrophages in the culture. The adipocytes, mesothelial cells, and fibroblasts are obtained\nfrom omental biopsies and cultured in vitro. Three separate donors are used for this study,\nalthough the authors imply that they have studied many more (see below). The monocytes\nare purified from peripheral blood of a single donor. Using these pentacultures, the authors\nreport several key findings. First, tumor cells seem to drive most of the macrophage\npolarization in the cultures, as shown by differences in transcriptomic and proteomic data,\nwhich had more effect than varying the origin (i.e. donors) of the other cell types. Second,\neach HGSOC cell line drives different degrees of macrophage polarization and different\nspatial architecture. Third, the authors report intriguing macrophage-directed effects on\ntour cell viability, which could, at least for one cell line, be modulated with monoclonal\nantibodies targeting “do not eat me” signals.\nOverall, this is an interesting, well written, and technically well-performed study that should\nbe of general interest to the readership of Nature Communications. There are, however,\nsome conceptual and technical issues that should be addressed in a revision.\nMajor Issues:\n(1) A penta-culture (actually, a sexta-culture, as it includes endothelium) of the ovarian\nomentum, incorporating a macrophage cell line, has been described previously (Estermann\net al., Biomaterials, 2023), albeit that work was more focused on dissemination of HGSC\nwithin the omentum than on the issues discussed here. In some ways, the authors of the\ncurrent paper have also reported a 5-cell type culture (see Malacrida et al. 2021, Figure 4F),\nalthough that work focused on platelets, not macrophages, The authors should discuss the\nsimilarities and differences between their system and the previous reports. Also, they should\nemphasize the novelty of this paper, which is the demonstration that tumour cells drive the\nTME (at least those components of the TME that are present in this culture), and the “don’t\neat me signal” aspects. The utility of their system in enabling the study of monocyte->\nmacrophage differentiation should also be discussed.\nWe appreciate the reviewer’s positive comments. We are aware of the contributions from\nother multicellular in vitro models, such as Estermann et al.4, as well as our own prior work5.\nWe agree that it is important to discuss how our system is similar to and different from these\nprevious reports, and to emphasise what makes our paper novel.\nRegarding Estermann et al.'s work, it represents an important step forward in mimicking the\nomental microenvironment. While both their model and ours aims to capture the complex\ncellular interactions in the ovarian cancer omentum, the critical difference comes down to the\norigin of the cells. Estermann et al used immortalised cell lines to create the\nmicroenvironment components. Notably, they used the acute monocytic leukemia cell line\nTHP-1 as a source of monocytic cells. In contrast, we build the microenvironment of our\ncurrent pentacultures, (and our previously published models with fewer cell types), using\nprimary normal human cells isolated directly from uninvolved omentum from patients and\nhuman peripheral blood monocytes. Successfully differentiating macrophages from human\nperipheral blood monocytes in a multi-cellular model has been one of the challenges in the\nfield of 3D models. As far as we are aware, there is also currently no ovarian organoid model\nwith successful incorporation of macrophages. As for our own previous work5, we focused on\nthe early stages of metastasis, particularly on how cancer cells stick and invade an omental\n8\nstructure comprising adipocytes, mesothelial cells and fibroblasts in the presence of\nplatelets. Our current paper focuses on later-stage metastatic mechanisms, specifically on\nthe role of macrophages. The overall design and the inclusion of five cell types is a\nprogression in our efforts to build more relevant in vitro models, and we are asking a\ndifferent question. We focused on macrophages because they are a dominant immune cell\nin HGSOC omental metastases, and they are increasingly seen as one of key reasons why\nimmunotherapy does not work in HGSOC treatment. We also explored mechanisms of\ntargeted therapy resistance by looking at the \"do-not-eat-me\" signals, especially CD47. This\nshows our model can be used to test therapies aimed at overcoming these resistance\npathways. Finally, we characterised the relevance of our model using existing scRNAseq\ndata from human HGSOC biopsies to confirm that the macrophage populations we see in\nour in vitro system are similar to those found in patients. We expanded on these details in\nthe revised manuscript (lines 72-75, 84, 412-415, 455-465).\n(2) Surprisingly, the authors do not comment on whether the tumor cells also drive fibroblast\ndifferentiation to CAFs (or induce additional fibroblast/CAF differentiation from mesothelium).\nAlternatively, does culturing the primary omental fibroblasts in vitro prior to their incorporation\ninto pentacultures induce activation similar to that of CAFs? Several reports in other tumor\nsystems have emphasized the role of CAFs in directing macrophage polarization, so this\npoint would seem to be important.\nWe appreciate the reviewer's comment regarding fibroblast differentiation. While our primary\nfocus in this study was cancer cell-macrophage interactions, we agree that other cell types,\nespecially fibroblasts, might contribute and might also be influenced by the malignant cells.\nOur preliminary results indicate that malignant cells might also drive fibroblast activation and\ndifferentiation transcriptional programmes (Reviewer Figure 7, new Supplementary Figure\n3G) as well as distinct fibroblast phenotypes within our model, similar to our observations\nwith macrophages, though less strongly. Using immunofluorescence staining, we found that\nαSMA expression was significantly higher in AOCS1 and G164 pentacultures compared to\nthe OvCAR3 pentacultures (Reviewer Figure 8, new Supplementary Figure 3H). However,\nthe expression of FAP (fibroblast activation protein) on the fibroblasts and mesothelial cells\nin the pentacultures measured using flow cytometry, showed no significant difference in the\npentacultures with the three malignant cell lines (Reviewer Figure 9, new Supplementary\nFigure 3I).\nWe have incorporated this information into the revised manuscript (lines 216-225). We feel\nthat any further analysis is outside the scope this paper, but we will be following this up in the\nfuture.\nReviewer Figure 7 (new Supplementary Figure 3G) - Heatmap of Fibroblast GOBPs with increased enrichment in\npenta-cultures compared to single cell-type monoculture controls (Welch t-test, BH adjusted p <= 0.05)\nimages from pentaculture models. DAPI (cyan), cancer cells (red), and αSMA-positive fibroblasts (magenta).\nαSMA fluorescence intensity was normalized to staining area. Data represent mean ± SEM (n = 3), Welch's\nunpaired t-test.\n60\nd\nn\na\ns ts\na\nlb\ns\nlle\nc\nla\n40\no\nrb\nile\nh\n+ P A\nif to\ns e m 20\nF\n%\nPenta Penta Penta\nReviewer Figure 9 (now Supplementary Figure 3I) – Percentage of FAP⁺ fibroblasts and mesothelial cells in\npentaculture cultures after 7 days, quantified by flow cytometry. Data presented as mean ± SEM (n = 3).\n(3) The authors state that they used 3 donors for the omentum-derived cells for this study,\nbut in the Discussion (line 366), they say there are >50 donors for the first three cell types\nand 5 for the monocytes. To what extent have these other donors been characterized in this\nsystem? Also, what types of surgeries were the patients who donated ometum undergoing\n(i.e., what were their underlying disorders)?\nWe thank the reviewer for this comment and have now clarified this in the revised\nmanuscript. We used three different donors for omentum-derived primary cells in the bulk\nRNA-sequencing experiment in Figure 2. All other experiments in the study have included\nmultiple primary cell donors, as reflected by the \">50\" and \"5\" stated in the Discussion.\nRegarding patient characteristics, primary adipocytes, fibroblasts, and mesothelial cells were\nisolated from macroscopically uninvolved omentum from gynae cancer patients undergoing\ndebulking surgery, including both neoadjuvant chemotherapy (NACT)-treated and\nchemotherapy-naïve individuals. This detail has been clarified in the revised manuscript\n(lines 126-128, 484-485).\n(4) In Figure 1, the authors indicate that monocytes spontaneously differentiate into\nmacrophages when introduced into the quad-culture model. An important missing control\nwould seem to be incubating the monocytes without any cancer cells; have the done this?\nAlso, even if the cancer cells are necessary to start the monocyte differentiation process, are\nthey sufficient? Or is there cross talk with other cell types (e.g., CAFs, as per point 2)?\nWe appreciate the reviewer's comment on monocyte differentiation and the need for\ncontrols. We have now performed experiments addressing the necessity and sufficiency of\n10\ncancer cells. We cultured monocytes in a 3D model of adipocytes, mesothelial cells and\nfibroblasts without cancer cells (tetraculture). While fibroblasts and mesothelial cells were\nsufficient to initiate monocyte-to-macrophage differentiation (Reviewer Figure 10 and new\nSupplementary Figure 3B), in these experiments the levels of macrophage markers and\nsecreted proteins were different from the pentaculture results shown in Figure 3E-H\n(Reviewer Figure 11 and 12 and new Supplementary Figure 3C and 3D). This suggests\nthat malignant cells are necessary for development of the different macrophage subsets.\nAlso, in a more simplistic 3D spheroid model (cancer cells + monocytes plated in ultra-low\nattachment plates), monocytes began to acquire some phenotypic differences. However, the\nabsence of cytokines, such as M-CSF and CCL2 which we believed are mainly secreted by\nfibroblasts and mesothelial cells (Reviewer Figure 13 and new Supplementary Figure 3F),\nresulted in poor macrophage viability after four days. While stromal cells initiate\ndifferentiation and cancer cells drive subset specification, we believe that the synergistic\ncross talk within the complete pentaculture is essential for sustained viability and the full\nphenotypic diversity of macrophages. These results are also described in the revised\nmanuscript (lines 192-204, 210-215).\n100 100\n)s\nlle\nc\ne v il fo 6\n8\n0\n0 %\nM\na c ro p\n( s 50 h a\n+\nlle\nc 40\ng\ne\nm\n5 a\n%\n20\nrk\ne rs\n0 0\nCD45+ CD163+CD206+HLA-DR+\nReviewer Figure 10 (now Supplementary Figure 3B) - Flow cytometry analysis of the tetraculture (pentaculture minus\ncancer cells) expression of CD45+ and CD163+, CD206+, and HLADR+ of viable CD45+ cells. Data presented as mean ± SEM\n(n=3).\nReviewer Figure 11 (now Supplementary Figure 3C) - Flow cytometry analysis of the tetraculture expression of\nCD169 and GPNMB macrophages alongside the relative expression in the different pentaculture (same graphs\nas Figure 3E and 3F). Data presented as mean ± SEM (n=3).\nthe pentaculture graphs from Figure 3G and 3H. Data presented as mean ± SEM (n=5).\n0.0015\n8000 0.0008\n0.0008 0.0033\nm\n/g\np\nF\nC\nM\n2000\nReviewer Figure 13 (now Supplementary Figure 3F) - MCSF Elisa using the supernatant from malignant cells and\nprimary cell types in monoculture. Data presented as mean ± SEM (n=3 or 5 different primary cells donors). Ordinary one-\nway ANOVA.\n(5) The authors perform the studies in this paper over 7 days. How long do the cultures last\nbeyond that? Also, they comment (and show data that) the viability of the cancer cells is\npreserved over the 7-day period, but what about the other cell types?\nThe original submission, Supplementary Figure 1A (now Supplementary Figure 1B)\nshowed the overall viability of the entire pentaculture, reflecting the viability of all cell types.\nWe have now included viability for each cell type within the pentacultures in the revised\nmanuscript (Reviewer Figure 14 and Supplementary Figure 1C). Regarding culture\nduration, our pentacultures could be maintained for up to 14 days. However, at this time\npoint we observed a significant decline in macrophage viability. Therefore, we did not test\ncultures beyond 14 days (see lines 99-104).\n12\n100\nla\nile 100 100\ns lle h to\ns\ne g\na\nc e h\nre\nc\nn a c fo\ny tilib\na iV\n%\n50\nm\nd\nn a o rb\nif fo\ny tilib\na\n50\np o\nrc\na m fo\ny tilib\na iV\n%\n50\n0\niV\n%\n0 0\nAOCS1 G164 OvCAR3 AOCS1 G164 OvCAR3 AOCS1 G164 OvCAR3\nPenta Penta Penta Penta Penta Penta Penta Penta Penta\nReviewer Figure 14 (now Supplementary Figure 1C) - Viability of the different cell populations at day 7. Data shown as\nmean ± SEM (n=3).\n(6) They note that a major limitation of the current work is the absence of adaptive immune\ncells (I would include other innate immune cells, e.g., gMDSC/neutrophils, NK cells). A priori,\nit would seem that at least some of these could be added. Could the authors comment on\nwhether they have tried to do so in the Discussion. Also, is there a reason why they chose to\nuse immortalized HGSOC lines instead of primary tumor cells or organoids?\nWe appreciate this comment from the reviewer. We completely agree that more immune\ncells would enhance the relevance of our model. While our current study focused on cancer\ncell-macrophage interactions, we are actively working to increase the complexity of our\nmodels. One ongoing project in our lab is integrating CAR T cells in the pentacultures, but\nwe believe this is outside the scope of the current paper. Regarding the use of immortalised\nHGSOC cell lines, we apologise to the reviewer for any confusion. Please also refer to our\nresponse to reviewer 1 - point 2. AOCS1 and G164 were established from patient samples\nand self-immortalised in culture. Although OvCAR3 was purchased from ATCC, all three cell\nlines were extensively characterised at proteomic, transcriptomic and genomic level. As\ndemonstrated in Figure 4E, these cell lines exhibit genomic and mutational profiles that are\nsimilar to those of HGSOC patient-derived organoids and HGSOC biopsies.\nWe have added a comment regarding the future directions of our models to the Discussion\nsection of the revised manuscript (lines 455-460).\n(7) Line 151-154: the authors state that CIBERSORTx ..revealed that fibroblast and\nmesothelial cells were present at similar abundance..”, but to my eyes, it looks like there are\nsignificantly more of these cells in AOCS1 cultures—am I wrong?\nWe appreciate the reviewer's observation regarding Figure 3A. We checked the results, and\nwhile the graph might visually suggest slight differences, we confirm there is no significant\ndifference in the abundance of fibroblasts and mesothelial cells across the various\npentaculture models, including AOCS1 (Reviewer Figure 15).\n13\n100 s\ns lle 100\nts\na\nlb\no\nc\nla\nile\nrb\nif\nfo\ne\nc\nn\na\n50\nh\nto\ns\ne\nm\nfo\ne\n50\nd c\nn n\nu a\nb d\nA n\n%\nu\nb\n%\nPenta Penta Penta Penta Penta Penta\nReviewer Figure 15 – Percentage abundance of fibroblasts and mesothelial cells from CIBERSORTx analysis\n(Figure 3A in the manuscript). No statistical difference found between the different pentacultures. One-Way\nANOVA and Welch’s t test.\nMoreover, as CIBERSORTx only provides indicative proportions based on RNA expression,\nwe have now provided flow cytometry quantification for each population in Supplementary\nFigure 1A (see also Reviewer 1, point 1).\n(8) Lines 159-60, the authors correctly note that the percentage of G164 cells is significantly\nhigher by flow, as predicted by CIBERSORTx. But why are AOCS1 and OVCAR1 identical\nwhen they are significantly different in the CIBERSORTx analysis?\nWe appreciate the reviewer's comment regarding the CIBERSORTx and flow cytometry data\nfor AOCS1 and OvCAR3 abundances. While CIBERSORTx is a robust way to deconvolute\nour bulk RNAseq data from the in vitro models using scRNAseq derived signatures from\nHGSOC biopsies, some gene expression signatures might overlap across cell populations\nand could lead to discrepancies between in silico predictions and actual measurements. For\nthis reason, some of our deconvolution results, including cancer cell abundance and\nmacrophage clusters, were validated at protein level using flow cytometry or ELISA (see\nFigure 3E-H).\n(9) Line 197, Why don’t the authors also comment on CDK12 loss in this line? And what\nabout CCNE1 amplification in OVCAR3?\nWe thank the reviewer for this comment. We agree that a discussion of these specific\ngenetic alterations is important for providing a more complete context for the cell line\ncharacteristics.\nCDK12 loss is well-documented for its association with defects in DNA repair, which is\nconsistent with the HRD-deficient profile of the G164 cell line. This genetic signature also\naligns with results from a previous publication6 showing higher sensitivity of this cell line to\nPARP inhibitors when compared to AOCS1. Regarding CCNE1 amplification, a common\nalteration in HGSOC, we observed that while all three cell lines show this amplification\n(Figure 4C), it is present at the highest levels in OvCAR3 and the lowest in G164. This\nfinding correlates with higher CIN signature 4 activity in OvCAR3 and AOCS1 and was also\nin line with whole genome duplication events observed in these lines, Figure 4E. We have\nincorporated these details into the revised manuscript (lines 240 and 249-251).\n(10) Lines 311-313, referring to the G164 pentacultures, the authors state that macrophages\nresided mainly in the stroma. But from the micrograph in 6D, it looks mainly like there are far\nfewer macrophages, period. Could they comment?\nlead to biased visual observation of cell abundance due to the heterogeneous spatial\ndistribution within a 3D culture model. Our primary goal for that specific image was to\ncapture a representative area proving the predominant stromal localisation of the\nmacrophages in the G164 pentaculture. As shown in new Supplementary Figures 1A and\n1C, we have now included detailed analyses presenting both the proportions of different cell\ntypes and the viability of each individual cell compartment at the end of the 7 days of culture\nfor each pentaculture model. These quantitative data, along with the results in Figure 1C,\nshow that the overall percentage of macrophages is comparable across the different\npentaculture models.\n(11) The quantification in Fig. 5D does not agree with the photo in 5C, at least to my eyes.\nWe appreciate the reviewer's observation regarding the visual discrepancy between Figure\n5C and its quantification in Figure 5D. As previously mentioned, static images are illustrative\nand may not fully capture the dynamic 3D interactions that we see in the videos. However,\nquantification in Figure 5D is derived from the average of 5 different time frames within the\n30-minute videos. This approach accounts for macrophage movement and heterogeneous\nlocalisation.\nMinor issues:\n1) Line 39, HGSOC is misspelled; Line 306 “the” should be capitalized.\n2) In Fig 1A, I found it difficult to see the fibroblasts in the cartoon. I suggest using a different\ncolor for more contrast.\n3) Lines 104-106; the authors state that “…pentacultures retained a substantial population of\nmonocytes, as indicated by CD45+ staining…” But macrophages are also CD45+? How can\nthis statement be used to support the retention of MONOCYTES?\nMinor Issues 1, 2 and 3 have now been addressed in the revised manuscript.\n4) Figs. 2C-E are too small to see without magnification (i.e., when printed out at 1x). So are\nFig. 4D and 6A and Supp Figs 2 and 3 (especially those numbers in blue boxes).\n5) Figure 1F. To facilitate reader interpretation, I recommend adding arrows in the graph that\nindicate the change of the center of mass of each population between days 3 and 7\nWe have now improved the quality of the figures mentioned in Minor Issues 4) and 5).\n6) Why aren’t all the chemokines and cytokines presented in the same order in Figure 3I for\npentaculture and 2D cultures?\n7) Line 205, CIN should be defined as chromosomal instability when used for the first time.\n8) Lines 212-214 is worded awkwardly. Also, flow cytometry does not measure “expression”;\nit measures “surface expression” (e.g., Fig 1 legend, and elsewhere in the paper”\nWe have now made the changes suggested.\nThe authors present an interesting preclinical model for high-grade serous ovarian cancer,\nincorporating tumor cells, primary fibroblasts, mesothelial cells, adipocytes, and monocytes\nusing three previously established cell lines.\nThis manuscript builds on their previous work on “tetracultures”, further incorporating\nmonocytes from healthy donors to their TME model. They show that the monocytes\ndifferentiated into macrophages, without exogenous cytokines, in the pentacultures. The\nauthors then describe the model using IHC, flow cytometry RNAsequencing and whole\ngenome sequencing. They highlighted the potential influence of malignant cell genomic,\ntranscriptomic and proteomic heterogeneity on the TME. The findings on the functional\ncharacterization are interesting. The work is highly relevant and timely; however, the strength\nof the conclusions and translational impact would be improved with more detailed\ncharacterisation of the models and source tumors (see also Reviewer 1 – point 5).\n1. For the representativeness of the models: Fig 1 B and S Fig 1b: is the human biopsy from\nthe same patient? It is difficult to assess the cell morphology. To conclude how well the\nmodels match the human tumors, it would be important to should show\n1) matching human tumor and model H&E stainings\nWhile we appreciate this point, due to our model setup, which combines cancer cell lines\nwith primary stromal and immune cells from multiple patients, direct patient-matched\nhistological comparisons are not feasible. However, the strength of our model lies in its\nability to replicate key characteristics observed in the human omental metastatic\nmicroenvironment.\n2) higher magnification of H&E to allow for assessing the cell morphology\nWe have now provided these images\n3) It is difficult to see any tumor cells in the models - the authors should show also\ntumor marker stainings e.g. CK7, PA8 in the models. Based on Fig1 B and SFig\n1B, HLA-DR is widely expressed and most of the cells seem to be macrophages\nand tumor cells cannot be identified from the figures.\nSee point below\n2. Row 101: “AOCS1 and OvCAR3 cancer cells were located close to the stromal cells in the\npentacultures, while G164 cancer cells generally formed clusters away from the stromal\ncells.” Where is this shown? Is this quantified? Deeper characterisation of the full\ncomposition (see point 1) would also better support the conclusion that can be made on the\ninfluence of the malignant cells on the morphology and composition of the models.\nTo respond to the reviewer’s points, we are providing here in Reviewer Figure 16 (now\nSupplementary Figures 1E and 1F) images of our different pentacultures. We believe that\nthese immunofluorescence images demonstrate the differences: AOCS1 and OvCAR3\nmalignant cells (red) appear integrated with αSMA+ fibroblasts (magenta – top panel) or\nCD14+ macrophages (green – bottom panel), whereas G164 cells form distinct aggregates\nsurrounded by the stromal components. These observations are also supported by\nSupplementary Videos 1–3 and Figure 1B (CD45 panel), which show that macrophages\nlocalize closer to cancer cells in the AOCS1 and OvCAR3 pentacultures, while in the G164\npentaculture, macrophages predominantly reside within the CD90⁺ stromal regions\n(fibroblasts and mesothelial cells).\nmodels. Representative maximum intensity projection images of the different pentacultures. Top panel: DAPI in\ncyan, cancer cells in red, αSMA in magenta, scale bar 20µm. Bottom panel: CD14+ macrophages, cancer cells in\nred, CD90+ stroma in magenta, scale bar 50µm.\n3. For the RNAseq and analysis: Fig 2 a - it is difficult to assess which conditions were\nsequenced and compared. This figure could be improved to better show the different\nconditions and comparisons. Figure 2 b is not really informative as part of the results, but\nrather shows sample-specific clustering. Was there a batch effect? Was that corrected for?\nWe thank the reviewer for this comment. All samples were processed under identical\nconditions, including collection, instrument, and run time, so there is no technical batch\neffect. The experimental variables in the setup were the type of malignant cells, the type of\nstromal cells, and the specific patient donor of the stromal cells. However, the observed\nclustering was primarily driven by the type of malignant cells used in the cultures.\n4. Tissue composition: bulkRNAseq deconvolution has known biases for decomposing the\nsignal - the authors should validate these findings using another technology, for example,\nimmunofluorescence or flow cytometry.\nWe appreciate the comment made by reviewer and we agree. We have indeed validated our\nfindings using another assay: Figure 3B presents validation of cellular proportions in the\npentacultures via flow cytometry, and Figures 3E-H demonstrate validation of specific\nmacrophages using both flow cytometry and ELISA. Also, the differences in cellular\nproportions can now be also appreciated from Supplementary Figure 1A (see response to\nReviewer 1, point 1 above).\n5. Fig 3: the tumor cell fractions were quite low after - could this be corrected using higher\nnumbers of cancer cells when establishing the cultures? Did the authors test different ratios\nof the cells in the setup of the pentacultures?\nchanged the absolute number of cancer cells present at the end of the culture, it did not\naffect the relative differences in phagocytosis observed between the malignant cell lines.\nOvCAR3 and AOCS1 consistently remained significantly more phagocytosed by the\nmacrophages than G164.\n6. How much of the transcriptional differences of the pentacultures stem from the\nsignificantly different gene expression profiles of the tumor cells? To show that the\ndifferences in the immune/stromal cell phenotypes are not just reflections of the (often\npoorly) tumor signal - scRNAseq (or bulk from flow-sorted populations) from the\npentacultures would significantly strengthen the conclusions.\nTo identify genes that significantly changed between pentacultures, but not between the\nmalignant cell monocultures, we explored the differential expression data for genes with a\nsignificant change in at least one of the penta-cultures vs the other pentaculture (BH\nadjusted p < 0.05 and logFC > |0.5|) AND a non-significant change in any of the malignant\ncell monoculture pairwise contrasts (BH adjusted p > 0.1). This resulted in 244 genes which\nare illustrated in the heatmap below and indicate transcriptional differences between penta-\ncultures beyond the basal malignant cell line differences. These genes reflected both\nadditional changes in malignant cells as well as in the stromal compartment when in\npentaculture. Of particular interest was cluster 1 containing PRGFRB, CCR2, TMEM132B,\nCD83, S100A11 and DCSTAMP which, we suggest, reflects the stromal compartment of the\npenta-cultures. The number of genes in the clusters was not sufficient for Gene Ontology\nand reactome pathway enrichment analysis. To inspect differences at the level of GO\nprocesses and reactome pathways, in a similar manner to gene level analysis, we explored\ngsva enrichment scores with a significant change between pentacultures (nominal p < 0.05)\nbut not between the malignant cell monocultures (nominal p > 0.1). We noticed that the\npathways that differed between pentacultures but not between monocultures were diverse,\nreflecting both further malignant cells changes such as axon guidance, metabolism and\nmorphogenesis, but also the stomal compartment of the penta-cultures (defence response to\ntumour cells, regulation of fibroblast proliferation and T cell homeostasis)(Reviewer Figure\n17 – panel A is also new Figure 2E).\nexpression of differentially expressed genes with a significant change in at least one of the pentacultures vs the\nother pentacultures (BH adjusted p < 0.05 and logFC > |0.5|) AND a non-significant change in any of the\nmalignant cell monoculture pairwise contrasts (BH adjusted p > 0.1) clustered with k-means clustering; (B) GSVA\nenrichment scores of MsigDB GO and REACTOME collections with a significant change between pentacultures\n(nominal p < 0.05) but not between the malignant cell monocultures (nominal p > 0.1), clustered with k-means\nclustering.\n7. Colour annotations are missing in Figure 4 B. The value of WGS exploration in only three\ntumor samples remains limited. Is G164 a functionally HRD tumor by e.g. functional RAD51\nstaining? Exploring more models with HRD vs HRP tumors would be really interesting!\nThe colour annotation for Figure 4B was originally placed below Figure 4A. We have\nmoved it closer to Figure 4B to make it clearer. We agree that whole-genome sequencing\non only three cell lines provides a limited view of genetic diversity. The purpose of this\nanalysis was to establish a link between specific genomic profiles of our cell lines and their\nresulting TME phenotypes. We agree that exploring a greater number of models with diverse\nHRD/HRP status would be an important future direction. Regarding the functional HRD\nstatus of the G164 cell line, published work by Tamura et al.6 reports its higher sensitivity to\nPARP inhibitors when compared to AOCS1.\n8. In the end, it remains unclear how well do the observed models match the clinical tumors\nin terms of composition and phenotype. It would strengthen the conclusion if the authors had\nthe original tumor samples, and were able to better show that they recapitulate the source\n19\ntumors. Further, the conclusion that the tumor genotypes shape the TME would need\nsignificantly more models to be established to reach statistical conclusions. E.g. the clinical\nrelevance of these phenotypes could then be expanded to other public clinical datasets for\nincreased impact.\nWe appreciate the reviewer's comment regarding the clinical relevance of our models in\nterms of composition and phenotype. Regarding the direct comparison with matched clinical\ntumours, it is technically impossible within the framework of our model. Our primary cells are\nisolated from macroscopically uninvolved omentum from patients. However, although original\nG164 and OvCAR3 tumour sequencing is not available, we performed a direct correlation of\nGSVA enrichment scores for Hallmark pathways of interest (related to the TME) using\npublicly available RNAseq data from the original AOCS1 tumour (see Reviewer 1, point 5 –\nReviewer Figure 5, new Supplementary Figure 2B). Furthermore, we utilised scRNAseq\nsignatures derived from human omental biopsies to deconvolute our data, confirming the\npresence of clinically relevant macrophage subsets. We further validated the abundance and\nfunctional states of these cells using flow cytometry and ELISA (see Figure 3). We have\nrevised the manuscript to improve the clarity of these points (lines 146-155).\n9. Figures 6 G-J only show two datapoints per condition and the statistical comparisons -\nplease add biological replicates to these experiments.\nWe appreciate the reviewer’s comment regarding the number of replicates in Figures 6G-J.\nWe acknowledge that more biological replicates would increase statistical power. However,\neach data point is the analysis from a single video representing the average measurement of\n40-50 macrophages over multiple timeframes. For this reason, we are now providing the\nresults of the two experiments separately, one in the main Figure 6G and the second\nexperiment in Supplementary Figure 6F (see below Reviewer Figure 18). Moreover, this\nanalysis only serves as second confirmation of the results provided in Figure 6E. Given the\ninternal consistency of each replicate, we believe these data points provide sufficient\nevidence to support our conclusions.\n80\n80\ne 0.0003\nc\nn <0.0001\na ts id\nm\nu 0 2\nM\n<\nta\nφ\ns lle c\nre\nc n a c\nm\no rf 2\n4\n6\n0\n0\n0 e c n a ts\nid\nm u 0 2\nM\n<\nt\n%\na\nφ\ns lle\nc\nre c n a c\nm\no rf2\n4\n6\n0\n0\n0\n%\n0\nControlAnti-CD47 Ab\nReviewer Figure 18 (now Figure 6G and Supplementary Figure 6F) - Average percentage of macrophages at a\ndistance less than 20µm from malignant cells. Data presented as mean ± SEM of 5 frames. The independent\nexperiments have been separated.\n606, 976-983 (2022). https://doi.org:10.1038/s41586-022-04789-9\ngrade serous ovarian cancer. Nat Commun 15, 10144 (2024).\nhttps://doi.org:10.1038/s41467-024-54295-x\novarian tumor slices reveals changes in cell motility after treatments. iScience 26,\n106514 (2023). https://doi.org:10.1016/j.isci.2023.106514\nthe formation of ovarian cancer metastasis. Biomaterials 294, 121996 (2023).\nhttps://doi.org:10.1016/j.biomaterials.2023.121996\nproduction of diseased extracellular matrix and tissue invasion. iScience (2021).\nTherapeutic Sensitivities in High-Grade Serous Ovarian Carcinoma. Cancer research\n80, 4946-4959 (2020). https://doi.org:10.1158/0008-5472.Can-19-0852\nReviewer #2 (Remarks to the Author):\nThis is a revised version of a manuscript that I reviewed earlier. The authors have made a\ngood faith effort to respond to my queries and suggestions as well as those of the other\nReviewers. In my opinion, this paper is quite important and warrants publication. However, I\ndo have a few remaining issues/concerns that should be addressed before publication (not\nrequiring re-review).\n1) Most importantly, in my previous review, I asked about the source of the adipocytes,\nmesothelial cells, and fibroblasts used for the pentacultures. The text of the original\nmanuscript (and this version) states only that they were obtained from patients undergoing\n“gynecological surgeries.” However, in their Response to the Reviewer’s Comments’,\nthey state that these samples were actually from “uninvolved regions” of omentum in patients\nundergoing surgery for HGSOC. Given that these cells are typically bathed in cancer-\nassociated ascites, and some have received neoadjuvant chemotherapy (!), it is highly likely,\nthat they are not normal. The authors should discuss this possibility and its implications for\ninterpreting their results. Also, I think that they should explicitly mention the source of these\ncells in the text on p. 4 and indicate whether the various donors used had received\nneoadjuvant chemotherapy or not.\nWe agree with the reviewer and have updated the manuscript to clarify the tissue sources\nand donor treatment status. We acknowledge that stromal cells from \"uninvolved\"\nomental regions may not be “normal” due to exposure to cancer-associated ascites or neoadjuvant\nchemotherapy, which could potentially pre-activate these cells. However, it is important to\nnote that the results of our experiments remained consistent across biological replicates\nwhen using primary cells isolated from both chemo-naïve and post-chemotherapy patients.\nHowever, to ensure consistent transcriptomic result and avoid any bias, the bulk RNAseq\nexperiment was specifically designed using primary cells from three chemo-naïve HGSOC\npatients. We have integrated these details and a discussion of their implications for our\nfindings throughout the revised manuscript to ensure full transparency regarding our cell\nsources.\n2) The flow plots in Fig. 1F should be described better—what is the y-axis, for example?\nAlso, why not show the actual values for each population in a bar graph/dot plot or table?\nWe have now clarified the axes in Figure 1F, specifying that the plots represent marker\nexpression versus side scatter area. We displayed these flow plots as we wanted a visual\nrepresentation of the shift in macrophage markers from day 3 to day 7 to demonstrate how\nthese cells differentiate over time. Moreover, the day 7 data shown are taken from one the\nexperiments shown in Figure 1D. We have updated the manuscript and the figure legend to\nprovide a more thorough explanation of these plots and enhance clarity.\n3) In Fig 3B, rather than stating the y-axis measures “% viable cancer cells”, it would be\nbetter if they stated the exact measurement made either in the figure proper or the legend.\nWe appreciate the reviewer's suggestion to clarify the measurement in Figure 3B. However,\nthe data represent the percentage of viable cancer cells as quantified by flow cytometry. This\nnormalization was necessary to ensure comparison across different experimental conditions,\naccounting for variations in absolute cell counts between replicates.\n4) The color code in Fig. 4B should be explained.\nColour code explanation has now been added to the figure legend.\n5) The text of the second paragraph of p. 8 needs editing. It should be “…such as\nfibroblasts”, and in the next sentence, “….and shows why external supplementation..”\nThis has now been corrected.\n6) The reference to Adzibolosu et al. in the legend to Fig. 5 should be number 40, not\nnumber 13.\nThis is now corrected."}]

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