Hypoxia-driven remodeling of SELENOP^+ macrophages shapes T cell dynamics and promotes ovarian cancer metastasis

Domain: biology
Published: 2026-01-12
Editorial outcome: major_revision

Abstract
High-grade serous ovarian cancer (HGSOC) is characterized by extensive transcoelomic dissemination and the accumulation of ascites. However, how site-specific tumor microenvironment (TME) drives progression remains unknown. Here we show the co-occurrence and spatial co-localization of SELENOP+ macrophages and precursor exhausted CD8+ T cells and demonstrate that SELENOP+ macrophages activate T cells via selenoprotein P in vitro and in vivo. We further identify a dynamic transition in the SELENOP+/SPP1+ macrophage populations as tumor metastasis, driven by increased hypoxia in malignant epithelial cells through VEGFA-EPHB2 signaling. We also reveal that anti-VEGFA intervention controls ovarian tumor growth by increasing SELENOP+ macrophages and cytotoxicity of CD8+ T cells in vivo. Taken together, these findings spotlight the role of tumor-induced TME remodeling in subverting immune-mediated tumor control and thus facilitating HGSOC metastasis in females. Collectively, our results provide a foundation for the development of targeted therapeutic interventions aimed at impeding HGSOC metastatic trajectory.

Introduction
Ovarian cancer (OC) is the most lethal gynecological malignancy, typically diagnosed at an advanced stage, marked by extensive transcoelomic dissemination and the accumulation of ascites. High-grade serous ovarian cancer (HGSOC) is the most prevalent and aggressive subtype of OC. Implantation metastasis serves as the primary cause of complications and mortality in HGSOC patients. Although several novel therapeutic strategies, such as anti-angiogenesis therapies, poly (ADP-ribose) polymerase inhibitors (PARPi) and immunotherapies, have been introduced, the disappointing response rates and the development of resistance in HGSOC patients underscore the urgent need to further investigate the spatial heterogeneity of the tumor ecosystem that contributed to treatment failures in HGSOC. Multiple ovarian cancer studies have shown that different metastasis sites are closely related to ovarian cancer staging, treatment resistance, and even prognosis. We previously documented site-specific alterations in the tumor-infiltrating lymphocytes (TILs) status using single-cell RNA-seq (scRNA-seq) and paired T cell receptor sequencing (scTCR-seq), and identified that ovarian tumors were enriched with clonally expanded exhausted CD8+ T cells, whereas omental lesions were predominantly infiltrated by non-tumor-specific bystander T cells. Recently, it is widely recognized that anatomical sites and genetic predisposition collectively determine the evolutionary phenotypic divergence and immune resistance mechanisms in the tumor microenvironment (TME) of HGSOC. However, the spatiotemporal heterogeneity of the tumor microenvironment associated with HGSOC metastasis, including the integrated analysis of immune cell diversity and intra-tumor heterogeneity, as well as the underlying cellular and molecular mechanisms remains to be thoroughly explored.

In this work, to provide a comprehensive analysis of the spatiotemporal heterogeneity of the tumor microenvironment during HGSOC metastasis and to explore the cellular and molecular events involved, we delineate the transcriptional architecture across multiple spatiotemporal anatomical sites pertinent to the metastasis HGSOC, utilizing single-cell resolution to illuminate the profound heterogeneity and site-specific distinctions of immune and malignant cell populations within the TME. Through an integrative, multi-scale analytical approach—encompassing scRNA-seq, scTCR-seq, spatial transcriptomics (ST), whole-exome sequencing (WES), bulk RNA sequencing (RNA-seq), organoid culture systems, and orthotopic syngeneic models—we identify the tumor microenvironmental remodeling associated with HGSOC metastatic progression. Mechanistically, our findings demonstrate that hypoxia-driven malignant epithelial cells reprogram SELENOP+ macrophages through VEGFA-EPHB2 signaling, thereby impairing the cytotoxic function of precursor exhausted CD8+ T cells within the solid tumor microenvironment, ultimately facilitating HGSOC metastasis. This research provides crucial theoretical foundations and insights for the development of more effective clinical therapeutic strategies against HGSOC.

Results
As a single-cell RNA-seq atlas reveals site-specific characteristics and remodeling of the tumor ecosystem in metastatic HGSOC

To comprehensively decipher the complex tumor ecosystem during metastasis, we conducted scRNA-seq on 34 samples from multiple spatiotemporal anatomical sites related to HGSOC metastasis, obtained from 17 patients. The eight spatiotemporal anatomical sites include: (1) the solid tumor sites (designated as solid sites) which encompass adnexa tumors from early-stage (EAT) and late-stage (LAT) HGSOC, as well as matched metastatic sites (Met), including omental (Met.Ome) and peritoneal metastases (Met.Per); (2) the malignant fluid sites (designated as fluid sites) included peritoneal lavage fluid from early-stage HGSOC (PLF.EOC) and ascites from advanced patients (Ascites); (3) the peritoneal lavage fluid from benign uterine fibroids (PLF.UF) and normal post-menopausal ovarian tissue (Nor.Ovr). Meanwhile, we also integrated the spatial transcriptomic profiling of about 2 million spots across distinct anatomical sites in our previous work.

To assess global transcriptomic variation across all samples from scRNA-seq cohort, we performed principal component analysis (PCA) of pseudo-bulk samples. The results revealed that the primary driver of heterogeneity in the HGSOC tumor microenvironment was the distinction between solid and fluid sites, as samples from these two compartments were clearly separated.

Following quality control and doublet removal, we cataloged 138,866 high quality single cells into five major cell lineages and assessed their tissue preference patterns by Ratio of Observed to Expected (Ro/e) analysis. Given that T/NK cells, myeloid cells, and epithelial cells constitute the major cellular components at both solid sites (EAT, LAT and Met) and fluid sites (PLF.EOC and Ascites), we next focused our analysis on the dynamic cellular states of these populations associated with metastatic HGSOC.

Reduced GZMH+ precursor exhausted CD8+ T cells with cytotoxic effects in the solid tumor microenvironment of metastatic HGSOC

Using paired scRNA-seq and scTCR-seq analyses, our previous work demonstrated that CD8+ T cells were tumor-specific in adnexa but functioned as bystanders in the omental metastasis of HGSOC. However, the dynamic changes in functional states and differentiation of CD8+ T cells during HGSOC metastasis remain to be fully understood. Here, we first identified CD4+ T cells, CD8+ T cells, NK cells, and cycling cells via unsupervised clustering. Further reclustering led to the identification of nine CD8+ T cell subclusters characterized by distinct gene signatures.

We first uncovered unconventional and conventional CD8+ T cells and their preferential anatomical sites. We then aimed to seek exhausted CD8+ T cells, which expressed multiple inhibitory receptors, including PDCD1 (encoding PD-1), CTLA4, LAG3, TIGIT, LAYN, and HAVCR2 and largely attributed to anti-tumor activity. Accordingly, subcluster T06_CD8T-CXCL13, exhibited a transcriptional signature indicative of exhaustion and was thus classified as exhausted CD8+ T cells (Tex). Additionally, the T04_CD8T-GZMH subpopulation, characterized by modest exhaustion scores but distinguished by elevated levels of GZMH, GZMA, and CCL4, reminds us of the precursor exhausted T (Tpex) cells. Tpex cells have been reported as a specialized subset of CD8+ T cells that develop into terminally exhausted effector cells, aiding in tumor control. We further used previously reported signature genes of terminally Tex and Tpex to characterize the conventional CD8+ T clusters in our study. CD8+GZMH+ (T04) and CD8+CXCL13+ (T06) cells exhibited the greatest similarities with the Tpex and Tex cell populations, respectively. Moreover, we also observed the elevated expression of TCF7 and EOMES (known to be highly expressed by precursor exhausted T cells) in CD8+GZMH+ (T04) compared with CD8+CXCL13+ (T06). Therefore, subcluster CD8+GZMH+ (T04) was classified as Tpex.

Then we assigned cell subcluster identities from ours on an independent OC cohort with matched scRNA-seq and scTCR-seq and evaluated TCR clonotypes of CD8+ T clusters using STARTRAC indices. CD8+CXCL13+ Tex (T06) and CD8+GZMH+ Tpex cells (T04) exhibited high clonal expansion index, indicating that these cells could recognize more tumor antigens. Also, TCR clonotypes of T04 had the most transition index with tumor-reactive T06 cells, suggesting that CD8+GZMH+ T cells may represent a critical reservoir for the emergence of tumor-specific CD8+ T cells. Cytotoxic CD8+ T cells reactive against tumor-specific neoantigens eradicate cancer cells by producing cytokines, chemokines, and granzymes. As expected, T04 and T06 exhibited elevated cytotoxic signature scores and tumor specific signature scores, suggesting that these cells might be activated under chronic antigen stimulation.

Numerous investigations have established a correlation between clinical outcomes and Tpex cells, which subsequently differentiate into a terminal Tex subset. To track GZMH+ Tpex dynamics in HGSOC metastasis, we performed R analysis and found enrichment of T06, which are potentially differentiated from T04, within solid tumor sites, with higher prevalence detected in the EAT and LAT groups in comparison to the Met group. This result prompted us to further characterize CD8+ T cell lineages with cytotoxic potential that respond to TCR-mediated stimulation in the solid tumor microenvironment. Thus, we performed pseudotime analysis on CD8+ T cell subsets T01, T02, T04 and T06, excluding T03 and T07. Two distinct differentiation trajectories for CD8+ T cells were observed: one path culminated in a memory and precursor exhausted state (cell fate 1), predominantly characterized by T02 and T04 cells, while the alternative trajectory progressed toward a terminally exhausted state (cell fate 2). The latter pathway was marked by an enrichment of T06 at its end, with T04 appearing at an intermediate position along this trajectory. Cytotoxic, tumor specific, PD-1 and immune response signatures were elevated at end of fate 2, in line with terminal dysfunction. Consistent with our previous findings, Met-derived CD8+ T cells enriched in the early pseudotime were likely to be in naïve state. Moreover, EAT-derived CD8+ T cells showed more enrichment in the terminally functional state compared to LAT-derived cells, either in cell fate 1 and 2. Although T04 abundance changed little across solid sites, its cytotoxic/tumor-specific functions, including GZMB and PRF1 expression, were markedly reduced in LAT and Met compared with EAT. In contrast, T06 functions remained stable across sites. These results suggest that ovarian cancer progression involves loss of cytotoxicity and tumor reactivity in Tpex cells, along side reduced exhausted T cell numbers during metastasis.

To delineate the heterogeneity of CD4+ T cells, we performed transcriptional profiling and identified distinct subsets with characteristic gene expressions signatures. Previous studies have reported that Tregs are more frequently observed in HGSOC compared to other histological subtypes and mediate poor prognosis. We next focused on Treg dynamics across disease progression and observed a stepwise increase in the enrichment of CD4+ FOXP3+ Treg from EAT to LAT and ultimately to Met. Together, these findings highlight a potential pro-metastatic role for Tregs in HGSOC, likely mediated by their robust immunosuppressive functions within the solid tumor microenvironment.

SELENOP+ macrophages co-localized with CD8+ GZMH+ Tpex and predict better prognosis

Tumor-associated macrophages (TAMs) demonstrate significant plasticity and heterogeneity, challenging the traditional M1/M2 polarization paradigm, with studies showing their essential role in modulating antitumor T cell responses within the tumor microenvironment, including in ovarian cancers. However, the complexity of TAMs in HGSOC during metastasis remains largely unexplored. We analyzed a total of 46,396 myeloid cells using unsupervised clustering. We identified dendritic cells (DCs), monocytes, TAMs, neutrophils and mast cells according to their markers with their spatial preference. Notably, SELENOP+ macrophages (M06) and SPP1+ macrophages (M09) were enriched in solid sites, whereas the other macrophage clusters were predominantly observed in fluid sites. Further analysis using KEGG enrichment, RNA expressions and signature scoring revealed that M06 preferentially exhibited antigen processing and presentation capabilities. In contrast, M09 were involved in extracellular matrix remodeling and glycolysis and gluconeogenesis pathways. Analysis of the TCGA-OV cohort showed that M06 were linked to favorable prognosis, whereas M09 were associated with poor outcomes, suggesting antitumor and protumor roles, respectively.

We further deconvoluted the proportions of cell subpopulations for each sample in TCGA-OV based on subcluster profiles from our scRNA-seq dataset, which showed that SELENOP+ macrophages, but not SPP1+ macrophages, correlated with diverse T cell subsets, including CD8+ GZMH+ Tpex (T04). We also spatially mapped cell types in high-resolution spatial transcriptomics using cell2location. Compared to SPP1+ macrophages, SELENOP+ macrophages showed stronger co-localization with CD8+ GZMH+ Tpex (T04), particularly in adnexal sites, rather than in omental metastases, as revealed by our spatial transcriptomics data using a machine-learning method mistyR. Similar results were also observed in the validation spatial transcriptomics cohorts using the 10x Visium platform. Furthermore, multiplex immunohistochemistry (mIHC) also confirmed the close proximity of SELENOP+ macrophages and CD8+ GZMH+ T cells or CD8+ PD-1+ T cells. These findings suggest that SELENOP+ macrophages are spatially linked with Tpex cells, prompting further investigation into their interactions.

SELENOP+ macrophages increased cytotoxicity of Tpex in vitro

Previous studies have reported that a subset of TAMs possesses antigen cross-presentation capabilities and efficiently activates antigen-specific CD8+ T cells. Similarly, the antigen processing and presentation signature score of SELENOP+ macrophages were positively correlated with the GZMB expression and the cytotoxic signature scores of T04. Additionally, SELENOP, the marker of M06, was primarily expressed in SELENOP+ macrophages rather than other macrophage subsets or cell types. SELENOP encodes a secreted protein, selenoprotein P (SELENOP), which contains up to 10 selenocysteine (Sec) residues. To elucidate the effect of SELENOP on CD8+ T cell function, CD8+ T cells derived from female OT-1 mice (OT-1 T cells) activated by ovalbumin (OVA) peptide (257-264) were cultured with or without exogenous recombinant mouse SELENOP (rmSELENOP) for 48h. Dose-dependent changes in both GZMB and PRF1 levels were observed. Moreover, rmSELENOP treatment also enhanced the tumor-specific cytotoxicity of OT-1 T cells, estimated by the apoptosis rates of the co-cultured OVA-expressing ID8 ovarian cancer cells. Furthermore, rmSELENOP also enhanced the expression of GPX1 in OT-1 T cells, which is synthesized by selenium.

To assess the functional impact of macrophage-derived SELENOP on CD8+ T cells, we established macrophage-derived conditioned medium (CM) and CD8+ T cell coculture system. Selenop expression in bone marrow-derived macrophages (BMDMs) was modulated via lentiviral-mediated overexpression (OE) or knockdown (KD) strategies. Then, the CM from these BMDMs were collected and co-culture with OT-1 T cells for 2 days. We observed that CM from Selenop-overexpressing BMDMs (BMDMs-OE-Selenop) enhanced GZMB and PRF1 expression in OT-1 T cells, along with increased tumor cell killing capacity as demonstrated by T cell cytotoxicity assay. In contrast, CM from Selenop-knockdown BMDMs showed a weaker cytotoxic response effect, with reduced both GZMB and PRF1 expression, as well as the cytotoxic activity of CD8+ T cells, which could be reversed by supplementation of rmSELENOP. Parallel experiments using CM from THP-1 cells overexpressed SELENOP and PBMCs derived CD8+ T (human CD8+ T) cells of HGSOC patients yielded consistent results. To further evaluate the direct priming capacity of SELENOP+ macrophages on CD8+ T cells, we also performed the antigen presentation assay using OVA-loaded BMDMs, OVA-loaded BMDMs-KD-Selenop, cocultured directly with OT-1 T cells. The results were also consistent with those observed in the macrophage-conditioned medium and CD8+ T cell coculture system.

Together, these results indicate that SELENOP+ macrophages possess immunostimulatory properties, potentially activating or maintaining the cytotoxicity of precursor exhausted CD8+ T cells through SELENOP, in contrast with pro-tumorigenic effects from SPP1+ macrophages. Thus, SPP1+ and SELENOP+ macrophages resembled two opposite functions in HGSOC.

Site-specific phenotypes of macrophages and functional reprogramming of SELENOP+ macrophages in the solid tumor microenvironment of metastatic HGSOC

To further elucidate the dynamical alterations and differentiation of SELENOP+ and SPP1+ macrophages during the metastasis, we undertook an analysis of the tissue preference. As the disease progressed from EAT to LAT and then to Met, there was a gradual reduction in the proportion of SELENOP+ macrophages (M06) and a corresponding increase in SPP1+ macrophages (M09). Validation using external HGSOC scRNA-seq data also confirmed the similarity of M06 to the previously reported M2.SELENOP population and showed a modest enrichment of these M2.SELENOP macrophages in adnexal tumors compared to the metastatic lesions. To further support this observation, we integrated our scRNA-seq dataset with the publicly available dataset GSE184880. Consistently, this analysis recapitulated the observed trend, showing a progressive decrease in the inferred SELENOP+ macrophages and a concomitant increase in the inferred SPP1+ macrophages from EAT to LAT and ultimately to Met. This trend of decreased SELENOP+ macrophages was further corroborated by mIHC using an independent sample set. These results elucidated that M06 and M09 not only serve opposing functional roles within the TME but also exhibited contrasting trends as the tumor progressed through metastasis.

We further characterized the dichotomy of macrophages enriched in solid tumor of HGSOC as the following attempts. First, pseudotime analysis was performed on monocyte-like cells (M05), SELENOP+ macrophages (M06) and SPP1+ macrophages (M09). This analysis revealed a distinct bifurcation: cell fate 1 dominated by SPP1+ macrophages (M09) and cell fate 2 dominated by SELENOP+ macrophages (M06). We found that antigen processing and presentation, interferon gamma (IFNγ) response and SELENOP expressions were associated with fate 2 (SELENOP+ macrophages enriched), whereas hypoxia, glycolysis, extracellular matrix degradation pathway and SPP1 expressions were progressively upregulated along the fate 1 trajectory (SPP1+ macrophages enriched). We also identified transcription factors driving TAM differentiation in HGSOC solid tumors. SELENOP+ macrophages (M06) showed activation of IRF8, CEBPA, and STAT1, whereas SPP1+ macrophages (M09) were enriched for HMGA2, MYB, and MX1. IRF8, a transcription factor regulated by interferon, associated with inflammatory macrophage polarization, showed high specificity and activity in SELENOP+ macrophages (M06), which were also positively correlated with SELENOP expression and the antigen processing and presentation signature scores of M06. In addition, we observed that the expression of HMGA2 and MX1 increased along cell fate trajectory 1, while IRF8, CEBPA, and STAT1 expression increased along cell fate trajectory 2. Overall, these results suggested that TAM exhibited bidirectional differentiation pathways, and the phenotype of SELENOP+ macrophages is associated with IFNγ induction and IRF8 regulation. Functionally, IFNγ stimulation enhanced SELENOP expression in THP-1 macrophages, human monocyte-derived macrophages (MDMs) of HGSOC patients and BMDMs, confirming IFNγ regulation. Selenium supplementation further boosted SELENOP, indicating dependence on selenium availability.

It is noteworthy that EAT- and LAT-derived cells preferentially localized at the terminal end of the pseudotime trajectory enriched with SELENOP+ macrophages, whereas Met-derived macrophages tended to reside in distinct phases enriched with SPP1+ macrophages. Additionally, SELENOP expression, IRF8 activity and expression as well as antigen processing and presentation signature scores in SELENOP+ macrophages (M06) were lower in the Met group compared to the EAT and LAT groups. These findings suggest site-specific differentiation tendencies of TAMs, potentially associated with interferon and IRF8. On the contrary, the matrix remodeling score of SPP1+ macrophages (M09) increased in the Met group compared to the EAT and LAT groups, further revealing that SPP1+ macrophages might promote HGSOC metastasis.

Malignant epithelial cells transcriptional heterogeneity and genomic alterations

According to the seed and soil theory of metastasis, the spread of tumors results from the interplay between malignant cells and the microenvironments. In addition to the immune landscape, malignant epithelial cells themselves exhibit substantial transcriptional heterogeneity and adaptive plasticity, which are critical in shaping the metastatic niche. Intratumor heterogeneity in previous scRNA-seq studies is reflected by expression programs, and the consensus among them, termed meta-programs (MPs), represent fundamental features of tumor biology.

In our scRNA-seq data, inferCNV analysis identified 20,956 malignant epithelial cells (mEpi). To characterize the heterogeneity within these ovarian malignant epithelial cells, we applied non-negative matrix factorization (NMF) and successfully identified seven MPs along with their top-scoring genes. To further profile the MP1-7 obtained, we first performed Gene Ontology to analyze the top 50 genes within each MP and indicate that MP1 and MP2 were related to nuclear division and DNA replication, MP4 was characterized by extracellular matrix organization, and MP6 was associated with hypoxia. Then, we analyzed their gene signature similarities with annotated MPs from previous tumor MPs, demonstrating the existence of these MPs in pan-cancer level. Notably, MP3 and MP6 were enriched in cancer cells characterized by immune (Cancer.cell.3) and hypoxia signaling pathways (Cancer.cell.6) in a HGSOC cohort. PROGENy pathway analysis also revealed consistent results, including enrichment of the JAK-STAT pathway in MP3, which is closely associated with interferon stimulation, and enrichment of the hypoxia pathway in MP6. Based on these findings, we designated the MPs we obtained as Cell Cycle-G2/M (MP1), Cell Cycle-G1/S (MP2), Interferon (MP3), EMT (MP4), Stress (MP5), Hypoxia (MP6), and Senescence (MP7). Among the identified metaprograms, MP2 showed a positive correlation with MP3, while MP4 was negatively correlated with MP3 and MP5. These results provide a molecular basis for understanding tumor heterogeneity in HGSOC.

To evaluate the relationship between these MPs and the progression of HGSOC metastasis, we first analyzed their association with the prognosis of HGSOC patients using TCGA data. Our findings confirmed that the signature score of MP6, MP4 and Senescence MP7 were associated with worse overall survival (OS) in OC, respectively. Further analysis revealed that, compared to the malignant epithelial cells from the EAT group, the signature scores of these three MPs were higher in those from the Met and LAT groups, particularly in the Met group. This finding was validated by independent bulk RNA sequencing we previously generated, though there was no statistically significant for MP7. These suggested that the hypoxia-driven, EMT, and senescence state of malignant epithelial cells was associated with HGSOC metastases. We also noted that the signature score of MP6 presented strong positive correlation with the burden of copy number variation (CNV). This finding supports the concept that hypoxia was correlated with genomic instability and tumor progression.

We constructed copy number alteration (CNA) phylogenetic trees for 8 patients with HGSOC and revealed specific clones associated with high Hypoxia (MP6) signature scores. We also revealed that FTL, one of the MP6 signature genes, induced by hypoxia, was elevated in metastasis and predicted poor survival.

Hypoxia-driven malignant epithelial cells of HGSOC reprogramed SELENOP+ macrophages via VEGFA-EPHB2

Tumor-host bi-directional interactions contribute to tumor adaptation and invasion within the microenvironment, leading to tumor progression, metastasis and recurrence. Cellphone DB analysis revealed that ligand-receptor-mediated intercellular interactions between malignant epithelial cells and macrophages were more pronounced than those between malignant epithelial cells and other cell types. In terms of macrophage subtypes, hypoxia-driven malignant epithelial cells negatively correlated with SELENOP+ macrophage infiltration, while positively correlating with the proportion of SPP1+ macrophages. Our spatial transcriptomics analysis revealed that SPP1+ macrophages, rather than SELENOP+ macrophages, were found in close proximity to malignant epithelial cells with MP6 signature. mIHC further validated that SPP1+ macrophages rather than SELENOP+ macrophages contact with hypoxic tumor cells. The above results suggest a phenotypic transition of SELENOP+ macrophages associated with hypoxia-driven tumor cells during HGSOC metastasis.

To probe the underlying mechanisms, we performed a comprehensive characterization of the ligand-receptor interactions between malignant epithelial cells and SELENOP+ macrophages. Our analysis identified the ligand-receptor pairs exhibiting stronger interaction strength between SELENOP+ macrophages and malignant epithelial cells with elevated MP6 signature scores, compared to those with lower scores. Subsequently, we examined the intersections between the ligands derived from malignant epithelial cells within the 7 ligand-receptor pairs and the top 50 genes in the Hypoxia (MP6) module, revealing two overlapping genes: vascular endothelial growth factor A (VEGFA) and macrophage migration inhibitory factor (MIF). In the interactions mediated by VEGFA or MIF, the VEGFA-EPHB2 interaction was notably more pronounced in peritoneal sites. Our single-cell RNA-seq data revealed a positive correlation between VEGFA expression and the MP6 signature score in the malignant epithelial cells, along with a negative correlation between VEGFA expression in malignant epithelial cells and SELENOP expression in SELENOP+ macrophages. Thus, MP6 malignant cells might suppress M06 macrophages through VEGFA.

Recent evidence suggests that the VEGFA-EPHB2 ligand-receptor pair mediates a synergistic interplay between VEGF and EPH signaling pathways, as shown in ovarian organoids where it facilitated follicular development. EPH receptor B2 (EphB2), an important member of the Eph receptor family, have been implicated in diverse cell-cell communication events associated with cancer progression. To validate the VEGFA-EPHB2 axis in modulating the crosstalk between SELENOP+ macrophages and hypoxia-driven malignant epithelial cells, we performed co-culture assay with CM from ovarian cancer cells (COV362, OVCAR3, CAOV3 and ID8). Hypoxia induced VEGFA upregulation in OVCAR3/CAOV3 (48h) and COV362/ID8 (72h). CM from these cells suppressed SELENOP expression in THP-1 macrophages, an effect reversed by VEGFA neutralization. Parallel experiments using hypoxia-conditioned media from ID8 and BMDMs yielded consistent results. Either CRISPR-mediated EPHB2 knockout in THP-1 cells or shRNA–mediated Ephb2 knockdown in BMDMs abolished VEGFA-induced repression of SELENOP. Patient-derived cancer organoids (PDOs) represent effective tools for personalized medicine. PDOs also confirmed the above findings: hypoxia increased VEGFA, and CM from hypoxic PDOs suppressed SELENOP in MDMs. Hypoxia also reduced PDOs volume. Together, these data show that hypoxia-driven epithelial VEGFA represses macrophage SELENOP expression via EPHB2.

Anti-VEGFA therapy decreased tumor burden in vivo

To investigate the effect of VEGFA on ovarian tumor metastasis and the role of VEGFA on ovarian tumor microenvironment in vivo, we established orthotopic ovarian cancer mouse model by injecting intrabursally (i.b.) luciferase-transfected ID8 cells into immuno-competent C57BL/6J (B6) mice. We found that VEGFA inhibition significantly reduced tumor burden at day 49, evidenced by decreased bioluminescence imaging (BLI) signal intensity, fewer peritoneal metastases, and smaller primary tumors. Additionally, VEGFA blockade increased the proportion of F4/80+CD11b+SELENOP+ macrophages in both primary tumors and peritoneal metastases, elevated CD3+CD8+GZMB+ T cell proportions in the primary tumors and CD3+CD8+PD-1+ cell proportions in the primary tumors. These suggested that anti-angiogenesis therapy, such as bevacizumab, could prolong survival not only by killing tumors, but also by increasing SELENOP+ macrophages, thus enhanced CD8+ T cell activities. mIHC further validated the spatial association between SELENOP+ macrophages and CD8+ GZMH+ T cells within the tumor microenvironment of the orthotopic ovarian cancer model. In addition, VEGFA inhibition did not significantly alter SELENOP concentrations in the serum of mice, thereby excluding a substantial systemic effect of TME-derived VEGFA on the circulating SELENOP levels.

To further evaluate the capacity of SELENOP+ macrophages to directly prime CD8+ T cells and to demonstrate the requirement of macrophage-derived SELENOP in modulating VEGFA’s effect in vivo, we performed adoptive cellular transfer experiments. Then we found that, compared with the BMDMs group, the proportion of CD45.2+CD3+CD8+GZMB+ T cells decreased, accompanied by reduced frequencies of CD45.1+F4/80+CD11b+SELENOP+ macrophages in the primary tumors of the BMDMs-KD1-Selenop group. Meanwhile, VEGFA inhibition led to a concomitant increase in CD45.2+CD3+CD8+GZMB+ T cells and CD45.1+F4/80+CD11b+SELENOP+ macrophages in the primary tumors of mice injected with BMDMs-KD1-Selenop, but had no significant effect in mice injected with BMDMs. These findings support the role of SELENOP+ macrophages in priming CD8+ T cell cytotoxicity within the HGSOC tumor microenvironment in vivo, and further emphasize a VEGFA-mediated regulatory mechanism that appears to be more pronounced in macrophages with low SELENOP expression.

Reduced interaction potential between SELENOP+ macrophages and GZMH+ precursor exhausted CD8+ T cells following neoadjuvant chemotherapy (NACT)

Chemotherapy is the classical first-line treatment regimen for HGSOC. However, the specific alterations it induces in TME and their subsequent impact on therapeutic efficacy remain largely unexplored. To investigate the impact of chemotherapy on macrophage populations in HGSOC, we analyzed an external scRNA-seq dataset (GSE266577). Chemotherapy reduced overall macrophage abundance, shifting them from anti- to pro-inflammatory states. Subtype analysis showed stable proportions of SELENOP+ and SPP1+ macrophages. However, SELENOP+ macrophages displayed reduced SELENOP expression and antigen presentation, while SPP1+ macrophages showed diminished matrix remodeling. Intercellular communication analysis revealed that SELENOP+ macrophages interacted strongly with T04 CD8+ T cells before NACT, but this interaction weakened post-NACT, shifting toward SPP1+ macrophages. Spatial profiling confirmed that the coexistence scores of SELENOP+ macrophages and CD8+ T cells remained unchanged in the regions of interest (ROIs) with the presence of CD8+ T cells and IBA1+ macrophages in the GeoMX data, regardless of tumor or stromal areas of interest (AOIs). These results suggest a disruption of the immunoregulatory axis between SELENOP+ macrophages and GZMH+ CD8+ T cells following NACT, which may be attributable, at least in part, to a reduction in their functional interaction potential rather than changes in spatial proximity.

Clinically, SELENOP+ macrophages were enriched in non-relapsed patients, whereas SPP1+ macrophages predominated in relapsed cases in our scRNA-seq cohort. In the pre-NACT samples from GSE266577, long-platinum-free interval (PFI) patients showed enrichment of SELENOP+ macrophages with higher SELENOP expression, while short-PFI patients exhibited SPP1+ macrophages with increased matrix remodeling. The finding related to SELENOP+ macrophages were validated by mIHC in an independent cohort.

Distinct immune cell subpopulations enriched in solid sites across different BRCA statuses

BRCA mutation testing in first-line OC treatment has been recommended as it provides an opportunity to optimize the use of PARPi in HGSOC patients. Recent studies have highlighted the association between the BRCA mutation and the suppressive TME. Therefore, a deeper understanding of the TME in relation to different BRCA status is crucial for improving the therapeutic response of HGSOC patients to PARPi. We identified BRCA-mutated patients according to clinical assessment or WES and their TME manifestations, suggesting a more active and coordinated antitumor immune response in BRCA-mutated HGSOC tumors, whereas BRCA wild-type HGSOC tumors are associated with a more suppressive TME. This distinction is crucial for developing effective therapeutic strategies for HGSOC.

Methods
This study was performed following the ethical guidelines of the Declaration of Helsinki and was approved by the Research Ethics Committee of Shengjing Hospital of China Medical University, the Research Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and the Research Ethics Committee of the First Hospital of China Medical University. Written informed consent was obtained from all patients involved in this study for the use of their tissue samples and clinical information and participant compensation was not implemented in this study. Sex was not considered a biological variable in the study, as ovarian cancer is a gender specific disease. Only female patients were included in our study. Patients’ self-reported gender were used for the analysis. All animal studies were reviewed and approved by the Ethics Committee of the Laboratory Animal Department of China Medical University. Measures were taken to minimize animal distress. According to institutional policies on tumor production, the maximum allowable tumor diameter in any direction is 2.0 cm, and this limit was not exceeded in any of our experiments.

Patients samples and ethics statement

The scRNA-seq cohort in our study included 34 fresh samples from 17 female patients with pathologically confirmed high-grade serous ovarian cancer (HGSOC) or uterine fibroids who underwent surgery at two medical centers: Shengjing Hospital of China Medical University and the First Hospital of China Medical University. The disease stages of the patients with HGSOC were classified according to the 2018 FIGO staging system. Specifically, the sample collection included three normal ovarian tissue samples from postmenopausal women (Nor.Ovr group) and six peritoneal lavage fluid samples (PLF.UF group) from nine patients diagnosed with uterine fibroids; two adnexa tumor lesion samples (EAT group) and two matched peritoneal lavage fluid samples (PLF.EOC group) from two patients with HGSOC at FIGO stage I–II; six adnexal tumor lesion samples (LAT group), six matched ascites samples (Ascites group) and nine matched metastases (Met group) including four omental metastatic lesion samples (Met.Ome group) and five peritoneal metastatic lesion samples (Met.Per group) from six patients with HGSOC at FIGO stage III–IV. Our scRNA-seq cohort included seven patients with HRD-positive HGSOC, comprising four with BRCA1/2-mutated HGSOC. The median age of the patients was 54 years. The spatial transcriptomics (ST) cohort in our study included 24 fresh samples from 6 female patients (median age: 51.5 years) diagnosed as HGSOC under went surgeries at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Fresh tumor samples were collected during surgeries and prepared for ST. None of these patients received chemotherapy, radiotherapy, or any other antitumor therapy before surgery. Clinical characteristics of the patients are summarized in Supplementary Data 1 and 2.

Sample collection and single-cell suspension processing

Fresh samples including adnexal tumor, omental metastatic lesion, peritoneal metastatic lesion, ascites, peritoneal lavage fluid and normal ovarian tissue were obtained from the patients during surgery. Tissue samples were placed in MACS Tissue Storage Solution (Cat#130-100-008, Miltenyi Biotec). Briefly, fresh tissue samples were first washed with Phosphate-Buffered Saline (PBS), cut into small pieces (approximately 1 mm3) and dissociated in Human Tumor Dissociation kit (Cat#130-095-929, Miltenyi Biotec) in RPMI 1640 (Cat#C11875500BT, Gibco) for 40 min at 37 °C. Tissue samples after digestion or fluid samples were subsequently filtered using 70 μm cell strainers (Cat#352350, Corning) and centrifuged at 300 × g for 5 min. The supernatant was removed and the pelleted cells were suspended in red blood cell lysis buffer (Cat#R1010, Solarbio). Lysis was quenched by adding PBS and the cell suspension was centrifuged at 300 × g for 5 min. Then the remaining pellet was washed twice with PBS and resuspended in PBS containing 2% fetal bovine serum (FBS, Cat#FB65015, CLARK Bioscience). Dissociated single cells were then stained with acridine orange/propidium iodide (AO/PI) (Cat#CS2-0106-5ML, Nexcelom Bioscience) for viability assessment. When the viability of single cell suspensions was higher than 85%, the cell concentration was set according to 10x Genomics protocols (800–1200 cells/µL).

Single-cell RNA library preparation and sequencing

Single-cell RNA library preparation and sequencing experiments were conducted according to the manufacturer’s instructions of the 10x Genomics Chromium single cell 3’ platform (10x Genomics). In short, 10x Chromium Controller (Model# GCG-SR-1, 10x Genomics), Chromium Next GEM Single Cell 3ʹ Reagent Kit v3.1 (Cat# PN-1000123, 10x Genomics) and Chromium Next GEM Chip G Single Cell Kit (Cat# PN-1000120, 10x Genomics) were used to generate single-cell nanoliter-scale Gel Beads-in-emulsion (GEM) and barcoded RNAs. Reverse transcription was performed using a Touch Thermal Cycler (Model# 9902, Applied Biosystems), the GEMs were programmed at 53 °C for 45 min, followed 85 °C for 5 min, and hold at 4 °C. The cDNA was generated and amplified, and the quality was assessed by the Agilent 4200 TapeStation system (Agilent Technologies). Libraries were sequenced on an Illumina Novaseq 6000 sequencer using a pair-end 150bp (PE150) reading strategy (Capital Bio Technology, China).

Single-cell RNA-seq data processing and quality control

Cell Ranger software (version 3.1.0) was used to process single-cell sequencing data and align it to the GRCh38 human reference genome. For subsequent quality control and analysis, we utilized the filtered feature barcode expression matrix generated by Cell Ranger. Low-quality cells and genes were removed based on several criteria: cells with fewer than 200 genes, fewer than 800 UMI counts, top 1% UMI count, over 20% mitochondrial gene content, and genes present in fewer than 3 cells. The “DoubletFinder” R package (version 2.0.1) identified and removed potential doublets. Post quality control, the cell count matrix was normalized using the NormalizeData function of the Seurat package (version 4.3.0).

Cell type identification and characterization

For each individual sample, the normalized expression matrix was subjected to dimensionality reduction and unsupervised clustering using the Seurat workflow. First, for the purpose of downstream analysis, a selection of 2000 highly variable genes was made utilizing the “FindVariableGenes” function. Next, the matrix was scaled using the “ScaleData” function. Each individual sample matrices were merged by sample and subsequently renormalized and scaled. Subsequently, principal component analysis (PCA) was conducted for dimensionality reduction on highly-variable genes using the RunPCA function from the Seurat R package, specifying npcs = 50 as the parameter. For primary analyses, unsupervised cell clusters were acquired by graph-based clustering approach using the “FindNeighbors” and “FindClusters” functions. For visualization of graph-based cell clustering, Uniform Manifold Approximation and Projection (UMAP) was applied with the “RunUMAP” function. The main cell populations were identified by first-run clustering and subsequently annotated according to the expression of canonical markers in previous literature. The cells expressing more than one major cell marker were considered doublets and removed from each cluster individually. Marker genes of each subcluster were identified by the “FindAllMarkers” function. Genes with adjusted p value < 0.05 by Wilcoxon rank-sum test were defined as cluster-specific marker genes.

Batch correction and integration

After the first round of dimensionality reduction and unsupervised cell clustering, the major cell types identified across samples were divided into ten supersets, with myeloid cells and T/NK cells being re-clustered for further analysis. For myeloid cells and T/NK cells superset, the R package harmony (version 1.0) was used for batch correction to account for sample-specific effects.

PCA plot for pseudo-bulk RNA-seq

Pseudo-bulk RNA-seq analysis was executed by isolating the raw counts for each specimen using the “AggregateExpression” function within the “Seurat” R package. The raw counts were consolidated for each specimen, effectively producing a collective gene expression profile from the single-cell data. Principal components for pseudo-bulk RNA-seq analysis were examined and visualized using “pcaExplorer” (version 2.14.2), and statistical analyses were performed using PERMANOVA.

Ro/e analysis

To assess tissue preference for each cluster, we calculated the ratio of observed to expected cell numbers (Ro/e) across various tissues, according to the following formula (Eq. (1)) described by Zhang et al. and Cheng et al.:

Ro/e = Observed / Expected (1)

Expected cell counts for each cluster-tissue pair were determined using the chi-square test. A cluster was deemed enriched in a tissue if its Ro/e ratio exceeded 1.

Definition of gene signatures and cell function scores

The CD4+ and CD8+ T cells signature list were obtained from previous studies. The macrophages signature list were acquired from previous publications by Huang et al. and Zheng et al. Antigen processing and presentation signature, as well as ECM-receptor interaction pathways, were sourced from KEGG. For signature quantification, we utilized “UCell” (version 2.5.0) to assess the signature/pathway activity of each subset. The signature genes were showed in Supplementary Data 6.

TCR analysis

The raw data of TCR sequences was obtained at GSA-Human under accession code HRA002184, following an application through the corresponding author, and processed using Cell Ranger (version 7.1.0). Only cells with both single-cell RNA sequencing and single-cell TCR sequencing data were retained, and cells containing only a single α or β chain were excluded from downstream analysis. We then presented two STARTRAC indices to analyze different aspects of T cells based on paired single-cell transcriptomes and TCR sequences using STARTRAC (version 0.1.0). STARTRAC-expa and STARTRAC-tran were used to measure the degree of clonal expansion and state transition of T cell clusters upon TCR tracking, respectively, while STARTRAC-migr was not used. Additionally, CD8+ GNLY+ NK-like cells (T05), CD8+ SLC4A10+ MAIT-like cells (T08), and CD8+ TRDV2+ γδ T cells (T09) were excluded from these analyses due to their distinct TCRs.

Monocle2 trajectory inference analysis

Macrophages and CD8+ T cells developmental trajectories were inferred using Monocle2 (version 2.30.0) with default parameters. Count matrices of specific cell type were obtained from “Seurat”. Subsequently, these matrices were used with “newCellDataSet” to construct a CellDataSet. Next, order genes were selected using the default parameters in “monocle”. The DDRTree method was then employed for subsequent dimensionality reduction. After ordering the cells, a trajectory was obtained. The visualization function “plot_cell_trajectory” was used to plot each group along the trajectory. BEAM (Branched Expression Analysis Modeling) was employed to identify genes regulated in a branch-dependent manner. Finally, the function “plot_genes_branched_pseudotime” was used to depict gene expression across different branches over pseudotime.

Survival analysis

The Cancer Genome Atlas Ovarian Cancer Cohort (TCGA OC) gene expression matrices and clinical data downloaded from UCSC Xena were used to evaluate the prognostic performance of gene sets. Previously reported blacklisted genes: ribosome-protein-coding genes, and tissue dissociation operation-induced genes, including heat shock protein encoding were removed. To assess the enrichment of the gene signatures in each sample, we employed the single-sample gene set enrichment analysis (ssGSEA) method in the GSVA (version 1.40.0) package. Subsequently, we perform survival analyses utilizing the log-rank test method. To establish an optimal cutoff value and to generate Kaplan–Meier survival curves, we employed the “ggsurvplot” function available in the survminer R package (version 0.4.9). Significance was assessed by the log-rank test statistics (p values) between two groups.

BayesPrism deconvolution analysis

We employed the BayesPrism computational method (version 2.2.2) with default hyperparameters for deconvolution, aiming to gauge the cell-type abundances within bulk transcriptomic data from TCGA. Initially, we annotated our single-cell RNA sequencing data. To alleviate computational load, we subsampled 1000 cells for each subpopulation, subsequently generating reference profiles for all cell states. To avert sex-specific transcriptional biases, genes located on the sex chromosomes were excluded from these reference profiles. Furthermore, we removed ribosomal protein-coding genes and mitochondrial genes to mitigate batch effects. The cell type frequencies estimated by BayesPrism were then utilized as cell type fractions in the TCGA data for subsequent analyses.

Spatial transcriptomic experiment

OCT-embedded tissue samples were prepared for spatial transcriptomic analysis, and RNA quality was assessed prior to Stereo-seq library construction and sequencing using BGI capture chips. Raw data were processed with the BGI Stereomics pipeline, followed by cell type spatial mapping with cell2location and spatial neighborhood analysis using MISTy to evaluate cell–cell abundance relationships (see Supplementary Methods for details).

Similarity analysis of clusters

We employed the strategy published by Cheng et al. to assess the similarity between cell subpopulations in our dataset and those in external datasets. Briefly, we trained a logistic regression model with elastic net regularization using the cv.glmnet function from the R glmnet package, performing 10-fold cross-validation for each model. By calculating predicted logit values for each cell in the test data and converting them to probabilities, we visualized the similarity of clusters from the test data to those from the training data.

SCENIC analysis

We performed SCENIC analysis (pySCENIC) (version 0.12.1) to predict and validate the potential transcriptional regulatory network in cell subsets with default settings. The activity of the 215 regulons for each subset was calculated using the AUCell function. To pinpoint the key transcription factors (TFs) driving the unique characteristics of each cell subsets, we further analyzed the specific regulons in different cell types by calculating the regulon specificity score (RSS).

Single-cell CNV and clonality analysis

Infer copy number variation (CNV) algorithm was applied to identify malignant cells with large-scale chromosomal CNV in the cell clusters labeled as epithelial cells. We filtered genes with an average read count < 0.1 among reference cells. We used endothelial cells, SMC cells, and pericyte cells to serve as references. Malignant epithelial cells were identified using inferCNV R package (version 1.10.1). Malignant cells and non-malignant cells were defined as the same as Gavish et al. Subclone CNV architecture of each HGSOC patient were determined using the subcluster method of inferCNV R package and visualized by a phylogenetic tree plot to illustrate tumor clonality and evolution, as reported before. Subclones with less than five cells were filtered out.

Defining NMF programs and metaprograms of malignant epithelial cells

Consensus Non-negative Matrix factorization (cNMF) process was carried out for each sample from patients with HGSOC to capture the intratumor heterogeneity by performing the computational scheme reported before. Briefly, the steps were conducted as follows: (1) cNMF was run by using seven different parameter values (K = 4, 5, 6, 7, 8, 9 and 10), producing 49 programs for each sample; (2) the cNMF programs were then defined by criteria involved the robust within the sample, robust across sample and non-redundant within the sample. We obtained a total of 980 robust cNMF programs, which were then selected by ranking. Sixty-seven robust cNMF program generated in this process were clustered based on Jaccard similarity; (3) we used the clustering method to further define clusters of cNMF programs, applying an alternative way proposed by Gavish et al. Robust cNMF programs were compared to assess overlapping genes. Programs sharing at least 13 genes were identified as potential initiators of new clusters. If multiple cNMF programs overlapped, the program with the most genes in common with the founder was selected to join and create a cluster; (4) genes with the top cNMF scores in either program was picked to expand the model program; (5) the same methodology was applied to create additional clusters, and seven MPs were finally identified. Subsequently, their enrichment in gene sets with functional annotations was evaluated.

PROGENy

PROGENy (version 1.20.0) was used to calculate 14 signaling pathways, whose scores were computed by a weighted sum of the product from expression and the weight of footprint genes. We select the top 100 significant genes for each pathway or filter by significance in responding to the recommendation.

Cell-cell interaction analysis

To evaluate the interaction strength between different cell types, we employed cellphoneDB (version 3.0.0) with database v.2.0.0, using default parameters. We calculated mean values (strength) and cell communication significance (p value < 0.05). For the same ligand-receptor pair, interactions where the strength between malignant epithelial cells with high MP6 signature scores and SELENOP+ macrophages exceeded that of malignant epithelial cells with low MP6 signature scores and SELENOP+ macrophages were selected (malignant epithelial cells providing ligands). These interactions are thought to potentially play a key role in the hypoxia-driven remodeling of SELENOP+ macrophages.

Multiplex immunohistochemistry

Ovarian cancer tissues were collected and sectioned into 4 μm paraffin sections. The sections were stained using the Goat Anti-Mouse/Rabbit Multiplex IHC Detection Kit (18006, zenbio) according to the manufacturer’s instructions. The following antibodies were used: anti-CD8a Antibody (1:300, Cat# PAQ6570, Abmart), anti-PD1 antibody (1:300, Cat# PH9964, Abmart), anti-GZMH antibody (1:300, Cat# BS2543, Bioworld), anti-SPP1 antibody (1:200, Cat# ab63856, Abcam), anti-SELENOP antibody (1:200, Cat# PA5-112707, Invitrogen), anti-HIF-1α antibody (1:200, Cat# PU774605, Abmart), anti-CD68 antibody (1:500, Cat# ab283654, Abcam), anti-Panck antibody (1:10, Cat# RAB-0050, MXB Biotechnologies), anti-F4/80 antibody (1:200, Cat# 30325T, Cell Signaling Technology). The sections were incubated at 4 °C for 10 h with the primary antibody. After staining, multispectral images were generated with the multichannel laser confocal microscope (Nikon AXR) and Microsystems (605025, Leica).

Cell lines and cell culture

The human ovarian cancer cell line OVCAR3 and CAOV3 were purchased from Wuhan Pricella Biotechnology Co., Ltd (CL-0178). The human ovarian cancer cell line COV362 were purchased from GuangZhou Jennio Biotech Co., Ltd. The mouse ovarian cancer cell line ID8 was provided by X.C. (China Medical University). THP-1 cells were provided by Y.S. (China Medical University). All cell lines were cultured in RPMI 1640 medium with 10% FBS. Cells were incubated at 37 °C with 5% CO2. The hypoxic condition was induced by adjusting the oxygen concentration to 2% for 24 h, 48 h, and 72 h, respectively. THP-1 cells were differentiated into M0 cells by incubating in 100 ng/mL phorbol myristate acetate (PMA, Cat# P6741, Solarbio) for 72 h for further experiments. Bone marrow-derived macrophages (BMDMs) were isolated from C57BL/6J (B6) mice as follows. Bone marrow cells were collected by flushing the femoral marrow cavity of mice, filtered through a 70-μm cell strainer (Cat# 352350, Corning), and subsequently centrifuged at 300 × g for 5 min at 4 °C. Red blood cells were lysed by treating the bone marrow cells with red blood cell lysis buffer (Cat# R1010, Solarbio). Subsequently, the bone marrow cells were cultured in a DMEM medium containing 10% FBS and 100 ng/mL Macrophage-Colony Stimulating Factor (M-CSF, Cat# Z03275, Genscript) for 7 d, thereby obtaining BMDMs. Peripheral blood mononuclear cells (PBMCs) were isolated from peripheral blood of HGSOC patients using the human peripheral blood lymphocyte isolation solution, and were cultured in RPMI 1640 medium supplemented with 10% FBS and 25 ng/mL Recombinant Human M-CSF (Cat# CR97, Novoprotein). The BMDMs and PBMCs were differentiated for 7 days for further experiments.

T cell isolation and SELENOP treatment assay

OT-1 T cells from the spleen cells of female OT-I mice and human CD8+ T cells of HGSOC patients were isolated by the MojoSort™ Mouse CD8 T Cell Isolation Kit (Cat# 480035, Biolegend) and MojoSort™ Human CD8 T Cell Isolation Kit (Cat# 480129, Biolegend), and subsequently stimulated with 2 μg/mL anti-mouse/human CD3 (Cat# 100339, Cat# 300331, Biolegend) and 0.5 μg/mL anti-mouse/human CD28 (Cat# 102115, Cat# 302933, Biolegend) antibodies for 2 days, respectively. OT-1 T cells and human CD8+ T cells were maintained in PRMI 1640 medium with 10% FBS and 30 U/mL mouse/human IL-2 (Cat# Eg0456, Cat# HZ-1015, Proteintech). Ovalbumin (OVA) peptide (257-264) (Cat# T510212, Sangon Biotech)-activated OT-1 T cells were stimulated with PBS (control) or 25 ng/mL, 50 ng/mL, 75 ng/mL, 100 ng/mL, 250 ng/mL and 500 ng/mL recombinant mouse SELENOP (Cat# PDMM100013, Elabscience) for 48 h. CD8+ T cells were collected and the expression of membrane and intracellular protein was detected by flow cytometry.

T cell killing assay

The mouse ovarian cancer ID8-OVA-luciferase cell line with stable OVA expression was constructed using lentivirus transfection. The ID8-OVA-luciferase cells and OVCAR3 cells were co-cultured with OT-1 T cells and human CD8+ T cells for 24 h, respectively. Flow cytometry was used to detect the total apoptosis of the ID8-OVA-luciferase cells and OVCAR3 cells collected.

Cell transfection via lentiviral vectors and CRISPR/Cas9 system

Cell transfection via lentiviral vectors and CRISPR/Cas9 system was conducted as described below. All lentiviral vectors and CRISPR/Cas9 system were constructed by Hanbio Biotechnology Co., Ltd (RNA sequences can be found in Supplementary Data 10). For the generation of SELENOP-overexpressing or EPHB2-deficient THP-1 cell lines, THP-1 cells were infected by lentivirus at a multiplicity of infection (MOI) of 60, with 10 μg/mL polybrene (Cat# HB-PB-500, Hanbio) added to enhance transduction efficiency. Fresh medium was changed 48 h post infection, supplemented with 1 mg/mL puromycin (Cat# P8230, Solarbio) for selection. For the generation of Selenop-overexpressing, Selenop-deficient or Ephb2-deficient BMDMs, BMDMs were infected by lentivirus at a MOI of 50, with 2 μg/mL polybrene added to enhance transduction efficiency and the culture medium with M-CSF was replaced after 48 h. Cell transfection efficiency was detected by Western blot.

Macrophages and CD8+ T cell coculture system

OT-1 T cells and human CD8+ T cells were coculture with medium from BMDMs or THP-1 for 48 h. The expression levels of GZMB and PRF1 in CD8+ T cells were analyzed by flow cytometry, and their tumor-specific cytotoxicity was evaluated using the T cell killing assay.

Antigen presentation assay

BMDMs were collected on day 7 and cocultured with the OVA peptide (257-264) for 6 h. Cells were washed and cocultured with naive OT-1 T cells for 48 h. The cross-priming capacity of macrophages was then evaluated by flow cytometry as GZMB and PRF1 production by OT-1 T cells, and the T cell killing assay was performed to evaluate their tumor-specific cytotoxicity.

Selenite pretreatment, IFNγ stimulation and VEGFA treatment assay in macrophages

THP-1 cells were pretreatment with 70 nM Na2SeO3 (Cat# HY-W686381, MedChemExpress) or equal volume PBS for 72 h, followed by stimulated with 40 ng/mL recombinant human IFNγ protein (Cat# C014, Novoprotein) or equal volume PBS for 48 h. BMDMs/MDMs were also stimulated with 40 ng/mL recombinant mouse/human IFNγ protein (Cat# C746, Cat# C014, Novoprotein) or equal volume PBS for 48 h. SELENOP expression was detected by extracting cellular proteins for Western blot, while SELENOP secretion was measured from collected cell culture supernatants. THP-1 cells/BMDMs were stimulated with 50 ng/mL recombinant human/mouse VEGFA (Cat# GMP-CR96, Cat# CX73, Novoprotein) or an equal volume of PBS for 48 h, respectively. CRISPR/Cas9-mediated EPHB2 deficient THP-1 cells, as well as vector control cells, were stimulated with 50 ng/mL recombinant human VEGFA for 48 h. Similarly, Ephb2 knockdown BMDMs and their corresponding negative control cells were treated with recombinant mouse VEGFA under the same conditions. SELENOP expression was analyzed by Western blot using protein extracted from cell lysates.

Enzyme-linked immunosorbent assay (ELISA)

The concentrations of SELENOP secreted by THP-1 cells, MDMs and in the serum of mice were measured by ELISA kits (Cat# E-EL-H2177, Cat# E-EL-M3044, Elabscience). Briefly, cell culture supernatant was centrifuged at 800 × g for 10 min at 4 °C to remove the cell pellet and the serum of mice were separated by centrifugation of whole blood at 1000 × g for 10 min. The detailed experiment processes were performed according to the manufacturer’s instructions of the ELISA kits. Finally, the concentrations of SELENOP were measured via microplate reader (Infinite M200 Pro, TECAN) capable of measuring absorbance at 450 nm.

Cancer cells and macrophages coculture system

The conditioned medium was collected from 5 × 105 OVCAR3, CAOV3, COV362, and ID8 cells cultured under normoxic or hypoxic (2% O2) conditions for 48 h or 72 h, respectively. THP-1 cells and BMDMs were incubated with conditioned medium for 48 h, and rescue assays were subsequently conducted by supplementing the cultures with 50 μg/mL anti-human/mouse VEGFA neutralizing antibody (Cat# HY-P9906, MedChemExpress; Cat# 512810, BioLegend). Following incubation, cells were harvested and total protein was extracted for Western blot analysis.

Patient-derived organoids (PDOs) culture

PDOs was derived from primary lesions of one patient with HGSOC. Fresh tumor tissues were minced into small pieces and incubated in 1 mg/mL collagenase type Ⅳ (Cat# 17104019, Thermo) at 37 °C for 30 min. The digested cell solution was filtered with 70 μm cell filters (Cat# 352350, Falcon) and centrifuged at 300 × g for 5 min. The cell pellets were washed by DMEM with 5% FBS and 1% penicillin/streptomycin (Cat# 15140163, Thermo). Subsequently, the cell pellets were suspended in 80% Matrigel (Cat# 356231, Corning) and drops of Matrigel cell suspension were allowed to solidify on pre-warmed 24-well culture plates at 37 °C for 30 min. After the drops solidified, advanced DMEM/F12 (Cat# 12634028, Invitrogen) containing 50% culture medium supernatant of L-WRN cell (Cat# CRL-3276, ATCC), 1× B27 (Cat# 17504-044, Thermo), 50 ng/mL EGF (Cat# AF-100-15, PeproTech), 200 ng/mL FGF10 (Cat# 100-26, PeproTech), 1.25 mM N-acetyl-L-cysteine (Cat# A9165, Sigma), 10 mM nicotinamide (Cat# N0636, Sigma), 0.5 μM A8301 (Cat# 2939, Tocris), 10 μM forskolin (Cat# 1099, R&D), 1× GlutaMAX (Cat# 35050-061, Thermo), 1× Primocin (Cat# ant-pm1, Invivogen), 1× HEPES (Cat# 15630080, Gibco) and 10 μM Y27632 (Cat# S1094, Selleck) were added to each well. Fresh medium was added every 3 days. TrypLE was used to passage cultures. For hypoxia culture, PDOs were incubated in normoxia (control) or hypoxia culture (2% O2) incubator for 72 h or 96 h, respectively. The protein of PDOs was collected using lysis buffer for Western blot analysis. The conditioned medium was collected from ~ 200 clones PDOs cultured under normoxic or hypoxic (2% O2) conditions for 96 h. MDMs were then incubated with the conditioned medium for 48 h. Following incubation, cells were harvested and total protein was extracted for Western blot analysis.

Mice

OT-1 (C57BL/6J (B6)-Tg(TcraTcrb)1100Mjb/J) and CD45.1 (B6.SJL-Ptprca Pepcb/BoyJ) mice were generously provided by the First Hospital of China Medical University. C57BL/6J (B6) mice were obtained from GemPharmatech Co., Ltd. Age-matched (6–10 weeks) female mice were used in all mouse experiments. Female mice were selected because ovarian cancer is a gender specific disease. The number of animals (biological replicates) are indicated in figure legends. The female C57BL/6J (B6) mice were housed at the Experimental Animal Center of China Medical University, with commercial standard mouse diet containing 0.13 mg/kg Se (Available from: https://www.spfbiotech.com/report). All the mice were raised in an SPF environment with a temperature of 24 °C, a relative humidity of 50% to 60%, and a 12-h light/12-h dark cycle. All procedures on animals were performed under aseptic conditions and using sterile instruments.

Tumor model and treatment

To generate orthotopic tumors, eight 10-week-old female C57BL/6J (B6) mice were anesthetized by inhalation of isoflurane (5% in oxygen) in an induction chamber and anesthesia maintained at 2.5–3.0% isoflurane delivered via nose cone during all procedures. A small incision was made at the dorso-medial position directly above the ovarian fat pad, with a secondary small incision through the peritoneal wall. The ovarian fat pad was externalized and stabilized with a bull clip. ID8-luciferase cells (3×106 cells suspended in 15 μl sterile PBS) were injected underneath the right ovarian bursa. The peritoneal wall was sutured closed using 6/0 suture and closure of the incision with surgical staples. The incisions were closed using sterile sutures. The general health of the mice was assessed daily, and the inoculated tumors were observed weekly using an in vivo imaging system. From day 14 after the initial injection, the mice were intraperitoneally administered a control treatment of IgG (2.5 mg/kg body weight, Cat# BE0089, Bioxcell) or experimental treatment of anti-VEGFA antibodies (2.5 mg/kg body weight, Cat# 512810, BioLegend) twice a week for 5 weeks. Thereafter, the mice were sacrificed on day 49, and serum samples were collected for ELISA analysis as detailed in the Methods section Enzyme-linked immunosorbent assay (ELISA). For subcutaneous xenograft model, a total of 1×107 cells in 200 μL of PBS were subcutaneously injected into the right dorsal flank of 6-week-old female C57BL/6J (B6) mice. For ethical considerations, mice were sacrificed when the tumor volumes reached 1000 mm3. Tumor tissue was minced then digested in a digestion solution (1 M HEPES, 1 mg/mL collagenase I, 1 mg/mL collagenase IV, 0.2 mg/mL DNase I, 0.2 mg/mL Hyaluronidase, 1% Penicillin-streptomycin complex and 10% FBS). The dissociated cells were used for subsequent flow cytometry. For adoptive cellular transfer experiments, on day 14 after the establishment of the orthotopic ovarian cancer model, BMDMs or BMDMs-KD1-Selenop (3×106) from female C57BL/6J (B6) mice (CD45.1) were administered via intraperitoneal injection. In the BMDMs & anti-VEGFA/BMDMs-KD1-Selenop & anti-VEGFA group, mice were administered with both BMDMs/BMDMs-KD1-Selenop (3×106) and anti-VEGFA antibodies (2.5 mg/kg body weight, Cat# 512810, BioLegend) on day 14, with dosing performed twice weekly for 2 weeks. Fourteen days after adoptive cellular therapy, mice were sacrificed, and orthotopic ovarian lesions as well as peritoneal metastases were collected. Tumor tissues were mechanically dissociated to obtain single-cell suspensions, and analyzed by flow cytometry.

Statistical analysis

Statistical significance was defined as p < 0.05 after Benjamini-Hochberg (BH) correction. Analyses were performed using R (v4.2.2), Python (v3.9.0) or GraphPad Prism (v8.0.2). Group comparisons involved Student’s t tests, Wilcoxon rank-sum tests, Kruskal–Wallis tests, chi-square tests, one-way ANOVA or two-way ANOVA. Survival analyses used log-rank tests, concentrating on the proportions of SELENOP+ and SPP1+ macrophage subclusters. The ggsurvplot function from the survminer package was employed to determine cutoff values and generate Kaplan–Meier survival curves. Further details on statistical methods can be found in the results section and figure legends. Owing to double-precision numerical constraints, p values less than 2.2 × 10−16 are assigned a standardized value of 2.2 × 10−16, as previously reported.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study of Liu et al., provides a comprehensive analysis of the tumor microenvironment (TME) in high-grade serous ovarian cancer (HGSOC) metastasis using advanced techniques like single-cell RNA sequencing and spatial transcriptomics. The authors identified critical cellular and molecular mechanisms influencing tumor progression and immune suppression. The SELENOP+ macrophages were found to enhance antitumor immunity by activating CD8+ T cells, whereas SPP1+ macrophages promoted tumor metastasis. Hypoxia-driven malignant epithelial cells were shown to reprogram SELENOP+ macrophages through VEGFA-EPHB2 signaling, diminishing the immune response. This interaction was associated with HGSOC metastasis. Then, anti-VEGFA therapy increased the proportion of SELENOP+ macrophages and CD8+ T cells, highlighting its potential for improving therapeutic outcomes. The study also revealed that BRCA-mutated HGSOC tumors exhibit a more active immune response compared to BRCA wild-type, suggesting the need for targeted immunotherapeutic strategies.\n\nKey findings of the study include:\n- Identification of two distinct macrophage subsets: SELENOP+ macrophages (M06) with antigen-processing and presentation capabilities, and SPP1+ macrophages (M09) involved in extracellular matrix remodeling. Also, M06 macrophages are associated with better patient outcomes, whereas M09 macrophages are linked to poor outcomes.\n- Demonstration that SELENOP+ macrophages possess immunostimulatory properties, potentially enhancing the cytotoxicity of CD8+ T cells via SeP. This is in contrast with pro-tumorigenic effects of SPP1+ macrophages.\n- SELENOP+ macrophages are enriched in adnexal tumors, but their presence decreases as the tumor metastasizes.\n- Identification of seven consensus metaprograms (MPs) within malignant epithelial cells, each with distinct gene signatures\n- Hypoxia-driven malignant epithelial cells reprogram SELENOP+ macrophages via VEGFA-EPHB2, as well as Hypoxia and FTL promote metastasis and therapy resistance. The study shows that VEGFA inhibition increases SELENOP+ macrophage proportions in primary tumors and peritoneal metastasis.\n- The study also characterizes the heterogeneity of CD8+ T cells and their functional states in different anatomical sites.\n\nAlhough the study is nicely presented as such, several major concerns should be addressed before the publication:\n\n1) Limited Sample Size: The authors acknowledge that the study may be limited by a relatively small sample size, especially in some groups such as EAT. This may reduce the significance of the findings. We suggest to increase the sample size, at least for profiling the macrophage subsets.\n\n2) Lack of Treatment Response Data: While the study provides an in-depth analysis of the TME, it does not incorporate the effects of chemotherapy or other treatments on the TME or link molecular findings to clinical outcomes like platinum-free interval (PFI). All the patient samples investigated by the authors are from treatment-naive cohorts. I suggest that the authors should investigate:\n- what is the effect of chemotherapy on macrophages distribution and characterization in HGSOC?\n- how does the chemotherapy affect the functionality of M06 and M09 populations, in terms of immunostimulatory properties?\n\n3) The authors demonstrate that hypoxia-driven malignant epithelial cells reprogram M06 macrophages, and for this they did experiment using only OVCAR3 cell-derived conditioned media. OVCAR3 is not the best representative of HGSOC cell lines. I suggest the authors should use at least 2 more cell lines, and choose some BRCA1 positive and some BRCA1 negative (See Domcke et al, 2013", "reviewer_2": "Previous work from this group showed that CD8+ T cells were tumour-specific in adnexal tumours but functioned as bystanders in HGSOC17 metastasis, which involved paired scRNA seq and scTCR-seq analyses. The intention of this manuscript is to explore the dynamic changes in CD8+ T cells during HGSOC metastasis, i.e., alteration to functional and differentiation states. The manuscript is a complicated paper full of bioinformatic data analysis (densely populated figures that are enriched with complex data). The information shown supports the narrative of the paper, showing intratumoral heterogeneity and complex epigenetic changes that causes remodelling of the tumour microenvironment in HGSOC. The finding that hypoxia-driven malignant epithelial cells could reprogram SELENOP+ macrophages is backed up with additional data. The addition of the mouse work adds weight to this study and a mechanism of tumour progression/survival/reduced immune surveillance, showing that VEGFA signalling is a key component and highlights that anti-angiogenic therapies could be used as a treatment these tumours. This work is an important contribution to our understanding of HGSOC and is a resource for future work.\n\n1) Last paragraph of the introduction is very long and acts as an abstract of the full manuscript. This is inappropriate, as the introduction should rather summarise the intention/rationale of the study and study approach.\n\n2) Should consider moving some methods into supplementary to reduce the length of the manuscript.\n\n3) Figure 2B, the dark colour of the extreme -1 and +1 are heavily blackened, which results in not knowing what end of the colour the T06_CD8T-CXCL13 cell population is for the exhausted and TEX boxes, either very dark/blackened red (+1) or very dark/blackened blue (-1). I suggest taking out the black shading from these boxes. While not as bad, same could be said for Figure 2A with several dark blue spots that could be misinterpreted in the figure as being over-expressed (especially as the spots are very small). I suggest to make all the dots larger, as there is room in the figure for this. Same can be said for Figure 3A and 3D.\n\n4) Line 178: ‘Subcluster T06_CD8T-CXCL13, defined by the expression of CXCL13, CTLA4, and TIGIT, exhibited a transcriptional signature indicative of exhaustion, thus classified as exhausted T cells (Tex) (Fig. 2a,b).’ Please add a sentence to explain why this expression signature is indicative of exhaustion and reference a paper if possible to help readers that might not be as knowledgable.\n\n5) Figure 2D, in annotation ‘expasion index’ is spelt wrong. In this figure there should also be axis annotations that have been omitted.\n\n6) Figure 2F: in result text, lack of annotation in the figure and a different description in the figure legend, i.e., what is ‘R o/e’? Currently, it is ambiguous to what the data represents. Can the authors improve the labelling of the figure and description in the text and figure legend to make this result clearer.\n\n7) Line 595: Figure 6h, siEPHB is not effective at knocking down siEPHB. I would suggest using other siRNA that are more effective and employ EPHB drug inhibitors to add validity to this work.\n\n8) Line 1191: Amplicon lengths should be provided here, and whether dissociation curves were also run routinely to ensure specificity of reactions.", "reviewer_3": "This study reveals a novel cross-talk between macrophages, T cells and epithelial cells during the progression of high-grade serous ovarian cancer which centres around SELENOP+ macrophages. This cell population was identified by an extensive single cell RNAseq approach considering both the temporal aspect of tumor development as well as different anatomical sides. While this first screening approach is very convincing and well justified to identify the relevant cellular players the mechanistic part analysed by cell culture and mouse experiments remains rather unclear and needs to be substantially improved and extended to support the conclusions drawn by the authors.\n\nThe identification of the proposed cross-talk within the tumor microenvironment might not only be relevant for ovarian cancer but could extend to additional cancer types given that this SELENOP+ macrophage population has also been identified in lung cancer before. Therefore, the manuscript would be of great interest to the broad readership of Nature Communications. However, in my view, the manuscript still lacks mechanistic details and controls and should be thoroughly revised to address the following comments.\n\nThe whole manuscript should be more clearly structured according to the information provided in the abstract. So far, it is very complicated for the reader to have data provided as figures, extended data figures, and supplementary figure. In my view, a fundamental restructuring of the data is really needed to support the story described in the abstract. Thus, later sections e.g. about immune checkpoints or the section starting at line 436 about dendritic cells are not really related to this story and should be excluded. Also the comparison between different anatomical sides within one patient could be left out. At least it is not getting clear how this relates to the main story line.\n\nBased on the abstract, the title could also more specifically describe the novel interactions identified.\n\nThroughout the manuscript: Please introduce the genes/proteins that were discussed in more detail. This is mandatory especially when addressing a broader readership.\n\nIntroduction, line 63: Please elaborate on the results gained in the previous publication (Ref 17) a bit more. Which questions remained unanswered after this publication? Why did you now follow up an alternative approach?\n\nIntroduction, lines 72-94: This section intensively summarizes the generated results but I miss a clear aim of the study.\n\nResults, line 114: The authors conclude that the anatomical site was the primary driver of heterogeneity. How does this relate to the following results which mainly take the tumor progression / stage into account? Here it would have been also helpful for my understanding to comment on the dependency of samples because different anatomical samples are taken form one patient (dependent variables) while different stages are analysed in independent samples from different patients.\n\nWhen looking at progression, the comparison between tumor and fluids is not conclusive. Why was this incorporated into the analyses? Effects of anatomical localization and tumor progression should be more clearly separated from each other (or even leave out anatomical localization results).\n\nFigure 1 and 2 extensively characterize the samples analysed. Those results could be narrowed down a bit to provide a clearer focus.\n\nFigure 3: the SELENOP+ macrophage cluster was identified. It is very important to keep in mind that this is only gene expression data which does not necessarily relate to protein expression because selenoprotein synthesis is strictly dependent on selenium availability which, under conditions with limited supply, overwrites a transcriptional regulation.\n\nPlease provide data showing the specificity of the SELENOP immunohistochemistry staining and Western Blot results (e.g. by staining cells cultured under conditions with and without selenium or by using specific blocking peptides). Unfortunately, there are many unspecific SELENOP antibodies available.\n\nAs only very shortly stated in line 334-336, SELENOP is secreted by hepatocytes but also other cell types (including macrophages?) and can be taken up by other cells via the receptor LRP8. SELENOP is broken down to release selenium which is used for the synthesis of other selenoproteins such as GPX or TXNRD. Those parameters definitely need to be analysed to show that SELENOP is taken up by T cells after culturing them with recombinant SELENOP. I would recommend to analyse GPX1 protein expression but other selenoproteins will work as well. Alternatively, the intracellular selenium concentration could be measured.\n\nFor all cell culture experiments, it is mandatory to know the basal selenium concentration in FCS and as a proof of concept, cells should be also treated /- selenite. Using THP1 macrophages, the authors should show that macrophages indeed secrete SELENOP. To strengthen the results, the authors could perform this experiment also with monocytes isolated from patient buffy coats. The conditioned medium from THP1 or other monocytes/macrophages could be used in addition to recombinant SELENOP to treat T cells. Also treating THP1 cells with IFNg should be performed in cells /- selenite pretreatment for 72 h.\n\nSection on epithelial cells (starting at line 461): The rational for the transition from immune cells to epithelial cells is not getting clear. This section should be shortened. For example, the link to ferritin is not supporting the story line. This is also not mentioned later in the discussion again.\n\nLine 587: Briefly introduce the VEGFA-EPHB2 pathway.\n\nThe knockdown efficiency of EPHB2 is quite poor. Is there an alternative way of inhibiting the pathway? A knockout would be more conclusive to show a dependency.\n\nLine 594: If the effect of VEGFA treatment should be reversed by the EPHB2 knockdown, please combine the VEGFA treatment with the knockdown to draw this conclusion.\n\nUsing patient-derived organoids is an interesting alternative approach but as stated above this should be combined with primary human monocytes/macrophages and not again with the THP1 cell line.\n\nLine 613, the in vivo model: The SELENOP+ macrophages at this stage really express more SELENOP protein as this has been characterized by FACS, in contrast to the RNAseq data which only rely on mRNA data (which might be quite irrelevant for selenoprotein expression). Please use another term for these macrophages to make this difference clearer to the reader.\n\nIn addition, the authors should provide information about the selenium concentration in the mouse chow. In addition, it would be very interesting to also measure SELENOP concentrations in the serum of mice to exclude a systemic effect of VEGF on SELENOP secreted from the liver.\n\nPlease quantify primary tumor, omental metastasis and peritoneal metastasis formation and not only provide fluorescence images and FACS results.\n\nTo really show that macrophage-derived SELENOP is needed to modulate the VEGF effect on tumor metastasis in vivo, the authors should use SELENOP knockout mice with a macrophage specific SELENOP knockout (SELENOPdeltaMye mice).\n\nLine 621: The last section of the results is again out of the focus of the manuscript and should be clearly shortened.\n\nThe model provided in Fig. 8 is very interesting but it is not really supported by the data provided in this manuscript (see comment above).\n\nDiscussion.\nThe discussion of the novel crosstalk should be extended. There is much more literature on SELENOP and its mode of action which should be discussed here. In addition, a putative role of the trace element selenium could be discussed as well.\n\nFor the discussion of hypoxia-induced effects on SELENOP refer to Becker et al., 2014, Hypoxia reduces and redirects selenoprotein biosynthesis.\n\nThe second part of the discussion starting in line 688 is again out of topic and has not much to do with the results described in the abstract.\n\nMinor comments:\nAbstract, line 37: malignant epithelial cells.\n\nPlease use SELENOP as abbreviation for selenoprotein P as this is the official nomenclature.\n\nLine 297: I would mention here again that “elevated phagocytosis function” is another result of the KEGG pathway analysis. Otherwise, one could expect a functional readout.\n\nLine 334: Please delete “theoretically”.\n\nFig. 6i is not very informative for the reader.\n\nLine 661, Ref 66-68: Only Ref 66 is supporting the respective information about the macrophage subset in lung cancer.\n\nLines 805-806: specify the title of the section.\n\nLine 1225: Is the amount injected (15 µl) correct?\n\nLine 1238: statistical analysis One-Way ANOVA is missing (is written in figure legends).", "reviewer_4": "This work by Liu et al. uses multiple state of the art single-cell multi-omics and spatial transcriptomics methods to map the spatiotemporal heterogeneity of HGSOC metastasis and tumor microenvironment (TME) and identifies two major populations of tumor-associated macrophages (TAMs): SELENOP+ and SPP1+, respectively, which undergo dynamic transition in the setting of hypoxic malignant ovarian cells to subvert immune-mediated tumor control, thus facilitating HGSOC metastasis. Some of the findings are consistent with what others have found in various types of cancers but not all of them have been functionally and definitively validated in patients or in vivo model systems. Given the limitation, it is important that, at a minimum, the pro-metastatic role of SPP1+ macrophages, as postulated in this study, should be unequivocally demonstrated by taking at least one of the suggested approaches as follows:\n1. SPP1-targeting antibodies: use neutralizing antibodies against SPP1 to block its activity and downstream signaling.\n2. SPP1 knockout: Global and conditional SPP1 KO mice are commercially available.\n\nThere are also concerns regarding the claim of direct involvement of SELENOP+ macrophages in priming T cells, as there is no direct evidence showing that SELENOP+ macrophages can activate CD8+ T cells. In contrast, previous studies (PMID: 18424738, 20530259) show that SELENOP is highly upregulated in M2 macrophages, which are generally considered more pro-tumorigenic than M1 macrophages.\n\nSome additional specific issues of concern:\n1.Fig. 2- Out of on the scRNA-seq data (Extended Dara Fig. 2a), only CD8+ T cells are analyzed in detail (Fig. 2). However, CD4+ T cells are also abundant in the tumor. Why no further analysis on the CD4+ T cell population? Are regulatory T cells enriched in late state tumors?\n\n2.Fig. 3h- The resolution of spatial transcriptomics data is too low. T04_CD8T-GZMH gene expression doesn’t show clear cluster on the spatial transcriptomics data. It appears that the gene expression is mapped to the whole slide. CD8+ T cell clusters can be observed on the immunofluorescence staining slides (Fig. 3j). However, based on the Visium data (Extended Data Fig. 3e), the correlation between SELENOP+ macrophage and T04_CD8T-GZMH abundance is very weak (R = 0.11, Extended Data Fig. 3e).\n\n3.Fig. 3k and Extended Data Fig. 3i- To study the effect of SELENOP+ macrophages on CD8+ T cell function, the authors use SeP protein to stimulate the OT1 cells. There is no dose-dependent change on the GZMB and PRF1. Can SELENOP+ macrophages directly prime CD8+ T cells? To verify the role of SELENOP+ macrophages, co-culturing of OT1-specific SIINFEKL peptide-loaded-SELENOP+ macrophages with OT1 cells should be performed. If it is not feasible to directly isolate SELENOP+ macrophages, the SELENOP protein can be overexpressed in macrophages, which can then be used to prime CD8+ T cells.\n\n4.Fig.6f- The experiment should be performed with purified macrophages from the spleen of mice or bone marrow derived macrophages for more physiological relevance.\n\n5.Fig.6g, h- The EPHB2 knockdown efficiency is very low. However, the SELENOP expression increases dramatically in the siEPHB2 cells. Is it due to off-target effect of siEPHB2? Anti-VEGF neutralizing antibody can be used to verify the role of VEGF signaling on macrophages. The result should be validated using purified macrophages or bone marrow derived macrophages.\n\n6.Fig.7b- On day 14, the mouse in the anti-VEGF group seems to show stronger luminescence intensity than the control mouse, suggesting that anti-VEGF may not efficiently suppress primary tumor growth. Quantification of luminescence intensity from multiple mice per group should be provided with statistics.\n\n7.Fig.7c- How are SELENOP+ macrophages identified using flow cytometry? The gating strategy should be provided. In this mouse model, do SELENOP+ macrophages also co-localize with CD8+ Tpex? What is the phenotype of this SELENOP+ macrophage population? Are they similar to M1 or M2 macrophages? Since the SELENOP+ macrophages can be found in this mouse model, those macrophages can be used to test their capacity to prime CD8+ T cells in vitro and in vivo.", "reviewer_5": "The paper maps the full spatiotemporal progression of high-grade serous ovarian cancer (HGSOC). Thus it improves over the current literature, which predominantly focuses on the tumor microenvironment (TME) of advanced HGSOC. The authors employ multimodal profiling techniques including scRNA-seq, spatial transcriptomics, scTCR-seq, RNA-seq, organoids, and orthotopic syngeneic models. They report spatial proximity between SELENOP+ macrophages and precursor exhausted CD8+ T cells. Their findings suggest that hypoxia-driven remodeling of the TME (via the malignant epithelial cells) plays a critical role in facilitating metastasis, and indicate the potential for combining anti-VEGFA therapy with immunotherapy.\n\nThe question addressed by this work is important, and overall its execution is promising. I think this will be a useful contribution to scientific progress in this field. I particularly appreciate that the authors chose to validate some of their findings using publicly available TCGA data (Fig. 3f). I have a few comments that might help improve the impact of this work and its presentation.\n\n1.**Ratio of observed to expected:** The usage of the ratio of observed to expected seems inconsistent. For example, in Fig. 2f, each row sums up to 1, whereas in Fig. 3b, this is not the case. This should either be investigated, or the methodology should be explained more clearly.\n\n2. **Methodology of NMF to define programs:** the way that the metaprograms are defined seems unnecessarily convoluted. I would rather start with the GO approach to get initial cluster annotations, and highlight the similarities with existing signatures afterwards.\n\n3. **Grammatical errors and typos:** there are some minor grammatical errors within the text (e. g. lines 31, 57, 115, 318, 320, 370, 444, 585, 630). Fig. 2b refers to precursor exhausted T cells as Texp, whereas Tpex is used everywhere else. Fig. 2d should be “expansion index”. Line 130: “mere” seems like the wrong word here.\n\n4. **Presentation of expression data:** the authors use a rainbow-style colormap to show expression values in Fig. 2a, 3a, 4]. There is however no standardization as to which color represents 0. I would instead encourage the use of a divergent color palette with a clear center, such as the coolwarm palette already used in Fig. 2b. The labels of the colorbar in Fig. 3a are not aligned properly.\n\n5. **Fig. 2g-i:** it is not clear why clusters T03 and T07 were excluded here.\n\n6. **Fig. 4d:** Once again, the choice of only showing clusters M05, M06, and M09 is not clearly explained in the text. I would also appreciate it if the same analysis was performed on all macrophage clusters (or at least the TAMs) and put in the supplement (same for the previous point).\n\n7. **Fig. 4e:** would benefit from adding plot titles directly in the figure, showing which enrichment analysis belongs to which state.\n\n8. **Fig. 6c:** there should be a distinction between 0 and negative importances.\n\n9. **Extended Data Fig. 4:** in every other R plot, the numbers are shown, but not in this one.\n\n0. **Code Availability:** the authors claim that the code is available (Line 1254), however there is no link to a GitHub repository (or equivalent).", "reviewer_6": "Liu et al. characterized the spatial heterogeneity of HGSOC across different progression stages and tissue sites using scRNA-seq and spatial transcriptomics. Their study identifies the co-occurrence and spatial colocalization of SELENOP+ macrophages and precursor exhausted CD8+ T (Tpex) cells, revealing that SELENOP+ macrophages promote CD8+ T cell activation in early tumors. They further showed that as the tumor progresses and metastasizes, SELENOP+ macrophages are reprogramed into SPP1+ macrophages by hypoxia-induced malignant epithelial cells through VEGFA-EPHB2 signaling. Notably, anti-VEGFA therapy restores SELENOP+ cells in ovarian cancer cell lines and mouse models. While the study provides insights into the role of macrophage subsets in HGSOC progression, it would benefit from a more rigorous analysis. The key findings are not effectively emphasized, some results are overly descriptive, and the mechanistic validation requires stronger supporting evidence.\n\nHere are my major concerns:\n1.The authors highlight the inclusion of early-stage patients as a strength, but similar comparisons have been made in previous studies (such as PMID35675036, 38278958). While one is cited in the discussion, these works should also be discussed. Additionally, the authors should compare their findings with these studies to clarify how their results align or differ from existing literature.\n\n2.The authors repeatedly use the term \"significant\" without indicating statistical tests, such as in the R analysis (Fig. 1f, Extended Fig. 2i, j). They should either remove or revise their claims or perform appropriate statistical tests. Specifically, as the R analysis is throughout the study and underpins several conclusions, a chi-square test should be applied to assess tissue preference. Additionally, the equation used for R analysis should be explicitly provided.\n\n3. Although Tregs are not the focus of this study, their absence is notable. The authors should address their relevance in HGSOC progression. Additionally, since T cells and NK cells were initially grouped together, separating them into distinct clusters would allow for more precise characterization.\n\n4. Quantifying the spatial distribution of macrophage and Tpex is valuable. However, the authors should include zoom-in images to visually support the computationally inferred spatial relationships between SELENOP+ or SPP1+ macrophage and Tpex.\n\n5. The SELENOP+ macrophages appear abundant in scRNA-seq and Visium data but are scarce in IF staining (Fig. 3] and 4b). The authors should provide an explanation for this discrepancy, such as differences in detection sensitivity, technical limitations, or biological factors affecting protein versus transcript expression.\n\n6. In the BRCA analysis, the BRCAmut group includes two EAT samples, comprising half of the total samples, while the wt group consists only of late-stage samples. This raises the concern that the observed high proportions of Tpex, Tex, and SELENOP+ macrophages in BRCAmut may be driven by differences in sample composition rather than the BRCA mutation itself. The authors should clarify how they account for these confounding factors.\n\n7. Given the significant growth differences between PDOs and the OVACR3 tumor cell line, both models were subjected to 48-hour normal and hypoxic cultures. Have alternative hypoxic culture durations been explored? Additionally, the authors should specify the cell numbers of PDOs and OVACR3 used to prepare the conditioned medium.\n\n8. The authors show that SELENOP+ macrophages activate CD8+ T cells via SeP, but validation is limited. While exogenous SeP increased GZMB and PRF1 expression and co-culture assays assessed cytotoxicity, these alone are insufficient. Blocking SeP secretion in macrophages would strengthen mechanistic validation.\n\n9. The bioluminescence images show only one representative mouse per group. Can the authors confirm whether the same mouse was imaged on days 7, 14, and 42? To enhance data reliability, it is recommended that images of at least three mice per group be included.\n\nMinor points:\n1.The authors should provide the full name of ISG and PARPi and others if they are first mentioned.\n\n2.The authors should indicate the cell fate on top of the corresponding barplot in Fig4e for better readability.\n\n3.In the first paragraph of the first results section, line 12, there is an extra \"and\" at the end of the sentence."}, "author_response": "We have addressed each comment and point-by-point responses are provided below:\nEDITORS’ Comment:................................................................................................................. 1\nREVIEWER 1: ............................................................................................................................ 3\nREVIEWER 2: 10\nREVIEWER 3: 16\nREVIEWER 4: 35\nREVIEWER 5: 48\nREVIEWER 6: 55\nIn our letter, the reviewer’s comments are shown in blue italics, all the responses are\nshown in black Arial. We have inserted some of the revised and newly added figures with\ntheir legends, and the corresponding revised text excerpts are provided (section name,\nline numbers) from the clean version of the manuscript for ease of accessibility.\nAlso, in the revised version of the manuscript, the removed parts from the original\npaper are shown in double strikethrough and all the changes and additions are highlighted\nin blue Times New Roman.\nDetailed response to the editors’ comment as following:\nEDITORS’ Comment:\n“In particular, we would expect your revision to address Reviewer #1’s concern on the lack\nof sample size and treatment response data, Reviewer #3’s concern on a lack of\nmechanistic details and controls to support the proposed crosstalk, Reviewer #4’s concern\non a lack of direct evidence to support the proposed direct involvement of SELENOP+\nmacrophages in activating CD8+ T cells, and Reviewer #6’s concerns on the relevance of\nTregs on HGSOC progression, discrepancy on the SELENOP+ macrophage abundance\ninferred from the two datasets, and the potential sample composition effect on the BRCA\nanalysis. That is not to say that we consider any of the remaining reviewer concerns to be\nany less important, and we would expect your revision to address them in full.”\nResponse and revision in the manuscript:\nFirst, we again thank the editors for giving us the option of responding to reviewer\ncomments and revising the manuscript for Nature Communications. It has taken us nearly 6\nmonths to complete the studies owing to the technically demanding and time-consuming\nnature of the in vivo experiments. We thank the editorial team for their patience.\n(1) To address concerns on the lack of sample size and treatment response data\nraised by Reviewers #1, we expand sample size by integrating our single-cell RNA\nsequencing (scRNA-seq) data with a publicly available dataset GSE184880, and the results\nregarding macrophages remained consistent with our original observations. We also\nanalyzed the GSE266577, which included paired samples from chemo-naive and post-\nneoadjuvant chemotherapy (interval debulking surgery, IDS) patients. The results revealed\na reduced interaction potential between SELENOP+ macrophages and GZMH+precursor\nexhausted CD8+ T cells following neoadjuvant chemotherapy (NACT).\n(2) In response to Reviewer #3, we conducted a comprehensive set of additional\nexperiments, including SELENOP antibody validation assay, Western blotting of GPX1 in\nCD8+ T cells, ELISA for SELENOP, selenite pretreatment assay, macrophage-derived\nCM and CD8+ T cell coculture system, and CRISPR-Cas9-mediated EPHB2 deficiency,\nserum SELENOP concentrations in orthotopic ovarian cancer mice, adoptive cellular\ntransfer assay in vivo, along with several other assays suggested by Reviewer #3. These\nexperiments have refined mechanistic details and controls to support the proposed\ncrosstalk.\n(3) To address Reviewer #4’s comments, we performed targeted experiments that\ndirectly support the role of SELENOP+ macrophages in CD8+ T cells activation, including\nantigen presentation assay in vitro and adoptive cellular transfer assay in vivo.\n(4) In response to Reviewer #6, we analyzed CD4+ T cell heterogeneity using our\nscRNA-seq data and identified a potential involvement of regulatory T cells (Tregs) in the\nmetastatic progression of HGSOC. We explained the discrepancy in SELENOP+\nmacrophage abundance between different detection methods. This methodological\n1\ndifference would not affect data explanation. In addition, we incorporated two previously\nunassigned LAT samples, classified as BRCA wild-type based on newly generated whole-\nexome sequencing (WES) results, into the subsequent analysis. The results remained\nconsistent with our original observations.\n(5) Furthermore, as recommended by Reviewers #2 and #5, we clarified\nmethodological details and improved figure quality and presentation throughout the\nmanuscript. To strengthen the reported data and conclusions, we now include the relevant\nstatistical information in figures and their legends, and have generated a new\nSupplementary Table (Source Data) detailing every result for statistical analysis.\nWe believe that the revisions made in response have adequately addressed the\nreviewers’ concerns, substantially improving the quality of the manuscript. We are\nconfident that the revised manuscript offers valuable insights into HGSOC, with\nimplications for future research, the development of effective therapeutic strategies, and\npotential translational applications.\nREVIEWER 1:\n“The study of Liu et al., provides a comprehensive analysis of the tumor microenvironment\n(TME) in high-grade serous ovarian cancer (HGSOC) metastasis using advanced\ntechniques like single-cell RNA sequencing and spatial transcriptomics. The authors\nidentified critical cellular and molecular mechanisms influencing tumor progression and\nimmune suppression. The SELENOP+ macrophages were found to enhance antitumor\nimmunity by activating CD8+ T cells, whereas SPP1+ macrophages promoted tumor\nmetastasis. Hypoxia-driven malignant epithelial cells were shown to reprogram SELENOP+\nmacrophages through VEGFA-EPHB2 signaling, diminishing the immune response. This\ninteraction was associated with HGSOC metastasis. Then, anti-VEGFA therapy increased\nthe proportion of SELENOP+ macrophages and CD8+ T cells, highlighting its potential for\nimproving therapeutic outcomes. The study also revealed that BRCA-mutated HGSOC\ntumors exhibit a more active immune response compared to BRCA wild-type, suggesting\nthe need for targeted immunotherapeutic strategies.\nKey findings of the study include:\n- Identification of two distinct macrophage subsets: SELENOP+ macrophages (M06) with\nantigen-processing and presentation capabilities, and SPP1+ macrophages (M09)\ninvolved in extracellular matrix remodeling. Also, M06 macrophages are associated with\nbetter patient outcomes, whereas M09 macrophages are linked to poor outcomes. -\nDemonstration that SELENOP+ macrophages possess immunostimulatory properties,\npotentially enhancing the cytotoxicity of CD8+ T cells via SeP. This is in contrast with pro-\ntumorigenic effects of SPP1+ macrophages.\n- SELENOP+ macrophages are enriched in adnexal tumors, but their presence decreases\nas the tumor metastasizes.\n- Identification of seven consensus metaprograms (MPs) within malignant epithelial cells,\neach with distinct gene signatures\n- Hypoxia-driven malignant epithelial cells reprogram SELENOP+ macrophages via\nVEGFA-EPHB2, as well as Hypoxia and FTL promote metastasis and therapy resistance.\nThe study shows that VEGFA inhibition increases SELENOP+ macrophage proportions in\nprimary tumors and peritoneal metastasis.\n- The study also characterizes the heterogeneity of CD8+ T cells and their functional states\nin different anatomical sites.\nAlhough the study is nicely presented as such, several major concerns should be\naddressed before the publication:”\nGeneral response: We thank the reviewer for the constructive summary of our study. We\nappreciate that the key findings and overall significance of the work were acknowledged,\nwhich was noted as “nicely presented”. Below, we provide a point-by-point response to\nthe reviewer’s concerns.\n3\n“1) Limited Sample Size: The authors acknowledge that the study may be limited by a\nrelatively small sample size, especially in some groups such as EAT. This may reduce the\nsignificance of the findings. We suggest to increase the sample size, at least for profiling\nthe macrophage subsets.”\nResponse and revision in the manuscript: We thank the reviewer for this insightful\ncomment. We integrated our scRNA-seq dataset with GSE184880, resulting in a\ncombined dataset including seven EAT, eight LAT and nine Met samples. By analyzing\nthe inferred macrophage subpopulations in the integrated dataset, we confirmed and\nfurther substantiated our original observations: a progressive decrease in SELENOP+\nmacrophages and a concomitant increase in SPP1+ macrophages from EAT to LAT and\nultimately to Met (Extended Data Fig. 4d) (Results, lines 295-299).\nExtended Data Fig. 4d. Tissue preference of each macrophage clusters estimated by Ro/e (integrated\nGSE184880 with our scRNA-seq data) (P < 2.2e-16). P-values: chi-squared test. n = 24 samples, n = 7 EAT, n = 8 LAT, n = 9 Met.\n“2) Lack of Treatment Response Data: While the study provides an in-depth analysis of\nthe TME, it does not incorporate the effects of chemotherapy or other treatments on the\nTME or link molecular findings to clinical outcomes like platinum-free interval (PFI). All the\npatient samples investigated by the authors are from treatment-naive cohorts. I suggest\nthat the authors should investigate:\n- what is the effect of chemotherapy on macrophages distribution and characterization in\nHGSOC?\n- how does the chemotherapy affect the functionality of M06 and M09 populations, in terms\nof immunostimulatory properties?”\nResponse and revision in the manuscript: We sincerely thank the reviewer for these\nthoughtful and important suggestions. We have expanded our analysis using GSE266577\nincluding chemo-naive and respective IDS patients following NACT. Our new findings are\nsummarized as below:\n(1) The abundance of macrophages within the myeloid cells decreased following NACT,\naccompanied a shift from anti-inflammatory to pro-inflammatory states (Extended Data\nlower antigen processing and presentation signature scores, while SPP1' macrophages\nexhibited decreased matrix remodeling signature scores following NACT, with no\nsignificant enrichment between the two groups (Extended Data Fig. 8d-g) (Results,\nlines 491-498).\nExtended Data Fig. 8a-g. a, Tissue preference of myeloid clusters estimated by Ro/e between chemo-\nnaive and post-neoadjuvant chemotherapy (interval debulking surgery, IDS) groups in GSE266577 (P <\n2.2e-16). Violin plots comparing the proinflammatory (b) and anti-inflammatory function scores (c)\nbetween groups. d, Tissue preference of inferred SELENOP+ and SPP1+ macrophages estimated by\nRo/e between chemo-naive and IDS groups in GSE266577. Violin plots comparing the SELENOP\nexpression (e), antigen processing and presentation of SELENOP+ macrophages (f) and matrix\nremodeling signature scores of SPP1+ macrophages (g) between groups. For a-g, a total of n = 18\nHGSOC paired samples, including n = 9 chemo-naive, n= 9 IDS. For a, d"}]

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