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Neutrophils physically interact with tumor cells to form a signaling niche promoting breast cancer aggressiveness

Nature Cancer volume 6, pages 540–558 (2025)Cite this article

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Abstract

Tissue remodeling and cell plasticity in the mammary gland are activated by multilineage communications; however, the dynamic signaling promoting breast cancer remains unclear. Here, by RNA sequencing of single cells and physically interacting cells (PICs) along mammary gland development and carcinogenesis, we uncovered that neutrophils appear transiently during early development and re-emerge in physical interaction with tumor cells in advanced carcinoma. Neutrophil heterogeneity analysis characterized transcriptional states linked to age and cancer stage. Integrating ligand–receptor and PIC sequencing analyses with various functional experiments unveiled a physical and secreted protumorigenic signaling niche. This approach revealed that neutrophils are recruited by tumor-activated macrophages and physically interact with tumor cells, increasing tumor cell proliferative and invasive properties, as well as endothelial proliferation and angiogenesis. The molecular program upregulated in neutrophil-PICs correlates with lower survival in advanced breast cancer patients. Our interaction-driven perspective highlights potential molecular targets and biomarkers for breast cancer treatment.

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Fig. 1: Thorough characterization of mammary gland immune and nonimmune lineages during physiological development and carcinogenesis.
Fig. 2: Dynamic analysis of mammary gland cellular composition revealed a tumor-specific cellular niche.
Fig. 3: PIC-seq analysis revealed neutrophil enrichment in physical interactions with epithelial cells in the TME.
Fig. 4: Heterogeneous neutrophil states during mammary gland development and tumor progression.
Fig. 5: TAN–tumor cell PICs form a signaling niche with ductal macrophages and the perivascular compartment in the breast TME.
Fig. 6: The physical interaction between neutrophils and cancer cells has high malignant molecular features.
Fig. 7: The protumoral effect of neutrophils in the breast TME depends on their physical interaction.

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Data availability

Raw and processed scRNA-seq and PIC-seq data supporting the findings of this study have been deposited in the Gene Expression Omnibus under accession code GSE278570. The publicly available neutrophil scRNA-seq and RNA-seq data used in this study is available in the Gene Expression Omnibus database under accession codes GSE232217 (ref. 16) and GSE243466 (ref. 12). The publicly available clinical information and RNA-seq data used in this study are available in the TCGA Research Network (https://www.cancer.gov/tcga). The remaining data are available within the article, supplementary information or source data files. Source data are provided with this paper.

Code availability

All original code used for analysis and generation of figures is freely available in the GitHub repository at https://github.com/MeravCohenLab/NeutrophilPIC.

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Acknowledgements

M.C. is supported by the European Research Council Starting Grant (number 101042232), the Research Career Development Award from the Israel Cancer Research Fund (number 949767) and the Israel Science Foundation (number 1966/23). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Sandra Camargo, Ori Moskowitz.

Authors and Affiliations

  1. Department of Clinical Microbiology and Immunology, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel

    Sandra Camargo, Ori Moskowitz, Maiia Levinson, Roi Balaban, Shani Gola, Alice Raizman, Kelly Lipczyc & Merav Cohen

  2. Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, Utrecht, the Netherlands

    Amir Giladi

  3. Department of Pathology, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel

    Alon Richter & Yaron Carmi

  4. Oncology Division, Tel Aviv Sourasky Medical Center, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel

    Noa Keren-Khadmy, Yael Dugach & Amir Sonnenblick

  5. Department of Systems Immunology, the Weizmann Institute of Science, Rehovot, Israel

    Oren Barboy

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Contributions

S.C. conceived and designed the project, developed experimental protocols, performed and analyzed experiments and wrote the paper. O.M. conceived and designed the project, analyzed experiments, developed computational methods, performed computational analysis and wrote the paper. A.G. and S.G. contributed to the computational analysis. M.L., R.B., A. Raizman and K.L. performed experiments and analyzed the data. A. Richter, Y.C. and O.B. contributed to experiments. N.K.K., Y.D. and A.S. collected human samples. M.C. directed and supervised the project; conceived, designed, and analyzed experiments and wrote the paper.

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Correspondence to Merav Cohen.

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Extended data

Extended Data Fig. 1 Isolation and characterization of immune and nonimmune populations from normal and tumor breast tissue in the MMTV-PyMT mouse model.

Related to Fig. 1. (a) Representative FACS plots showing the gating strategy for isolation of immune (CD45+), nonimmune (CD45−) and epithelial (EpCAM+) cells. (b, c) The distribution of total UMI counts (b) binned across the 3 FACS gates and (c) across cell states in passed QC cells. (d–g) Heatmaps of log-normalized expression of signature genes for (d) lymphoid, (e) myeloid, (f) stromal and (g) epithelial cells.

Source data

Extended Data Fig. 2 Changes in cellular composition of each cell population during breast tissue development and tumor progression.

Related to Fig. 2. (a) FACS quantification of CD45+ and EpCAM+ cell percentage across time points in PyMT+ (nCD45 = 27, nEpCAM = 23) and PyMT− (nCD45 = 23, nEpCAM = 19) mammary glands. P value was calculated by the two-sided Mann-Whitney U test. Error bars represent the mean ± SE of percentage across biological replicates for each time point and condition where n > 2. (b–e) Cell-type distribution of (b) lymphoid, (c) myeloid, (d) stromal and (e) epithelial populations isolated from PyMT+ and PyMT− mammary glands across time points. Heatmaps on the right of the annotations represent P values for the relationship between age and condition with population fraction. P values were determined using a two-way ANOVA followed by Tukey’s post-hoc test and FDR correction for multiple comparisons. PyMT+: n10d = 3, n3w = 5, n6w = 2, n8w = 3, n10w = 5, n12w = 4; PyMT−: n10d = 3, n3w = 4, n6w = 2, n8w = 3, n10w = 3, n12w = 2 biological replicates, Supplementary Table 1. (f–h) Dynamic changes in the abundance of (f) Schwann cells, (g) pericytes and (h) Myo/Lum in tumor and normal mammary glands across time points, represented as each cell’s fraction out of total cells in the relevant compartment. Error bars represent the mean ± SE of fractions across biological replicates for each time point and condition where n > 2. P values were determined using a two-way ANOVA followed by Tukey’s post-hoc test and FDR correction for multiple comparisons. The presented P values are the significance of the relationship between conditions (tumor vs normal) and population fraction. PyMT+: n(f-g) = 14, n(h) = 16; PyMT−: n(f-g) = 13, n(h) = 12 biological replicates, Supplementary Table 1. (i) Representative IF staining of EpCAM+ epithelial cells, F4/80+ macrophages and DAPI+ nuclei in the TME of PyMT+ vs PyMT− 6 w old mice (n = 2). Scale bars 50–100 μm. *P < 0.05, **P < 0.01, ***P < 0.001.

Source data

Extended Data Fig. 3 Quality control of immune-epithelial PICs.

Related to Fig. 3. (a) Distribution of total UMI counts for CD45+EpCAM+ PICs, on a log-scale. Solid lines signify quartiles. (b–d) We tested the accuracy of the PIC-seq pipeline on simulated PICs with various total UMI sizes between the minimum UMI on the X axis and 50 UMIs above the minimum, calculating (b) R2 of the correlation between simulated and inferred mixing factors, (c) fraction of correct metacell inference and (d) fraction of correct annotation inference. Lines represent locally estimated scatterplot smoothing (LOESS) of the data points, with the shaded area representing a 95% confidence interval. n = 10,000 simulated PICs. (e, f) Heatmaps showing the density of correlation between true (simulated) and inferred (e) epithelial metacells and (f) immune metacells, grouped by annotation. n = 10,000 simulated PICs. (g) The correlation between mixing factor α in simulated PICs vs the inferred α. n = 10,000 simulated PICs. (h) The fraction of singlets vs simulated PICs that would be excluded from the analysis for each threshold of the difference between PIC simulation and singlet simulation, defining 1.6 as the threshold which keeps the most PICs while excluding the most singlets. (i) Distribution of experimental PIC events excluded (red) or kept (green), across time points and conditions. (j) Heatmap of log-normalized expression of epithelial or immune marker genes across singlets, excluded PICs and good PICs that were kept for later analysis. Data analyzed was collected from PyMT-: nDevelopment = 7,2, nYouth = 5,2, nAdulthood = 4,4; PyMT+: nDevelopment = 8,2, nEarly-carcinoma = 5,3, nAdvanced-carcinoma = 9,8, (singlets, PICs), biological replicates.

Source data

Extended Data Fig. 4 Characterization of immune-epithelial cell PICs.

Related to Fig. 3. (a) A heatmap of log-normalized expression for selected CD45+ immune and EpCAM+ epithelial genes in immune singlets, epithelial singlets and immune-epithelial PICs. The heatmap also specifies the mixing factor (α; lower bar), representing the relative share of the immune and epithelial partners in the PIC UMIs. While α = 0 would imply all UMIs originate from the immune partner, α = 0.5 value exhibits an even distribution for each partner and α = 1 value shows all UMIs originate from the epithelial partner. (b) Cell-type distribution of EpCAM+ epithelial cells in singlets and PICs across time points in tumor vs normal. (PyMT−: nDevelopment = 6,2, nYouth = 3,2, nAdulthood = 3,4; PyMT+: nDevelopment = 6,2, nEarly-carcinoma = 2,3, nAdvanced-carcinoma = 8,8; singlet,PIC). P values were determined by the two-sided Mann-Whitney U test followed by FDR correction, *PProg.1 = 0.0438, *PAlveolar = 0.0435, *PTumor cells = 0.0438. (c) ImageStream representative images showing interacting neutrophil-tumor cell PICs (CD45+Ly6G+EpCAM+). Images are representative of two independent experiments. Scale bar, 10 μm.

Source data

Extended Data Fig. 5 Neutrophil singlet isolation and analysis.

Related to Fig. 4. (a) A representative FACS plot of singlet neutrophils (Ly6G+) gated from CD45+EpCAM− population, following purification from PyMT+ 12 w old mice (n = 4); population frequencies represent mean ± SE. (b–d) UMAPs of all CD45+ and Ly6G+ immune singlets, colored by (b) Leiden clusters, (c) FACS gates and (d) log-normalized expression of neutrophil-specific genes. (e) Density plot of neutrophil UMI counts. (f) A two-dimensional map of 1,759 neutrophils generated by the MetaCell algorithm and colored by annotation. (g) Distribution of neutrophil annotations for each Ly6G+ enriched sample from PyMT+ and PyMT− mice across time points. (h) The Distribution of TAN gene scores assigned to single cells from each population (n = 8,261 single cells from 10 biological replicates). The box plots’ center lines represent the median values, with the hinges indicating the first and third quartiles. Whiskers extend to the lowest and highest values within 1.5 times the interquartile range. The distribution of the scores in TANs was significantly higher than the distribution in each of the other populations according to the two-sided t-test with FDR multiple testing correction. (i) Kaplan–Meier curve showing survival over time of stage I and II breast cancer patients (n = 396) belonging to the top and bottom quartiles of 792 patients provided a gene score based on expression of the TAN signature gene list. P value was calculated using the Log-rank test. (j, k) LFCs in mean normalized expression for tumor vs blood neutrophils collected from our breast cancer model (n = 5, 4) and compared to (j) a PDAC model (n = 2, 2) and (k) a CRC model (n = 4, 4). In both cases, linear regression was performed to generate the dotted line, and P value and Pearson correlation coefficient were calculated to assess the statistical significance and strength of the correlation.

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Extended Data Fig. 6 Neutrophil-tumor cell PIC analysis and TME signaling.

Related to Fig. 5. (a, b) Density of correlation between true (simulated) and inferred (a) epithelial metacells and (b) neutrophil metacells, grouped by annotation. (c) The correlation between mixing factor α in simulated PICs vs the inferred α. (d) Cell-type distribution of neutrophil states (CD45+Ly6G+) and epithelial cells (EpCAM+) in singlets and PICs in early and advanced carcinoma. (e–g) Counts of interactions with LIANA aggregate rank < 0.05 from the (e) tumor niche into neutrophils, (f) from neutrophils to the tumor niche cells and (g) from the tumor niche into neutrophil-tumor cell PICs. (h) Heatmap showing significant ligand–receptor pairs representing potential signaling from the tumor niche to neutrophils, colored by LIANA aggregate rank. (i) Total number of Ly6G+ neutrophils that migrated to the bottom chamber when they are exposed to RM-MACs cultured with/wo anti-CCL3 treatment; n = 4 replicates derived from two independent experiments. Lines represent the mean. (j) Heatmap showing significant ligand–receptor pairs representing potential signaling from neutrophil-tumor cell PICs to vasculature (endothelial Mcamhi and pericytes), colored by LIANA aggregate rank. * aggregate rank < 0.05, ** aggregate rank < 0.01, *** aggregate rank < 0.001. Exact aggregate ranks are provided in Supplementary Table 4.

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Extended Data Fig. 7 Molecular and functional programs induced by neutrophil-tumor cell physical crosstalk.

Related to Figs. 6 and 7. (a) Comparison of mean expression (normalized to median cell size) in observed vs simulated neutrophil-tumor cell PICs in advanced carcinoma mice. Highlighted genes are colored by their expected specificity to the neutrophil (green) or tumor cell (red) compartments, calculated as mean epithelial expression / (mean epithelial + mean neutrophil expression). A total of 942 PICs from 7 biological replicates were analyzed. (b) Representative scratch assay images at 0 h and 12 h of incubation of Met-1 cells seeded alone (n = 6 individual scratch wounds), in co-culture with neutrophils (Neut, n = 6), with conditioned medium of neutrophils (Neut CM, n = 4) or with neutrophils pretreated with cytochalasin D (n = 6). (c) The distribution of NeuTME-PIC (Including Ptgs2+ Neut, TAN1 and TAN2) gene scores assigned to single cells from each population (n = 8,261 single cells from 10 biological replicates) and to the NeuTME-PICs (n = 711 PICs from 8 biological replicates). The box plots’ center lines represent the median values, with the hinges indicating the first and third quartiles. Whiskers extend to the lowest and highest values within 1.5 times the interquartile range. P values comparing each population to the NeuTME-PICs were assigned using the two-sided t-test with FDR multiple testing correction. (d) Kaplan–Meier curve showing survival over time of stage I and II breast cancer patients (n = 396) belonging to the top and bottom quartiles of 792 patients provided a gene score based on expression of the NeuTME-PIC gene set. P value was calculated using the Log-rank test. (e) FACS quantification of KI67+ proliferating Met-1 cells derived from monocultures, transwells and cocultures with neutrophils; n = 3 replicates derived from two independent experiments (represented by dot shape) (f) FACS quantification of CD45+Ly6G+ cells from mammary glands of PyMT+ 12 w, comparing IgG isotype injected mice (n = 7) vs. anti-Ly6G treated mice (n = 8). Error bars represent the mean ± SEM. P value was determined by two-sided t-test. (g) FACS quantification of CD45+Gr-1+ cells, following lineage-negative gating excluding non-neutrophil populations, isolated from mammary glands of Ly6G-depleted and isotype-treated PyMT+ mice. Each dot represents a mammary gland isolated from one mouse in each condition. (h) Representative IF staining image of CD45+S100A9+ neutrophil accumulation in the TME of 12 w PyMT+ mammary glands following anti-Ly6G or isotype injections. Neutrophils were counted and their quantity was normalized per same number of fields of view (FOV). FOVIsotype = 287, FOVdepletion = 455 derived from one mouse in each condition. Scale bar, 100 µm. (i, j) UMAP projection of genes used to exclude problematic populations from (i) epithelial cells and (j) endothelial cells before differential gene expression analysis. (k) Enrichment of the Gene Ontology gene list among our analyzed gene set sorted by two-tailed T-scores generated by comparing tumor cells from the anti-Ly6G treated mice vs. IgG isotype injected mice. q-value represents the FDR adjusted P value of the enrichment.

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Camargo, S., Moskowitz, O., Giladi, A. et al. Neutrophils physically interact with tumor cells to form a signaling niche promoting breast cancer aggressiveness. Nat Cancer 6, 540–558 (2025). https://doi.org/10.1038/s43018-025-00924-3

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