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Solid tumour-induced systemic immunosuppression involves dichotomous myeloid–B cell interactions

Nature Cell Biology (2024)Cite this article

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Abstract

Solid tumours induce systemic immunosuppression that involves myeloid and T cells. B cell-related mechanisms remain relatively understudied. Here we discover two distinct patterns of tumour-induced B cell abnormality (TiBA; TiBA-1 and TiBA-2), both associated with abnormal myelopoiesis in the bone marrow. TiBA-1 probably results from the niche competition between pre-progenitor-B cells and myeloid progenitors, leading to a global reduction in downstream B cells. TiBA-2 is characterized by systemic accumulation of a unique early B cell population, driven by interaction with excessive neutrophils. Importantly, TiBA-2-associated early B cells foster the systemic accumulation of exhaustion-like T cells. Myeloid and B cells from the peripheral blood of patients with triple-negative breast cancer recapitulate the TiBA subtypes, and the distinct TiBA profile correlates with pathologic complete responses to standard-of-care immunotherapy. This study underscores the inter-patient diversity of tumour-induced systemic changes and emphasizes the need for treatments tailored to different B and myeloid cell abnormalities.

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Fig. 1: Remote tumours disrupt early B cell development in two different patterns.
Fig. 2: TiBA-1 and TiBA-2 drive distinct changes to systemic B cell profiles.
Fig. 3: TiBA-2 impairs anti-tumour immunity and ICB efficacy.
Fig. 4: Tie-B cells promote the systemic accumulation of exhaustion-like T cells.
Fig. 5: Both TiBA-1 and TiBA-2 are associated with abnormal myelopoiesis in the BM.
Fig. 6: Development of TiBA-2 involves abnormal neutrophil–early B crosstalk.
Fig. 7: Distinct TiBA patterns in human patients with TNBC correlate with myeloid cells and clinical outcomes.

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Data availability

scRNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus and the Sequence Read Archive database under accession codes GSE222854, GSE274772 and PRJNA923661. We obtained RAN-seq profiles of breast cancer from the TCGA data portal (https://portal.gdc.cancer.gov/) and chose samples profiled at UNC to avoid any potential batch effects, stored in a folder named ‘RNASeqV2’. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

All analyses were performed in R using previously generated codes. This paper does not report original codes.

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Acknowledgements

X.H.-F.Z. is supported by US Department of Defense 1W81XWH-21-1-0790, W81XWH-20-1-0375, National Cancer Institute (NCI) CA183878, NCI CA251950, NCI CA221946, NCI CA227904, NCI CA253533, NCI P50CA186193, Breast Cancer Research Foundation and McNair Medical Institute. The scRNA-seq was performed at Single Cell Genomics Core at Baylor College of Medicine (BCM) supported by NIH S10OD025240, and CPRIT RP200504. The flow cytometry analysis in this work was supported by the Cytometry and Cell Sorting Core at BCM with funding from the CPRIT Core Facility Support Award (CPRIT-RP180672) and NIH (CA125123 and RR024574). Y.G. is supported by NCI K99CA279899.

Author information

Author notes

  1. These authors contributed equally: Xiaoxin Hao, Yichao Shen.

Authors and Affiliations

  1. Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA

    Xiaoxin Hao, Yichao Shen, Jun Liu, Ling Wu, Zhan Xu, Liqun Yu, Yang Gao, Fengshuo Liu, Hilda L. Chan, Yunfeng Ding, David G. Edwards, Nan Chen, Bora Lim & Xiang H.-F. Zhang

  2. Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA

    Xiaoxin Hao, Yichao Shen, Jun Liu, Ling Wu, Zhan Xu, Liqun Yu, Yang Gao, Fengshuo Liu, Hilda L. Chan, Yunfeng Ding, David G. Edwards, Nan Chen, Bora Lim & Xiang H.-F. Zhang

  3. National Key Laboratory of Immunity and Inflammation, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Suzhou, China

    Xiaoxin Hao

  4. McNair Medical Institute, Baylor College of Medicine, Houston, TX, USA

    Xiaoxin Hao, Yichao Shen & Xiang H.-F. Zhang

  5. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA

    Yichao Shen, Jun Liu, Ling Wu, Zhan Xu, Liqun Yu, Yang Gao, Fengshuo Liu, Hilda L. Chan, Yunfeng Ding, David G. Edwards, Bora Lim & Xiang H.-F. Zhang

  6. Graduate Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, Houston, TX, USA

    Yichao Shen

  7. Department of Breast Medical Oncology and Morgan Welch IBC Research Program and Clinic, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Angela Alexander, Azadeh Nasrazadani & Naoto T. Ueno

  8. Graduate Program in Cancer and Cell Biology, Baylor College of Medicine, Houston, TX, USA

    Fengshuo Liu

  9. Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA

    Hilda L. Chan

  10. Graduate Program in Immunology and Microbiology, Baylor College of Medicine, Houston, TX, USA

    Che-Hsing Li

  11. Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, China

    Weijie Zhang

  12. Department of Orthopaedic Surgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China

    Weijie Zhang

  13. University of Hawai’i Cancer Center, Honolulu, HI, USA

    Naoto T. Ueno

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Contributions

X.H. and X.H.-F.Z. developed the concept, discussed experiments and wrote the paper; X.H. performed animal studies, flow cytometry analysis, in vitro co-culture assay and imaging experiments and analysed the data; X.H. and Y.S. performed scRNA-seq and processed and analysed the scRNA-seq data; A.A., N.C., A.N., N.T.U. and B.L. contributed to clinical sample collection; J.L. contributed to maintaining mouse strains; L.W., Z.X., L.Y., Y.G, F.L., H.L.C., C.-H.L., Y.D., W.Z., D.G.E. and other authors assisted in animal studies and contributed to paper editing. X.H.-F.Z. supervised the research.

Corresponding author

Correspondence to Xiang H.-F. Zhang.

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Extended data

Extended Data Fig. 1 Remote tumors disrupt early B cell development in two different patterns.

a, Representative flow cytometry gating of mouse B cell subsets in the BM of TiBA-1 tumor-bearing (LLC), TiBA-2 tumor-bearing (PyMT-N) and Sham mice. b, The percentages of BM B cell subsets among total B cells in tumor-bearing and Sham mice were analyzed by flow cytometry. B6 background (LLC, PyMT-N, E0771), BALB/c background (4T1, 2208L, T12, T11). BM was analyzed by flow cytometry when tumor models had similar tumor sizes (around 1 cm3). Sham-B6 (n = 14 mice), PyMT-N (n = 13 mice), LLC (n = 10 mice), 2208L (n = 7 mice), and other groups (n = 5 mice). Two-way ANOVA with Dunnett’s multiple comparisons test compared to Sham. c, d, The absolute cell numbers of BM B cell subsets in tumor-bearing and Sham mice were analyzed by flow cytometry. BM was analyzed by flow cytometry when tumor models had similar tumor sizes (around 1 cm3). Sham-B6 (n = 14 mice), PyMT-N (n = 13 mice), LLC (n = 10 mice), 2208L (n = 7 mice), and other groups (n = 5 mice). One-way ANOVA (Brown-Forsythe and Welch ANOVA) with Dunnett’s T3 multiple comparisons test compared to Sham. e, Flow cytometry analyzed B220+ B cell numbers in the BM across different tumor models (similar tumor sizes). Sham-B6 (n = 27 mice), PyMT-N (n = 24 mice), LLC (n = 15 mice) and other groups (n = 5 mice). One-way ANOVA with Dunnett’s multiple comparisons test compared to Sham. f, Diagram summarizing TiBA phenotypic characteristics in the BM. For all boxplots (b–e), the line inside the box is the median value, and the bottom/top bars of the box indicate min to max values.

Source data

Extended Data Fig. 2 scRNA-seq of BM cells in naïve mice and different TiBA tumor-bearing models.

a, Diagram showing FACS-sorting of BM cells from Sham and TiBA tumor-bearing mice for scRNA-seq. b, Heatmap of top 5 markers expression of BM cell clusters in the BALB/c dataset. c, Heatmap of top 5 markers expression of BM cell clusters in the C57BL/6 dataset. d, Feature plots show the expression of early B cell marker genes across BM cell clusters. e, Diagram showing the firefly-luciferase/GFP-labeled 4T1 cells were transplanted into the mammary fat pad (MFP). f, Representative image showing negative luc-signals at different bones. g, Heatmaps of log10 fold change in the percentage of various B cell subsets in BM of tumor-bearing mice compared with Sham. n = 3 mice/group.

Source data

Extended Data Fig. 3 TiBA-1 and TiBA-2 drive distinct changes to systemic B cell profiles.

a, Representative flow cytometry gating of mouse B cell subsets in the PB of Sham and 4T1 tumor-bearing mice. b, Quantification of Tie-B cell numbers in the PB and spleen in different tumor models by flow cytometry, Sham-B6 (n = 11 mice), PyMT-N (n = 6 mice) and other groups (n = 5 mice). One-way ANOVA with Dunnett’s multiple comparisons test compared to Sham. c, d, Absolute cell numbers of various B cell subsets in the PB (c) and spleen (d) of tumor-bearing and Sham mice. PB was analyzed by flow cytometry when tumor models had similar tumor sizes (around 1 cm3). n = 6 mice (for Sham-B6 or PyMT-N) and n = 5 mice (for other groups) mice, respectively. One-way ANOVA (Brown-Forsythe and Welch ANOVA) with Dunnett’s T3 multiple comparisons test compared to Sham. e, Heatmap of top 5 markers expression of PB cell clusters. f, Feature plots show the expression of Breg cell marker genes across PB cell clusters. g, h, Flow cytometry analysis of IL10 expression on different cell subsets in TiBA-2 tumor-bearing mice. MFI, mean fluorescent intensity. n = 5 mice/group. One-way ANOVA with Dunnett’s multiple comparisons test compared to NC, mean ± s.d. i–k, Flow cytometry analyzed tumor-infiltrating B cell subsets in different TiBA tumors (tumor sizes around 1 cm3). TiBA-1 (n = 11 mice), TiBA-2 (n = 14 mice), TiBA-0 (n = 14 mice). One-way ANOVA with Dunnett’s multiple comparisons test compared to TiBA-2, mean ± s.d. l, Flow cytometry analyzed tumor-infiltrating plasma cells in TiBA tumors. n = 5. One-way ANOVA with Tukey’s multiple comparisons test compared to TiBA-2, mean ± s.d. m, Flow cytometry analyzed Tie-B cell numbers in tumors (tumor sizes around 1.5 cm3), blood and spleen of TiBA-2 models. n = 8 mice. One-way ANOVA with Dunnett’s multiple comparisons test, mean ± s.d. For all boxplots (b–d), the line inside the box is the median value, and the bottom/top bars of the box indicate min to max values.

Source data

Extended Data Fig. 4 Tie-B cells impair anti-tumor immunity and ICB efficacy.

a, Representative flow cytometry images of B cells in the BM and PB. b, Flow cytometry analyzed B cells in 2208L tumor-bearing mice after B cell depletion (day 27). n = 5 mice/group. Unpaired two-tailed Student’s t-test. c, Flow cytometry analyzed total B cells or Tie-B cells in PyMT-N tumor-bearing mice after B cell depletion (day 27). n = 5 mice/group. Unpaired two-tailed Student’s t-test. d, e, Adoptive transfer of B220+IgM−CD93− or B220+IgM+ non-Tie-B cells into μMT mice bearing PyMT-N tumors and quantification of tumor growth curves. n = 5 mice/group. f, E0771 tumor growth curves during IgG or anti-PD-1&CTLA-4 treatment. n = 5 mice/group, unpaired two-tailed Student’s t-test on day 22. g, ICB-responsive E0771 cells were orthotopically transplanted into μMT mice, 5 days later, adoptively transfer of PBS, B220+IgM−CD93− or B220+IgM+ cells every 5 days. Meanwhile, anti-PD1&CTLA4 were treated every 3 days. h, E0771 tumor growth curves in mice from the experiment (g). n = 5 mice/group. i, Tie-B cells were stained with CFSE and injected into WT mice. j, Flow cytometry analysis of CFSE+ Tie-B cell numbers across different organs 18 h after injection. n = 3 mice per group. One-way ANOVA with Dunnett’s multiple comparisons test compared to the spleen group, mean ± s.d. k, Tie-B cells were isolated from TiBA-2 tumor-bearing CD45.1 mice, then intravenously injected into different TiBA tumor mice. l–n, Flow cytometry analysis of CD45.1+ Tie-B cell numbers in the spleen (l), BM (m) and tumor (n) of different TiBA models 5 days after injection. n = 4 mice/group. One-way ANOVA with Dunnett’s multiple comparisons test compared to TiBA-2. o–t, Flow cytometry analyzed the percentages of indicated B cell subsets in total CD45.1+B220+ cells in the spleen or BM of different recipients. n = 4 mice/group. One-way ANOVA with Dunnett’s multiple comparisons test compared to TiBA-2. For all boxplots (b, c, l–t), the line inside the box is the median value, and the bottom/top bars of the box indicate min to max values.

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Extended Data Fig. 5 Tie-B promotes the systemic accumulation of exhaustion-like T cells.

a, b, Tumor cell numbers four days after cocultured with Tie-B cells. n = 3 independent repeats. Unpaired two-tailed Student’s t-test, mean ± s.d. c, PyMT-M tumor growth curves during T-cell transfer and ICB treatment. Unpaired two-tailed Student’s t-test on day 20. d, PD-1+CTLA-4+ T cells in 2208L-bearing mice after B cell depletion. n = 5 mice per group. One-way ANOVA with Tukey’s multiple comparisons test (left panel). The line inside the box is the median value, and the bottom/top bars of the box indicate min to max values. e, B220+ cells and PD-1+CTLA-4+ T cells in naïve mice after B cell depletion. n = 5 mice. Unpaired two-tailed Student’s t-test, mean ± s.d. f, Mean fluorescent intensity (MFI) of ID2 protein in T cells from naïve or TiBA-2 tumor-bearing mice. n = 4 mice. Unpaired two-tailed Student’s t-test, mean ± s.d. g, PD1+CTLA4+ proportions after CD8T cells were co-cultured with Tie-B cells. n = 3 biological replicates. Unpaired two-tailed Student’s t-test, mean ± s.d. h, The expression level of MHC-II genes in B cell clusters based on the scRNA-seq data. i, j, MFI of MHC-II protein in B cell subsets in TiBA-2 tumor-bearing mice. n = 10 mice. Two-way ANOVA with Dunnet’s multiple comparisons test (i) and one-way ANOVA with Tukey’s multiple comparisons test (j), mean ± s.d. k, l, Tie-B cells were stimulated with ovalbumin323-339 peptides, co-cultured with OT-II CD4+ T cells, then detected with MHC-II-OVA-tetramer antibodies. n = 3 biological replicates, unpaired two-tailed Student’s t-test, mean ± s.d. DC, dendritic cells (positive control) m, Number of migrated T cells. n = 3 biological replicates, unpaired two-tailed Student’s t-test, mean ± s.d. n, Representative IF staining images of Tie-B cells (B220+CD93+) and CD8 T cells in spleen. Yellow arrows indicate aggregations. Representative of three independent experiments. o, Establishment of tumor-bearing Cd79a-CreER;Hmgb1fl/fl mice for tamoxifen treatment. p, PD1+CTLA4+ proportions after co-culture of CD3T and Tie-B cells. n = 3 biological replicates. One-way ANOVA with Dunnet’s multiple comparisons test, mean ± s.d. q, MFI of ID2 protein in T cells after co-culturing with Tie-B. n = 3 biological replicates. One-way ANOVA with Dunnet’s multiple comparisons test, mean ± s.d.

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Extended Data Fig. 6 TiBA-1/2 cannot be induced solely by direct interactions between cancer cells and early B cells.

a, Flow cytometry analysis of myeloid cell numbers in E0771 tumors from the experiment in Fig. 4t, u. n = 5 mice/group. b–e, Flow cytometry analysis of myeloid cell subsets in the PB, spleen, BM and tumors of PyMT-N tumor-bearing Cd79a-CreER or Cd79a-CreER;Hmgb1fl/fl mice after tamoxifen treatment. n = 4 mice/group. Unpaired two-tailed Student’s t-test, mean ± s.d. f–h, Flow cytometry analysis of B cell subsets in the PB, spleen and BM of PyMT-N tumor-bearing Cd79a-CreER or Cd79a-CreER;Hmgb1fl/fl mice after tamoxifen treatment. n = 4 mice/group. Unpaired two-tailed Student’s t-test, mean ± s.d. i, Diagram showing co-culture of TiBA-1 or TiBA-2 tumor cells with early B cells in a non-direct contact manner to mimic the remote effect of cancer cells. j, k, Flow cytometry analysis of the absolute cell numbers of early B cell subsets 2 days after co-culture with TiBA-1 (LLC) or TiBA-2 (PyMT-N) tumor cells. n = 3 (B cells were isolated from 3 biological replicate mice). Two-way ANOVA with Dunnett’s multiple comparisons test compared to “B+NC” group, mean ± s.d. l, Diagram illustrates the 2208 L (TiBA-1) tumor-bearing CD11b-DTR or WT mice that received DT treatment. m, Flow cytometry analysis of absolute cell numbers of mature B cells (Fr. F) in the BM of 2208L tumor-bearing WT or CD11b-DTR mice after DT treatment (day 21). n = 4 mice/group. The line inside the box is the median value, and the bottom/top bars of the box indicate min to max values. n, Feature plot showing the CD11b expression in BM cell clusters. o, Representative flow cytometry images of CD11b and B220 expression in the BM of 2208L tumor-bearing mice.

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Extended Data Fig. 7 Development of TiBA-2 involves abnormal neutrophil–early B cell crosstalk.

a, Flow cytometry quantification of monocytes (CD45+CD11b+Ly6ChiLy6G−) numbers in PyMT-N tumor-bearing mice after anti-Ly6C depletion. n = 5 mice/group. Unpaired two-tailed Student’s t-test, mean ± s.d. b–d, Flow cytometry analysis of indicated B cell subsets in the PB, spleen and BM of PyMT-N tumor-bearing mice after IgG or anti-Ly6C treatment. n = 5 mice/group, mean ± s.d. e, Diagram showing early-B cells were co-cultured with neutrophils (isolated from E0771 models) in the presence of E0771 tumor cells. The numbers of pre-B cell subsets were analyzed by flow cytometry 3 days after co-culture. Mean ± s.d. f, Dot plot showing the expression level of predicted target genes in pro-B (Fr. B-C) and pre-B (Fr. C’-D) clusters. g, h, Volcano plots show the differential gene expression of Fr. B/C and Fr. D cell clusters between PyMT-N tumor-bearing and Sham mice. Wilcoxon rank-sum test. i, Feature plot showing the Tnfsf13b and Il15 expression distribution in BM cell clusters. j, GSEA shows down- and up-regulated gene sets in the BM neutrophil cluster of TiBA-1 tumor-bearing mice compared to Sham. Hypergeometric p-values were adjusted for multiple testing using the Benjamini-Hochberg method. k, l, Feature plots and Violin plots show the Itgb2 expression in the BM neutrophil clusters between PyMT-N tumor-bearing and Sham mice. m, Diagram showing early-B cells and neutrophils cocultured and treated with anti-ITGB2 blocking antibodies. n, Dot plot showing the expression levels of ITGB2 receptors in all BM B cell clusters. o, Diagram showing early-B cells and neutrophils cocultured and treated with anti-ICAM2 blocking antibodies. p, Flow cytometry analysis of the numbers of pre-B cells 4 days after co-culture in the experiment (o). n = 3 biological replicates. One-way ANOVA with Tukey’s multiple comparisons test, mean ± s.d.

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Extended Data Fig. 8 Chemotherapy plus ICB treatment on TiBA-2 models and human PB cell flow cytometry analysis.

a, Diagram showing PyMT-N tumor-bearing mice (or Sham mice) treated with vehicle or paclitaxel in combination with ICB (anti-PD-1&CTLA-4). b–d, Flow cytometry analysis of transitional B cells in the PB of PyMT-N tumor-bearing mice after six doses of treatment as described in experiment (a). TiBA-2 groups (n = 5 mice), Sham (n = 4 mice). One-way ANOVA with Dunnett’s multiple comparisons test compared to the “TiBA-2+Vec” group, mean ± s.d. e–g, Flow cytometry analysis of BM Fr. D subsets and PB Tie-B cells in PyMT-N tumor-bearing mice after six doses of treatment as described in experiment (a). TiBA-2 groups (n = 5 mice), Sham (n = 4 mice). One-way ANOVA with Dunnett’s multiple comparisons test compared to the “TiBA-2+Vec” group, mean ± s.d. h, PyMT-N tumor growth curves during paclitaxel in combination with anti-PD-1&CTLA4 therapy. n = 5 mice per group. i, Representative flow cytometry gating of human B cell subsets and cell counting beads. j, The age of healthy females and different TiBA patients. Healthy (N = 34), TiBA-1 TNBC patients (N = 18), TiBA-2 TNBC patients (N = 9), TiBA-0 TNBC patients (N = 15). Mean ± s.d. k, Human T cells were co-cultured with TiBA-2 patient CD19+IgM+IgD+ cells. Flow cytometry analysis of PD1+CTLA4+ T cell numbers 3 days after co-culture. n = 3 biologically replicates, mean ± s.d. l, Working model of distinct TiBA phenotypes. Created with BioRender.com.

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Hao, X., Shen, Y., Liu, J. et al. Solid tumour-induced systemic immunosuppression involves dichotomous myeloid–B cell interactions. Nat Cell Biol (2024). https://doi.org/10.1038/s41556-024-01508-6

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