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Lymphotoxin-β promotes breast cancer bone metastasis colonization and osteolytic outgrowth

Nature Cell Biology volume 26, pages 1597–1612 (2024)Cite this article

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Abstract

Bone metastasis is a lethal consequence of breast cancer. Here we used single-cell transcriptomics to investigate the molecular mechanisms underlying bone metastasis colonization—the rate-limiting step in the metastatic cascade. We identified that lymphotoxin-β (LTβ) is highly expressed in tumour cells within the bone microenvironment and this expression is associated with poor bone metastasis-free survival. LTβ promotes tumour cell colonization and outgrowth in multiple breast cancer models. Mechanistically, tumour-derived LTβ activates osteoblasts through nuclear factor-κB2 signalling to secrete CCL2/5, which facilitates tumour cell adhesion to osteoblasts and accelerates osteoclastogenesis, leading to bone metastasis progression. Blocking LTβ signalling with a decoy receptor significantly suppressed bone metastasis in vivo, whereas clinical sample analysis revealed significantly higher LTβ expression in bone metastases than in primary tumours. Our findings highlight LTβ as a bone niche-induced factor that promotes tumour cell colonization and osteolytic outgrowth and underscore its potential as a therapeutic target for patients with bone metastatic disease.

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Fig. 1: Single-cell RNA-seq analysis of cancer cells from different stages of bone metastasis.
Fig. 2: A barcoded mini cDNA library screen identifies candidate genes for bone colonization and metastatic progression.
Fig. 3: LTβ emerges as the top candidate for promoting bone metastasis.
Fig. 4: LTβ reactivates indolent cancer cells to form osteolytic bone metastasis.
Fig. 5: Expression of LTβ is induced in the bone microenvironment.
Fig. 6: Tumour-derived LTβ stimulates chemokine production in osteoblasts through NF-κB2 signalling.
Fig. 7: LTβ and CCL2/5 promote bone metastasis by enhancing cancer cell–osteoblast adhesion and osteoclast differentiation.
Fig. 8: LTβ is a therapeutic target for bone metastasis.

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Data availability

Further information and requests for resources and reagents should be directed to—and will be fulfilled by—H.Z. RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession codes GSE241494 and GSE241165. Source data are provided with this paper. All of the other data supporting the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

All of the custom code used for analysis in this paper is available from the corresponding authors upon request.

References

  1. Esposito, M., Guise, T. & Kang, Y. The biology of bone metastasis. Cold Spring Harb. Perspect. Med. 8, a031252 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Zhang, W. et al. The bone microenvironment invigorates metastatic seeds for further dissemination. Cell 184, 2471–2486.e20 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Nagrath, S. et al. Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature 450, 1235–1239 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Braun, S. et al. A pooled analysis of bone marrow micrometastasis in breast cancer. N. Engl. J. Med. 353, 793–802 (2005).

    Article  CAS  PubMed  Google Scholar 

  5. Zheng, H. et al. Therapeutic antibody targeting tumor- and osteoblastic niche-derived Jagged1 sensitizes bone metastasis to chemotherapy. Cancer Cell 32, 731–747.e6 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wang, H. et al. The osteogenic niche promotes early-stage bone colonization of disseminated breast cancer cells. Cancer Cell 27, 193–210 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lu, X. et al. VCAM-1 promotes osteolytic expansion of indolent bone micrometastasis of breast cancer by engaging α4β1-positive osteoclast progenitors. Cancer Cell 20, 701–714 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ghajar, C. M. et al. The perivascular niche regulates breast tumour dormancy. Nat. Cell Biol. 15, 807–817 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Yin, J. J. et al. TGF-β signaling blockade inhibits PTHrP secretion by breast cancer cells and bone metastases development. J. Clin. Invest. 103, 197–206 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ross, M. H. et al. Bone-induced expression of integrin β3 enables targeted nanotherapy of breast cancer metastases. Cancer Res. 77, 6299–6312 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sethi, N., Dai, X., Winter, C. G. & Kang, Y. Tumor-derived JAGGED1 promotes osteolytic bone metastasis of breast cancer by engaging notch signaling in bone cells. Cancer Cell 19, 192–205 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Satcher, R. L. & Zhang, X. H. Evolving cancer-niche interactions and therapeutic targets during bone metastasis. Nat. Rev. Cancer 22, 85–101 (2022).

    Article  CAS  PubMed  Google Scholar 

  13. Massague, J. & Obenauf, A. C. Metastatic colonization by circulating tumour cells. Nature 529, 298–306 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Minn, A. J. et al. Distinct organ-specific metastatic potential of individual breast cancer cells and primary tumors. J. Clin. Invest. 115, 44–55 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kang, Y. et al. A multigenic program mediating breast cancer metastasis to bone. Cancer Cell 3, 537–549 (2003).

    Article  CAS  PubMed  Google Scholar 

  16. Lelekakis, M. et al. A novel orthotopic model of breast cancer metastasis to bone. Clin. Exp. Metastasis 17, 163–170 (1999).

    Article  CAS  PubMed  Google Scholar 

  17. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  19. Wang, X. L., He, Y., Zhang, Q. M., Ren, X. W. & Zhang, Z. M. Direct comparative analyses of 10X Genomics chromium and Smart-seq2. Genom. Proteom. Bioinf. 19, 253–266 (2021).

    Article  Google Scholar 

  20. Reid, J. E. & Wernisch, L. Pseudotime estimation: deconfounding single cell time series. Bioinformatics 32, 2973–2980 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Jolly, M. K., Ware, K. E., Gilja, S., Somarelli, J. A. & Levine, H. EMT and MET: necessary or permissive for metastasis? Mol. Oncol. 11, 755–769 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Thiery, J. P., Acloque, H., Huang, R. Y. & Nieto, M. A. Epithelial–mesenchymal transitions in development and disease. Cell 139, 871–890 (2009).

    Article  CAS  PubMed  Google Scholar 

  23. Minn, A. J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518–524 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lanczky, A. & Gyorffy, B. Web-based survival analysis tool tailored for medical research (KMplot): development and implementation. J. Med. Internet Res. 23, e27633 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Rose, A. A. et al. Osteoactivin promotes breast cancer metastasis to bone. Mol. Cancer Res. 5, 1001–1014 (2007).

    Article  CAS  PubMed  Google Scholar 

  26. Maric, G., Rose, A. A., Annis, M. G. & Siegel, P. M. Glycoprotein non-metastatic b (GPNMB): a metastatic mediator and emerging therapeutic target in cancer. Onco Targets Ther. 6, 839–852 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Tang, X. et al. GPR116, an adhesion G-protein-coupled receptor, promotes breast cancer metastasis via the Gαq-p63RhoGEF-Rho GTPase pathway. Cancer Res. 73, 6206–6218 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Lu, T. T. & Browning, J. L. Role of the lymphotoxin/LIGHT system in the development and maintenance of reticular networks and vasculature in lymphoid tissues. Front. Immunol. 5, 47 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Haybaeck, J. et al. A lymphotoxin-driven pathway to hepatocellular carcinoma. Cancer Cell 16, 295–308 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bauer, J. et al. Lymphotoxin, NF-kB, and cancer: the dark side of cytokines. Dig. Dis. 30, 453–468 (2012).

    Article  PubMed  Google Scholar 

  31. Das, R. et al. Lymphotoxin-β receptor–NIK signaling induces alternative RELB/NF-κB2 activation to promote metastatic gene expression and cell migration in head and neck cancer. Mol. Carcinog. 58, 411–425 (2019).

    Article  CAS  PubMed  Google Scholar 

  32. Garrett, I. R. et al. Production of lymphotoxin, a bone-resorbing cytokine, by cultured human myeloma cells. N. Engl. J. Med. 317, 526–532 (1987).

    Article  CAS  PubMed  Google Scholar 

  33. Shultz, L. D., Ishikawa, F. & Greiner, D. L. Humanized mice in translational biomedical research. Nat. Rev. Immunol. 7, 118–130 (2007).

    Article  CAS  PubMed  Google Scholar 

  34. Johnson, R. W. et al. Induction of LIFR confers a dormancy phenotype in breast cancer cells disseminated to the bone marrow. Nat. Cell Biol. 18, 1078–1089 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Wan, L. et al. MTDH–SND1 interaction is crucial for expansion and activity of tumor-initiating cells in diverse oncogene- and carcinogen-induced mammary tumors. Cancer Cell 26, 92–105 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Nobre, A. R. et al. Bone marrow NG2+/Nestin+ mesenchymal stem cells drive DTC dormancy via TGFβ2. Nat. Cancer 2, 327–339 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ren, G. et al. Mesenchymal stem cell-mediated immunosuppression occurs via concerted action of chemokines and nitric oxide. Cell Stem Cell 2, 141–150 (2008).

    Article  CAS  PubMed  Google Scholar 

  38. Simonet, W. S. et al. Osteoprotegerin: a novel secreted protein involved in the regulation of bone density. Cell 89, 309–319 (1997).

    Article  CAS  PubMed  Google Scholar 

  39. Hu, K. & Olsen, B. R. Osteoblast-derived VEGF regulates osteoblast differentiation and bone formation during bone repair. J. Clin. Invest. 126, 509–526 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Shupp, A. B., Kolb, A. D., Mukhopadhyay, D. & Bussard, K. M. Cancer metastases to bone: concepts, mechanisms, and interactions with bone osteoblasts. Cancers (Basel) 10, 182 (2018).

    Article  PubMed  Google Scholar 

  41. Upadhyay, V. & Fu, Y. X. Lymphotoxin signalling in immune homeostasis and the control of microorganisms. Nat. Rev. Immunol. 13, 270–279 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hultgren, O., Eugster, H. P., Sedgwick, J. D., Korner, H. & Tarkowski, A. TNF/lymphotoxin-α double-mutant mice resist septic arthritis but display increased mortality in response to Staphylococcus aureus. J. Immunol. 161, 5937–5942 (1998).

    Article  CAS  PubMed  Google Scholar 

  43. Roach, D. R. et al. Secreted lymphotoxin-α is essential for the control of an intracellular bacterial infection. J. Exp. Med. 193, 239–246 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ehlers, S. et al. The lymphotoxin β receptor is critically involved in controlling infections with the intracellular pathogens Mycobacterium tuberculosis and Listeria monocytogenes. J. Immunol. 170, 5210–5218 (2003).

    Article  CAS  PubMed  Google Scholar 

  45. Madge, L. A., Kluger, M. S., Orange, J. S. & May, M. J. Lymphotoxin-α1β2 and LIGHT induce classical and noncanonical NF-κB-dependent proinflammatory gene expression in vascular endothelial cells. J. Immunol. 180, 3467–3477 (2008).

    Article  CAS  PubMed  Google Scholar 

  46. Binder, N. B. et al. Estrogen-dependent and C-C chemokine receptor-2-dependent pathways determine osteoclast behavior in osteoporosis. Nat. Med. 15, 417–424 (2009).

    Article  CAS  PubMed  Google Scholar 

  47. Lee, J. W. et al. The HIV co-receptor CCR5 regulates osteoclast function. Nat. Commun. 8, 2226 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Wilkinson, A. C. et al. Long-term ex vivo haematopoietic-stem-cell expansion allows nonconditioned transplantation. Nature 571, 117–121 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Romero-Moreno, R. et al. The CXCL5/CXCR2 axis is sufficient to promote breast cancer colonization during bone metastasis. Nat. Commun. 10, 4404 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Stashenko, P., Dewhirst, F. E., Peros, W. J., Kent, R. L. & Ago, J. M. Synergistic interactions between interleukin 1, tumor necrosis factor, and lymphotoxin in bone resorption. J. Immunol. 138, 1464–1468 (1987).

    Article  CAS  PubMed  Google Scholar 

  51. Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  52. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  55. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Apweiler, R. et al. UniProt: the Universal Protein knowledgebase. Nucleic Acids Res. 32, D115–D119 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Cante-Barrett, K. et al. Lentiviral gene transfer into human and murine hematopoietic stem cells: size matters. BMC Res. Notes 9, 312 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Green, M. R. & Sambrook, J. Preparation of genomic DNA from mouse tails and other small samples. Cold Spring Harb. Protoc. 2017, pdb.prot093518 (2017).

    Article  PubMed  Google Scholar 

  61. Colic, M. et al. Identifying chemogenetic interactions from CRISPR screens with drugZ. Genome Med. 11, 52 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  63. Meng, J. et al. Tumor-derived Jagged1 promotes cancer progression through immune evasion. Cell Rep. 38, 110492 (2022).

    Article  CAS  PubMed  Google Scholar 

  64. Wilkinson, A. C., Ishida, R., Nakauchi, H. & Yamazaki, S. Long-term ex vivo expansion of mouse hematopoietic stem cells. Nat. Protoc. 15, 628–648 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Cao, X. et al. Next generation of tumor-activating type I IFN enhances anti-tumor immune responses to overcome therapy resistance. Nat. Commun. 12, 5866 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kos, C. H. et al. The calcium-sensing receptor is required for normal calcium homeostasis independent of parathyroid hormone. J. Clin. Invest. 111, 1021–1028 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank J. Massagué (Memorial Sloan Kettering Cancer Center) and Y. Kang (Princeton University) for the generous gift of multiple bone metastatic cell lines, including SCP28 and PD2R cells. We thank Y. Kang for helpful discussions. We thank all members of the Zheng Laboratory for helpful discussions and technical assistance. We thank the Technology Center for Protein Science at Tsinghua University for FACS support. We also thank the Laboratory Animal Research Center and Center of Biomedical Analysis at Tsinghua University for providing animal support. This study was partially supported by the National Key Research and Development Program of China (2020YFA0509400 to H.Z.), National Natural Science Foundation of China (81772981 and 81972462 to H.Z.), Tsinghua University Initiative Scientific Research Program and Tsinghua-Peking Center for Life Sciences. 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: Xuxiang Wang, Tengjiang Zhang, Bingxin Zheng.

  2. These authors jointly supervised this work: Bin Yue, Hanqiu Zheng.

Authors and Affiliations

  1. Center for Cancer Biology, School of Basic Medical Sciences, Tsinghua University, Beijing, China

    Xuxiang Wang, Tengjiang Zhang, Youxue Lu, Yong Liang, Guoyuan Xu, Luyang Zhao, Yuwei Tao, Qianhui Song, Huiwen You, Haitian Hu, Xuan Li & Keyong Sun

  2. Department of Orthopedic Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China

    Bingxin Zheng & Bin Yue

  3. School of Life Sciences and Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, China

    Tianqi Li & Jianbin Wang

  4. Department of Joint Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China

    Zian Zhang

  5. State Key Laboratory of Molecular Oncology and Center for Cancer Biology, School of Basic Medical Sciences, Tsinghua University, Beijing, China

    Xun Lan, Deng Pan, Yang-Xin Fu & Hanqiu Zheng

  6. SXMU-Tsinghua Collaborative Innovation Center for Frontier Medicine, Shanxi Medical University, Taiyuan, China

    Hanqiu Zheng

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Contributions

X.W. and H.Z. designed and performed the experiments and wrote and revised the manuscript. T.L., K.S., J.W. and X. Lan provided technical support for the scRNA-seq experiments. T.Z. and X.W. performed the scRNA-seq analysis and provided in-house bioinformatics analysis. B.Z., Z.Z. and B.Y. collected and provided paired clinical samples of primary breast cancer and bone metastatic tumour tissues from patients with breast cancer. Y. Lu and D.P. provided critical reagents and mouse models for mouse HSC isolation, knockout and transplantation experiments. Y. Liang and Y.-X.F. provided the humanized mouse model for this study. G.X., L.Z., Y.T., Q.S., H.Y., H.H. and X. Li provided experimental assistance with molecular cloning and animal works. H.Z. developed the concept, designed the experiments and supervised the overall study.

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Correspondence to Bin Yue or Hanqiu Zheng.

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Extended data

Extended Data Fig. 1 Representative images of different stages of bone metastasis.

a, 4T1 or 4T1.2 cells were IC injected into 6-week-old female BALB/c mice. Bone metastasis burden was monitored by BLI imaging. Kaplan-Meier curves for BMFS were displayed. n = 12 for 4T1 group, and n = 14 for 4T1.2 group. b, Quantification of BLI signal from experiment (a). n = 12 for 4T1 group, and n = 14 for 4T1.2 group. c, Representative BLI, μCT, H&E, and TRAP staining images for different stages of bone metastasis generated by 4T1 cells. Arrows indicate the osteolytic bone lesion areas. B, bone tissue area; T, tumor area. Scale bar = 100 μm. d, Representative BLI, μCT, H&E, and TRAP staining images for different stages of bone metastasis generated by 4T1.2 cells. Scale bar = 100 μm. Arrows indicate the osteolytic bone lesion areas (c,d). B, bone tissue area; T, tumor area (c,d). Data presented as mean ± SD (b). The P values were determined by two-way repeated measures ANOVA (b) or log-rank test (a).

Source data

Extended Data Fig. 2 Tumor cells from different sources present distinct gene expression patterns.

a, t-SNE plots of the specific marker gene expression in the cell population. Firefly-luciferase and mCherry were exogenously labeled markers for tumor cells, Krt8 marks epithelial cell lineage, Vcam-1 is a previously identified bone metastatic gene. b, t-SNE analysis of 4T1 cells from 4T1-CL, 4T1-FP, 4T1-D4, 4T1-D10, and 4T1-D16. Cells from different sources were color coded. c, Pseudotime analysis of 4T1 cells from 4T1-CL, 4T1-FP, 4T1-D4, 4T1-D10, and 4T1-D16. Cells from different sources were color coded. d, Violin plot depicts the epithelial score of 4T1 cells of 4T1-CL, 4T1-FP, 4T1-D4, 4T1-D10, and 4T1-D16 based on scRNA-seq results. e, Violin plot depicts the epithelial score of 4T1.2-CL and 4T1.2-D4 based on scRNA-seq results. f, Heatmap of the differentially expressed epithelial and mesenchymal marker genes by comparing 4T1-CL, 4T1-FP, 4T1-D4, 4T1-D10, and 4T1-D16 was presented. g, Gene set enrichment analysis (GSEA) plot showing “HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION” enriched in scRNA-seq from 4T1-D4 cells as compared with 4T1-CL. NES, normalized enrichment score; FDR, false discovery rate. The P values were determined by two-tailed unpaired t-test (d,e).

Source data

Extended Data Fig. 3 Prognosis analysis of candidate genes in breast cancer patient dataset.

a, A diagram illustrating the gene selection process for cDNA screening is shown. By comparing 4T1-D4 vs. 4T1-CL group and by comparing 4T1.2-D4 vs. 4T1-D4, the top 200 genes with increased expression from either comparation were selected in a combined gene list, which is included in Supplementary Table 2. Genes encoding membrane or secreted proteins were subsequently chosen for the construction of the human cDNA overexpression library. b, The t-SNE plots displayed the representative gene expression patterns of the top five candidate genes with the highest enrichment scores shown in Supplementary Table 2. c, The t-SNE plots presented the gene expression patterns of the top five candidate genes identified from in vivo mini-cDNA library screening, as depicted in Fig. 2f. d, Gene ranked by the p-values from Kaplan-Meier curve of relapse-free survival in lymph node positive breast cancer patients. Patient dataset is from Kaplan-Meier Plotter database (kmplot.com), stratified by the expression of individual candidate gene. Genes on the left are those whose higher expressions are associated with better patient survival; while genes on the right are those whose higher expressions are associated with worse patient survival. e-i, Kaplan-Meier plots of relapse-free survival in lymph node positive breast cancer patients, stratified by the expression of five top candidate genes (MMP9, GPNMB, LTB, ADGRF5, and PTHLH). The P values were determined by log-rank test, light color represents ± 95% confidence intervals (d-i).

Source data

Extended Data Fig. 4 LTβ promotes bone metastasis.

a-b, The mRNA expression levels of LTB (a) and ADGRF5 (b) in SCP28 cells with LTβ- (a) or ADGRF5- (b) OE were determined by qPCR. n = 3 biologically independent samples. c, 103 SCP28 cells with -Vector control, LTβ-OE, or ADGRF5-OE were seeded into 96 well plate for cell culture. Relative cell number was monitored by in vitro luciferase assay. n = 3 biologically independent samples. d, 105 SCP28 cells with -Vector control, LTβ-OE, or ADGRF5-OE were IC injected into female nude mice. Bone metastasis burden was monitored by BLI imaging. n = 5, 7 or 7 for Vector control, LTβ group, or ADGRF5 group. e, Representative BLI, μCT, X-ray, H&E staining, and TRAP staining images of mice from experiment in (d). Scale bar = 50 μm. BLI scale: x 107 p/sec/cm2/sr. f, Quantification of the number of TRAP+ osteoclasts from experiment performed in (e). n = 4 biologically independent samples. g, Quantification of osteolytic lesion areas based on X-ray images from experiment performed in (e). Group size as in (d). h-i, LTβ (h) and ADGRF5 (i) in SCP28 cells were knocked down by shRNA constructs, mRNA levels were determined by qPCR. n = 3 biologically independent samples. j, SCP28 cells with -Vector control or LTβ-OE were injected in the mammary fat pads of female nude mice. Tumor volume was measured weekly. n = 5 mice. k, 105 4T1 cells with -Vector control or LTβ-OE were tail-vein injected into 6-week-old female BALB/c mice. Lung metastasis burden was monitored by BLI imaging. n = 8 mice. l, Representative BLI, lung image, and H&E staining of mice from experiment performed in (k). Scale bar = 5 mm. BLI scales: x107 p/sec/cm2/sr. m, Quantification of lung metastatic nodules from (k). n = 8 lungs per group. Arrows indicate the osteolytic bone lesion areas (e). B, bone tissue area; T, tumor area (e). Data presented as mean ± SEM (c,f,g,j,k) or SD (a,b,d,h,i,m). The P values were determined by two-way repeated measures ANOVA (c,d,j,k) or two-tailed unpaired t-test (a,b,f,g,h,i,m).

Source data

Extended Data Fig. 5 Pro-metastatic activity of LTβ in additional bone metastasis models.

a, SCP28 or PD2R cells were IC injected into female nude mice. Bone metastasis burden was monitored by BLI imaging. n = 6 or 7 mice for SCP28 group or PD2R group. b, Kaplan-Meier curve of mice in experiments performed in (a). Group sizes as in (a). c, Representative BLI and μCT images from mice in (a). BLI scale: x 108 p/sec/cm2/sr. d, Representative H&E and TRAP staining images from mice in (a). Scale bar = 100 μm. e, Representative IF images of Ki-67 staining in the bone tissues from PD2R or SCP28 IC-injected mice. Mice were sacrificed Week 1 post injection for hind limb bone and IF stained against Ki-67. GFP: cancer cells. Scale bar = 20 μm, white dotted lines: nuclear border of GFP+ cells. f, Percentage of Ki-67+ cells detected by IF staining from experiment in (e). n = 3 mice. g, Similar experiment was performed as in Fig. 4a, except that NSG mice were used. Bone metastasis burden was determined by weekly BLI. Quantification of BLI signal. n = 8 mice. h, Representative BLI, X-ray, μCT, H&E staining, and TRAP staining images of bone metastasis from experiment in (g). Scale bar = 100 μm. BLI scales: x107 p/sec/cm2/sr. i, Quantification of the number of TRAP+ osteoclasts based on TRAP staining images from experiment performed in (g). n = 8 biologically independent samples. j, Quantification of osteolytic lesion areas based on X-ray images of mice from experiment performed in (g). n = 8 biologically independent samples. k, Quantification of the percentage of BLI-positive legs from ex vivo BLI imaging of mice from experiment in (g). Arrows indicate the osteolytic bone lesion areas (c,h). B, bone tissue area; T, tumor area (d,h). Data presented as mean ± SEM (f,g,i,j) or SD (a). The P values were determined by two-way repeated measures ANOVA (a,g), log-rank test (b), or two-tailed unpaired t-test (f,i,j).

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Extended Data Fig. 6 LTβ expression is indued by RANKL.

a, A scratch wound healing assay was performed to assess the migration potential of 4T1 and SCP28 cells with vector control and LTβ overexpression. The closure of the wounded cell layer was monitored under a microscope 24 hours after wounding. Scale bar = 400 μm. b, 4T1 and SCP28 cells with vector control and LTβ overexpression were used for trans-well migration assay. Representative images of trans-well migration assay. Scale bar = 400 μm. c, Quantification of the number of migrated cells in (b). n = 3 biologically independent samples. d, Diagram of the trans-endothelial migration assay. HUVEC cells were seeded in gelatin pre-coated membrane of trans-well, cancer cells were then cultured on the surface of HUVEC cell layer. After 24-hour culture, migrated cancer cells on the surface of lower well of plate were counted. e, 4T1 and SCP28 cells with vector control and LTβ overexpression were used for trans-endothelial migration assay. Representative images of trans-endothelial migration assay. Scale bar = 500 μm. f, Quantification of the number of migrated cells in (e). n = 3 biologically independent samples. g, The mRNA expression level of LTB in SCP28 cells was determined by qPCR after treated with recombinant proteins. Data presented as mean ± SD. n = 3 biologically independent samples. h, SCP28 cells were treated with PBS or recombinant RANKL protein. Additional recombinant OPG protein was further added into the RANKL-treated group. The mRNA expression level of LTB in SCP28 cells after these treatments was determined by qPCR. n = 3 biologically independent samples. i, The mRNA expression levels of Lta and Ltb in cells isolated from the lymph nodes of BALB/c mice and in cultured 4T1 cells were determined by qPCR. n = 3 biologically independent samples. Data presented as mean ± SEM (c,f) and SD (g-i). The P values were determined by two-tailed unpaired t-test (c,f,g-i).

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Extended Data Fig. 7 Bone-seeding tumor cells interact with osteoblasts to generate a cytokine-rich microenvironment.

a, SCP28 cells with either scramble control or LTα KD were utilized for bone metastasis assay. 105 cancer cells were IC injected into 6-week-old female nude mice. Bone metastasis burden was quantified by weekly BLI imaging. n = 11 mice for control group, and n = 12 mice for LTα KD group. b, Representative BLI, μCT, H&E staining, and TRAP staining images of bone metastasis bearing mice from experiment in (a). Scale bar = 100 μm. BLI scale: x107 p/sec/cm2/sr. c, Heatmap represents differentially expressed genes in MC3T3 which were treated with either PBS or LTα1β2 for 24 and 48 hours. Scale bar indicates Z-scores. d, PCA analysis demonstrated that the gene expression profiles of LTα1β2-treated MC3T3 cells were significantly different from that of PBS-treated cells. e, GO analysis of enriched terms in MC3T3 treated with LTα1β2 compared to that of PBS treatment. f, Relative mRNA expression levels of chemokines in MC3T3 cells treated with either PBS as control, low concentration or high concentration of recombinant LTα1β2. n = 3 biologically independent samples per group. g, Representative IF images of ALP and CCL2 staining in the bone tissues from SCP28 injected nude mice. Mice were sacrificed at Week 2 post injection for hind limb bone collection and IF staining against ALP and CCL2. GFP: cancer cells. ALP: osteoblasts. Scale bar = 20 μm. Arrows indicate the osteolytic bone lesion areas (b) and CCL2 staining (g). B, bone tissue area; T, tumor area (b). Data presented as mean ± SEM (a) and SD (f). The P values were determined by two-way repeated measures ANOVA (a), two-tailed unpaired t-test (f) or Benjamini-Hochberg procedure using the clusterProfiler package (e).

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Extended Data Fig. 8 CCL2/5 promotes osteoclastogenesis.

a, Representative microscopic imaging of adhered GFP-labeled SCP28 cells was presented from experiment in Fig. 7b. Scale bar = 400 μm. b, mCherry-labeled MC3T3 cells were seeded onto cell culture plates to reach 100% confluency. GFP-labeled PD2R cells with either -Vector or -LTβ expression were seeded on top of the MC3T3 cells for 2 minutes. The number of adhered PD2R cells was determined using microscopic imaging. n = 6 biologically independent samples. c, Bone marrow CD117+ cells from Cas9 mice were FACS analyzed for Ccr2 and Ccr5 expression a week after sgRNA transduction. Notice a clear down-regulation of Ccr5 expression after the transduction of Ccr sgRNA (sgCcr5 group). Ccr2 expression is very limited even in control group (sgNT). d, Same sgRNAs against Ccr2 and Ccr5 from experiment in (c) were tested in Raw264.7 cells. DNA from these cells were then PCR and sequenced to examine the knockout efficiency. e, Control CD117+ cells or Ccr2s knockout (sgCcrs) CD117+ cells from Fig. 7f were further induced for osteoclast differentiation in vitro. Osteoclast cells were visualized by an TRAP-staining kit. f, The peripheral blood of transplanted recipient mice was collected and analyzed by FACS analysis, using the gating schemes as shown in the figure panels. FSC-A, forward scatter area; SSC-A, side scatter area; FSC-H, forward scatter height; SSC-H, side scatter height. For transplanted recipient mice: GFP+ cells, which were differentiated from successfully transfected CD117+ cells, were used for further analysis of the percentage of B cells, T cells, and myeloid cells. For wild type mice without transplantation, GFP- cells were similarly analyzed as negative control. g, Quantification of the percentages of B cells, T cells, and myeloid cells from mice without or with transplantation. n = 5 mice for each group. Data presented as mean ± SD (b,g). The P values were determined by two-tailed unpaired t-test (f).

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Extended Data Fig. 9 LTβ-mediated signaling is essential to bone metastasis progression.

a, SCP28 cells were IC injected into 6-week-old female nude mice. Mice were treated with LTβR-Ig recombinant protein three days later and continued until the experimental endpoint. b, Bone metastasis burden was monitored by BLI imaging from experiment in (a). n = 9 mice for IgG treated group, n = 12 for LTβR-Ig treated group. c, Kaplan-Meier BMFS curve of mice bearing bone metastasis in the experiment performed in (a). Group sizes as in (b).d, Representative BLI, μCT, H&E staining, and TRAP staining images of bone metastasis from the experiment in (a). Scale bar = 100 μm. BLI scale: x107 p/sec/cm2/sr. e, PD2R-LTβ cells were IC injected into 6-week-old female NSG mice. Mice were treated with LTβR-Ig recombinant protein three days later and continued until the experimental endpoint. f, Bone metastasis burden was monitored by BLI imaging from experiment in (e). n = 9 mice per each group. g, Kaplan-Meier BMFS curve of mice bearing bone metastasis in the experiment performed in (e). Group sizes as in (f). h, Representative BLI, μCT, H&E staining, and TRAP staining images of bone metastasis from experiment in (e). Scale bar = 100 μm. BLI scale: x107 p/sec/cm2/sr. Arrows indicate the osteolytic bone lesion areas (d,h). B, bone tissue area; T, tumor area (d,h). Data presented as mean ± SEM (b,f). The P values were determined by two-way repeated measures ANOVA (b,f) or log-rank test (c,g).

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Extended Data Fig. 10 LTβ promotes bone metastatic colonization in humanized mouse model.

a, Humanized mouse model: human HSC cells were inoculated into lethally irradiated NSG mice to reconstitute their hematopoietic system. Peripheral blood from these mice was FACS analyzed for the presence of human CD45+ cells. Representative FACS-plot from this experiment was shown. b, Quantification of the percentage of human CD45+ cells from control NSG mice and from humanized mice. n = 3 mice for control group and n = 6 mice for humanized mouse group. c, GFP-labeled SCP28 cells with Vector control or LTβ OE were IC injected into humanized NSG mice. Mice were sacrificed 5 days later to collect hindlimbs for IF staining. Samples were counter-stained with DAPI. Scale bar = 100 μm. d, Quantification of GFP+ cells per field from experiment in (c). n = 8 fields per group. e, The α-LTβ antibody for IHC staining in Fig. 8f was verified by detecting LTβ expression in lymph node from patients with breast cancer. Rabbit IgG was utilized as a negative control. Data presented as mean ± SEM (b,d). P values were determined by two-tailed unpaired t-test (b,d).

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Supplementary information

Reporting Summary

Supplementary Videos 1–5

Supplementary Video 1. SCP28 control. Video 2. SCP28-LTB knockdown. Video 3. SCP28-ADGRF5 knockdown. Video 4. PD2R vector. Video 5. PD2R-LTB.

Supplementary Tables 1–6

Supplementary Table 1. The distribution of cells among different cell types and clusters in t-SNE plot. Table 2. Candidates for cDNA library construction. Table 3. DNA oligos for plasmid construction. Table 4. Oligos for NGS library construction. Table 5. shRNA sequences. Table 6. Oligos for qPCR.

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Source Data Figs. 1–8 and Extended Data Figs. 1–10

Statistical source data.

Source Data Fig. 6

Unprocessed western blots.

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Wang, X., Zhang, T., Zheng, B. et al. Lymphotoxin-β promotes breast cancer bone metastasis colonization and osteolytic outgrowth. Nat Cell Biol 26, 1597–1612 (2024). https://doi.org/10.1038/s41556-024-01478-9

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