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Lymphatic endothelial-like cells promote glioblastoma stem cell growth through cytokine-driven cholesterol metabolism

Nature Cancer volume 5, pages 147–166 (2024)Cite this article

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Abstract

Glioblastoma is the most lethal primary brain tumor with glioblastoma stem cells (GSCs) atop a cellular hierarchy. GSCs often reside in a perivascular niche, where they receive maintenance cues from endothelial cells, but the role of heterogeneous endothelial cell populations remains unresolved. Here, we show that lymphatic endothelial-like cells (LECs), while previously unrecognized in brain parenchyma, are present in glioblastomas and promote growth of CCR7-positive GSCs through CCL21 secretion. Disruption of CCL21–CCR7 paracrine communication between LECs and GSCs inhibited GSC proliferation and growth. LEC-derived CCL21 induced KAT5-mediated acetylation of HMGCS1 on K273 in GSCs to enhance HMGCS1 protein stability. HMGCS1 promoted cholesterol synthesis in GSCs, favorable for tumor growth. Expression of the CCL21–CCR7 axis correlated with KAT5 expression and HMGCS1K273 acetylation in glioblastoma specimens, informing patient outcome. Collectively, glioblastomas contain previously unrecognized LECs that promote the molecular crosstalk between endothelial and tumor cells, offering potentially alternative therapeutic strategies.

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Fig. 1: LEC-like cells are present in glioma.
Fig. 2: The location of LEC-like cells in glioma.
Fig. 3: LEC-like cells contribute to glioma and promote growth of GSCs.
Fig. 4: LEC-like cells promote growth of CCR7+ GSCs.
Fig. 5: LEC-like cell-secreted CCL21, but not CCL19, stimulates proliferation of CCR7+ GSCs in vitro.
Fig. 6: HMGCS1 is modified at the post-translational level and contributes to tumor growth.
Fig. 7: KAT5 acetylates HMGCS1 to regulate its stability.
Fig. 8: HMGCS1 acetylation alters tumor growth and metabolism.

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Data availability

The RNA-seq data related to this study are available at the NCBI Gene Expression Omnibus under accession no. GSE218043, the SMART-seq data in this study are available at the Genome Sequence Archive for Human under accession no. HRA003200 and the MS data related to this study are available at OMIX (China National Center for Bioinformation) under accession code PRJCA014088. All other supporting data of this study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

Code availability

The analyses presented in this study were conducted using standard workflows.

References

  1. Ostrom, Q. T. et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2013–2017. Neuro. Oncol. 22, iv1–iv96 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Stupp, R. et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 10, 459–466 (2009).

    Article  CAS  PubMed  Google Scholar 

  3. Quail, D. F. & Joyce, J. A. The microenvironmental landscape of brain tumors. Cancer Cell 31, 326–341 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Singh, S. K. et al. Identification of human brain tumour initiating cells. Nature 432, 396–401 (2004).

    Article  CAS  PubMed  Google Scholar 

  5. Seano, G. & Jain, R. K. Vessel co-option in glioblastoma: emerging insights and opportunities. Angiogenesis 23, 9–16 (2020).

    Article  PubMed  Google Scholar 

  6. Gilbert, M. R. et al. A randomized trial of bevacizumab for newly diagnosed glioblastoma. N. Engl. J. Med. 370, 699–708 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Chinot, O. L. et al. Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma. N. Engl. J. Med. 370, 709–722 (2014).

    Article  CAS  PubMed  Google Scholar 

  8. Obernier, K. & Alvarez-Buylla, A. Neural stem cells: origin, heterogeneity and regulation in the adult mammalian brain. Development 146, 156059 (2019).

    Article  Google Scholar 

  9. Zhou, W. et al. Periostin secreted by glioblastoma stem cells recruits M2 tumour-associated macrophages and promotes malignant growth. Nat. Cell Biol. 17, 170–182 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Osswald, M. et al. Brain tumour cells interconnect to a functional and resistant network. Nature 528, 93–98 (2015).

    Article  CAS  PubMed  Google Scholar 

  11. Plate, K. H., Breier, G., Weich, H. A. & Risau, W. Vascular endothelial growth factor is a potential tumour angiogenesis factor in human gliomas in vivo. Nature 359, 845–848 (1992).

    Article  CAS  PubMed  Google Scholar 

  12. Ricci-Vitiani, L. et al. Tumour vascularization via endothelial differentiation of glioblastoma stem-like cells. Nature 468, 824–828 (2010).

    Article  CAS  PubMed  Google Scholar 

  13. Wang, R. et al. Glioblastoma stem-like cells give rise to tumour endothelium. Nature 468, 829–833 (2010).

    Article  CAS  PubMed  Google Scholar 

  14. Cheng, L. et al. Glioblastoma stem cells generate vascular pericytes to support vessel function and tumor growth. Cell 153, 139–152 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Winkler, E. A. et al. A single-cell atlas of the normal and malformed human brain vasculature. Science 375, eabi7377 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Louveau, A. et al. Structural and functional features of central nervous system lymphatic vessels. Nature 523, 337–341 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Da Mesquita, S. et al. Functional aspects of meningeal lymphatics in ageing and Alzheimer’s disease. Nature 560, 185–191 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Da Mesquita, S. et al. Meningeal lymphatics affect microglia responses and anti-Aβ immunotherapy. Nature 593, 255–260 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Louveau, A. et al. CNS lymphatic drainage and neuroinflammation are regulated by meningeal lymphatic vasculature. Nat. Neurosci. 21, 1380–1391 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Song, E. et al. VEGF-C-driven lymphatic drainage enables immunosurveillance of brain tumours. Nature 577, 689–694 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ahn, J. H. et al. Meningeal lymphatic vessels at the skull base drain cerebrospinal fluid. Nature 572, 62–66 (2019).

    Article  CAS  PubMed  Google Scholar 

  22. Hu, X. et al. Meningeal lymphatic vessels regulate brain tumor drainage and immunity. Cell Res. 30, 229–243 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lee, E. et al. Breast cancer cells condition lymphatic endothelial cells within pre-metastatic niches to promote metastasis. Nat. Commun. 5, 4715 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Wei, J. C. et al. Tumor-associated Lymphatic endothelial cells promote lymphatic metastasis by highly expressing and secreting SEMA4C. Clin. Cancer Res. 23, 214–224 (2017).

    Article  CAS  PubMed  Google Scholar 

  25. Wei, Y. et al. IL-17A secreted from lymphatic endothelial cells promotes tumorigenesis by upregulation of PD-L1 in hepatoma stem cells. J Hepatol. 71, 1206–1215 (2019).

    Article  CAS  PubMed  Google Scholar 

  26. Xie, Y. et al. Key molecular alterations in endothelial cells in human glioblastoma uncovered through single-cell RNA sequencing. JCI Insight 6, 150861 (2021).

    Google Scholar 

  27. LeBlanc, V. G. et al. Single-cell landscapes of primary glioblastomas and matched explants and cell lines show variable retention of inter- and intratumor heterogeneity. Cancer Cell 40, 379–392 (2022).

    Article  CAS  PubMed  Google Scholar 

  28. Mabbott, N. A., Baillie, J. K., Brown, H., Freeman, T. C. & Hume, D. A. An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics 14, 632 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hansen, A. et al. KSHV-encoded miRNAs target MAF to induce endothelial cell reprogramming. Genes Dev. 24, 195–205 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wong, H. L. et al. MT1-MMP sheds LYVE-1 on lymphatic endothelial cells and suppresses VEGF-C production to inhibit lymphangiogenesis. Nat. Commun. 7, 10824 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Pan, Y. et al. Podoplanin requires sialylated O-glycans for stable expression on lymphatic endothelial cells and for interaction with platelets. Blood 124, 3656–3665 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chakarov, S. et al. Two distinct interstitial macrophage populations coexist across tissues in specific subtissular niches. Science 363, aau0964 (2019).

    Article  Google Scholar 

  33. Lim, H. Y. et al. Hyaluronan receptor LYVE-1-expressing macrophages maintain arterial tone through hyaluronan-mediated regulation of smooth muscle cell collagen. Immunity 49, 326–341 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Drieu, A. et al. Parenchymal border macrophages regulate the flow dynamics of the cerebrospinal fluid. Nature 611, 585–593 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gravendeel, L. A. et al. Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. Cancer Res. 69, 9065–9072 (2009).

    Article  CAS  PubMed  Google Scholar 

  36. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

    Article  Google Scholar 

  37. Mack, S. C. et al. Chromatin landscapes reveal developmentally encoded transcriptional states that define human glioblastoma. J. Exp. Med. 216, 1071–1090 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Buonamici, S. et al. CCR7 signalling as an essential regulator of CNS infiltration in T-cell leukaemia. Nature 459, 1000–1004 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Vaahtomeri, K. et al. Locally triggered release of the chemokine CCL21 promotes dendritic cell transmigration across lymphatic endothelia. Cell Rep. 19, 902–909 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Maire, C. L. et al. Glioma escape signature and clonal development under immune pressure. J. Clin. Invest. 130, 5257–5271 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Narita, T., Weinert, B. T. & Choudhary, C. Functions and mechanisms of non-histone protein acetylation. Nat. Rev. Mol. Cell Biol. 20, 156–174 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. Brasaemle, D. L. et al. Perilipin A increases triacylglycerol storage by decreasing the rate of triacylglycerol hydrolysis. J. Biol. Chem. 275, 38486–38493 (2000).

    Article  CAS  PubMed  Google Scholar 

  44. Jaeger, K. et al. Structural basis for allosteric ligand recognition in the human CC chemokine receptor 7. Cell 178, 1222–1230 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Alegaon, S. G. et al. Synthesis, molecular docking and ADME studies of thiazole-thiazolidinedione hybrids as antimicrobial agents. J. Biomol. Struct. Dyn. https://doi.org/10.1080/07391102.2021.1880479 (2022).

  46. Fu, C. et al. Oral bioavailability comparison of artemisinin, deoxyartemisinin, and 10-deoxoartemisinin based on computer simulations and pharmacokinetics in rats. ACS Omega 6, 889–899 (2021).

    Article  CAS  PubMed  Google Scholar 

  47. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  48. Kuczynski, E. A., Vermeulen, P. B., Pezzella, F., Kerbel, R. S. & Reynolds, A. R. Vessel co-option in cancer. Nat. Rev. Clin. Oncol. 16, 469–493 (2019).

    Article  CAS  PubMed  Google Scholar 

  49. Tavora, B. et al. Tumoural activation of TLR3-SLIT2 axis in endothelium drives metastasis. Nature 586, 299–304 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Calabrese, C. et al. A perivascular niche for brain tumor stem cells. Cancer Cell 11, 69–82 (2007).

    Article  CAS  PubMed  Google Scholar 

  51. Chen, J. et al. Cerebrovascular injuries induce lymphatic invasion into brain parenchyma to guide vascular regeneration in zebrafish. Dev. Cell 49, 697–710 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Jin, X. et al. Targeting glioma stem cells through combined BMI1 and EZH2 inhibition. Nat. Med. 23, 1352–1361 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Han, M. et al. Therapeutic implications of altered cholesterol homeostasis mediated by loss of CYP46A1 in human glioblastoma. EMBO Mol. Med. 12, e10924 (2020).

    Article  CAS  PubMed  Google Scholar 

  54. Wang, X. et al. MYC-regulated mevalonate metabolism maintains brain tumor-initiating cells. Cancer Res. 77, 4947–4960 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Villa, G. R. et al. An LXR–cholesterol axis creates a metabolic co-dependency for brain cancers. Cancer Cell 30, 683–693 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Cheng, C. et al. Glucose-mediated N-glycosylation of SCAP is essential for SREBP-1 activation and tumor growth. Cancer Cell 28, 569–581 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Yuan, H. et al. Lysine catabolism reprograms tumour immunity through histone crotonylation. Nature 617, 818–826 (2023).

    Article  CAS  PubMed  Google Scholar 

  58. Ye, Z. et al. Targeting microglial metabolic rewiring synergizes with immune-checkpoint blockade therapy for glioblastoma. Cancer Discov. 13, 974–1001 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Yu, H., Xing, S., Nierves, L., Lange, P. F. & Huan, T. Fold-change compression: an unexplored but correctable quantitative bias caused by nonlinear electrospray ionization responses in untargeted metabolomics. Anal. Chem. 92, 7011–7019 (2020).

    Article  CAS  PubMed  Google Scholar 

  60. Yu, H. & Huan, T. Patterned signal ratio biases in mass spectrometry-based quantitative metabolomics. Anal. Chem. 93, 2254–2262 (2021).

    Article  CAS  PubMed  Google Scholar 

  61. Tsugawa, H. et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 12, 523–526 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 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 

  65. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Kong, S. et al. Global analysis of lysine acetylome reveals the potential role of CCL18 in non-small cell lung cancer. Proteomics 21, e2000144 (2021).

    Article  PubMed  Google Scholar 

  69. Gong, Y. et al. Age-associated proteomic signatures and potential clinically actionable targets of colorectal cancer. Mol. Cell. Proteom. 20, 100115 (2021).

    Article  CAS  Google Scholar 

  70. Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Chou, M. F. & Schwartz, D. Biological sequence motif discovery using motif-x. Curr. Protoc. Bioinform. 13, 13 15–13 24 (2011).

    Google Scholar 

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Acknowledgements

The authors thank S. Ma from UCSD for the plasmid. This work was supported by grants from the National Institutes of Health (NIH) (T32 CA094186 to K.Y.; F30 CA217065 to R.C.G.; F31 CA243296 to D. Lee; R35 CA197718, R01 CA238662, R01 CA268634 and R01 NS103434 to J.N.R.; and R01NS106155, R01CA159859 and R01CA255369 to M.D.T.) and from the National Natural Science Foundation of China (82273255, 81822034 and 81821002 to S.Z.; 82273473 to Z.Q.; and 82072779 to Xiuxing W.), The National Key Research and Development Program of China (2022YFA1106600, 2017YFA0106800 and 2018YFA0109200 to S.Z.), The Sichuan Science-Technology International Cooperation Project (22ZYZYTS0070 and 2019YFH0144 to S.Z.) and The Shanghai Municipal Health Commission (2022YQ062 to Z.Q.). All graphics were created with BioRender.com.

Author information

Author notes

  1. These authors contributed equally: Linjie Zhao, Zhixin Qiu, Zhengnan Yang.

Authors and Affiliations

  1. University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA, USA

    Linjie Zhao, Zhixin Qiu, Qiulian Wu, Deguan Lv, Derrick Lee, Brandon M. Lehrich & Jeremy N. Rich

  2. Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, USA

    Linjie Zhao & Deobrat Dixit

  3. Department of Anesthesiology, Zhongshan Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China

    Zhixin Qiu

  4. Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of the Ministry of Education, and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University, and Collaborative Innovation Center, Chengdu, China

    Zhengnan Yang & Shengtao Zhou

  5. Department of Pathology, West China Second Hospital, Sichuan University, Chengdu, China

    Lian Xu

  6. Department of Pathology, Division of Neuropathology, University of Pittsburgh, Pittsburgh, PA, USA

    Thomas M. Pearce

  7. Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, USA

    Kailin Yang

  8. Department of Pharmacology and Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA

    FuLong Li

  9. The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada

    Olivier Saulnier, Liam D. Hendrikse, Vernon Fong, Jiao Zhang & Michael D. Taylor

  10. Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada

    Olivier Saulnier, Liam D. Hendrikse, Vernon Fong, Jiao Zhang & Michael D. Taylor

  11. Department of Neurosurgery, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China

    Fan Fei

  12. Department of Chemistry, University of British Columbia, Vancouver, British Columbia, Canada

    Huaxu Yu & Tao Huan

  13. Department of Pathology, School of Medicine, Case Western Reserve University, Cleveland, OH, USA

    Ryan C. Gimple

  14. Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute Cleveland Clinic, Cleveland, OH, USA

    Venkateshwari Varadharajan & J. Mark Brown

  15. Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

    Venkateshwari Varadharajan & J. Mark Brown

  16. Division of Obstetrics, Key Laboratory of Birth Defects and Related Disease of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University, Chengdu, China

    Juxiu Liu & Xiang Wang

  17. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Liam D. Hendrikse & Vernon Fong

  18. Department of Gynecology, Huzhou Maternity & Child Health Care Hospital, Huzhou, China

    Wei Wang

  19. Howard Hughes Medical Institute, Department of Medicine, University of California, San Diego, La Jolla, CA, USA

    Chunyu Jin

  20. State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Collaborative Innovation Center for Biotherapy, Chengdu, China

    Liang Ouyang

  21. Huaxi MR Research Center, Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China

    Haoxing Wu

  22. Department of Neurosurgery, Case Western Reserve University, Cleveland, OH, USA

    Andrew E. Sloan

  23. School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China

    Xiuxing Wang

  24. University of Rochester Medical Center, Rochester, NY, USA

    Steven A. Goldman

  25. University of Copenhagen, Copenhagen, Denmark

    Steven A. Goldman

  26. Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario, Canada

    Michael D. Taylor

  27. Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    Jeremy N. Rich

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  1. Linjie Zhao
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  2. Zhixin Qiu
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Contributions

Conceptualization was the responsibility of L.Z., S.Z. and J.N.R. Methodology was the responsibility of L.Z., Z.Q., Z.Y., L.X., Q.W., H.Y., V.V., J.L. and V.F. Software was the responsibility of Z.Q., O.S., L.D.H., V.F., J.Z. and W.W. Investigation was the responsibility of L.Z., Z.Q., Z.Y., F.L., L.O., T.M.P., W.W., Xiang W. and T.H. Resources were the responsibility of F.F., A.E.S., H.W. and Xiuxing W. Data curation was carried out by L.Z., Z.Q. and Z.Y. Writing of the original draft was the responsibility of L.Z. and Z.Q. Review and editing of the manuscript was carried out by J.N.R., S.Z., K.Y., S.A.G., R.C.G., D. Lv, D. Lee, B.M.L., C.J. and D.D. Supervision was carried out by J.M.B., M.D.T., S.Z. and J.N.R.

Corresponding authors

Correspondence to Shengtao Zhou or Jeremy N. Rich.

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Competing interests

The authors declare no competing interests.

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Nature Cancer thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 LEC-like cells are present in glioblastoma parenchyma.

a, UMAP plot showing cell clusters in scRNA-seq analysis from 4 integrated glioblastoma patients colored by individuals (GSE162631). b, Bubble plot showing expression levels of marker genes in cell clusters in scRNA-seq analysis from 4 integrated glioblastoma patients (GSE162631). c, UMAP plot showing cell clusters in scRNA-seq analysis from 4 integrated glioblastoma patients could express endothelial cell makers ESAM. d-g, Prediction of percentage of predicted LECs in endothelial cell clusters in scRNA-seq analysis from a panel of glioblastoma patients (GSE162631). h, Expression of key LEC-specific and BEC-specific markers, respectively in the GSE162631 dataset. i, Bubble plot showing expression levels of marker genes in cell clusters in scRNA-seq analysis from 12 integrated glioblastoma patients (GSE173278). j–k, UMAP showing expression levels of endothelial cell specific genes ESAM and PECAM in two endothelial cell populations in scRNA-seq analysis of 12 glioblastoma patients. l, LEC signature scores between predicted LECs and BECs from 12 integrated glioblastoma patients (GSE173278). For box-and-whisker plot, the box indicates interquartile range (IQR), the line in the box indicates the median, the whiskers indicate points within Q3 + 1.5 × IQR and Q1 – 1.5 × IQR, and the points beyond whiskers indicate outliers. Q1 and Q3 denote the first and third quartiles, respectively. P values were calculated using unpaired two-tailed t-test. m, Bubble plot showing expression levels of LEC marker genes Podoplanin, FOXC2, PROX1, and GATA2 are highly enriched in LEC clusters compared with BEC clusters in scRNA-seq analysis from 12 integrated glioblastoma patients (GSE173278). n, The large-scale chromosomal copy number variations (CNVs) in single cells (GSE173278) were inferred using inferCNV packages. The myeloid cells, oligodendrocytes and pericytes were used as normal references. CNVs in endothelial cell cluster and malignant cluster were shown in the heat map.

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Extended Data Fig. 2 Flow cytometry gating strategies for the LEC-like cell in glioblastoma patients.

Gating strategies for FACS analysis shown in Fig. 1n and the percentage of LYVE1+podoplanin+ LECs in CD45− CD31+ endothelial cells of a panel of GBM patient fresh samples.

Extended Data Fig. 3 LEC-like cell abundance is correlated with patient survival.

a-b, Gating strategies for FACS analysis shown in Fig. 2a,b. c, Representative images of immunohistochemical staining of pan-endothelial cell maker CD31 and LEC marker Podoplanin in non-neoplastic brain tissues and glioblastoma tissues. Glioblastoma tissue fractions exhibit enriched LECs. Scale bars, 50 μm. Images are representative of three independent experiments. d, Two-tailed Spearman correlation between HBEC cells and GBM patient 25-derived BECs using proteomics data. e, Two-tailed Spearman correlation between HBEC cells and glioblastoma patient 26-derived BECs using proteomics data. f, Spearman correlation between HBE-5i cells and glioblastoma patient-derived BECs using SMART-seq data. g, Spearman correlation between commercial LECs and glioblastoma patient-derived LECs using SMART-seq data. h, FACS plots demonstrating isolation of LEC-like cells from GL261 glioma cells. Cells from mouse tumor tissue were stained with CD45-APC-Cy7, CD31-PE, and Podoplanin-FITC.

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Extended Data Fig. 4 CCR7 mediates the interplay between LECs and GSCs.

a, Differential overall survival of glioblastoma patients stratified by different CCR6 expressions in the TCGA glioblastoma dataset. P values were calculated using log-rank test. b, Differential overall survival of glioblastoma patients stratified by CXCR4 expression in TCGA glioblastoma data. P values were calculated using log-rank test. c, mRNA levels of top 5 upregulated chemokine receptors in GSCs and NSCs analyzed by qPCR assay. Data are presented as mean ± SD from 3 independent experiments. P values were calculated using two-tailed unpaired Student’s t-test. d, Differential expression of CCR7 between normal and glioblastoma patients in Gravendeel data. GBM (n = 159) vs. normal brain (n = 8). P values were calculated using two-tailed unpaired Student’s t-test. e, Differential expression of CCR7 between normal and glioblastoma patients in TCGA glioblastoma dataset. GBM (n = 528) vs. normal brain (n = 10). P values were calculated using two-tailed unpaired Student’s t-test. f, CCR7+SOX2+/SOX2+ cells in both GSCs and NSCs were detected (n = 5). P values were calculated using two-tailed unpaired Student’s t-test. g, FACS dot plots showing the CD133− CCR7+ and CD133− CCR7− DGC2907. 96.3% of CD133− DGC2907 were CCR7 negative, 95% CI. h, FACS dot plots showing CD133-CCR7+ and CD133−CCR7− DGC20. 96.3% of CD133− DGC2907 were CCR7 negative, 95% CI. i, Spheroid formation assay was performed for GSCs transduced with either shCONT, shCCR7.291, or shCCR7.183 and co-cultured with LECs. n = 6 each group. j, Statistics for spheroid formation assay were performed for GSCs expressing shCONT, shCCR7.291, or shCCR7.183 co-cultured with LECs. Data are presented as mean ± SD from six independent experiments. P values were calculated using two-tailed unpaired Student’s t-test. k and l, Representative FACS dot plots showing CCR7high and CCR7low GSC2907(k) and GSC20(l). For box-and-whisker plot, the box indicates interquartile range (IQR), the line in the box indicates the median, the top of whiskers indicates the maxima and the bottom of the whiskers indicate the minima, Q1 and Q3 denote the first and third quartiles, respectively.

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Extended Data Fig. 5 LEC-secreted CCL21, but not CCL19, stimulates proliferation of CCR7+ GSCs in vitro.

a, Cell counting was performed for GSC1552 0, 2, 4, and 6 days after adding either IgG (10 ng ml−1), CCL19 recombinant protein (10 ng ml−1), or CCL21 recombinant protein (10 ng ml−1). b, Quantification of spheres formation of GSC2907 (left), GSC20 (middle), or GSC1552 (right) when treated with either IgG, CCL19 recombinant protein, or CCL21 recombinant protein. Data are presented as mean ± SD from 6 independent experiments. P-values were calculated using two-tailed unpaired Student’s t-test. c, Representative images of spheres formed by GSCs when treated with either IgG, CCL19 recombinant protein, or CCL21 recombinant protein. Scale bar, 50 μm. Images are representative of six independent experiments. d, Cell counting was performed 0, 2, 4, 6 days after adding either IgG or CCL21 monoclonal antibody. P-values were calculated using two-tailed unpaired Student’s t-test. e, Statistics for spheroid formation of GSCs treated with CCL21 monoclonal antibody when co-cultured with LECs. Data are presented as mean ± SD from 6 independent experiments. P-values were calculated using two-tailed unpaired Student’s t-test. f, Cell counting was performed for GSC1552 cells transfected with either shCONT or shCCR7 and treated with CCL21(n = 6). g and h, Representative FACS dot plots showing CCR7+ and CD133+ CT2A cells (g) or GL261 cells (h). i, Schematic model that illustrates cell sorting strategy of CD133+CCR7+ murine glioma cell lines (GL261 and CT2A) and implantation into CCL21−/− mice. j, qPCR analysis showing mRNA expression of CCL21 following shRNA-mediated knockdown. Data are presented as mean ± SD from three independent experiments. P-values were calculated using two-tailed unpaired Student’s t-test. k, Cell counting for GSCs co-cultured with either LECs-shCONT, LECs-shCCL21.242, or LECs-shCCL21.214 was performed on Day 0, 2, 4, and 6. (a-b, d–f, k) Data are presented as mean ± SD from six independent experiments. P-values were calculated using one-way ANOVA test.

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Extended Data Fig. 6 Acetylproteome of GSCs stimulated with CCL21.

a, Distribution of subcellular localization of proteins with quantified acetylation sites in GSC20. b, Protein-protein interaction modules for metabolic processes were demonstrated. Proteins with differential acetylation sites obtained from GSC20 cells when treated with either IgG or CCL21 recombinant protein. c, Protein-protein interaction modules for proteins with differential acetylation sites obtained from GSC20 cells when treated with either IgG or CCL21 recombinant protein.

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Extended Data Fig. 7 HMGCS1 is acetylated at K273 in GSCs stimulated with CCL21.

a, Specificity of the anti-acetylated lysine 273 antibody was confirmed by its ability to recognize the acetylated, but not unacetylated, peptide in GSC1552 cells. Images are representative of three independent experiments. b, Flag-tagged wild-type HMGCS1-expressing GSC20 cells were lysed, which were subjected to immunoprecipitation with a Flag antibody. The immunoprecipitated HMGCS1 was de-acetylated by CobB. c, Enzymatic activity of HMGCS1WT or HMGCS1K273Q was tested using an in vitro assay using purified His-tagged HMGCS1 or HMGCS1K273Q protein with acetyl-CoA. The reactions were performed at 30 °C for 30 minutes. Data are presented as mean ± SD from three independent experiments. P-values were calculated using two-tailed unpaired Student’s t-test. d, HMGCS1 was stably knocked down with shRNA in GSC20 and GSC23 cells. shRNA-resistant wild-type and K273Q mutant of HMGCS1 were stably re-expressed in the knockdown cells to a level compatible with endogenous HMGCS1. HMGCS1 knockdown efficiency and re-expression level were determined by western blot. Images are representative of three independent experiments.

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Extended Data Fig. 8 The BBB-penetrating properties of CMP2105.

a, ADME properties of conformers of Cmp2105 using Qikprop. b, HRMS Extracted ion chromatogram (XIC) of CMP2105, m/z 476.1962 (−0.01ppm mass error from theoretical) at retention time 4.6min in homogenized brain extract. c, HRMS HCD MS2 fragmentation spectrum of CMP2105, m/z 476.1962 parent ion, showing diagnostic fragment of 115.1122.

Supplementary information

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Zhao, L., Qiu, Z., Yang, Z. et al. Lymphatic endothelial-like cells promote glioblastoma stem cell growth through cytokine-driven cholesterol metabolism. Nat Cancer 5, 147–166 (2024). https://doi.org/10.1038/s43018-023-00658-0

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