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Integrated analysis of multiple methods reveals characteristics of the immune microenvironment in medulloblastoma

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Abstract

To explore the characteristics of the immune microenvironment (IME) of medulloblastoma (MB) by four methods: flow cytometry (FCM), immunohistochemical (IHC), bulk RNA expression and single cell RNA sequencing (scRNA-seq), we collected the intraoperative specimens of MB, ependymoma (EPN), high-grade glioma (HGG), and low-grade glioma (LGG) to make a cross-cancer comparison. The specimens were subjected to FCM and IHC respectively, and deconvolution from bulk RNA expression data and scRNA-seq analysis were performed in MB from the GEO database. FCM and IHC analysis found that the proportion of lymphocytes (LC) and T cells between MB and other brain tumors were significantly different. The deconvolution of bulk RNA expression data showed that only the proportion of cell types in MCPCOUNTER changed greatly. scRNA-seq found that the proportion of various immune cells in the IME of MB differed between different subtypes. Techniques such as FCM, IHC, bulk RNA expression, and scRNA-seq can sort out different immune cell subsets to a certain extent and quantify their proportions. The four methods have their own strengthens and limitations, but for highly heterogeneous tumor such as MB, integrated analysis of multiple methods is a better choice.

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Data availability

The data that support the findings of this study are available from the corresponding author, Jian Gong, upon reasonable request.

References

  1. Khong PL, Kwong DL, Chan GC, Sham JS, Chan FL, Ooi GC (2003) Diffusion-tensor imaging for the detection and quantification of treatment-induced white matter injury in children with medulloblastoma: a pilot study. AJNR Am J Neuroradiol 24:734–740

    PubMed  PubMed Central  Google Scholar 

  2. Nagel BJ, Palmer SL, Reddick WE, Glass JO, Helton KJ, Wu S, Xiong X, Kun LE, Gajjar A, Mulhern RK (2004) Abnormal hippocampal development in children with medulloblastoma treated with risk-adapted irradiation. AJNR Am J Neuroradiol 25:1575–1582

    PubMed  PubMed Central  Google Scholar 

  3. Kovanlikaya A, Panigrahy A, Krieger MD, Gonzalez-Gomez I, Ghugre N, McComb JG, Gilles FH, Nelson MD, Blüml S (2005) Untreated pediatric primitive neuroectodermal tumor in vivo: quantitation of taurine with MR spectroscopy. Radiology 236:1020–1025. https://doi.org/10.1148/radiol.2363040856

    Article  PubMed  Google Scholar 

  4. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114:97–109. https://doi.org/10.1007/s00401-007-0243-4

    Article  PubMed  PubMed Central  Google Scholar 

  5. Douglas-Akinwande AC, Payner TD, Hattab EM (2009) Medulloblastoma mimicking Lhermitte-Duclos disease on MRI and CT. Clin Neurol Neurosurg 111:536–539. https://doi.org/10.1016/j.clineuro.2009.01.008

    Article  PubMed  Google Scholar 

  6. Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G et al (2021) The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. https://doi.org/10.1093/neuonc/noab106

    Article  PubMed  PubMed Central  Google Scholar 

  7. Li Z, Wei Y, Shao Y, Tang L, Gong J (2021) Multi-omics analysis of intertumoral heterogeneity within medulloblastoma uncharted-pathway subtypes. Brain Tumor Pathol 38:234–242. https://doi.org/10.1007/s10014-021-00400-7

    Article  CAS  PubMed  Google Scholar 

  8. Khanna V, Achey RL, Ostrom QT, Block-Beach H, Kruchko C, Barnholtz-Sloan JS, de Blank PM (2017) Incidence and survival trends for medulloblastomas in the United States from 2001 to 2013. J Neurooncol 135:433–441. https://doi.org/10.1007/s11060-017-2594-6

    Article  PubMed  Google Scholar 

  9. Gudrunardottir T, Lannering B, Remke M, Taylor MD, Wells EM, Keating RF, Packer RJ (2014) Treatment developments and the unfolding of the quality of life discussion in childhood medulloblastoma: a review. Childs Nerv Syst 30:979–990. https://doi.org/10.1007/s00381-014-2388-5

    Article  PubMed  Google Scholar 

  10. Zhu C, Preissl S, Ren B (2020) Single-cell multimodal omics: the power of many. Nat Methods 17:11–14. https://doi.org/10.1038/s41592-019-0691-5

    Article  CAS  PubMed  Google Scholar 

  11. Zhang ZY, Xu J, Ren Y, Li KK, Ng HK, Mao Y, Zhong P, Yao Y, Zhou LF (2014) Medulloblastoma in China: clinicopathologic analyses of SHH, WNT, and non-SHH/WNT molecular subgroups reveal different therapeutic responses to adjuvant chemotherapy. PLoS ONE 9:e99490. https://doi.org/10.1371/journal.pone.0099490

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Quinlan A, Rizzolo D (2017) Understanding medulloblastoma. JAAPA 30:30–36. https://doi.org/10.1097/01.Jaa.0000524717.71084.50

    Article  PubMed  Google Scholar 

  13. Gieryng A, Pszczolkowska D, Walentynowicz KA, Rajan WD, Kaminska B (2017) Immune microenvironment of gliomas. Lab Invest 97:498–518. https://doi.org/10.1038/labinvest.2017.19

    Article  CAS  PubMed  Google Scholar 

  14. Shen DD, Pang JR, Bi YP, Zhao LF, Li YR, Zhao LJ, Gao Y, Wang B, Wang N, Wei L et al (2022) LSD1 deletion decreases exosomal PD-L1 and restores T-cell response in gastric cancer. Mol Cancer 21:75. https://doi.org/10.1186/s12943-022-01557-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Creaney J, Patch AM, Addala V, Sneddon SA, Nones K, Dick IM, Lee YCG, Newell F, Rouse EJ, Naeini MM et al (2022) Comprehensive genomic and tumour immune profiling reveals potential therapeutic targets in malignant pleural mesothelioma. Genome Med 14:58. https://doi.org/10.1186/s13073-022-01060-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Aziz HM, Saida L, de Koning W, Stubbs AP, Li Y, Sideras K, Palacios E, Feliu J, Mendiola M, van Eijck CHJ et al (2022) Spatial genomics reveals a high number and specific location of B cells in the pancreatic ductal adenocarcinoma microenvironment of long-term survivors. Front Immunol 13:995715. https://doi.org/10.3389/fimmu.2022.995715

    Article  CAS  PubMed  Google Scholar 

  17. Sakamoto S, Kagawa S, Kuwada K, Ito A, Kajioka H, Kakiuchi Y, Watanabe M, Kagawa T, Yoshida R, Kikuchi S et al (2019) Intraperitoneal cancer-immune microenvironment promotes peritoneal dissemination of gastric cancer. Oncoimmunology 8:e1671760. https://doi.org/10.1080/2162402X.2019.1671760

    Article  PubMed  PubMed Central  Google Scholar 

  18. Saraiva DP, Matias AT, Braga S, Jacinto A, Cabral MG (2020) Establishment of a 3D co-culture With MDA-MB-231 breast cancer cell line and patient-derived immune cells for application in the development of immunotherapies. Front Oncol 10:1543. https://doi.org/10.3389/fonc.2020.01543

    Article  PubMed  PubMed Central  Google Scholar 

  19. Delmonte OM, Fleisher TA (2019) Flow cytometry: surface markers and beyond. J Allergy Clin Immunol 143:528–537. https://doi.org/10.1016/j.jaci.2018.08.011

    Article  CAS  PubMed  Google Scholar 

  20. Cossarizza A, Chang HD, Radbruch A, Abrignani S, Addo R, Akdis M, Andra I, Andreata F, Annunziato F, Arranz E et al (2021) Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition). Eur J Immunol 51:2708–3145. https://doi.org/10.1002/eji.202170126

    Article  CAS  PubMed  Google Scholar 

  21. Kanegane H, Hoshino A, Okano T, Yasumi T, Wada T, Takada H, Okada S, Yamashita M, Yeh TW, Nishikomori R et al (2018) Flow cytometry-based diagnosis of primary immunodeficiency diseases. Allergol Int 67:43–54. https://doi.org/10.1016/j.alit.2017.06.003

    Article  CAS  PubMed  Google Scholar 

  22. Griesinger AM, Birks DK, Donson AM, Amani V, Hoffman LM, Waziri A, Wang M, Handler MH, Foreman NK (2013) Characterization of distinct immunophenotypes across pediatric brain tumor types. J Immunol 191:4880–4888. https://doi.org/10.4049/jimmunol.1301966

    Article  CAS  PubMed  Google Scholar 

  23. Oshige H, Yamahara T, Oishi T, Li Y, Zhen Y, Numa Y, Kawamoto K (2010) Flow cytometric analysis for the mechanism of the new antineoplastic agent temozolomide in glioma cells. Brain Tumor Pathol 27:7–15. https://doi.org/10.1007/s10014-009-0259-7

    Article  CAS  PubMed  Google Scholar 

  24. Tan WCC, Nerurkar SN, Cai HY, Ng HHM, Wu D, Wee YTF, Lim JCT, Yeong J, Lim TKH (2020) Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy. Cancer Commun (Lond) 40:135–153. https://doi.org/10.1002/cac2.12023

    Article  PubMed  Google Scholar 

  25. Bronckers IM, Paller AS, van Geel MJ, van de Kerkhof PC, Seyger MM (2015) Psoriasis in children and adolescents: diagnosis. Manag Comorbidities Paediatr Drugs 17:373–384. https://doi.org/10.1007/s40272-015-0137-1

    Article  CAS  Google Scholar 

  26. Jovic D, Liang X, Zeng H, Lin L, Xu F, Luo Y (2022) Single-cell RNA sequencing technologies and applications: a brief overview. Clin Transl Med 12:e694. https://doi.org/10.1002/ctm2.694

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Papalexi E, Satija R (2018) Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol 18:35–45. https://doi.org/10.1038/nri.2017.76

    Article  CAS  PubMed  Google Scholar 

  28. Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA (2015) The technology and biology of single-cell RNA sequencing. Mol Cell 58:610–620. https://doi.org/10.1016/j.molcel.2015.04.005

    Article  CAS  PubMed  Google Scholar 

  29. Cheng S, Li Z, Gao R, Xing B, Gao Y, Yang Y, Qin S, Zhang L, Ouyang H, Du P et al (2021) A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184:792-809 e723. https://doi.org/10.1016/j.cell.2021.01.010

    Article  CAS  PubMed  Google Scholar 

  30. Qian J, Olbrecht S, Boeckx B, Vos H, Laoui D, Etlioglu E, Wauters E, Pomella V, Verbandt S, Busschaert P et al (2020) A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res 30:745–762. https://doi.org/10.1038/s41422-020-0355-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

This work was funded by National Natural Science Foundation of China (Grant No. 62276027).

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Correspondence to Jian Gong.

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Fan, K., Wei, Y., Ou, Y. et al. Integrated analysis of multiple methods reveals characteristics of the immune microenvironment in medulloblastoma. Brain Tumor Pathol 40, 191–203 (2023). https://doi.org/10.1007/s10014-023-00467-4

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