T-cell dysfunction in the glioblastoma microenvironment is mediated by myeloid cells releasing interleukin-10.
Adult
Aged
Brain Neoplasms
/ drug therapy
Cell Communication
/ immunology
Cell Line, Tumor
Female
Glioblastoma
/ drug therapy
Healthy Volunteers
Heme Oxygenase-1
/ metabolism
Humans
Immunotherapy
/ methods
Interleukin-10
/ metabolism
Janus Kinase Inhibitors
/ pharmacology
Janus Kinases
/ antagonists & inhibitors
Male
Middle Aged
Myeloid Cells
/ metabolism
Neocortex
/ cytology
Primary Cell Culture
RNA-Seq
STAT Transcription Factors
/ metabolism
Signal Transduction
/ drug effects
Single-Cell Analysis
T-Lymphocytes
/ drug effects
Tissue Culture Techniques
Tumor Escape
Tumor Microenvironment
/ immunology
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
17 02 2022
17 02 2022
Historique:
received:
26
02
2021
accepted:
26
01
2022
entrez:
18
2
2022
pubmed:
19
2
2022
medline:
4
3
2022
Statut:
epublish
Résumé
Despite recent advances in cancer immunotherapy, certain tumor types, such as Glioblastomas, are highly resistant due to their tumor microenvironment disabling the anti-tumor immune response. Here we show, by applying an in-silico multidimensional model integrating spatially resolved and single-cell gene expression data of 45,615 immune cells from 12 tumor samples, that a subset of Interleukin-10-releasing HMOX1
Identifiants
pubmed: 35177622
doi: 10.1038/s41467-022-28523-1
pii: 10.1038/s41467-022-28523-1
pmc: PMC8854421
doi:
Substances chimiques
IL10 protein, human
0
Janus Kinase Inhibitors
0
STAT Transcription Factors
0
Interleukin-10
130068-27-8
HMOX1 protein, human
EC 1.14.14.18
Heme Oxygenase-1
EC 1.14.14.18
Janus Kinases
EC 2.7.10.2
Types de publication
Journal Article
Observational Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
925Informations de copyright
© 2022. The Author(s).
Références
Darmanis, S. et al. Single-cell RNA-seq analysis of infiltrating neoplastic cells at the migrating front of human glioblastoma. Cell Rep. 21, 1399–1410 (2017).
pubmed: 29091775
pmcid: 5810554
doi: 10.1016/j.celrep.2017.10.030
Woroniecka, K. et al. T-cell exhaustion signatures vary with tumor type and are severe in glioblastoma. Clin. Cancer Res. 24, 4175–4186 (2018).
pubmed: 29437767
pmcid: 6081269
doi: 10.1158/1078-0432.CCR-17-1846
Chen, Z. & Hambardzumyan, D. Immune microenvironment in glioblastoma subtypes. Front. Immunol. 9, 1004 (2018).
pubmed: 29867979
pmcid: 5951930
doi: 10.3389/fimmu.2018.01004
Sankowski, R. et al. Mapping microglia states in the human brain through the integration of high-dimensional techniques. Nat. Neurosci. 22, 2098–2110 (2019).
pubmed: 31740814
doi: 10.1038/s41593-019-0532-y
Hara, T. et al. Interactions between cancer cells and immune cells drive transitions to mesenchymal-like states in glioblastoma. Cancer Cell 39, 779–792.e11 (2021).
pubmed: 34087162
doi: 10.1016/j.ccell.2021.05.002
Gangoso, E. et al. Glioblastomas acquire myeloid-affiliated transcriptional programs via epigenetic immunoediting to elicit immune evasion. Cell 184, 2454–2470.e26 (2021).
pubmed: 33857425
pmcid: 8099351
doi: 10.1016/j.cell.2021.03.023
Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).
pubmed: 30479382
doi: 10.1038/s41586-018-0694-x
Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356.e16 (2017).
pubmed: 28622514
doi: 10.1016/j.cell.2017.05.035
Baitsch, L. et al. Exhaustion of tumor-specific CD8
pubmed: 21555851
pmcid: 3104769
doi: 10.1172/JCI46102
Singer, M. et al. A distinct gene module for dysfunction uncoupled from activation in tumor-infiltrating T cells. Cell 166, 1500–1511.e9 (2016).
pubmed: 27610572
pmcid: 5019125
doi: 10.1016/j.cell.2016.08.052
Anderson, A. C., Joller, N. & Kuchroo, V. K. Lag-3, Tim-3, and TIGIT: co-inhibitory receptors with specialized functions in immune regulation. Immunity 44, 989–1004 (2016).
pubmed: 27192565
pmcid: 4942846
doi: 10.1016/j.immuni.2016.05.001
Sawant, D. V. et al. Adaptive plasticity of IL-10+ and IL-35+ Treg cells cooperatively promotes tumor T cell exhaustion. Nat. Immunol. 20, 724–735 (2019).
pubmed: 30936494
pmcid: 6531353
doi: 10.1038/s41590-019-0346-9
Jiang, Y., Li, Y. & Zhu, B. T-cell exhaustion in the tumor microenvironment. Cell Death Dis. 6, e1792 (2015).
pubmed: 26086965
pmcid: 4669840
doi: 10.1038/cddis.2015.162
Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).
pubmed: 27501248
pmcid: 5297183
doi: 10.1038/nature19330
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
pubmed: 27124452
pmcid: 4944528
doi: 10.1126/science.aad0501
Platten, M., Ochs, K., Lemke, D., Opitz, C. & Wick, W. Microenvironmental clues for glioma immunotherapy. Curr. Neurol. Neurosci. Rep. 14, 440 (2014).
pubmed: 24604058
doi: 10.1007/s11910-014-0440-1
Filley, A. C., Henriquez, M. & Dey, M. Recurrent glioma clinical trial, CheckMate-143: the game is not over yet. Oncotarget 8, 91779–91794 (2017).
pubmed: 29207684
pmcid: 5710964
doi: 10.18632/oncotarget.21586
Weller, M. et al. Rindopepimut with temozolomide for patients with newly diagnosed, EGFRvIII-expressing glioblastoma (ACT IV): a randomised, double-blind, international phase 3 trial. Lancet Oncol. 18, 1373–1385 (2017).
pubmed: 28844499
doi: 10.1016/S1470-2045(17)30517-X
Henrik Heiland, D. et al. Tumor-associated reactive astrocytes aid the evolution of immunosuppressive environment in glioblastoma. Nat. Commun. 10, 2541 (2019).
pubmed: 31186414
pmcid: 6559986
doi: 10.1038/s41467-019-10493-6
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
pubmed: 34062119
pmcid: 8238499
doi: 10.1016/j.cell.2021.04.048
Mathewson, N. D. et al. Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell 184, 1281–1298.e26 (2021).
pubmed: 33592174
pmcid: 7935772
doi: 10.1016/j.cell.2021.01.022
Pombo Antunes, A. R. et al. Understanding the glioblastoma immune microenvironment as basis for the development of new immunotherapeutic strategies. Elife 9, e52176 (2020).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
pubmed: 24925914
pmcid: 4123637
doi: 10.1126/science.1254257
Miragaia, R. J. et al. Single-cell transcriptomics of regulatory T cells reveals trajectories of tissue adaptation. Immunity 50, 493–504.e7 (2019).
pubmed: 30737144
pmcid: 6382439
doi: 10.1016/j.immuni.2019.01.001
Miggelbrink, A. M. et al. CD4 T-cell exhaustion: does it exist and what are its roles in cancer? Clin. Cancer Res. 27, 5742–5752 (2021).
Cho, J.-H. et al. Unique features of naive CD8+ T cell activation by IL-2. J. Immunol. 191, 5559–5573 (2013).
pubmed: 24166977
doi: 10.4049/jimmunol.1302293
Mould, A. W., Morgan, M. A. J., Nelson, A. C., Bikoff, E. K. & Robertson, E. J. Blimp1/Prdm1 functions in opposition to Irf1 to maintain neonatal tolerance during postnatal intestinal maturation. PLoS Genet. 11, e1005375 (2015).
pubmed: 26158850
pmcid: 4497732
doi: 10.1371/journal.pgen.1005375
Kamimoto, K., Hoffmann, C. M. & Morris, S. A. CellOracle: Dissecting cell identity via network inference and in silico gene perturbation. Preprint at bioRxiv https://doi.org/10.1101/2020.02.17.947416 (2020).
Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178, 835–849.e21 (2019).
pubmed: 31327527
pmcid: 6703186
doi: 10.1016/j.cell.2019.06.024
Ravi, V. M. et al. Spatiotemporal heterogeneity of glioblastoma is dictated by microenvironmental interference. Preprint at bioRxiv https://doi.org/10.1101/2021.02.16.431475 (2021).
Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021).
pubmed: 33544846
pmcid: 8136778
doi: 10.1093/nar/gkab043
Naito, Y., Takagi, T. & Higashimura, Y. Heme oxygenase-1 and anti-inflammatory M2 macrophages. Arch. Biochem. Biophys. 564, 83–88 (2014).
pubmed: 25241054
doi: 10.1016/j.abb.2014.09.005
Sebastián, V. P. et al. Heme Oxygenase-1 as a Modulator of Intestinal Inflammation Development and Progression. Front. Immunol. 9, 1956 (2018).
pubmed: 30258436
pmcid: 6143658
doi: 10.3389/fimmu.2018.01956
Kaiser, S. et al. Neuroprotection after hemorrhagic stroke depends on cerebral heme oxygenase-1. Antioxidants 8, 496 (2019).
Woo, J.-I. et al. IL-10/HMOX1 signaling modulates cochlear inflammation via negative regulation of MCP-1/CCL2 expression in cochlear fibrocytes. J. Immunol. 194, 3953–3961 (2015).
pubmed: 25780042
doi: 10.4049/jimmunol.1402751
Giladi, A. et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. 38, 629–637 (2020).
pubmed: 32152598
doi: 10.1038/s41587-020-0442-2
Ravi, V. M. et al. Human organotypic brain slice culture: a novel framework for environmental research in neuro-oncology. Life Sci. Alliance 2, e201900305 (2019).
Kling, T. et al. Integrative modeling reveals annexin A2-mediated epigenetic control of mesenchymal glioblastoma. EBioMedicine 12, 72–85 (2016).
pubmed: 27667176
pmcid: 5078587
doi: 10.1016/j.ebiom.2016.08.050
Bell, K. F. et al. Mild oxidative stress activates Nrf2 in astrocytes, which contributes to neuroprotective ischemic preconditioning. Proc. Natl Acad. Sci. USA 108, E1–E2 (2011).
pubmed: 21177433
doi: 10.1073/pnas.1015229108
Liao, W., Lin, J.-X. & Leonard, W. J. IL-2 family cytokines: new insights into the complex roles of IL-2 as a broad regulator of T helper cell differentiation. Curr. Opin. Immunol. 23, 598–604 (2011).
pubmed: 21889323
pmcid: 3405730
doi: 10.1016/j.coi.2011.08.003
Venteicher, A. S. et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355, eaai8478 (2017).
Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).
pubmed: 27806376
pmcid: 5465819
doi: 10.1038/nature20123
Blank, C. U. et al. Defining “T cell exhaustion”. Nat. Rev. Immunol. 19, 665–674 (2019).
pubmed: 31570879
pmcid: 7286441
doi: 10.1038/s41577-019-0221-9
Wherry, E. J. T cell exhaustion. Nat. Immunol. 12, 492–499 (2011).
pubmed: 21739672
doi: 10.1038/ni.2035
Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789.e18 (2019).
pubmed: 30595452
doi: 10.1016/j.cell.2018.11.043
Woroniecka, K. I., Rhodin, K. E., Chongsathidkiet, P., Keith, K. A. & Fecci, P. E. T-cell dysfunction in glioblastoma: applying a new framework. Clin. Cancer Res. 24, 3792–3802 (2018).
pubmed: 29593027
pmcid: 6095741
doi: 10.1158/1078-0432.CCR-18-0047
Winkler, F. & Bengsch, B. Use of mass cytometry to profile human T cell exhaustion. Front. Immunol. 10, 3039 (2019).
pubmed: 32038613
doi: 10.3389/fimmu.2019.03039
Wurm, J. et al. Astrogliosis releases pro-oncogenic chitinase 3-like 1 causing MAPK signaling in glioblastoma. Cancers 11, 1437 (2019).
Priego, N. et al. STAT3 labels a subpopulation of reactive astrocytes required for brain metastasis. Nat. Med. 24, 1024–1035 (2018).
pubmed: 29892069
doi: 10.1038/s41591-018-0044-4
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).
pubmed: 29750242
pmcid: 6137996
doi: 10.1093/bioinformatics/bty191
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).
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).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
Parker, H. S., Fertig, E. J., Jaffe, A. E. & Storey, J. D. Package “sva.” (2014).
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).
pubmed: 22455463
pmcid: 3339379
doi: 10.1089/omi.2011.0118
Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
pubmed: 31178118
pmcid: 6687398
doi: 10.1016/j.cell.2019.05.031
Hahsler, M., Piekenbrock, M. & Doran, D. dbscan: fast density-based clustering with R. J. Stat. Softw. 91, 1–30 (2019).
Sievert, C. Interactive Web-Based Data Visualization with R, Plotly, and Shiny (Chapman and Hall/CRC, 2020).
Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S. & Theis, F. J. Single-cell RNA-seq denoising using a deep count autoencoder. Nat. Commun. 10, 390 (2019).
pubmed: 30674886
pmcid: 6344535
doi: 10.1038/s41467-018-07931-2
Bååth, R. Bayesian first aid: a package that implements Bayesian alternatives to the classical*. Test functions in R. Proceedings of UseR (2014).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
pubmed: 30089906
pmcid: 6130801
doi: 10.1038/s41586-018-0414-6
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).
pubmed: 32747759
doi: 10.1038/s41587-020-0591-3
Lange, M., Bergen, V., Klein, M. et al. CellRank for directed single-cell fate mapping. Nat Methods. https://doi.org/10.1038/s41592-021-01346-6 (2022).
Kueckelhaus, J. et al. Inferring spatially transient gene expression pattern from spatial transcriptomic studies. Preprint at bioRxiv https://doi.org/10.1101/2020.10.20.346544 (2020).
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).
pubmed: 16199517
pmcid: 1239896
doi: 10.1073/pnas.0506580102
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinforma. 14, 7 (2013).
doi: 10.1186/1471-2105-14-7
Zhang, Z. et al. SCINA: a semi-supervised subtyping algorithm of single cells and bulk samples. Genes 10, 531 (2019).
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).
pubmed: 30643263
pmcid: 6340744
doi: 10.1038/s41590-018-0276-y
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).
pubmed: 31819264
doi: 10.1038/s41592-019-0667-5
Müller, S., Cho, A., Liu, S. J., Lim, D. A. & Diaz, A. CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones. Bioinformatics 34, 3217–3219 (2018).
pubmed: 29897414
pmcid: 7190654
doi: 10.1093/bioinformatics/bty316
Maier, J. P. et al. Inhibition of metabotropic glutamate receptor III facilitates sensitization to alkylating chemotherapeutics in glioblastoma. Cell Death Dis. 12, 723 (2021).
pubmed: 34290229
pmcid: 8295384
doi: 10.1038/s41419-021-03937-9