Immune-awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy.
Journal
Nature cancer
ISSN: 2662-1347
Titre abrégé: Nat Cancer
Pays: England
ID NLM: 101761119
Informations de publication
Date de publication:
02 2020
02 2020
Historique:
entrez:
29
2
2020
pubmed:
29
2
2020
medline:
29
2
2020
Statut:
ppublish
Résumé
Our understanding of how checkpoint inhibitors (CPI) affect T cell evolution is incomplete, limiting our ability to achieve full clinical benefit from these drugs. Here we analyzed peripheral T cell populations after one cycle of CPI and identified a dynamic awakening of the immune system revealed by T cell evolution in response to treatment. We sequenced T cell receptors (TCR) in plasma cell-free DNA (cfDNA) and peripheral blood mononuclear cells (PBMC) and performed phenotypic analysis of peripheral T cell subsets from metastatic melanoma patients treated with CPI. We found that early peripheral T cell turnover and TCR repertoire dynamics identified which patients would respond to treatment. Additionally, the expansion of a subset of immune-effector peripheral T cells we call T
Identifiants
pubmed: 32110781
doi: 10.1038/s43018-019-0022-x
pmc: PMC7046489
mid: EMS85253
pii: 10.1038/s43018-019-0022-x
doi:
Substances chimiques
Immunologic Factors
0
Receptors, Antigen, T-Cell
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Pagination
210-221Subventions
Organisme : Wellcome Trust
ID : 100282
Pays : United Kingdom
Organisme : Cancer Research UK
ID : A22902
Pays : United Kingdom
Organisme : Cancer Research UK
ID : A27412
Pays : United Kingdom
Commentaires et corrections
Type : CommentIn
Déclaration de conflit d'intérêts
Conflict of Interest: RM is a consultant for Pfizer and has a drug discovery programme with Basilea Pharmaceutica. PL serves as paid advisor/speaker for Bristol-Myers Squibb, Merck Sharp and Dohme, Roche, Novartis, Amgen, Pierre Fabre, Nektar, Melagenix. PL reports travel support from Bristol-Myers Squibb and Merck Sharp and Dohme, and receives research support from Bristol-Myers Squibb. AG received honoraria and consultancy fees from BMS and Novartis.
Références
Badovinac, V. P., Porter, B. B. & Harty, J. T. Programmed contraction of CD8
pubmed: 12055624
doi: 10.1038/ni804
Ugurel, S. et al. Survival of patients with advanced metastatic melanoma: the impact of novel therapies—update 2017. Eur. J. Cancer 83, 247–257 (2017).
pubmed: 28756137
doi: 10.1016/j.ejca.2017.06.028
Wykes, M. N. & Lewin, S. R. Immune checkpoint blockade in infectious diseases. Nat. Rev. Immunol. 18, 91–104 (2018).
pubmed: 28990586
doi: 10.1038/nri.2017.112
Goldszmid, R. S., Dzutsev, A. & Trinchieri, G. Host immune response to infection and cancer: unexpected commonalities. Cell Host Microbe 15, 295–305 (2014).
pubmed: 24629336
pmcid: 3996827
doi: 10.1016/j.chom.2014.02.003
Vance, R. E., Eichberg, M. J., Portnoy, D. A. & Raulet, D. H. Listening to each other: infectious disease and cancer immunology. Sci. Immunol. 2, eaai9339 (2017).
pubmed: 28783669
pmcid: 5927821
doi: 10.1126/sciimmunol.aai9339
Dunn, G. P., Old, L. J. & Schreiber, R. D. The three Es of cancer immunoediting. Annu. Rev. Immunol. 22, 329–360 (2004).
pubmed: 15032581
doi: 10.1146/annurev.immunol.22.012703.104803
Huang, A. C. et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017).
pubmed: 28397821
pmcid: 5554367
doi: 10.1038/nature22079
Krieg, C. et al. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat. Med. 24, 144–153 (2018).
doi: 10.1038/nm.4466
pubmed: 29309059
Jacquelot, N. et al. Predictors of responses to immune checkpoint blockade in advanced melanoma. Nat. Commun. 8, 592 (2017).
pubmed: 28928380
pmcid: 5605517
doi: 10.1038/s41467-017-00608-2
Huang, A. C. et al. A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma. Nat. Med. 25, 454–461 (2019).
pubmed: 30804515
pmcid: 6699626
doi: 10.1038/s41591-019-0357-y
Hozumi, N. & Tonegawa, S. Evidence for somatic rearrangement of immunoglobulin genes coding for variable and constant regions. Proc. Natl Acad. Sci. USA 73, 3628–3632 (1976).
pubmed: 824647
doi: 10.1073/pnas.73.10.3628
pmcid: 431171
Schatz, D. G. & Baltimore, D. Uncovering the V(D)J recombinase. Cell 116, S103–S106 (2004).
pubmed: 15055595
doi: 10.1016/S0092-8674(04)00042-X
Janeway C. A. Jr et al. Immunobiology: The Immune System in Health and Disease 5th edn (Garland Science, 2001).
Kohler, S. & Thiel, A. Life after the thymus: CD31
pubmed: 18583570
doi: 10.1182/blood-2008-02-139154
Steinmann, G. G., Klaus, B. & Muller-Hermelink, H. K. The involution of the ageing human thymic epithelium is independent of puberty. A morphometric study. Scand. J. Immunol. 22, 563–575 (1985).
pubmed: 4081647
doi: 10.1111/j.1365-3083.1985.tb01916.x
Geenen, V. et al. Quantification of T cell receptor rearrangement excision circles to estimate thymic function: an important new tool for endocrine-immune physiology. J. Endocrinol. 176, 305–311 (2003).
pubmed: 12630915
doi: 10.1677/joe.0.1760305
Mangul, S. M. I. et al. Profiling adaptive immune repertoires across multiple human tissues by RNA sequencing. Preprint at bioRxiv https://doi.org/10.1101/089235 (2016).
Amaria, R. N. et al. Neoadjuvant immune checkpoint blockade in high-risk resectable melanoma. Nat. Med. 24, 1649–1654 (2018).
pubmed: 30297909
pmcid: 6481682
doi: 10.1038/s41591-018-0197-1
Coffey, D. LymphoSeq: Analyze high-throughput sequencing of T and B cell receptors. R package version 1.4.1 (2017).
Alves Sousa, A. P. et al. Comprehensive analysis of TCR-β repertoire in patients with neurological immune-mediated disorders. Sci. Rep. 9, 344 (2019).
pubmed: 30674904
pmcid: 6344574
doi: 10.1038/s41598-018-36274-7
Radziewicz, H., Uebelhoer, L., Bengsch, B. & Grakoui, A. Memory CD8
pubmed: 17828816
pmcid: 4611763
doi: 10.3748/wjg.v13.i36.4848
Mahnke, Y. D., Brodie, T. M., Sallusto, F., Roederer, M. & Lugli, E. The who's who of T-cell differentiation: human memory T-cell subsets. Eur. J. Immunol. 43, 2797–2809 (2013).
pubmed: 24258910
doi: 10.1002/eji.201343751
Ribas, A. et al. PD-1 blockade expands intratumoral memory T cells. Cancer Immunol. Res. 4, 194–203 (2016).
pubmed: 26787823
pmcid: 4775381
doi: 10.1158/2326-6066.CIR-15-0210
Greenplate, A. R. et al. Computational immune monitoring reveals abnormal double-negative T cells present across human tumor types. Cancer Immunol. Res. 7, 86–99 (2019).
pubmed: 30413431
doi: 10.1158/2326-6066.CIR-17-0692
Gremel, G. et al. Distinct subclonal tumour responses to therapy revealed by circulating cell-free DNA. Ann. Oncol. 27, 1959–1965 (2016).
pubmed: 27502704
pmcid: 5035787
doi: 10.1093/annonc/mdw278
Robert, C. et al. Nivolumab in previously untreated melanoma without BRAF mutation. N. Engl. J. Med. 372, 320–330 (2015).
pubmed: 25399552
doi: 10.1056/NEJMoa1412082
Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).
pubmed: 28854175
doi: 10.1038/nbt.3973
Venken, K. et al. Natural naive CD4
pubmed: 18424765
doi: 10.4049/jimmunol.180.9.6411
Herati, R. S. et al. Successive annual influenza vaccination induces a recurrent oligoclonotypic memory response in circulating T follicular helper cells. Sci. Immunol. 2, eaag2152 (2017).
pubmed: 28620653
pmcid: 5469419
doi: 10.1126/sciimmunol.aag2152
DeWitt, W. S. et al. Dynamics of the cytotoxic T cell response to a model of acute viral infection. J. Virol. 89, 4517–4526 (2015).
pubmed: 25653453
pmcid: 4442358
doi: 10.1128/JVI.03474-14
Martin, M. D. & Badovinac, V. P. Defining memory CD8 T cell. Front. Immunol. 9, 2692 (2018).
pubmed: 30515169
pmcid: 6255921
doi: 10.3389/fimmu.2018.02692
Tomiyama, H., Takata, H., Matsuda, T. & Takiguchi, M. Phenotypic classification of human CD8
pubmed: 15048710
doi: 10.1002/eji.200324478
Rossi, J. F., Ceballos, P. & Lu, Z. Y. Immune precision medicine for cancer: a novel insight based on the efficiency of immune effector cells. Cancer Commun. (Lond.) 39, 34 (2019).
doi: 10.1186/s40880-019-0379-3
Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).
pubmed: 31359002
pmcid: 6689255
doi: 10.1038/s41591-019-0522-3
Cha, E. et al. Improved survival with T cell clonotype stability after anti-CTLA-4 treatment in cancer patients. Sci. Transl. Med. 6, 238ra270 (2014).
doi: 10.1126/scitranslmed.3008211
Robert, L. et al. CTLA4 blockade broadens the peripheral T-cell receptor repertoire. Clin. Cancer Res. 20, 2424–2432 (2014).
pubmed: 24583799
pmcid: 4008652
doi: 10.1158/1078-0432.CCR-13-2648
Wieland, A. et al. T cell receptor sequencing of activated CD8 T cells in the blood identifies tumor-infiltrating clones that expand after PD-1 therapy and radiation in a melanoma patient. Cancer Immunol. Immunother. 67, 1767–1776 (2018).
pubmed: 30167863
pmcid: 6196100
doi: 10.1007/s00262-018-2228-7
Wei, S. C. et al. Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 170, 1120–1133.e17 (2017).
pubmed: 28803728
pmcid: 5591072
doi: 10.1016/j.cell.2017.07.024
Fritsch, R. D. et al. Stepwise differentiation of CD4 memory T cells defined by expression of CCR7 and CD27. J. Immunol. 175, 6489–6497 (2005).
pubmed: 16272303
doi: 10.4049/jimmunol.175.10.6489
Hendriks, J., Xiao, Y. & Borst, J. CD27 promotes survival of activated T cells and complements CD28 in generation and establishment of the effector T cell pool. J. Exp. Med. 198, 1369–1380 (2003).
pubmed: 14581610
pmcid: 2194245
doi: 10.1084/jem.20030916
Britschgi, M. R., Link, A., Lissandrin, T. K. & Luther, S. A. Dynamic modulation of CCR7 expression and function on naive T lymphocytes in vivo. J. Immunol. 181, 7681–7688 (2008).
pubmed: 19017956
doi: 10.4049/jimmunol.181.11.7681
Larbi, A. & Fulop, T. From “truly naïve” to “exhausted senescent” T cells: when markers predict functionality. Cytometry A 85, 25–35 (2014).
pubmed: 24124072
doi: 10.1002/cyto.a.22351
Sallusto, F. et al. Switch in chemokine receptor expression upon TCR stimulation reveals novel homing potential for recently activated T cells. Eur. J. Immunol. 29, 2037–2045 (1999).
pubmed: 10382767
doi: 10.1002/(SICI)1521-4141(199906)29:06<2037::AID-IMMU2037>3.0.CO;2-V
Geginat, J., Lanzavecchia, A. & Sallusto, F. Proliferation and differentiation potential of human CD8
pubmed: 12576317
doi: 10.1182/blood-2002-11-3577
Valpione, S. et al. Plasma total cell-free DNA (cfDNA) is a surrogate biomarker for tumour burden and a prognostic biomarker for survival in metastatic melanoma patients. Eur. J. Cancer 88, 1–9 (2018).
pubmed: 29175734
pmcid: 5769519
doi: 10.1016/j.ejca.2017.10.029
Falci, C. et al. Immune senescence and cancer in elderly patients: results from an exploratory study. Exp. Gerontol. 48, 1436–1442 (2013).
pubmed: 24120567
doi: 10.1016/j.exger.2013.09.011
Richardson, M. W. et al. Analysis of telomere length and thymic output in fast and slow/non-progressors with HIV infection. Biomed. Pharmacother. 54, 21–31 (2000).
pubmed: 10721459
doi: 10.1016/S0753-3322(00)88637-0
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
pubmed: 24695404
pmcid: 4103590
doi: 10.1093/bioinformatics/btu170
Dobin, A. et al. STAR: ultrafast universal RNA-Seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Thapa, D. R. et al. Longitudinal analysis of peripheral blood T cell receptor diversity in patients with systemic lupus erythematosus by next-generation sequencing. Arthritis Res. Ther. 17, 132 (2015).
pubmed: 26001779
pmcid: 4458014
doi: 10.1186/s13075-015-0655-9
Spreafico, R. et al. A circulating reservoir of pathogenic-like CD4
pubmed: 25498120
doi: 10.1136/annrheumdis-2014-206226
Nowicka, M. et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Res 6, 748 (2017).
pubmed: 28663787
doi: 10.12688/f1000research.11622.1
Gribov, A. et al. SEURAT: visual analytics for the integrated analysis of microarray data. BMC Med. Genomics 3, 21 (2010).
pubmed: 20525257
pmcid: 2893446
doi: 10.1186/1755-8794-3-21
Kotecha, N., Krutzik, P. O. & Irish, J. M. Web-based analysis and publication of flow cytometry experiments. Curr. Protoc. Cytom. 53, 10.17.1–10.17.24 (2010).
doi: 10.1002/0471142956.cy1017s53
Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007).
pubmed: 17695343
doi: 10.3758/BF03193146
Faul, F., Erdfelder, E., Buchner, A. & Lang, A.-G. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav. Res. Methods 41, 1149–1160 (2009).
pubmed: 19897823
doi: 10.3758/BRM.41.4.1149