Abstract

Glioblastoma is the most common primary malignant brain tumor in adults and is associated with poor survival. The Ivy Foundation Early Phase Clinical Trials Consortium conducted a randomized, multi-institution clinical trial to evaluate immune responses and survival following neoadjuvant and/or adjuvant therapy with pembrolizumab in 35 patients with recurrent, surgically resectable glioblastoma. Patients who were randomized to receive neoadjuvant pembrolizumab, with continued adjuvant therapy following surgery, had significantly extended overall survival compared to patients that were randomized to receive adjuvant, post-surgical programmed cell death protein 1 (PD-1) blockade alone. Neoadjuvant PD-1 blockade was associated with upregulation of T cell– and interferon-γ-related gene expression, but downregulation of cell-cycle-related gene expression within the tumor, which was not seen in patients that received adjuvant therapy alone. Focal induction of programmed death-ligand 1 in the tumor microenvironment, enhanced clonal expansion of T cells, decreased PD-1 expression on peripheral blood T cells and a decreasing monocytic population was observed more frequently in the neoadjuvant group than in patients treated only in the adjuvant setting. These findings suggest that the neoadjuvant administration of PD-1 blockade enhances both the local and systemic antitumor immune response and may represent a more efficacious approach to the treatment of this uniformly lethal brain tumor.

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Code availability

R code is available in packages as described in the manuscript.

Data availability

RNA sequencing data are available in the Gene Expression Omnibus under accession number GSE121810, which includes source data for Fig. 2b and Extended Data Figs. 4 and 5. The remainder of data that support the findings of this study are available from the corresponding author on reasonable request.

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References

  1. 1.

    Ostrom, Q. T. et al. CBTRUS Statistical Report: primary brain and other central nervous system tumors diagnosed in the United States in 2010-2014. Neuro. Oncol. 19, v1–v88 (2017).

  2. 2.

    Stupp, R. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352, 987–996 (2005).

  3. 3.

    Lamborn, K. R. et al. Progression-free survival: an important end point in evaluating therapy for recurrent high-grade gliomas. Neuro. Oncol. 10, 162–170 (2008).

  4. 4.

    Wu, W. et al. Joint NCCTG and NABTC prognostic factors analysis for high-grade recurrent glioma. Neuro. Oncol. 12, 164–172 (2010).

  5. 5.

    Clarke, J. L. et al. Is surgery at progression a prognostic marker for improved 6-month progression-free survival or overall survival for patients with recurrent glioblastoma? Neuro. Oncol. 13, 1118–1124 (2011).

  6. 6.

    Brahmer, J. R. et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N. Engl. J. Med. 366, 2455–2465 (2012).

  7. 7.

    Burki, T. K. Pembrolizumab for patients with advanced melanoma. Lancet Oncol. 16, e264 (2015).

  8. 8.

    Garon, E. B. et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N. Engl. J. Med. 372, 2018–2028 (2015).

  9. 9.

    Hamid, O. et al. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N. Engl. J. Med. 369, 134–144 (2013).

  10. 10.

    Ribas, A. Tumor immunotherapy directed at PD-1. N. Engl. J. Med. 366, 2517–2519 (2012).

  11. 11.

    Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).

  12. 12.

    Wolchok, J. D. et al. Nivolumab plus ipilimumab in advanced melanoma. N. Engl. J. Med. 369, 122–133 (2013).

  13. 13.

    Ribas, A. Adaptive immune resistance: how cancer protects from immune attack. Cancer Discov. 5, 915–919 (2015).

  14. 14.

    Ribas, A. et al. Association of pembrolizumab with tumor response and survival among patients with advanced melanoma. JAMA 315, 1600–1609 (2016).

  15. 15.

    Reck, M. et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N. Engl. J. Med. 375, 1823–1833 (2016).

  16. 16.

    Redman, J. M., Gibney, G. T. & Atkins, M. B. Advances in immunotherapy for melanoma. BMC Med. 14, 20 (2016).

  17. 17.

    Liu, J. et al. Improved efficacy of neoadjuvant compared to adjuvant immunotherapy to eradicate metastaticdisease. Cancer Discov. 6, 1382–1399 (2016).

  18. 18.

    Forde, P. M. et al. Neoadjuvant PD-1 blockade in resectable lung cancer. N. Engl. J. Med. 378, 1976–1986 (2018).

  19. 19.

    Blank, C. U. et al. Neoadjuvant versus adjuvant ipilimumab plus nivolumab in macroscopic stage III melanoma. Nat. Med. 24, 1655–1661 (2018).

  20. 20.

    Amaria, R. N. et al. Neoadjuvant immune checkpoint blockade in high-risk resectable melanoma. Nat. Med. 24, 1649–1654 (2018).

  21. 21.

    Blumenthal, D. T. et al. Pembrolizumab: first experience with recurrent primary central nervous system (CNS) tumors. J. Neurooncol. 129, 453–460 (2016).

  22. 22.

    Bouffet, E. et al. Immune checkpoint inhibition for hypermutant glioblastoma multiforme resulting from germline biallelic mismatch repair deficiency. J. Clin. Oncol. 34, 2206–2211 (2016).

  23. 23.

    Johanns, T. M. et al. Immunogenomics of hypermutated glioblastoma: a patient with germline POLE deficiency treated with checkpoint blockade immunotherapy. Cancer Discov. 6, 1230–1236 (2016).

  24. 24.

    Reardon, D. A. et al. Randomized phase 3 study evaluating the efficacy and safety of nivolumab vs bevacizumab in patients with recurrent glioblastoma: Checkmate 143. Neuro. Oncol. 19, 21–21 (2017).

  25. 25.

    Antonios, J. P. et al. Immunosuppressive tumor-infiltrating myeloid cells mediate adaptive immune resistance via a PD-1/PD-L1 mechanism in glioblastoma. Neuro. Oncol. 19, 796–807 (2017).

  26. 26.

    Antonios, J. P. et al. PD-1 blockade enhances the vaccination-induced immune response in glioma. JCI Insight 1, e87059 (2016).

  27. 27.

    Bloch, O. et al. Gliomas promote immunosuppression through induction of B7-H1 expression in tumor-associated macrophages. Clin. Cancer Res. 19, 3165–3175 (2013).

  28. 28.

    Reardon, D. A. et al. Glioblastoma eradication following immune checkpoint blockade in an orthotopic, immunocompetent model. Cancer Immunol.Res. 4, 124–135 (2016).

  29. 29.

    Wainwright, D. A. et al. Durable therapeutic efficacy utilizing combinatorial blockade against IDO, CTLA-4, and PD-L1 in mice with brain tumors. Clin. Cancer Res. 20, 5290–5301 (2014).

  30. 30.

    Zeng, J. et al. Anti-PD-1 blockade and stereotactic radiation produce long-term survival in mice with intracranial gliomas. Int. J. Radiat. Oncol. Biol. Phys. 86, 343–349 (2013).

  31. 31.

    Okada, H. et al. Immunotherapy response assessment in neuro-oncology: a report of the RANO working group. Lancet Oncol. 16, e534–e542 (2015).

  32. 32.

    Pollack, I. F. et al. Antigen-specific immune responses and clinical outcome after vaccination with glioma-associated antigen peptides and polyinosinic-polycytidylic acid stabilized by lysine and carboxymethylcellulose in children with newly diagnosed malignant brainstem and nonbrainstem gliomas. J. Clin. Oncol. 32, 2050–2058 (2014).

  33. 33.

    Prins, R. M. et al. Gene expression profile correlates with T-cell infiltration and relative survival in glioblastoma patients vaccinated with dendritic cell immunotherapy. Clin. Cancer Res. 17, 1603–1615 (2011).

  34. 34.

    Ayers, M. et al. IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).

  35. 35.

    Urup, T. et al. Transcriptional changes induced by bevacizumab combination therapy in responding and non-responding recurrent glioblastoma patients. BMC Cancer 17, 278 (2017).

  36. 36.

    Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).

  37. 37.

    Chen, J. et al. Interferon-gamma-induced PD-L1 surface expression on human oral squamous carcinoma via PKD2 signal pathway. Immunobiology 217, 385–393 (2012).

  38. 38.

    Spranger, S. et al. Up-regulation of PD-L1, IDO, and T(regs) in the melanoma tumor microenvironment is driven by CD8+ T cells.Sci. Transl. Med. 5, 200ra116 (2013).

  39. 39.

    Taube, J. M. et al. Colocalization of inflammatory response with B7-h1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape. Sci. Transl. Med. 4, 127ra137 (2012).

  40. 40.

    Farmer, P. et al. Identification of molecular apocrine breast tumours by microarray analysis. Oncogene 24, 4660–4671 (2005).

  41. 41.

    Li, G. & Wang, X. Prediction accuracy measures for a nonlinear model and for right-censored time-to-eventd ata. J. Am. Stat. Assoc. https://doi.org/10.1080/01621459.2018.1515079 (2018).

  42. 42.

    Einat, M., Resnitzky, D. & Kimchi, A. Close link between reduction of c-myc expression by interferon and G0/G1 arrest. Nature 313, 597–600 (1985).

  43. 43.

    Shearer, M. & Taylor-Papadimitriou, J. Regulation of cell growth by interferon. Cancer Metastasis Rev. 6, 199–221 (1987).

  44. 44.

    Yung, W. K., Steck, P. A., Kelleher, P. J., Moser, R. P. & Rosenblum, M. G. Growth inhibitory effect of recombinant alpha and beta interferon on human glioma cells. J. Neurooncol. 5, 323–330 (1987).

  45. 45.

    Schalper, K. A. Neoadjuvant nivolumab modifies the tumor immune microenvironment in resectable glioblastoma. Nat. Med. https://doi.org/10.1038/s41591-018-0339-5 (2019).

  46. 46.

    Robert, L. et al. Distinct immunological mechanisms of CTLA-4 and PD-1 blockade revealed by analyzing TCR usage in blood lymphocytes. Oncoimmunology 3, e29244 (2014).

  47. 47.

    Omuro, A. et al. Nivolumab with or without ipilimumab in patients with recurrent glioblastoma: results from exploratory phase I cohorts of CheckMate 143. Neuro. Oncol. 20, 674–686 (2018).

  48. 48.

    Kamphorst, A. O. et al. Proliferation of PD-1+ CD8 T cells in peripheral blood after PD-1-targeted therapy in lung cancer patients. Proc. Natl Acad. Sci. USA 114, 4993–4998 (2017).

  49. 49.

    Huang, A. C. et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017).

  50. 50.

    Krieg, C. et al. High-dimensional single-cell analysis predicts response to anti-PD-1immunotherapy. Nat. Med. 24, 144–153 (2018).

  51. 51.

    Arbour, K. C. et al. Impact of baseline steroids on efficacy of programmed cell death-1 and programmed death-ligand 1 blockade in patients with non-small-cell lung cancer. J. Clin. Oncol. 36, 2872–2878 (2018).

  52. 52.

    Weber, J. S. et al. Safety profile of nivolumab monotherapy: a pooled analysis of patients with advanced melanoma. J. Clin. Oncol. 35, 785–792 (2017).

  53. 53.

    Hamza, M. A. et al. Survival outcome of early versus delayed bevacizumab treatment in patients with recurrent glioblastoma. J. Neurooncol. 119, 135–140 (2014).

  54. 54.

    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

  55. 55.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

  56. 56.

    Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).

  57. 57.

    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).

  58. 58.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

  59. 59.

    Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

  60. 60.

    Grossman, R. L. et al. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016).

  61. 61.

    Carlson, C. S. et al. Using synthetic templates to design an unbiased multiplex PCR assay. Nat. Commun. 4, 2680 (2013).

  62. 62.

    Robins, H. et al. Ultra-sensitive detection of rare T cell clones. J. Immunol. Methods 375, 14–19 (2012).

  63. 63.

    Robins, H. S. et al. Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells. Blood 114, 4099–4107 (2009).

  64. 64.

    Emerson, R. O. et al. High-throughput sequencing of T-cell receptors reveals a homogeneous repertoire of tumour-infiltrating lymphocytes in ovarian cancer. J. Pathol. 231, 433–440 (2013).

  65. 65.

    Daley, T. & Smith, A. D. Predicting the molecular complexity of sequencing libraries. Nat. Methods 10, 325–327 (2013).

  66. 66.

    Nowicka, M. et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Res. 6, 748 (2017).

  67. 67.

    Ellingson, B. M. et al. Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro. Oncol. 17, 1188–1198 (2015).

  68. 68.

    Ellingson, B. M. et al. Volumetric response quantified using T1 subtraction predicts long-term survival benefit from cabozantinib monotherapy in recurrent glioblastoma. Neuro. Oncol. 20, 1411–1418 (2018).

  69. 69.

    Ellingson, B. M. et al. Recurrent glioblastoma treated with bevacizumab: contrast-enhanced T1-weighted subtraction maps improve tumor delineation and aid prediction of survival in a multicenter clinical trial. Radiology 271, 200–210 (2014).

  70. 70.

    Ellingson, B. M. et al. Validation of post-operative residual contrast enhancing tumor volume as an independent prognostic factor for overall survival in newly diagnosed glioblastoma.Neuro. Oncol. 20, 1240–1250 (2018).

  71. 71.

    Ellingson, B. M. et al. Baseline pretreatment contrast enhancing tumor volume including central necrosis is a prognostic factor in recurrent glioblastoma: evidence from single and multicenter trials. Neuro. Oncol. 19, 89–98 (2017).

  72. 72.

    Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

  73. 73.

    Zhao, K., Lu, Z. X., Park, J. W., Zhou, Q. & Xing, Y. GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data. Genome Biol. 14, R74 (2013).

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Acknowledgements

This study was funded in part by the National Institutes of Health SPORE in Brain Cancer (grant no. P50CA211015), the Parker Institute for Cancer Immunotherapy (grant no. 20163828), the Cancer Research Institute, the Musella Foundation, the Ben and Catherine Ivy Foundation, the Uncle Kory Foundation, the Defeat GBM Program of the National Brain Tumor Society, the Ziering Family Foundation and by Merck & Co., Inc. Research and/or financial support was also provided by Adaptive Biotechnologies. The authors also thank A. Garcia, N. Akkad, M. Attiah, S. Khattab, J. Reynoso, M. Wong and M. Guemes for their contributions to the experiments. ImmunoSEQ assays are for research use only and not for use in diagnostic procedures.

Author information

Author notes

  1. These authors contributed equally: Timothy F. Cloughesy, Aaron Y. Mochizuki

  2. These authors jointly supervised this work: Patrick Y. Wen, Robert M. Prins.

Affiliations

  1. Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    • Timothy F. Cloughesy
    •  & Phioanh L. Nghiemphu
  2. Department of Medical and Molecular Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    • Timothy F. Cloughesy
    • , Alexander H. Lee
    •  & Robert M. Prins
  3. Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA

    • Timothy F. Cloughesy
    • , Tom B. Davidson
    • , Benjamin M. Ellingson
    • , Gang Li
    • , Linda M. Liau
    •  & Robert M. Prins
  4. Division of Hematology/Oncology, Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    • Aaron Y. Mochizuki
    •  & Tom B. Davidson
  5. Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    • Joey R. Orpilla
    • , Alexander H. Lee
    • , Anthony C. Wang
    • , Linda M. Liau
    •  & Robert M. Prins
  6. Division of Dermatology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    • Willy Hugo
  7. Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    • Benjamin M. Ellingson
  8. Adaptive Biotechnologies, Seattle, WA, USA

    • Julie A. Rytlewski
    •  & Catherine M. Sanders
  9. Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA

    • Eric S. Kawaguchi
    • , Lin Du
    •  & Gang Li
  10. Department of Pathology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    • William H. Yong
  11. Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    • Sarah C. Gaffey
    • , Eudocia Q. Lee
    • , David A. Reardon
    •  & Patrick Y. Wen
  12. Department of Neurosurgery, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA

    • Adam L. Cohen
    •  & Howard Colman
  13. Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    • Ingo K. Mellinghoff
    •  & Thomas J. Kaley
  14. Department of Neuro-Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    • Barbara J. O’Brien
    •  & John F. de Groot
  15. Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA

    • Nicholas A. Butowski
    •  & Jennifer L. Clarke
  16. Department of Neurology, Massachusetts General Hospital Cancer Center, Boston, MA, USA

    • Isabel C. Arrillaga-Romany
  17. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA

    • Robert M. Prins

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Contributions

The conceptualization, methodology and supervision were carried out by R.M.P., P.Y.W. and T.F.C.; the investigation was carried out by R.M.P., T.F.C., P.Y.W., J.R.O., A.H.L., A.Y.M., A.C.W., T.B.D., W.H.Y., J.L.C., I.C.A.-R., H.C., T.J.K., J.F.d.G., D.A.R., I.K.M., A.L.C., E.Q.L., P.L.N., B.J.O. and N.A.B.; writing of the original draft was handled by A.Y.M.; and the draft was reviewed and edited by all the authors; funding was acquired by L.M.L., R.M.P., T.F.C. and P.Y.W.; the data were curated by J.R.O. and S.C.G.; formal analysis was carried out by J.R.O., B.M.E., A.Y.M., G.L., L.D., E.S.K., W.H., C.M.S. and J.A.R.; and project administration was carried out by J.R.O. and S.C.G.

Competing interests

J.A.R. and C.M.S. have a financial interest in Adaptive Biotechnologies. T.F.C. and D.A.R. have received compensation from Merck as consultants on advisory boards. P.Y.W. and H.C. have received honoraria from Merck. J.F.d.G. has done consulting and/or received honoraria with Merck and Bristol-Myers Squibb. I.K.M. reports research funding from General Electric, Amgen and Lilly; advisory roles with Agios, Puma Biotechnology and Debiopharm Group; and honoraria from Roche for a presentation.

Corresponding authors

Correspondence to Timothy F. Cloughesy or Robert M. Prins.

Extended data

  1. Extended Data Fig. 1 CONSORT diagram.

    Flow diagram of disposition of patients enrolled in the study.

  2. Extended Data Fig. 2 Kaplan–Meier plot of progression-free survival.

    Median progression-free survival (PFS) for patients who received pembrolizumab only in the adjuvant setting was 72.5 d; patients who received neoadjuvant and adjuvant pembrolizumab had a median PFS of 99.5 d (hazard ratio 0.43, 95% confidence interval 0.20–0.90; two-sided P = 0.03 by log-rank test).

  3. Extended Data Fig. 3 Eighteen gene interferon-γ-related signature scores in neoadjuvant versus adjuvant-only groups.

    Line in middle of box represents the median; box extends from the 25th to 75th percentiles; whiskers represent minimum and maximum values; n = 28 independent biological samples; P = 0.025, U = 49 by two-sided Mann–Whitney U-test. *: P < 0.05. Source Data

  4. Extended Data Fig. 4 RNA sequencing comparison to other recurrent glioblastoma samples.

    We combined our RNA sequencing dataset to that of GSE79671 (an RNA sequencing dataset of recurrent glioblastoma pre- and post-bevacizumab treatment; only pre-treatment (Pre-Tx) samples were used, and The Cancer Genome Atlas (TCGA) glioblastoma samples. We applied appropriate batch correction on log-transformed, normalized mRNA expression values using the removeBatchEffect function in the R package limma to estimate the fraction of glioblastoma patients with positive enrichment of cell cycle/cancer proliferation signatures (GSVA score ≥ 0.2). The proportion of positive enrichment of cell cycle/cancer proliferation signatures in our dataset as a whole is similar to GSE79671 (14 out of 29 (48%) versus 11 out of 20 (55%)). The number of samples with positive enrichment in the TCGA GBM is lower, at 41%. We observed that the neoadjuvant PD-1 monoclonal antibody therapy group is associated with a lower fraction of tumors with cell cycle signatures. Only 3 out of 14 tumors in the neoadjuvant group demonstrated positive enrichment, with 11 of 15 tumors in the adjuvant group and 11 of 20 tumors in the GSE79671 set (one-sided Fisher exact test, P = 0.01 and P = 0.05, respectively). GSVA, gene set variation analysis. Source Data

  5. Extended Data Fig. 5 RNA sequencing comparison to TCGA.

    We combined our RNA sequencing dataset to the TCGA glioblastoma dataset, with appropriate batch correction, to estimate the fraction of glioblastoma patients with positive enrichment of cell cycle/cancer proliferation signatures (GSVA score ≥ 0.2). Three out of 14 tumors in the neoadjuvant group demonstrated positive enrichment, with 11 of 15 tumors in the adjuvant group and 73 of 166 tumors in The Cancer Genome Atlas set. TCGA: The Cancer Genome Atlas. GSVA: gene set variation analysis. Source Data

  6. Extended Data Fig. 6 Mass cytometry dimension reduction.

    a, Diffusion map of peripheral blood mononuclear cells (PBMCs) sampled from n = 28 patients at baseline, the time of surgery and on-treatment. Phenotypically similar cells are depicted in an unsupervised manner along the same continuous axes in a pseudotemporal progression. b, t-distributed stochastic neighbor-embedding (tSNE) plot of PBMCs from n = 28 patients at all three time points. Phenotypically similar cells are clustered in an unsupervised manner. All represented cells in both panels are colored by algorithmically assigned cluster numbers using the FlowSOM package. CD3+CD4+, CD3+CD8+, CD3CD19+ and CD3CD14+CD16+CD11b+CD11c+ cells are labeled to demonstrate how clustered cells in close proximity to one another are plotted.

Supplementary information

Source data

  1. Source Data Fig. 2

    Statistical source data for panel b

  2. Source Data Extended Data Fig. 3

    Statistical source data

  3. Source Data Extended Data Fig. 4

    Statistical source data

  4. Source Data Extended Data Fig. 5

    Statistical source data

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https://doi.org/10.1038/s41591-018-0337-7