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A peripheral immune signature of responsiveness to PD-1 blockade in patients with classical Hodgkin lymphoma

Abstract

PD-1 blockade is highly effective in classical Hodgkin lymphomas (cHLs), which exhibit frequent copy-number gains of CD274 (PD-L1) and PDC1LG2 (PD-L2) on chromosome 9p24.1. However, in this largely MHC-class-I-negative tumor, the mechanism of action of anti-PD-1 therapy remains undefined. We utilized the complementary approaches of T cell receptor (TCR) sequencing and cytometry by time-of-flight analysis to obtain a peripheral immune signature of responsiveness to PD-1 blockade in 56 patients treated in the CheckMate 205 phase II clinical trial (NCT02181738). Anti-PD-1 therapy was most effective in patients with a diverse baseline TCR repertoire and an associated expansion of singleton clones during treatment. CD4+, but not CD8+, TCR diversity significantly increased during therapy, most strikingly in patients who had achieved complete responses. Additionally, patients who responded to therapy had an increased abundance of activated natural killer cells and a newly identified CD3CD68+CD4+GrB+ subset. These studies highlight the roles of recently expanded, clonally diverse CD4+ T cells and innate effectors in the efficacy of PD-1 blockade in cHL.

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Fig. 1: Analyses of peripheral TCR repertoire diversity at baseline and following PD-1 blockade.
Fig. 2: Clonal expansion following PD-1 blockade.
Fig. 3: Analyses of circulating CD3+ cells in healthy donors and patients with newly diagnosed or R/R cHL.
Fig. 4: Comparative analyses of CD3+ clusters in healthy donors and patients with newly diagnosed and R/R cHL.
Fig. 5: Analyses of circulating CD3 cells in healthy donors and patients with newly diagnosed or R/R cHL.
Fig. 6: Comparative analyses of CD3 clusters in healthy donors and patients with newly diagnosed and R/R cHL.

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Data availability

The TCR sequences for this study were processed through the immunoSEQ platform of Adaptive Biotechnologies. The TCR sequences are publicly available (https://doi.org/10.21417/FZC2020NM) through this link: https://adaptivebiotech.com/pub/cader-2020-nm. The raw CyTOF.fcs files are publicly available through login at Cytobank, https://premium.cytobank.org/cytobank/experiments#project-id=2539 and https://premium.cytobank.org/cytobank/experiments/310927. Source data for all main and Extended Data figures are available in the Supplementary Dataset.

Code availability

CyTOF data was processed by VorteX (26 April 2018) and the output was processed by a custom R script. TCR-seq data was processed and analyzed by custom Python and R scripts. The code is available at https://github.com/huxihao/cHL-PBMC.

References

  1. Mathas, S., Hartmann, S. & Kuppers, R. Hodgkin lymphoma: pathology and biology. Semin. Hematol. 53, 139–147 (2016).

    Article  PubMed  Google Scholar 

  2. Green, M. R. et al. Integrative analysis reveals selective 9p24.1 amplification, increased PD-1 ligand expression, and further induction via JAK2 in nodular sclerosing Hodgkin lymphoma and primary mediastinal large B-cell lymphoma. Blood 116, 3268–3277 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Roemer, M. G. et al. PD-L1 and PD-L2 genetic alterations define classical hodgkin lymphoma and predict outcome. J. Clin. Oncol. 34, 2690–2697 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Roemer, M. G. M. et al. Major histocompatibility complex class II and programmed death ligand 1 expression predict outcome after programmed death 1 blockade in classic hodgkin lymphoma. J. Clin. Oncol. 36, 942–950 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wienand, K. et al. Genomic analyses of flow-sorted hodgkin reed-sternberg cells reveal complementary mechanisms of immune evasion. Blood Adv. 3, 4065–4080 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ansell, S. M. et al. PD-1 blockade with nivolumab in relapsed or refractory Hodgkin’s lymphoma. N. Engl. J. Med. 372, 311–319 (2015).

    Article  PubMed  CAS  Google Scholar 

  8. Armand, P. et al. Nivolumab for relapsed/refractory classic hodgkin lymphoma after failure of autologous hematopoietic cell transplantation: Extended follow-up of the multicohort single-arm phase II CheckMate 205 trial. J. Clin. Oncol. 36, 1428–1439 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Armand, P. et al. Programmed death-1 blockade with pembrolizumab in patients with classical hodgkin lymphoma after brentuximab vedotin failure. J. Clin. Oncol. 34, 3733–3739 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Chen, R. et al. Phase II study of the efficacy and safety of pembrolizumab for relapsed/refractory classic hodgkin lymphoma. J. Clin. Oncol. 35, 2125–2132 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Shi, Y. et al. Safety and activity of sintilimab in patients with relapsed or refractory classical Hodgkin lymphoma (ORIENT-1): a multicentre, single-arm, phase 2 trial. Lancet Haematol. 6, e12–e19 (2019).

    Article  PubMed  Google Scholar 

  12. Song, Y. et al. Treatment of relapsed or refractory classical Hodgkin lymphoma with the anti-PD-1, tislelizumab: results of a phase 2, single-arm, multicenter study. Leukemia 34, 533–542 (2020).

    Article  CAS  PubMed  Google Scholar 

  13. Song, Y. et al. A single-arm, multicenter, phase 2 study of camrelizumab in relapsed or refractory classical Hodgkin lymphoma. Clin. Cancer Res. 25, 7363–7369 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Merryman, R. W., Armand, P., Wright, K. T. & Rodig, S. J. Checkpoint blockade in Hodgkin and non-Hodgkin lymphoma. Blood Adv. 1, 2643–2654 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ramchandren, R. et al. Nivolumab for newly diagnosed advanced-stage classic hodgkin lymphoma: safety and efficacy in the phase II CheckMate 205 study. J. Clin. Oncol. 37, 1997–2007 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kamphorst, A. O. et al. Rescue of exhausted CD8 T cells by PD-1-targeted therapies is CD28-dependent. Science 355, 1423–1427 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Juneja, V. R. et al. PD-L1 on tumor cells is sufficient for immune evasion in immunogenic tumors and inhibits CD8 T cell cytotoxicity. J. Exp. Med. 214, 895–904 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Reichel, J. et al. Flow sorting and exome sequencing reveal the oncogenome of primary Hodgkin and Reed–Sternberg cells. Blood 125, 1061–1072 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Roemer, M. G. et al. Classical hodgkin lymphoma with reduced β2M/MHC class I expression is associated with inferior outcome independent of 9p24.1 status. Cancer Immunol. Res. 4, 910–916 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Johnson, D. B. et al. Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy. Nat. Commun. 7, 10582 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kreiter, S. et al. Mutant MHC class II epitopes drive therapeutic immune responses to cancer. Nature 520, 692–696 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Linnemann, C. et al. High-throughput epitope discovery reveals frequent recognition of neo-antigens by CD4+ T cells in human melanoma. Nat. Med. 21, 81–85 (2015).

    Article  CAS  PubMed  Google Scholar 

  25. Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Choi, I. K. et al. Signaling by the Epstein–Barr virus LMP1 protein induces potent cytotoxic CD4+ and CD8+ T cell responses. Proc. Natl Acad. Sci. USA 115, E686–E695 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Alspach, E. et al. MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature 574, 696–701 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kanzler, H., Kuppers, R., Hansmann, M. L. & Rajewsky, K. Hodgkin and Reed–Sternberg cells in Hodgkin’s disease represent the outgrowth of a dominant tumor clone derived from (crippled) germinal center B cells. J. Exp. Med. 184, 1495–1505 (1996).

    Article  CAS  PubMed  Google Scholar 

  29. Weniger, M. A. et al. Human CD30+ B cells represent a unique subset related to Hodgkin lymphoma cells. J. Clin. Invest. 128, 2996–3007 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Carey, C. D. et al. Topological analysis reveals a PD-L1-associated microenvironmental niche for Reed–Sternberg cells in Hodgkin lymphoma. Blood 130, 2420–2430 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Cader, F. Z. et al. Mass cytometry of Hodgkin lymphoma reveals a CD4+ regulatory T-cell-rich and exhausted T-effector microenvironment. Blood 132, 825–836 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bakhru, P. et al. Combination central tolerance and peripheral checkpoint blockade unleashes antimelanoma immunity. JCI Insight 2, e93265 (2017).

  33. Hogan, S. A. et al. Peripheral blood TCR repertoire profiling may facilitate patient stratification for immunotherapy against melanoma. Cancer Immunol. Res. 7, 77–85 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Jacquelot, N. et al. Predictors of responses to immune checkpoint blockade in advanced melanoma. Nat. Commun. 8, 592 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  36. Manjarrez-Orduno, N. et al. Circulating T cell subpopulations correlate with immune responses at the tumor site and clinical response to PD1 inhibition in non-small cell lung cancer. Front Immunol. 9, 1613 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Spitzer, M. H. et al. Systemic immunity is required for effective cancer immunotherapy. Cell 168, 487–502 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zuazo, M. et al. Functional systemic CD4 immunity is required for clinical responses to PD-L1/PD-1 blockade therapy. EMBO Mol. Med 11, e10293 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Mackall, C. L. T-cell immunodeficiency following cytotoxic antineoplastic therapy: a review. Stem Cells 18, 10–18 (2000).

    Article  CAS  PubMed  Google Scholar 

  40. Glowala-Kosinska, M. et al. Thymic activity and T cell repertoire recovery after autologous hematopoietic stem cell transplantation preceded by myeloablative radiotherapy or chemotherapy. Biol. Blood Marrow Transpl. 22, 834–842 (2016).

    Article  Google Scholar 

  41. Ljungman, P. et al. Vaccination of hematopoietic cell transplant recipients. Bone Marrow Transpl. 44, 521–526 (2009).

    Article  CAS  Google Scholar 

  42. Samusik, N., Good, Z., Spitzer, M. H., Davis, K. L. & Nolan, G. P. Automated mapping of phenotype space with single-cell data. Nat. Methods 13, 493–496 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Godfrey, D. I., Koay, H. F., McCluskey, J. & Gherardin, N. A. The biology and functional importance of MAIT cells. Nat. Immunol. 20, 1110–1128 (2019).

    Article  CAS  PubMed  Google Scholar 

  44. Pillay, J., Tak, T., Kamp, V. M. & Koenderman, L. Immune suppression by neutrophils and granulocytic myeloid-derived suppressor cells: similarities and differences. Cell. Mol. Life Sci. 70, 3813–3827 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Freud, A. G., Mundy-Bosse, B. L., Yu, J. & Caligiuri, M. A. The broad spectrum of human natural killer cell diversity. Immunity 47, 820–833 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Vari, F. et al. Immune evasion via PD-1/PD-L1 on NK cells and monocyte/macrophages is more prominent in Hodgkin lymphoma than DLBCL. Blood 131, 1809–1819 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Sen, D. R. et al. The epigenetic landscape of T cell exhaustion. Science 354, 1165–1169 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kurtulus, S. et al. Checkpoint blockade immunotherapy induces dynamic changes in PD-1CD8+ tumor-infiltrating T cells. Immunity 50, 181–194 e186 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Siddiqui, I. et al. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211 e110 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 e1020 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Acharya, N. & Anderson, A. C. New clones on the block. Immunity 51, 606–608 (2019).

    Article  CAS  PubMed  Google Scholar 

  54. Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Perez-Diez, A. et al. CD4 cells can be more efficient at tumor rejection than CD8 cells. Blood 109, 5346–5354 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Porichis, F. et al. Immune checkpoint blockade restores HIV-specific CD4 T cell help for NK cells. J. Immunol. 201, 971–981 (2018).

    Article  CAS  PubMed  Google Scholar 

  57. Barry, K. C. et al. A natural killer-dendritic cell axis defines checkpoint therapy-responsive tumor microenvironments. Nat. Med. 24, 1178–1191 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Elavazhagan, S. et al. Granzyme B expression is enhanced in human monocytes by TLR8 agonists and contributes to antibody-dependent cellular cytotoxicity. J. Immunol. 194, 2786–2795 (2015).

    Article  CAS  PubMed  Google Scholar 

  59. Hu, X. et al. Landscape of B cell immunity and related immune evasion in human cancers. Nat. Genet. 51, 560–567 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  61. Rempala, G. A. & Seweryn, M. Methods for diversity and overlap analysis in T-cell receptor populations. J. Math. Biol. 67, 1339–1368 (2013).

    Article  PubMed  Google Scholar 

  62. Cuzick, J. A Wilcoxon-type test for trend. Stat. Med. 4, 87–90 (1985).

    Article  CAS  PubMed  Google Scholar 

  63. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).

    Google Scholar 

  64. Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported in part by Bloodwise Fellowship 14042 and a Helen Gurley Brown Fellowship (F.Z.C.), a R01 CA161026 (M.A.S.), the Miller Family Fund (M.A.S.), the BMS International Immuno-Oncology Network (M.A.S. and S.J.R.) and a R01 CA234018 (X.S.L.). The authors thank L. Boyne (Dana-Farber Cancer Institute) for providing editorial assistance.

Author information

Authors and Affiliations

Authors

Contributions

F.Z.C., X.H., X.S.L. and M.A.S. conceived and led the project and analyzed the data. F.Z.C., X.H., W.L.G., J.O., E.M., R.R., P.-H.C., J.L.W., R.C.J.S. and S.J.R. performed experiments and analyzed the data. K.W., L.N.L. B.L., W.M., P.A. and D.N. contributed to the analysis and scientific discussions. F.Z.C., X.H., X.S.L. and M.A.S. wrote the paper.

Corresponding authors

Correspondence to X. Shirley Liu or Margaret A. Shipp.

Ethics declarations

Competing interests

After completing the current studies at DFCI, F.Z.C. and X.H. became full-time employees at Astra Zeneca and GV20, respectively. P.A. consults for Merck, Bristol Myers Squibb (BMS), Pfizer, Affimed, Adaptive, Infinity, ADC Therapeutics and Celgene and receives institutional research funding from Merck, BMS, Affimed, Adaptive, Roche, Tensha, Otsuka, Sigma Tau, Genentech and IGM and honoraria from Merck and BMS. S.J.R. has received research funding from BMS, Merck, Affimed and Kite/Gilead. X.S.L. is a cofounder and board member of GV20 Oncotherapy, SAB of 3DMedCare, consultant for Genentech, and stockholder of BMY, TMO, WBA, ABT, ABBV, and JNJ. X.S.L. has received funding from Takeda and Sanofi. M.A.S. has received research funding from BMS, Merck and Bayer and has served on advisory boards for BMS and Celgene. The remaining authors declare no competing financial interests.

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Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Analyses of peripheral TCR repertoire diversity at baseline and following PD-1 blockade.

a, Number of prior therapies in trial patients who were treated with nivolumab ≥ 1 yr after ASCT by best overall response to PD-1 blockade (CR n = 14, PR n = 18, PD n = 12). b, Percentages of CD3 + and CD3- viable cells at baseline in trial patients with relapsed/refractory cHL. Viable singlet cells identified by manual gating of CyTOF data were divided according to CD3 expression (CD3-, grey and CD3 + , orange, n = 38). Individual samples from patients with available CyTOF files who had relapsed/refractory cHL with ≥ 1 year between nivolumab and prior myeloablative ASCT are shown (n = 38) (CR n = 13, PR n = 15, PD n = 10). c, Comparison of baseline CD3 + populations in trial patients with relapsed/refractory cHL (from b) according to their subsequent response to PD-1 blockade. d, Total number of TCR-seq detected clones at baseline in trial patients (from a) according to their subsequent response to PD-1 blockade. e, Percentages of CD4 + (blue) and CD8 + (purple) cells at baseline in trial patients with relapsed/refractory cHL. CD3 + cells identified (from b) and divided according to CD4 + or CD8 + expression by manual gating of CyTOF data. Additional cryopreserved samples from indicated cases (*) were available for CD4 + and CD8 + sorting (n = 18, 2 excluded from this analysis as no CyTOF files available). f, Comparison of baseline CD4 + populations in all trial patients with relapsed/ refractory cHL (from e) according to their subsequent response to PD-1 blockade (CR, PR, PD). g, Comparison of baseline CD4 + populations in trial patients with relapsed/refractory cHL (from e*) with additional PBMC samples sorted for CD4 + and CD8 + T cells (n = 18). h, Total numbers of CD4 + and CD8 + TCR-seq detected clones at baseline in trial patients (from g) according to their subsequent response to PD-1 blockade. Differences between groups in panels a, c, d, f, g and h were assessed with a Wilcoxon rank sum test of the median with two-tailed p values. i, Changes in TCR diversity from C1D1 to C4D1 in the subset of trial patients with known HRS cell expression of MHC class I and MHC class II and CD4 + and CD8 + TCRseq data (n = 9). Definitions of positive (positive or decreased) and negative expression of MHC class I and class II on HRS cells previously described in (Roemer et al 20184). j, Changes in TCR diversity from C1D1 to C4D1 separated by HRS cell expression of MHC class II only, samples from i. Differences in panels i and j were assessed by Wilcoxon rank sum test with one-sided p-values. k, The ratio of maximum expansion of singleton clones (0 or 1 copy at baseline)/ non-singleton clones which have 2 or more copies at baseline in patients with BOR of CR (n = 9), PR (n = 17) or PD (n = 8) to PD-1 blockade. Only patients with all 3 timepoints are included in the analysis. Differences between groups were assessed with a Wilcoxon rank sum test of the median, two-tailed p values. (l and m) The ratio of expanded singleton / non-singleton clones from CD4 + only T cells (l) or CD8 + only T cells (m) from patients with CR, PR or PD to PD-1 blockade (n = 20). Differences in panels l and m were assessed by Wilcoxon rank sum test with one-sided p-values. Graphpad Prism (v8) or R (ggplot function) was used to generate box plots (GraphPad Prism panels b, c, e-g and R panels a, d, h-m). The box corresponds to the first and third quartiles and whiskers define minimum and maximum values. Outliers beyond 1.5x IQR in R- generated plots are plotted individually.

Extended Data Fig. 2

Forced-directed layout of CD3+ populations at baseline in a) healthy donors and patients with newly diagnosed cHL and b) patients with newly diagnosed and relapsed/refractory cHL. Each FDL shows expression of individual proteins ranging from no/low expression in blue to high expression in red. Clusters can be assigned a phenotype on the basis of these FDLs. Shown here are 12 proteins which allow identification of differentiation status (CCR7, CD45RO), polarization (CCR5, CCR4, CD161), activation (PD-1, T-bet, Eomes, Granzyme B), Tregs (FoxP3, CD25) and CXCR5 + cells.

Extended Data Fig. 3 Comparison of CD3+ populations in healthy donors versus patients with newly diagnosed cHL.

To quantify differences between these 2 groups, healthy donors (n = 11) and patients with newly diagnosed cHL (n = 9), we determined the number of cells that each sample contributed to a given cluster and applied a Wilcoxon rank sum test with two-sided p-values. Nominal p-values with Benjamini-Hochberg (BH) corrections for p≤0.05 (CD4 + and CD8 + cells separately). Shown here graphically are box plots (generated in GraphPad Prism) defining the 25th and 75th percentile and median values and whiskers for minimum and maximum values: a, CD4 + clusters; b, CD8 + clusters; and c, CD3 + CD4-CD8- subsets.

Extended Data Fig. 4 Comparison of CD3+ populations in patients with newly diagnosed cHL versus relapsed/refractory cHL (all) at baseline.

To quantify differences between these 2 groups, newly diagnosed cHL (n = 9) and relapsed/refractory cHL (n = 36), we determined the number of cells that each sample contributed to a given cluster and applied a Wilcoxon rank sum test with two-sided p-values. Nominal p-values with Benjamini-Hochberg corrections for p≤0.05 (CD4 + and CD8 + cells separately). Shown here graphically are box plots (generated in GraphPad Prism) defining the 25th and 75th percentile and median values and whiskers for minimum and maximum values: a, CD4 + clusters; b, CD8 + clusters; and c, CD3 + CD4-CD8- subsets. d, PD-1 expression on CD3 + T cell clusters identified by Vortex in patients with newly diagnosed cHL vs. relapsed/refractory disease. Only clusters with z-score normalized PD-1 expression greater than 0 (ie. greater than the mean) in the PD-1 columns in the Fig. 3h heat-maps are shown. The differences in PD-1 expression in T-cell subsets from patients with newly diagnosed and relapsed/refractory cHL were measured by the Wilcoxon rank sum test with two-sided p-values, significance denoted by asterisks.

Extended Data Fig. 5 Comparison of CD3+ populations in patients with relapsed/refractory cHL at baseline split by best overall response to subsequent PD-1 blockade (CR, PR, PD).

To quantify differences between these groups, (CR n = 12, PR n = 15, PD n = 9) we determined the number of cells that each sample contributed to a given cluster and applied a Cuzick trend test (two-sided nominal p-values). Shown here graphically are box plots (generated in GraphPad Prism) defining the 25th and 75th percentile and median values and whiskers for minimum and maximum values: a) CD4 + clusters; b) CD8 + clusters; and c) CD3 + CD4-CD8- subsets.

Extended Data Fig. 6 Comparison of CD3- populations in healthy donors versus patients with newly diagnosed cHL.

To quantify differences between these 2 groups, patients with newly diagnosed cHL (n = 10) and relapsed/refractory cHL (n = 35), we determined the number of cells that each sample contributed to a given cluster and applied a Wilcoxon rank sum test (two-sided nominal p-values) with Benjamini-Hochberg (BH) corrections for p ≤ 0.05 (Classical Monocytes, Neutrophils, B cells and NK cells separately). One patient with newly diagnosed cHL who had sufficient numbers of CD3- sampled events in Extended Data Fig. 68 had insufficient numbers of CD3 + sampled events and was excluded from the CD3 + analysis in Extended Data Figs. 35). One patient with relapsed/refractory cHL had sufficient numbers of CD3 + sampled events for inclusion in Extended Data Figs. 4, 5 but had insufficient numbers of CD3- sampled events and was excluded from the CD3- analyses in Extended Data Fig. 68. Shown here graphically are box plots (generated in GraphPad Prism) defining the 25th and 75th percentile and median values and whiskers for minimum and maximum values: a) Monocyte clusters; b) B cell clusters [(1) CXCR5- CD73- IRF4-, (2) CXCR5 + CD73-IRF4-, (3)CXCR5 + CD73 + IRF4 + ]; c) Neutrophils; d) NK cell clusters and e) CD68 + CD4 + GrB+ cells.

Extended Data Fig. 7 Comparison of CD3- populations in patients with newly diagnosed cHL versus relapsed/refractory cHL (all) at baseline.

To quantify differences between these 2 groups, patients with newly diagnosed cHL (n = 10) and relapsed/refractory cHL (n = 35), we determined the number of cells that each sample contributed to a given cluster and applied a Wilcoxon rank sum test (two-sided nominal p-values) with Benjamini-Hochberg (BH) corrections for p ≤ 0.05 (Classical Monocytes, Neutrophils, B cells and NK cells separately). Shown here graphically are box plots (generated in GraphPad Prism) defining the 25th and 75th percentile and median values and whiskers for minimum and maximum values: a) Monocyte clusters; b) B cell clusters [(2) CXCR5 + CD73-IRF4-, (3) CXCR5 + CD73 + IRF4 + ]; c) Neutrophils; d) NK cell clusters and e) CD68 + CD4 + GrB+ cells.

Extended Data Fig. 8 Comparison of CD3- populations in patients with relapsed/refractory cHL split by best overall response at baseline (CR, PR, PD).

To quantify differences between these groups (CR n = 12, PR n = 15, PD n = 8), we determined the number of cells that each sample contributed to a given cluster and applied Cuzick trend test (two-sided nominal p-values) with Benjamini-Hochberg (BH) corrections for p ≤ 0.05 (B cells and NK cells separately). Shown here graphically are box plots (generated in GraphPad Prism) defining the 25th and 75th percentile and median values and whiskers for minimum and maximum values: a) Monocyte clusters; b) B-cell clusters [(2) CXCR5 + CD73-IRF4-, (3) CXCR5 + CD73 + IRF4 + ]; c) Neutrophils; d) NK-cell clusters and e) CD68 + CD4 + GrB+ cells.

Extended data Fig. 9 CyTOF analyses of CD3- cell populations from viable singlet cells from 7 primary cHLs and 10 reactive lymph nodes/tonsils from31.

a, Force-directed layouts generated from X-shift analysis within VorteX visualization environment by sampling 4500 events from each sample and pooling resulting events together prior to clustering. The X-shift algorithm clusters events according to similarities in expression of CyTOF panel proteins, grouping events with shared lineage, differentiation and polarization within the pool. Every identified unique population is labeled with a specific color based on the Hex color code. b, Expression of CD68, CD4 and Granzyme B across all samples. c, Separate force-directed layouts (FDLs) of reactive lymph node and primary cHL cell suspensions. In each FDL, the events pertaining to the group of interest retain their Hex color code. Events belonging to the other group are represented in grey. d, Comparison of CD3-CD68 + CD4 + GrB+ Cluster 3341 between reactive lymph nodes and primary cHLs. Shown here graphically are box plots (generated in GraphPad Prism) defining the 25th and 75th percentile and median values and whiskers for minimum and maximum values. To quantify differences between these 2 groups, we determined the number of cells that each sample contributed to a given cluster and applied a Wilcoxon rank sum test with two-sided p-values.

Supplementary information

Supplementary Information

Supplementary Tables 2 and 3

Reporting Summary

Supplementary Table 1

Summary of patient samples included in the study. No sample available (blue), or available specimen had less than the required number of sampled events for the indicated analysis (purple). a, Samples from healthy donors (Sheet 1). b, Samples from patients with newly diagnosed cHL (Sheet 2). c, Samples from patients with relapsed/refractory cHL who commenced anti-PD-1 (nivolumab) <1 yr after ASCT (Sheet 3). d, Samples from patients with relapsed/refractory disease who commenced anti-PD-1 (nivolumab) ≥1 yr after ASCT (Sheet 4). For all patients with R/R cHL we include sample ID, cohort, BOR as determined by IRRC on May 2018 in these patients, number of lines of prior treatment, duration of nivolumab and number of TCR sequences detected. For those patients who commenced anti-PD-1 (nivolumab) ≥1 yr after ASCT, we also indicate availability of samples for CD4+ and CD8+ sorted and where available the associated number of TCR sequences. Two of the evaluated 20 patients had C3D1, sample rather than C4D1, indicated with an asterisk.

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Cader, F.Z., Hu, X., Goh, W.L. et al. A peripheral immune signature of responsiveness to PD-1 blockade in patients with classical Hodgkin lymphoma. Nat Med 26, 1468–1479 (2020). https://doi.org/10.1038/s41591-020-1006-1

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