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The PD-1 expression balance between effector and regulatory T cells predicts the clinical efficacy of PD-1 blockade therapies

Abstract

Immune checkpoint blockade has provided a paradigm shift in cancer therapy, but the success of this approach is very variable; therefore, biomarkers predictive of clinical efficacy are urgently required. Here, we show that the frequency of PD-1+CD8+ T cells relative to that of PD-1+ regulatory T (Treg) cells in the tumor microenvironment can predict the clinical efficacy of programmed cell death protein 1 (PD-1) blockade therapies and is superior to other predictors, including PD ligand 1 (PD-L1) expression or tumor mutational burden. PD-1 expression by CD8+ T cells and Treg cells negatively impacts effector and immunosuppressive functions, respectively. PD-1 blockade induces both recovery of dysfunctional PD-1+CD8+ T cells and enhanced PD-1+ Treg cell–mediated immunosuppression. A profound reactivation of effector PD-1+CD8+ T cells rather than PD-1+ Treg cells by PD-1 blockade is necessary for tumor regression. These findings provide a promising predictive biomarker for PD-1 blockade therapies.

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Fig. 1: Responders to PD-1 blockade therapies have high PD-1+CD8+ T cell infiltration in the TME.
Fig. 2: PD-1 expression is highly induced by CD8+ T cells with high-affinity antigen peptides.
Fig. 3: Nonresponders to PD-1 blockade therapies have high PD-1 expression by eTreg cells in the TME.
Fig. 4: PD-1+ Treg cell–mediated immunosuppression is enhanced by treatment with monoclonal antibody to PD-1.
Fig. 5: TCR and CD28 signals are reactivated by PD-1 blockade in CD8+ T cells and Treg cells.
Fig. 6: PD-1 expression balance of T cells in the TME is related to responses to PD-1 blockade in vivo.
Fig. 7: PD-1 expression balance of T cells predicts clinical responses to PD-1 blockade therapies.
Fig. 8: PD-1 expression balance of T cells accurately predicts responses to PD-1 blockade therapies.

Data availability

The data that support the findings of this study are available from the corresponding author upon request. The exome sequencing data of patients’ tumor samples were deposited in the Japanese Genotype-phenotype Archive (accession number: JGAS00000000244).

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Acknowledgements

This article is dedicated to the memory of T. Kamada. We thank T. Kobayashi, Y. Tada, T. Takaku, M. Nakai, K. Onagawa, M. Takemura, C. Haijima, M. Hoshino, K. Yoshida, M. Sugaya, Y. Ishige, E. Tanji, M. Ozawa and C. Notake for their technical assistance. This study was supported by Grants-in-Aid for Scientific Research (S grant 17H06162 (H. Nishikawa), Challenging Exploratory Research grant 16K15551 (H. Nishikawa), Young Scientists grant 17J09900 (Y. Togashi), JSPS Research Fellowship 17K18388 (Y. Togashi) and JSPS Research Fellowship 18J21161 (T. Kamada) and 18J21551 (S.K.)) from the Ministry of Education, Culture, Sports, Science and Technology of Japan; by the Project for Cancer Research and Therapeutic Evolution (P-CREATE, 16cm0106301h0001 (H. Nishikawa), 16cm0106301h0001 (H.M.), 18cm0106502s0403 (M.K.) and 18cm0106340h0001 (Y. Togashi)); by a Development of Technology for Patient Stratification Biomarker Discovery grant (19ae0101074s0401 (H. Nishikawa)) and Leading Advanced Projects for Medical Innovation (LEAP, 18am0001001h9905 (H.M.)) from the Japan Agency for Medical Research and Development (AMED); by the National Cancer Center Research and Development Fund (28-A-7 and 31-A-7 (H. Nishikawa)); by the Naito Foundation (Y. Togashi and H. Nishikawa); by the Takeda Science Foundation (Y. Togashi); by the Kobayashi Foundation for Cancer Research (Y. Togashi); by a Novartis research grant (Y. Togashi); by a Bristol-Myers Squibb research grant (Y. Togashi) and by the SGH Foundation (Y. Togashi). This study was executed in part as a research program supported by Ono Pharmaceutical Co. Ltd.

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Authors

Contributions

S.K., Y. Togashi, T. Kamada, E.S., H. Nishinakamura, Y. Takeuchi, K.V., K.I., Y.M., S.M., T.I., A.T., S.F., G.I., T. Kuwata, M.K., T.U., H.M. and H. Nishikawa performed the experiments and analyzed the data. A.K., H.U., K.K., K.A., K.N., N.Y., K.G., M.T., T.D. and K.S. collected the clinical specimens and performed the analyses of the clinical data. T.S. and Y.Y. designed the architecture of the statistical models. S.K., Y. Togashi, T. Kamada and H. Nishikawa conceived the project and wrote the paper.

Corresponding author

Correspondence to Hiroyoshi Nishikawa.

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Competing interests

Y. Togashi has received honoraria from Bristol-Myers Squibb, Ono Pharmaceutical, Chugai Pharmaceutical, MSD and Boehringer Ingelheim and has received research funding from AstraZeneca and KOTAI Biotechnologies outside the scope of this work. T.S. and Y.Y. are employees of Hitachi, Ltd. (Hitachi provided support in the form of salaries for authors T.S. and Y.Y. but did not have any additional role in the study). T. Kuwata has received research funding from Ono Pharmaceutical and has received honoraria from Chugai Pharmaceutical outside this work. K.G. has received honoraria and research funding from Bristol-Myers Squibb, Chugai Pharmaceutical and Ono Pharmaceutical and has received research funding from AstraZeneca, MSD, Merck Serono and F. Hoffmann–La Roche outside the scope of this work. H.M. has served as a member of the scientific advisory board for Chugai Pharmaceutical and has received research funding from Ono Pharmaceutical outside the scope of this work. T.D. has received research funding from Merck Serono, MSD, Chugai Pharmaceutical and Bristol-Myers Squibb outside the scope of this work. K.S. has served as a member of the scientific advisory board for Ono Pharmaceutical, Eli Lilly, Bristol-Myers Squibb, Astellas Pharma, Takeda and Pfizer; has received research funding from Ono Pharmaceutical, Eli Lilly, Sumitomo Dainippon Pharma, Daiichi Sankyo, Taiho Pharmaceutical, Chugai Pharmaceutical and MSD; and has received honoraria from Novartis, AbbVie and Yakult outside the scope of this work. H. Nishikawa received research funding from Ono Pharmaceutical and BD Japan for this work and has received honoraria and research funding from Chugai Pharmaceutical, MSD and Bristol-Myers Squibb; honoraria from Ono Pharmaceutical; and research funding from Taiho Pharmaceutical, Daiichi Sankyo, Kyowa Kirin, Zenyaku Kogyo, Oncolys BioPharma, Debiopharm, Asahi-Kasei, SRL, Fujifilm, Astellas Pharma, Sumitomo Dainippon Pharma and Sysmex outside the scope of this study. H. Nishikawa is the primary inventor on pending patents PCT/JP2019/02163 belonging to the National Cancer Center Japan and Ono Pharmaceutical and PCT/JP2020/0059919 belonging to the National Cancer Center Japan and BD Biosciences. T.S. has a pending US patent 16/117260 belonging to Hitachi, Ltd. All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 CyTof tSNE plots of TILs and the relation between PD-1 expression and antigen affinity are shown.

(a) Fresh tumor samples obtained from primary or metastatic tumors by endoscopic or needle biopsy within two weeks before the initial drug administration were subjected to CyTOF. (b) Binding affinities of each peptide restricted by HLAA*0201 were predicted with NetMHCpan (version 2.8). Sequences and predicted IC50 values of peptides used in this study are shown. (c) Predicted IC50 values for Melan A and Flu are shown. (d) Representative FCM staining of PD-1 expression (left) and summary for expression levels detected by Mean Fluorescence Intensity (MFI) of PD-1 in tetramer+CD8+ T cells (right). 1 × 105 HLAA*0201 CD8+ T cells prepared from healthy individuals (n = 3) were cultured with antigen-presenting cells pulsed with natural Melan A or Flu peptides. Seven to 10 days later, PD-1 expression by Melan A or Flu-specific CD8+ T cells detected by MHC/peptide tetramers was examined. Paired t test was used. MelA, Melan A; Bars, mean; error bars, s.e.m.

Extended Data Fig. 2 Cytokine production by CD8+ T cells is attenuated by PD-L1 and recovered by anti-PD-1 mAb.

(a, c) Representative FCM staining (left) and summary (right) of IFN-γ production by CD8+ T cells are shown. CD8+ T cells purified from splenocytes of C57BL/6J (a) or Pdcd1-/-C57BL/6 (c) mice were stimulated with anti-CD3 mAb and anti-CD28 mAb, PD-L1-Ig and/or anti-PD-1 mAb as indicated for 48 hours (n = 3 per group). IFN-γ production by CD8+ T cells was analyzed with FCM. One-way ANOVA test with Bonferroni corrections was used. (b) MC-38 cells (5×105) were inoculated subcutaneously into C57BL/6J mice on day 0 and anti-PD-1 mAb or control mAb was administered on days 3, 6 (n = 6 tumors per group). On day 7, mice were sacrificed, and TILs were extracted from tumors and analyzed with FCM. Representative FCM staining (left) and summary (right) of IFN-γ and TNF-α production by CD8+ T cells from TILs are shown. Unpaired t test was used. These experiments were performed at least twice with similar results. Bars, mean; error bars, s.e.m.

Extended Data Fig. 3 Treg cell activation markers are down-regulated by PD-1 signaling and recovered by anti-PD-1 mAb.

(a, c) Representative FCM staining (left) and summary (right) of GITR, ICOS, and CTLA4 expression by Foxp3+CD4+ T cells are shown. CD25+CD4+ T cells purified from splenocytes of C57BL/6J (wild-type; WT) (a) or Pdcd1-/-C57BL/6 (PD-1 KO) (c) mice were stimulated with anti-CD3 mAb and anti-CD28 mAb, PD-L1-Ig and/or anti-PD-1 mAb as indicated for 48 hours (n = 3 per group). The expression of GITR, ICOS and CTLA-4 in Foxp3+CD4+ T cells was analyzed with FCM. One-way ANOVA test with Bonferroni corrections was used. (b) MC-38 cells (5 × 105) were inoculated subcutaneously into C57BL/6J mice on day 0 and anti-PD-1 mAb or control mAb was administered on days 3, 6 (n = 5 per group). On day 7, mice were sacrificed, and TILs were extracted from tumors and analyzed with FCM. Representative FCM staining (left) and summary (right) of GITR, ICOS and CTLA-4 expression in Foxp3+CD4+ T cells from TILs are shown. Unpaired t-test was used. The experiments were performed at least twice with similar results. Bars, mean; error bars, s.e.m..

Extended Data Fig. 4 The activation of TCR and CD28 signals in human T cells is affected by PD-1 blockade.

(a, b) Representative FCM staining (left) and summary (right) of pZAP70, pAKT and pSHP2 expression by CD8+ T cells (a) and CD45RA-FoxP3hiCD4+ T cells (b) are shown. CD8+ T cells (a) or CD45RA-FoxP3hiCD4+ T cells (b) sorted from PBMCs of healthy individuals (n = 4 per group) were stimulated with anti-CD3 mAb and anti-CD28 mAb, PD-L1-Ig and/or anti-PD-1 mAb as indicated for 48 hours. The expression of pZAP70, pAKT and pSHP2 by CD8+ T cells and CD45RA-FoxP3hiCD4+ T cells was analyzed with FCM. One-way ANOVA test with Bonferroni corrections was used. (c) Representative FCM staining (left) and summary (right) for the fold changes of Treg cell activation markers after treatment with anti-PD-1 mAb are shown. The expression of pZAP70, pAKT and pSHP2 by eTreg cells from TILs was analyzed with FCM after 48 hours of stimulation by anti-CD3 mAb and anti-CD28 mAb in the presence/absence of anti-PD-1 mAb (n = 5 per group). Paired t-test was used. Bars, mean; error bars, s.e.m.

Extended Data Fig. 5 PD-1 expression balance of T cells in the TME, not in PBMCs, correlates with PD-1 blockade efficacy.

(a) The boundaries classified by the piece-wise linear model was generated by training with the discovery cohort. (b) The boundaries were applied into the validation cohort. The red color shading in each panel represents the responder probability. The AUC values of the piece-wise linear model in the discovery cohort (a) and the validation cohort (b) were 0.991 and 0.968, respectively. (c-g) Fresh tumor samples obtained from primary or metastatic tumors by endoscopic or needle biopsy and PBMCs within 2 weeks before the initial drug administration were subjected to FCM. Correlation of PD-1+ (%) in CD8+ T cells (c) and eTreg cells (d) in PBMCs and TILs in the validation cohort is shown. (e, f) PD-1 expression by CD8+ T cells (e) or eTreg cells (f) in PBMCs from responders and non-responders is shown. Unpaired t test was used (All, R, n = 13, NR, n = 31; NSCLC, R, n = 5, NR, n = 7; GC, R, n = 3, NR, n = 19; MM, R, n = 5, NR, n = 5). (g) Scatter plots using %PD-1+ in CD8+ T cells and eTreg cells in PBMCs is shown.

Extended Data Fig. 6 tSNE plots are shown for representative markers in CyTOF analysis for PBMCs.

Blood samples obtained from patients in the validation cohort within two weeks before the initial drug administration were subjected to CyTOF. Representative expression patterns of markers are shown.

Extended Data Fig. 7 Any biomarkers with PBMCs do not predict clinical responses to PD-1 blockade therapies.

(ad) CD45RA+CCR7+ (%) (a), CD45RA+CCR7(%) (b), CD45RACCR7+ (%) (c), and CD45RACCR7 (%) (d) in CD4+ cells in PBMCs from responders and non-responders are shown. (e-h) CD45RA+CCR7+ (%) (e), CD45RA+CCR7 (%) (f), CD45RACCR7+ (%) (g), and CD45RACCR7 (%) (h) in CD8+ cells in PBMCs from responders and non-responders are shown. (k) Ki-67+ (%) in CD8+ cells in PBMCs from responders and non-responders is shown. (j) CD14+CD16HLA-DRhigh (%) in CD45+ cells in PBMCs from responders and non-responders is shown. (k) CD69+ (%) in CD3CD56+ cells in PBMCs from responders and non-responders is shown. Unpaired t-test was used (All, R, n = 12, NR, n = 29; NSCLC, R, n = 5, NR, n = 7; GC, R, n = 2, NR, n = 17; MM, R, n = 5, NR, n = 5).

Extended Data Fig. 8 TMB and PD-L1 expression show a slight correlation with efficacy of PD-1 blockade therapies.

(a) Kaplan–Meier curves for PFS of GC patients treated with anti-PD-1 mAb according to MMR status in the validation cohort. (b, c) Eight NSCLC, nine GC and five MM samples in the validation cohort were subjected to whole-exome sequencing. ROC curves for TMB (missense variants) were developed by plotting the true positive rate against the false positive rate at each threshold setting. AUC (0.64, 95%CI: 0.38-0.91) shown in each plot summarizes the performance of TMB (b). Kaplan–Meier curves for PFS of patients (the validation cohort) treated with PD-1 blockade therapies in the whole (upper left), NSCLC (upper right), GC (lower left) and MM (lower right) according to TMB. To compare PFS, the cut-off of TMB derived from ROC analysis was used (c). (d) Kaplan–Meier curves for PFS of patients (the validation cohort) treated with anti-PD-1 mAb in the whole (left), NSCLC (middle) and GC (right) according to PD-L1 expression. To compare PFS, PD-L1 expression by tumor cells of ≥ 1% was defined as positive. (a, c, d) Log-rank test was employed.

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Point-wise linear models and feature importance are shown.

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Kumagai, S., Togashi, Y., Kamada, T. et al. The PD-1 expression balance between effector and regulatory T cells predicts the clinical efficacy of PD-1 blockade therapies. Nat Immunol 21, 1346–1358 (2020). https://doi.org/10.1038/s41590-020-0769-3

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