Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Connecting blood and intratumoral Treg cell activity in predicting future relapse in breast cancer

Nature Immunology volume 20, pages 1220–1230 (2019)Cite this article

Subjects

Abstract

Regulatory T (Treg) cells play a major role in the development of an immunosuppressive tumor microenvironment. The origin of intratumoral Treg cells and their relationship with peripheral blood Treg cells remain unclear. Treg cells consist of at least three functionally distinct subpopulations. Here we show that peripheral blood CD45RA−FOXP3hi Treg cells (Treg II cells) are phenotypically closest to intratumoral Treg cells, including in their expression of CCR8. Analyses of T cell antigen receptor repertoires further support the hypothesis that intratumoral Treg cells may originate primarily from peripheral blood Treg II cells. Moreover, the signaling responsiveness of peripheral blood Treg II cells to immunosuppressive, T helper type 1 (TH1) and T helper type 2 (TH2) cytokines reflects intratumoral immunosuppressive potential, and predicts future relapse in two independent cohorts of patients with breast cancer. Together, our findings give important insights into the relationship between peripheral blood Treg cells and intratumoral Treg cells, and highlight cytokine signaling responsiveness as a key determinant of intratumoral immunosuppressive potential and clinical outcome.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: IL-2 induces higher levels of phoshorylation of STAT5, STAT1 and STAT3 in peripheral blood Treg II cells from patients with BC.
Fig. 2: Peripheral blood Treg II cells have immune phenotypes similar to those of paired intratumoral Treg cells.
Fig. 3: Peripheral blood Treg II cells and intratumoral Treg cells have similar patterns of chemokine receptor expression.
Fig. 4: Intratumoral Treg cells share more clonal overlap with peripheral blood Treg II cells than with Treg I or Treg III cells.
Fig. 5: Cytokine signaling responses in peripheral blood Treg II cells at diagnosis predict future relapse of patients with BC.
Fig. 6: Treg cell suppressive capacity could be reflected by CSI.
Fig. 7: The CSI of peripheral Treg II cells reflects the immunosuppressive potential of intratumoral Treg cells.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Josefowicz, S. Z., Lu, L. F. & Rudensky, A. Y. Regulatory T cells: mechanisms of differentiation and function. Annu. Rev. Immunol. 30, 531–564 (2012).

    Article  CAS  Google Scholar 

  2. Lu, L., Barbi, J. & Pan, F. The regulation of immune tolerance by FOXP3. Nat. Rev. Immunol. 17, 703–717 (2017).

    Article  CAS  Google Scholar 

  3. Sakaguchi, S., Miyara, M., Costantino, C. M. & Hafler, D. A. FOXP3+ regulatory T cells in the human immune system. Nat. Rev. Immunol. 10, 490–500 (2010).

    Article  CAS  Google Scholar 

  4. Kumar, V. Adenosine as an endogenous immunoregulator in cancer pathogenesis: where to go? Purinergic Signal. 9, 145–165 (2013).

    Article  CAS  Google Scholar 

  5. Vignali, D. A., Collison, L. W. & Workman, C. J. How regulatory T cells work. Nat. Rev. Immunol. 8, 523–532 (2008).

    Article  CAS  Google Scholar 

  6. Tanaka, A. & Sakaguchi, S. Regulatory T cells in cancer immunotherapy. Cell Res. 27, 109–118 (2017).

    Article  CAS  Google Scholar 

  7. Chaudhary, B. & Elkord, E. Regulatory T cells in the tumor microenvironment and cancer progression: role and therapeutic targeting. Vaccines 4, 28 (2016).

    Article  Google Scholar 

  8. Bates, G. J. et al. Quantification of regulatory T cells enables the identification of high-risk breast cancer patients and those at risk of late relapse. J. Clin. Oncol. 24, 5373–5380 (2006).

    Article  Google Scholar 

  9. Shang, B., Liu, Y., Jiang, S. J. & Liu, Y. Prognostic value of tumor-infiltrating FoxP3+ regulatory T cells in cancers: a systematic review and meta-analysis. Sci. Rep. 5, 15179 (2015).

    Article  CAS  Google Scholar 

  10. Rech, A. J. et al. CD25 blockade depletes and selectively reprograms regulatory T cells in concert with immunotherapy in cancer patients. Sci. Transl. Med. 4, 134ra162 (2012).

    Article  Google Scholar 

  11. Liakou, C. I. et al. CTLA-4 blockade increases IFNγ-producing CD4+ICOShi cells to shift the ratio of effector to regulatory T cells in cancer patients. Proc. Natl Acad. Sci. USA 105, 14987–14992 (2008).

    Article  CAS  Google Scholar 

  12. Ladoire, S. et al. Pathologic complete response to neoadjuvant chemotherapy of breast carcinoma is associated with the disappearance of tumor-infiltrating FoxP3+ regulatory T cells. Clin. Cancer Res. 14, 2413–2420 (2008).

    Article  CAS  Google Scholar 

  13. Sugiyama, D. et al. Anti-CCR4 mAb selectively depletes effector-type FoxP3+CD4+ regulatory T cells, evoking antitumor immune responses in humans. Proc. Natl Acad. Sci. USA 110, 17945–17950 (2013).

    Article  CAS  Google Scholar 

  14. Miyara, M. et al. Functional delineation and differentiation dynamics of human CD4+ T cells expressing the FoxP3 transcription factor. Immunity 30, 899–911 (2009).

    Article  CAS  Google Scholar 

  15. Liyanage, U. K. et al. Prevalence of regulatory T cells is increased in peripheral blood and tumor microenvironment of patients with pancreas or breast adenocarcinoma. J. Immunol. 169, 2756–2761 (2002).

    Article  CAS  Google Scholar 

  16. Martens, A. et al. Baseline peripheral blood biomarkers associated with clinical outcome of advanced melanoma patients treated with ipilimumab. Clin. Cancer Res. 22, 2908–2918 (2016).

    Article  CAS  Google Scholar 

  17. Yao, X. et al. Levels of peripheral CD4+FoxP3+ regulatory T cells are negatively associated with clinical response to adoptive immunotherapy of human cancer. Blood 119, 5688–5696 (2012).

    Article  CAS  Google Scholar 

  18. Chinen, T. et al. An essential role for the IL-2 receptor in Treg cell function. Nat. Immunol. 17, 1322–1333 (2016).

    Article  CAS  Google Scholar 

  19. Fontenot, J. D., Rasmussen, J. P., Gavin, M. A. & Rudensky, A. Y. A function for interleukin 2 in Foxp3-expressing regulatory T cells. Nat. Immunol. 6, 1142–1151 (2005).

    Article  CAS  Google Scholar 

  20. Antonioli, L., Pacher, P., Vizi, E. S. & Hasko, G. CD39 and CD73 in immunity and inflammation. Trends Mol. Med. 19, 355–367 (2013).

    Article  CAS  Google Scholar 

  21. Plitas, G. et al. Regulatory T cells exhibit distinct features in human breast cancer. Immunity 45, 1122–1134 (2016).

    Article  CAS  Google Scholar 

  22. De Simone, M. et al. Transcriptional landscape of human tissue lymphocytes unveils uniqueness of tumor-infiltrating T regulatory cells. Immunity 45, 1135–1147 (2016).

    Article  Google Scholar 

  23. Campbell, D. J. & Koch, M. A. Phenotypical and functional specialization of FOXP3+ regulatory T cells. Nat. Rev. Immunol. 11, 119–130 (2011).

    Article  CAS  Google Scholar 

  24. Vu, M. D. et al. OX40 costimulation turns off Foxp3+ Tregs. Blood 110, 2501–2510 (2007).

    Article  CAS  Google Scholar 

  25. Carstens, J. L. et al. Spatial computation of intratumoral T cells correlates with survival of patients with pancreatic cancer. Nat. Commun. 8, 15095 (2017).

    Article  Google Scholar 

  26. Baras, A. S. et al. The ratio of CD8 to Treg tumor-infiltrating lymphocytes is associated with response to cisplatin-based neoadjuvant chemotherapy in patients with muscle invasive urothelial carcinoma of the bladder. Oncoimmunology 5, e1134412 (2016).

    Article  Google Scholar 

  27. Hendry, S. et al. Assessing tumor-infiltrating lymphocytes in solid tumors: a practical review for pathologists and proposal for a standardized method from the International Immunooncology Biomarkers Working Group: part 1: assessing the host immune response, TILs in invasive breast carcinoma and ductal carcinoma in situ, metastatic tumor deposits and areas for further research. Adv. Anat. Pathol. 24, 235–251 (2017).

    Article  Google Scholar 

  28. Barsheshet, Y. et al. CCR8+FOXp3+ Treg cells as master drivers of immune regulation. Proc. Natl Acad. Sci. USA 114, 6086–6091 (2017).

    Article  CAS  Google Scholar 

  29. Loyher, P. L. et al. CCR2 influences T regulatory cell migration to tumors and serves as a biomarker of cyclophosphamide sensitivity. Cancer Res. 76, 6483–6494 (2016).

    Article  CAS  Google Scholar 

  30. Curiel, T. J. et al. Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival. Nat. Med. 10, 942–949 (2004).

    Article  CAS  Google Scholar 

  31. Facciabene, A. et al. Tumour hypoxia promotes tolerance and angiogenesis via CCL28 and Treg cells. Nature 475, 226–230 (2011).

    Article  CAS  Google Scholar 

  32. Redjimi, N. et al. CXCR3+ T regulatory cells selectively accumulate in human ovarian carcinomas to limit type I immunity. Cancer Res. 72, 4351–4360 (2012).

    Article  CAS  Google Scholar 

  33. Tan, M. C. et al. Disruption of CCR5-dependent homing of regulatory T cells inhibits tumor growth in a murine model of pancreatic cancer. J. Immunol. 182, 1746–1755 (2009).

    Article  CAS  Google Scholar 

  34. Adeegbe, D. O. & Nishikawa, H. Natural and induced T regulatory cells in cancer. Front. Immunol. 4, 190 (2013).

    Article  CAS  Google Scholar 

  35. Su, S. et al. Blocking the recruitment of naive CD4+ T cells reverses immunosuppression in breast cancer. Cell Res. 27, 461–482 (2017).

    Article  CAS  Google Scholar 

  36. Walker, M. R., Carson, B. D., Nepom, G. T., Ziegler, S. F. & Buckner, J. H. De novo generation of antigen-specific CD4+CD25+ regulatory T cells from human CD4+CD25− cells. Proc. Natl Acad. Sci. USA 102, 4103–4108 (2005).

    Article  CAS  Google Scholar 

  37. Tran, D. Q., Ramsey, H. & Shevach, E. M. Induction of FOXP3 expression in naive human CD4+FOXP3− T cells by T-cell receptor stimulation is transforming growth factor-β-dependent but does not confer a regulatory phenotype. Blood 110, 2983–2990 (2007).

    Article  CAS  Google Scholar 

  38. Marie, J. C., Letterio, J. J., Gavin, M. & Rudensky, A. Y. TGF-β1 maintains suppressor function and Foxp3 expression in CD4+CD25+ regulatory T cells. J. Exp. Med. 201, 1061–1067 (2005).

    Article  CAS  Google Scholar 

  39. Schlenner, S. M., Weigmann, B., Ruan, Q., Chen, Y. & von Boehmer, H. Smad3 binding to the foxp3 enhancer is dispensable for the development of regulatory T cells with the exception of the gut. J. Exp. Med. 209, 1529–1535 (2012).

    Article  CAS  Google Scholar 

  40. Ouyang, W., Beckett, O., Ma, Q. & Li, M. O. Transforming growth factor-β signaling curbs thymic negative selection promoting regulatory T cell development. Immunity 32, 642–653 (2010).

    Article  CAS  Google Scholar 

  41. Konkel, J. E. et al. Transforming growth factor-β signaling in regulatory T cells controls T helper-17 cells and tissue-specific immune responses. Immunity 46, 660–674 (2017).

    Article  CAS  Google Scholar 

  42. Murai, M. et al. Interleukin 10 acts on regulatory T cells to maintain expression of the transcription factor Foxp3 and suppressive function in mice with colitis. Nat. Immunol. 10, 1178–1184 (2009).

    Article  CAS  Google Scholar 

  43. Chaudhry, A. et al. Interleukin-10 signaling in regulatory T cells is required for suppression of Th17 cell-mediated inflammation. Immunity 34, 566–578 (2011).

    Article  CAS  Google Scholar 

  44. Wei, J. et al. Antagonistic nature of T helper 1/2 developmental programs in opposing peripheral induction of Foxp3+ regulatory T cells. Proc. Natl Acad. Sci. USA 104, 18169–18174 (2007).

    Article  CAS  Google Scholar 

  45. Caretto, D., Katzman, S. D., Villarino, A. V., Gallo, E. & Abbas, A. K. Cutting edge: the Th1 response inhibits the generation of peripheral regulatory T cells. J. Immunol. 184, 30–34 (2010).

    Article  CAS  Google Scholar 

  46. Overacre-Delgoffe, A. E. et al. Interferon-γ drives Treg fragility to promote anti-tumor immunity. Cell 169, 1130–1141 (2017).

    Article  CAS  Google Scholar 

  47. Sun, W. et al. A positive-feedback loop between tumour infiltrating activated Treg cells and type 2-skewed macrophages is essential for progression of laryngeal squamous cell carcinoma. Br. J. Cancer 117, 1631–1643 (2017).

    Article  CAS  Google Scholar 

  48. Tiemessen, M. M. et al. CD4+CD25+Foxp3+ regulatory T cells induce alternative activation of human monocytes/macrophages. Proc. Natl Acad. Sci. USA 104, 19446–19451 (2007).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank M. Kirschenbaum for obtaining consent from patients and acquiring samples at City of Hope Comprehensive Cancer Center. We would like to thank D. Dunkley for organizing the collection of blood samples from age-matched healthy controls. This work was supported by the Department of Defense Breast Cancer Research Program, Stand Up to Cancer, the Breast Cancer Research Foundation and the V Foundation. Research reported in this publication included work performed in the Analytical Cytometry Core and Pathology Research Services Core supported by the National Cancer Institute of the National Institutes of Health under award number P30CA33572. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations

  1. Department of Immuno-Oncology, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA

    Lei Wang, Diana L. Simons, Travis Y. Tu, Shawn Solomon, Roger Wang, Anthony Rosario, Christian Avalos & Peter P. Lee

  2. Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA

    Xuyang Lu

  3. Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, CA, USA

    Daniel Schmolze

  4. Department of Surgery, City of Hope Comprehensive Cancer Center, Duarte, CA, USA

    John Yim

  5. Department of Medical Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA, USA

    James Waisman

Authors
  1. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diana L. Simons
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuyang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Travis Y. Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shawn Solomon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Roger Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Anthony Rosario
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Christian Avalos
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Daniel Schmolze
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. John Yim
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. James Waisman
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Peter P. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.W. and P.P.L. designed experiments; L.W., D.L.S., T.Y.T., S.S., R.W., D.S., A.R. and C.A. conducted experiments; L.W. and X.L. analyzed experimental data; J.Y. and J.W. identified and recruited patients into this study; L.W. and P.P.L. wrote the manuscript.

Corresponding author

Correspondence to Peter P. Lee.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Treg cell gating strategy for flow and phosflow cytomery.

(a) Representative flow plots for peripheral and intratumoral Treg cells. (b-c) Representative flow plots showing the percentages of IL-2Rβ+ (b) and IL-2Rγ+ (c) in peripheral blood Tconv, Treg I, II and III cells. (d) Representative flow plots for peripheral blood Treg cells in phosflow cytometry.

Supplementary Figure 2 Expressions of immune-regulating proteins in peripheral blood Treg cell subpopulations and intratumoral Treg cells.

The percentages of CD73+ (n=40) (a), PD1+ (n=40), ****p<0.0001 (b), Tim3+ (n=40), ****p<0.0001 (c), LAG3+ (n=40), ****p<0.0001; ***p=0.0002 (d), GITR+ (n=20), ****p<0.0001; **p=0.007 (e) or HLA-DR+ (n=40), ****p<0.0001; ***p=0.0003 (f) in peripheral Tconv, Treg I, II and III cells from newly diagnosed patients with BC were determined by flow cytometry. Friedman test. Shown are mean ± s.e.m. Representative flow plots showing the percentages of LAG3+ (d), GITR+ (e), or HLA-DR+ (f) in intratumor Treg cells from untreated primary breast tumors (n=8). Data from intratumoral Treg cells were highlighted with purple box.

Supplementary Figure 3 Flow sort gating strategy of peripheral blood Tconv and Treg cell subpopulations.

Peripheral Treg I (CD45RA+CD25lo), II (CD45RA−CD25hi), III (CD45RA−CD25lo) and Tconv (CD45RA−CD25−) cells were sorted from PBMCs of newly diagnosed patients with BC.

Supplementary Figure 4 Chemokine receptors CCR2, CCR10 and CXCR3 not differentially expressed in intratumoral Treg cells.

The percentages and representative flow plots of CCR2+, ***p=0.0003; *p=0.03 (a), CCR10+, ****p<0.0001; **p=0.009 (b), CXCR3+, ****p<0.0001; *p=0.025 (c) in peripheral Tconv, Treg I, II and III cells from newly diagnosed patients with BC (n=20) and in intratumor Treg cells from untreated primary breast tumors (n=8). Friedman test. Shown are mean ± s.e.m. Data from intratumoral Treg cells were highlighted with purple box.

Supplementary Figure 5 Intratumoral Treg cells have higher TCR clonal overlap with peripheral Treg II cells.

(a) TCRβ CDR3 regions of flow sorted intratumor Treg cells (CD4+CD25+CD127−) and paired peripheral Treg I, II or III cells from patients with BC (n=3) were sequenced. Pair-wise scatter plots showing the overlapping TCR clones between intratumor Treg cells and peripheral Treg cell subpopulations. (b) The percentages of unique TCR nucleotide clonal overlap between peripheral blood Treg I, II or III cells. Shown are mean ± s.e.m.

Supplementary Figure 6 Representative flow plots showing TGFβ-induced signaling in peripheral blood Treg II cell subpopulation.

TGFβ (25ng/ml for 30 mins)-induced Smad2/3 phosphorylation in peripheral blood Treg II cells from newly diagnosed patients with BC (n=40) was determined by phosphoflow cytometry with anti-p-Smad2 (pS465/pS467)/p-Smad3 (pS423/pS425) antibody. Plots from relapsed patients with BC were highlighted with red box.

Supplementary Figure 7 Representative flow plots showing IL-10-induced signaling in peripheral blood Treg II cell subpopulation.

IL-10 (100ng/ml for 15 mins)-induced STAT1 phosphorylation in peripheral blood Treg II cells from newly diagnosed patients with BC (n=40) was determined by phosphoflow cytometry with anti-p-STAT1 (pY701) antibody. Plots from relapsed patients with BC were highlighted with red box.

Supplementary Figure 8 Representative flow plots showing IL-4-induced signaling in peripheral blood Treg II cell subpopulation.

IL-4 (50ng/ml for 15 mins)-induced STAT6 phosphorylation in peripheral blood Treg II cells from newly diagnosed patients with BC (n=40) was determined by phosphoflow cytometry with anti-p-STAT6 (pY641) antibody. Plots from relapsed patients with BC were highlighted with red box.

Supplementary Figure 9 Representative flow plots showing IFNγ-induced signaling in peripheral blood Treg II cell subpopulation.

IFNγ (50ng/ml for 15 mins)-induced STAT1 phosphorylation in peripheral blood Treg II cells from newly diagnosed patients with BC (n=40) was determined by phosphoflow cytometry with anti-p-STAT1 (pY701) antibody. Plots from relapsed patients with BC were highlighted with red box.

Supplementary Figure 10 Cytokine signaling index of Tconv, Treg I or Treg III cells not correlated with clinical outcome.

Relapse-free survival (RFS) was compared between patients with BC (n=40) with above median CSI and below median CSI in peripheral Tconv (p=0.22) (a), Treg I (p=0.68) (b) or Treg III cells (p=0.07) (c) using Kaplan-Meier estimate and log rank test. All blood were collected from patients with BC at diagnosis before surgery or any therapy who had been clinically followed for at least 36 months. NS, not significant.

Supplementary Figure 11 Cytokine plasma levels in patients with BC at diagnosis do not correlate with clinical outcome.

(a) Plasma levels of TGFβ, IL-10, IL-4 and IFNγ were determined by ELISA and compared between relapse-free (n=25) and relapsed (n=15) BC patients. Shown are mean ± s.e.m. (b) The association between plasma level of TGFβ (p=0.08), IL-10 (p=0.79), IL-4 (p=0.36) or IFNγ (p=0.58) and signaling response in Treg II cells, respectively. Spearman correlation coefficient test. NS, not significant.

Supplementary Figure 12 Treg II CSI in healthy donors were lower than in relapsed patients with BC.

(a-b) PBMCs from age-matched healthy donors (HD) (n=10) were stimulated with TGFβ (25ng/ml, 30 mins), IL-10 (100ng/ml, 15 mins), IL-4 (50ng/ml, 15 mins) or IFNγ (50ng/ml, 15 mins). TGFβ-induced p-Smad2/3 and IL-10-induced p-STAT1, ***p<0.001 (a), IFNγ-induced p-STAT1 and IL-4-induced p-STAT6 **p=0.008; *p=0.028 (b) in peripheral Treg II cells were determined by phosphoflow cytometry and were compared between healthy donors (n=10) and relapsed patients with BC (n=15). (c) Treg II CSI was compared between HD (n=10) and relapsed patients with BC (n=15), ****p<0.001, two-tailed, Mann-Whitney test. Shown are mean ± s.e.m.

Supplementary Figure 13 Cytokine signaling index (CSI) of peripheral Treg I or III cells not associated with immunosuppressive potential of intratumoral Treg cells.

Untreated breast primary tumor tissue sections were stained for FoxP3, CD8, CD68, CD123, CK, and DAPI. (a) The density of Treg cells within primary tumors (FoxP3+ cell number per mm2) from patients with BC who later relapsed (n=9) and from patients who remained relapse-free (n=11), p=0.07, two-tailed, Mann-Whitney test, Shown are mean ± s.e.m. (b) The association between Treg cell density and the percentage of Treg cells within 20μm from TAMs (n=20) (p=0.14). (c-d) The association between Treg I CSI (p=0.52) (C) or Treg III CSI (p=0.15) (d) and the percentage of Treg cells within 20μm from TAMs (n=20). Spearman correlation coefficient test. NS, not significant.

Supplementary Figure 14 Consistent cytokine signaling responses in reference PBMCs between discovery and validation runs.

Different vials of reference PBMC from the same healthy donors (n=5) were thawed and run together with samples from patient with BC in the discovery and validation cohorts to determine consistency between runs. (a) TGF-induced Smad2/3 phosphorylation, (b) IL-10-induced STAT1 phosphorylation, (c) IL-4-induced STAT6 phosphorylation, or (d) IFNγ-induced STAT1 phosphorylation in Treg II cells.

Supplementary information

Supplementary Information

Supplementary Figures 1–14 and Supplementary Tables 1 and 2

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Simons, D.L., Lu, X. et al. Connecting blood and intratumoral Treg cell activity in predicting future relapse in breast cancer. Nat Immunol 20, 1220–1230 (2019). https://doi.org/10.1038/s41590-019-0429-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-019-0429-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer