Stress–glucocorticoid–TSC22D3 axis compromises therapy-induced antitumor immunity

Article metrics

Abstract

Psychological distress has long been suspected to influence cancer incidence and mortality. It remains largely unknown whether and how stress affects the efficacy of anticancer therapies. We observed that social defeat caused anxiety-like behaviors in mice and dampened therapeutic responses against carcinogen-induced neoplasias and transplantable tumors. Stress elevated plasma corticosterone and upregulated the expression of glucocorticoid-inducible factor Tsc22d3, which blocked type I interferon (IFN) responses in dendritic cell (DC) and IFN-γ+ T cell activation. Similarly, close correlations were discovered among plasma cortisol levels, TSC22D3 expression in circulating leukocytes and negative mood in patients with cancer. In murine models, exogenous glucocorticoid injection, or enforced expression of Tsc22d3 in DC was sufficient to abolish therapeutic control of tumors. Administration of a glucocorticoid receptor antagonist or DC-specific Tsc22d3 deletion reversed the negative impact of stress or glucocorticoid supplementation on therapeutic outcomes. Altogether, these results indicate that stress-induced glucocorticoid surge and Tsc22d3 upregulation can subvert therapy-induced anticancer immunosurveillance.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: SD accelerates tumor progression and hampers chemotherapeutic responsiveness.
Fig. 2: SD impairs the efficacy of anticancer therapies and induces immunosuppression.
Fig. 3: The secretion of stress-related hormones and neurotransmitters and their impact on anticancer therapies.
Fig. 4: Mental stress modulates the function and transcriptome of tumor-infiltrating dendritic cells (TIDC).
Fig. 5: DC-targeted genetic manipulation of Tsc22d3 modulates the efficacy of anticancer therapies and the transcriptome of TIDCs.
Fig. 6: Prognostic value of TSC22D3 in patients with cancer.

Data availability

All requests for raw and analyzed data and materials will be promptly reviewed by Suzhou Institute of Systems Medicine to verify if the request is subject to any intellectual property or confidentiality obligations. Patient-related data not included in the paper were generated as parts of clinical investigation and may be subject to patient confidentiality. Any data and materials that can be shared will be released via a material transfer agreement. All raw RNA sequencing data can be found at the NCBI Sequence Read Archive (accession number: PRJNA555789, PRJNA556553).

Code availability

RNA-seq data analysis was performed with DESeq2, pheatmap and clusterProfiler packages using R and R studio software (R Foundation for Statistical Computing). All requests for the code are promptly reviewed by Suzhou Institute of Systems Medicine to verify if the request is subject to any intellectual property or confidentiality obligations.

References

  1. 1.

    Schoemaker, M. J. et al. Psychological stress, adverse life events and breast cancer incidence: a cohort investigation in 106,000 women in the United Kingdom. Breast Cancer Res. 18, 72 (2016).

  2. 2.

    Font-Burgada, J., Sun, B. & Karin, M. Obesity and cancer: the oil that feeds the flame. Cell Metab. 23, 48–62 (2016).

  3. 3.

    Dagogo-Jack, I. & Shaw, A. T. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol. 15, 81–94 (2018).

  4. 4.

    Batty, G. D., Russ, T. C., Stamatakis, E. & Kivimaki, M. Psychological distress in relation to site specific cancer mortality: pooling of unpublished data from 16 prospective cohort studies. BMJ 356, j108 (2017).

  5. 5.

    Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).

  6. 6.

    Galluzzi, L., Buque, A., Kepp, O., Zitvogel, L. & Kroemer, G. Immunological effects of conventional chemotherapy and targeted anticancer agents. Cancer Cell 28, 690–714 (2015).

  7. 7.

    Ma, Y. et al. Anticancer chemotherapy-induced intratumoral recruitment and differentiation of antigen-presenting cells. Immunity 38, 729–741 (2013).

  8. 8.

    Ma, Y., Pitt, J. M., Li, Q. & Yang, H. The renaissance of anti-neoplastic immunity from tumor cell demise. Immunol. Rev. 280, 194–206 (2017).

  9. 9.

    Vacchelli, E. et al. Chemotherapy-induced antitumor immunity requires formyl peptide receptor 1. Science 350, 972–978 (2015).

  10. 10.

    Gonzalo, J. A., Gonzalez-Garcia, A., Martinez, C. & Kroemer, G. Glucocorticoid-mediated control of the activation and clonal deletion of peripheral T cells in vivo. J. Exp. Med. 177, 1239–1246 (1993).

  11. 11.

    Michaud, K., Matheson, K., Kelly, O. & Anisman, H. Impact of stressors in a natural context on release of cortisol in healthy adult humans: a meta-analysis. Stress 11, 177–197 (2008).

  12. 12.

    Cain, D. W. & Cidlowski, J. A. Immune regulation by glucocorticoids. Nat. Rev. Immunol. 17, 233–247 (2017).

  13. 13.

    Webster, J. I., Tonelli, L. & Sternberg, E. M. Neuroendocrine regulation of immunity. Ann. Rev. Immunol. 20, 125–163 (2002).

  14. 14.

    Chavan, S. S., Pavlov, V. A. & Tracey, K. J. Mechanisms and therapeutic relevance of neuro-immune communication. Immunity 46, 927–942 (2017).

  15. 15.

    Golden, S. A., Covington, H. E. 3rd, Berton, O. & Russo, S. J. A standardized protocol for repeated social defeat stress in mice. Nat. Protoc. 6, 1183–1191 (2011).

  16. 16.

    Papadopoulou, A. et al. Acute and chronic stress differentially regulate cyclin-dependent kinase 5 in mouse brain: implications to glucocorticoid actions and major depression. Transl. Psychiatry 5, e578 (2015).

  17. 17.

    Obeid, M. et al. Calreticulin exposure dictates the immunogenicity of cancer cell death. Nat. Med. 13, 54–61 (2007).

  18. 18.

    Spitz, I. M. & Bardin, C. W. Mifepristone (RU 486)-a modulator of progestin and glucocorticoid action. N. Engl. J. Med. 329, 404–412 (1993).

  19. 19.

    Fleseriu, M. et al. Mifepristone, a glucocorticoid receptor antagonist, produces clinical and metabolic benefits in patients with Cushing’s syndrome. J. Clin. Endocrinol. Metab. 97, 2039–2049 (2012).

  20. 20.

    Bai, Y. Y. et al. ProBDNF signaling regulates depression-like behaviors in rodents under chronic stress. Neuropsychopharmacology 41, 2882–2892 (2016).

  21. 21.

    David, D. J. et al. Neurogenesis-dependent and -independent effects of fluoxetine in an animal model of anxiety/depression. Neuron 62, 479–493 (2009).

  22. 22.

    Cryan, J. F., Page, M. E. & Lucki, I. Differential behavioral effects of the antidepressants reboxetine, fluoxetine, and moclobemide in a modified forced swim test following chronic treatment. Psychopharmacology 182, 335–344 (2005).

  23. 23.

    Roni, M. A. & Rahman, S. Effects of lobeline and reboxetine, fluoxetine, or bupropion combination on depression-like behaviors in mice. Pharmacol. Biochem. Behav. 139, 1–6 (2015).

  24. 24.

    Oh, K. S. et al. Anti-inflammatory chromatinscape suggests alternative mechanisms of glucocorticoid receptor action. Immunity 47, 298–309 e295 (2017).

  25. 25.

    Ayroldi, E. et al. Modulation of T-cell activation by the glucocorticoid-induced leucine zipper factor via inhibition of nuclear factor kappaB. Blood 98, 743–753 (2001).

  26. 26.

    Ronchetti, S., Migliorati, G. & Riccardi, C. GILZ as a mediator of the anti-inflammatory effects of glucocorticoids. Front. Endocrinol. 6, 170 (2015).

  27. 27.

    Calmette, J. et al. Glucocorticoid-induced leucine zipper enhanced expression in dendritic cells is sufficient to drive regulatory T cells expansion in vivo. J. Immunol. 193, 5863–5872 (2014).

  28. 28.

    Calmette, J. et al. Glucocorticoid-induced leucine zipper protein controls macropinocytosis in dendritic cells. J. Immunol. 197, 4247–4256 (2016).

  29. 29.

    Ngo, D. et al. Divergent effects of endogenous and exogenous glucocorticoid-induced leucine zipper in animal models of inflammation and arthritis. Arthritis Rheumatol. 65, 1203–1212 (2013).

  30. 30.

    Deng, L. et al. STING-dependent cytosolic DNA sensing promotes radiation-induced type I interferon-dependent antitumor immunity in immunogenic tumors. Immunity 41, 843–852 (2014).

  31. 31.

    Sistigu, A. et al. Cancer cell-autonomous contribution of type I interferon signaling to the efficacy of chemotherapy. Nat. Med. 20, 1301–1309 (2014).

  32. 32.

    Grove, R. & Prapavessis, H. Preliminary evidence for the reliability and validity of an abbreviated profile of mood states. Int. J. Spot Psychol. 23, 93–109 (1992).

  33. 33.

    Baker, F., Denniston, M., Zabora, J., Polland, A. & Dudley, W. N. A POMS short form for cancer patients: psychometric and structural evaluation. Psychooncology 11, 273–281 (2002).

  34. 34.

    Li, T. et al. TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 77, e108–e110 (2017).

  35. 35.

    Gyorffy, B., Surowiak, P., Budczies, J. & Lanczky, A. Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer. PloS One 8, e82241 (2013).

  36. 36.

    Szasz, A. M. et al. Cross-validation of survival associated biomarkers in gastric cancer using transcriptomic data of 1,065 patients. Oncotarget 7, 49322–49333 (2016).

  37. 37.

    Thaker, P. H. et al. Chronic stress promotes tumor growth and angiogenesis in a mouse model of ovarian carcinoma. Nat. Med. 12, 939–944 (2006).

  38. 38.

    Cui, B. et al. Stress-induced epinephrine enhances lactate dehydrogenase A and promotes breast cancer stem-like cells. J. Clin. Invest. 129, 1030–1046 (2019).

  39. 39.

    Bereshchenko, O. et al. GILZ promotes production of peripherally induced Treg cells and mediates the crosstalk between glucocorticoids and TGF-beta signaling. Cell Rep. 7, 464–475 (2014).

  40. 40.

    Hamdi, H. et al. Induction of antigen-specific regulatory T lymphocytes by human dendritic cells expressing the glucocorticoid-induced leucine zipper. Blood 110, 211–219 (2007).

  41. 41.

    Futterleib, J. S., Feng, H., Tigelaar, R. E., Choi, J. & Edelson, R. L. Activation of GILZ gene by photoactivated 8-methoxypsoralen: potential role of immunoregulatory dendritic cells in extracorporeal photochemotherapy. Transfus. Apher. Sci. 50, 379–387 (2014).

  42. 42.

    Li, C. C., Munitic, I., Mittelstadt, P. R., Castro, E. & Ashwell, J. D. Suppression of dendritic cell-derived IL-12 by endogenous glucocorticoids is protective in LPS-induced sepsis. PLoS Biol. 13, e1002269 (2015).

  43. 43.

    Norbiato, G., Bevilacqua, M., Vago, T. & Clerici, M. Glucocorticoids and interferon-alpha in the acquired immunodeficiency syndrome. J. Clin. Endocrinol. Metab. 81, 2601–2606 (1996).

  44. 44.

    Thormann, V. et al. Genomic dissection of enhancers uncovers principles of combinatorial regulation and cell type-specific wiring of enhancer-promoter contacts. Nucleic Acids Res. 46, 2868–2882 (2018).

  45. 45.

    Navari, R. M. & Aapro, M. Antiemetic prophylaxis for chemotherapy-induced nausea and vomiting. N. Engl. J. Med. 374, 1356–1367 (2016).

  46. 46.

    Cheng, K. K. F., Lim, Y. T. E., Koh, Z. M. & Tam, W. W. S. Home-based multidimensional survivorship programmes for breast cancer survivors. Cochrane Database Syst. Rev. 8, CD011152 (2017).

  47. 47.

    Bradt, J., Dileo, C., Magill, L. & Teague, A. Music interventions for improving psychological and physical outcomes in cancer patients. Cochrane Database Syst. Rev. https://doi.org/10.1002/14651858.CD006911.pub3 (2016).

  48. 48.

    Tritos, N. A. & Biller, B. M. Advances in medical therapies for Cushing’s syndrome. Discov. Med. 13, 171–179 (2012).

  49. 49.

    Zilionis, R. et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity 50, 1317–1334 e1310 (2019).

  50. 50.

    Caton, M. L., Smith-Raska, M. R. & Reizis, B. Notch-RBP-J signaling controls the homeostasis of CD8- dendritic cells in the spleen. J. Exp. Med. 204, 1653–1664 (2007).

  51. 51.

    Berton, O. et al. Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress. Science 311, 864–868 (2006).

  52. 52.

    Krishnan, V. et al. Molecular adaptations underlying susceptibility and resistance to social defeat in brain reward regions. Cell 131, 391–404 (2007).

  53. 53.

    Ducottet, C. & Belzung, C. Behaviour in the elevated plus-maze predicts coping after subchronic mild stress in mice. Physiol. Behav. 81, 417–426 (2004).

  54. 54.

    Aguirre-Gamboa, R. et al. SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis. PloS One 8, e74250 (2013).

  55. 55.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

  56. 56.

    Lambrechts, D. et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med. 24, 1277–1289 (2018).

  57. 57.

    Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).

  58. 58.

    Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 e1624 (2017).

  59. 59.

    Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 e1236 (2018).

  60. 60.

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

Download references

Acknowledgements

Y.M. is supported by China Ministry of Science and Technology (National key research and development program, Grant 2017YFA0506200), Natural Science Foundation of China (NSFC) Grant 81972701, 81722037 and 81671630), Natural Science Foundation of Jiangsu Province (Grant BK20170006 and BK20160379), CAMS Innovation Fund for Medical Sciences (CIFMS; 2016-I2M-1-005 and 2019-I2M-1-003), Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2017NL31004 and 2017RC31008), National Thousand Talents Recruitment Program (China) and Innovative and Entrepreneurial Team Program (Jiangsu Province). G.K. and L.Z. are supported by the Recruitment Program of High-end Foreign Experts in China (GDW20171100085, GDW20181100051). Y.H. is supported by NSFC Grant 81802870 and Natural Science Foundation of Jiangsu Province Grant BK20170407. Z.S.Q. is supported by NSFC Grant 31600959 and Natural Science Foundation of Jiangsu Province Grant BK20160378. G.K. is supported by the Ligue contre le Cancer (équipe labellisée); Agence National de la Recherche (ANR); Association pour la recherche sur le cancer (ARC); Cancéropôle Ile-de-France; Chancelerie des universités de Paris (Legs Poix), a donation by Elior; European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR); European Union Horizon 2020 (OncoBiome), Fondation Carrefour; Fondation pour la Recherche Médicale (FRM); Institut Gustave Roussy (Odysssea), Institut National du Cancer (INCa); INSERM (HTE); Institut Universitaire de France; the LabEx Immuno-Oncology (ANR-18-IDEX-0001); the RHU Torino Lumière; the Seerave Foundation; the SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); and the SIRIC Cancer Research and Personalized Medicine (CARPEM). J. Calmette was supported by a doctoral fellowship from the ‘Innovation Thérapeutique du fundamental à l’appliqué’ Doctoral School (ED 569), LabEX LERMIT. We acknowledge C. Sévoz-Couche and C. Brouillard (UMR_S 1158, Unité de Neurophysiologie Respiratoire Expérimentale et Clinique, Sorbonne Université), and P. Li and J. Zhu (Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College) for technical assistance. We thank F. Bachelerie for access to an animal facility (UMS/IPSIT, Clamart).

Author information

Y.M. and G.K. designed this study. Y.M., H.Y., L.X. and S.L. performed experiments involving murine tumor models. Jian Chen and M.S. performed the POMS assessment on patients with cancer. Jian Chen, M.S., Z.P., L.F., J.Y. and P.Z. collected blood samples from patients and healthy volunteers. L.X., S.L., V.M. and Y.M. performed behavioral tests. L.L. provided quality control for some behavioral tests. L.X., Y.M., H.Y., S.L. and Q.L. performed FACS and RNA-seq analyses. L.X., Y.M. and S.Z. performed ELISA and ELISPOT. Q.L. and Y.M. performed survival analyses using a public cancer database. B.K., Z.Z., A.L., K.Y. and J.H. analyzed single-cell-sequencing datasets. L.X., S.Z., Q.L., S.L., M.L. and Jinfeng Chen performed cell culture, qRT–PCR and immunohistochemistry experiments. J. Calmette, E.M., G.S.-L. and R.K. provided support in the experiments using Tsc22d3-Tg and Tsc22d3-cKO mice. G.K. and Y.M. wrote the manuscript with help of L.Z.

Correspondence to Guido Kroemer or Yuting Ma.

Ethics declarations

Competing interests

L.Z. and G.K. are founders and shareholders of EVERIMMUNE. The other authors declare no competing interests.

Additional information

Peer review information: Kate Gao 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.

Extended data

Extended Data Fig. 1 Stress-induced behavioral changes.

ac, Apparatuses for the SD protocol model (a), the AR model (b) and the light–dark box test (c). d,e, Total entry times in light box (d) and total locomotion (e) of Ctrl (n = 14) and SD (n = 13) preconditioned mice in the light–dark box test. Two-tailed Mann–Whitney test was applied for statistical analysis (P values are shown between indicated groups). A representative result from five independent experiments is shown. fh, A typical image showing virtual zone division in the open-field test (f). Total entry times (g) and the duration of stay (h) in the central zone were compared between Ctrl (n = 10) and SD (n = 18) mice. Two-tailed Mann–Whitney test was applied for statistical analysis (P values are shown between indicated groups). A representative result from five independent experiments is shown. i, Total duration of grooming activities of Ctrl (n = 12) and SD (n = 11) mice in the splash test. A two-tailed Mann–Whitney test was applied for statistical analysis (P = 0.0012). A representative result from three independent experiments is shown. jl, A diagram illustrating the apparatus used in the social avoidance test and its procedures (j). Duration of stay in the ‘interaction zone’ (k) and social-interaction ratio (l) in the absence or presence of the ‘target (CD-1)’ mouse were measured in Ctrl (n = 12) and SD (n = 33) mice. A two-tailed Man–Whitney test was applied for statistical analysis (P values are shown between indicated groups). A representative result from four independent experiments is shown. m, The survival of Ctrl and SD mice that received chemotherapy with MTX or PBS in an AOM–DSS-induced colon carcinogenesis model were compared with log-rank test (n = 13 mice for Ctrl, n = 14 mice for SD, n = 12 mice for Ctrl OXA, n = 15 for SD OXA). Results from two independent experiments are combined and shown. n,o, On day 55 after the first injection of AOM, the number (n) and total size (o) of neoplastic nodules in the colon and rectum were calculated in Ctrl (n = 6) and SD (n = 7) groups with a stereo microscope. Two-tailed Mann–Whitney test was applied for statistical analysis ({ values are shown between indicated groups). A representative result of two independent experiments is shown. pr, The consumption of food (p) (n = 10 mice for Ctrl, n = 17 mice for SD) and water (q) (n = 9 mice for Ctrl, n = 20 mice for SD) and body weight (r) (n = 19 mice for Ctrl, n = 20 mice for SD) were measured in Ctrl and SD mice daily during the 10-d SD procedure. Two-tailed Mann–Whitney test was used for statistical analysis (P values are shown between indicated groups). A representative result of two independent experiments is shown. All dot plots are shown as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant.

Extended Data Fig. 2 Therapeutic regimens in mental-stress settings and individual tumor-growth curves.

ac, Schematic showing the timing of SD, tumor injection and chemotherapy with MTX (a). Individual tumor-growth curves (mouse per mouse) of the results regarding MCA205 fibrosarcomas (b) and TC-1 lung carcinomas (c), as plotted in Fig. 2a,b, respectively. d,e, Schematic diagram showing the timing of vaccination with dying MCA205 cancer cells, SD and live MCA205-tumor-cell rechallenge (d). Individual tumor-growth curves of Ctrl and SD mice that received the prophylactic tumor vaccine (VAC) or PBS control (e), as shown in Fig. 2c. fg, A schematic diagram illustrating the timing of SD, tumor injection and immunotherapy with PD-1 blockade (f). Individual tumor growth curves of Ctrl and SD mice treated with anti-PD-1 or ISO (g), as shown in Fig. 2d. hj, A schematic diagram indicating the timing of AR, tumor-cell inoculation and MTX-based chemotherapy (h). Individual tumor-growth curves of MCA 205 fibrosarcomas (i) and TC-1 lung cancer (j) in this setting, as shown in Fig. 2e,f. k, Gating strategies for determining the frequency of DCs (CD11c+I-A/I-E+), macrophages (F4/80+I-A/I-E-), neutrophils (CD11b+Ly6G+) and T lymphocytes (CD3+CD11b-) in CD45+ tumor-infiltrating leukocytes.

Extended Data Fig. 3 Secretion of stress hormones and neurotransmitters and their roles in anticancer therapies.

ad, Plasma corticosterone (a,b), serotonin (c) and norepinephrine (d) levels were measured at the indicated time points before or after AR (n = 24 for all time points) (a), or SD procedures (n = 22 for before SD and after SD, n = 13 for after SD day 24 in b, n = 27 for before SD, n = 15 after SD, n = 13 for after SD day 24 in c, n = 18 for before SD, n = 10 for after SD, n = 13 for after SD day 24 in d). Results from two independent experiments are combined and shown. Two-tailed Mann–Whitney test was used for statistical analyses (P values are shown between indicated groups). e, Individual tumor growth curves of Ctrl and SD mice treated with PBS (left) or MTX (right) in the presence of MIFE, as plotted in Fig. 3h. f,g, The locomotion in the light–dark box test of Ctrl or SD mice with or without MIFE treatment in the light box (n = 8 for Ctrl and Ctrl MIFE, n = 7 for SD and SD MIFE) (f). A representative result of two independent experiments is shown. Plasma corticosterone concentrations were compared in Ctrl and SD mice, with or without MIFE treatment (n = 21 for Ctrl and SD, n = 15 for Ctrl MIFE, n = 14 for SD MIFE) (g). Results from two independent experiments are combined and shown. Two-tailed Mann–Whitney test was applied for statistical analyses. h,i, Individual tumor-growth curves of Ctrl (left panel in h,i) and SD mice (right panel in h,i), treated with anti-PD-1 (h), tumor vaccination (VAC) (i), or corresponding controls, in the absence or presence of MIFE, as plotted in Fig. 3i,j. j,k, The efficacy of MTX-based chemotherapy against MCA205 tumors was compared between Ctrl and SD mice, in the presence or absence of serotonin-norepinephrine reuptake inhibitors FLU and REB. Group mean (j) and individual growth curves (k) are shown (n = 6 for Ctrl, SD MTX, SD PBS REX, SD MTX REX, n = 5 for Ctrl MTX, n = 7 for SD, SD PBS FLU, SD MTX FLU and SD MTX REX). A representative result of two independent experiments is shown. Two-tailed Mann–Whitney test was used for statistical analyses. All dot plots and growth curves are shown as mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant.

Extended Data Fig. 4 Impact of synthetic glucocorticoid on MTX-based chemotherapy and immune cell compositions in tumor microenvironment.

a, Plasma serotonin, norepinephrine and corticosterone concentrations in Ctrl and SD mice, with or without FLU or REB treatment (n = 11 for Ctrl and SD, n = 7 for SD FLU, n = 6 for SD REB). A representative result of two independent experiments is shown. Two-tailed Mann–Whitney test was used for statistical analyses (P values are shown between indicated groups). b, The correlation between final tumor sizes with plasma corticosterone levels after the 10-d SD procedure was evaluated with linear regression test, for Ctrl (n = 15) and SD (n = 9) mice in the vaccination setting. cf, Tumor-bearing mice were treated with MTX (against MCA205) (c,e,f), or with DX (against CT26 colon cancer) (d), in the presence of DEX (in c,d), or prednisone (PRED, in e,f) or solvent control. Tumor progression was compared between the indicated groups. DEX and PRED doses are in µg per 20 g body weight. For CT26 model, n = 4 for PBS and PBS DEX, n = 5 for DX and DEX (d). For MCA205 model, n = 6 for PBS, n = 5 for all the other groups (e). A representative result of two independent experiments is shown. Two-tailed Mann–Whitney test was applied for statistical analyses (p values are shown between indicated groups). gl, Effect of low-dose DEX on thymic weights (g) and cell numbers (h), as well as on the frequency of tumor-infiltrating DCs (i), macrophages (j), neutrophils (k), and IFN-γ-producing CD3+ T cells (l). For each group in gk, n = 5 mice. For each group in l, n = 10 mice. A representative result of two independent experiments is shown. Two-tailed Mann–Whitney test was used for statistical analyses (P values are shown between indicated groups). All dot plots and growth curves are shown as means ± s.e.m. * P < 0.05, ** P < 0.01, *** P < 0.001, ns, not significant.

Extended Data Fig. 5 Gene-expression profiling of DCs and T cells in stress conditions or on encountering endogenous or exogenous glucocorticoid.

a, GO network analysis of significantly upregulated genes in tumor-infiltrating DCs from the SD group, as compared with DCs from Ctrl group. Nodes represent enriched GO-terms. b,c, Gene transcription involved in lineage commitment and differentiation of DC (b), or related to DEX induced immunosuppression (c) was determined by qRT–PCR (n = 6 for Ctrl MTX, n = 8 for SD MTX). A representative result of two independent experiments is shown. Two-tailed Mann–Whitney test was used for statistical analyses (P values are shown between indicated groups). dg, Heat maps showing the expression of genes encoding TLR family members (d), chemokines, cytokines and receptors (e), immune checkpoint molecules (f), and antigen uptake and adhesion molecules (g) in TIDCs from SD and Ctrl mice after MTX. hi, BMDCs were stimulated with serum collected freshly from Ctrl or acute restraint (AR) mice, in the absence or presence of MIFE. The correlation between Tsc22d3 expression in BMDCs and serum corticosterone levels were explored with linear regression test (n = 6 for Ctrl serum, n = 7 for SD serum) (h). The impact of MIFE on Tsc22d3 expression in BMDCs (n = 6 for Ctrl, Ctrl MIFE, n = 7 for SD, SD MIFE) (i) were analyzed with two-tailed Mann–Whitney test. A representative result of two independent experiments is shown. j, Relative expression of Tsc22d3 in BMDCs co-cultured with live or dying tumor cells in the presence of DEX (n = 5 for all groups). A representative result of two independent experiments is shown. Two-tailed Mann–Whitney test was applied for statistical analyses (P values are shown between indicated groups). k, Relative Tsc22d3 expression in tumor-infiltrating T cells isolated from Ctrl or SD mice (n = 6 for all groups). A representative result of two independent experiments is shown. Two-tailed Mann–Whitney test was applied for statistical analyses (P values are shown between indicated groups). l, Splenic T cells were isolated and stimulated with fresh serum collected from Ctrl, SD or AR mice. Linear regression analysis was applied between Tsc22d3 expression in splenic T cells and serum corticosterone levels. For left panel, n = 6 for Ctrl serum, n = 7 for AR serum. For right panel, n = 5 for Ctrl serum, n = 15 for SD serum. A representative result of two independent experiments is shown. Two-tailed Mann–Whitney test was applied for statistical analyses (P values are shown between indicated groups). m, Relative expression of the glucocorticoid receptor (gene Nr3c1) in different tumor-infiltrating leukocyte populations from control versus social defeat mice (n = 11 for Ctrl MTX, n = 15 for SD MTX). A representative result of two independent experiments is shown. Two-tailed Mann–Whitney test was applied for statistical analyses (P values are shown between indicated groups). Data from qRT–PCR analyses are shown as mean ± s.d. Ppia was used as the reference gene for qRT–PCR-based quantification of target gene expression. * P < 0.05, ** P < 0.01, *** P < 0.001, ns, not significant.

Extended Data Fig. 6 The impact of DC-targeted genetic manipulation of Tsc22d3 on the responses to anticancer therapies and stress.

a, Individual tumor-growth curves of Tsc22d3-Tg versus littermate control mice in response to DO-based chemotherapy, corresponding to Fig. 6a. bd, WT and Tsc22d3-cKO harboring MCA205 tumors were either left unstressed and treated with MTX, in the absence or presence of DEX injections (b), or stressed with SD procedures (c,d) and treated with isotype versus PD-1 antibody (c), or MTX versus PBS (d). Individual tumor-growth curves correspond to Fig. 6c–e. e,f, Effects of DC-specific deletion of Tsc22d3 gene on total locomotion (e) and plasma corticosterone levels (f) in Ctrl and SD conditions (n = 11 for WT mice, n = 10 for Tsc22d3-cKO mice). A representative result of two independent experiments is shown. Two-tailed Mann–Whitney test was used for statistical analyses (P values are shown between indicated groups). gi, Heat maps summary of the relative expression of TLR family members (g), antigen uptake and adhesion molecules (h) and chemokines, cytokines and receptors (i) in TIDCs from WT or Tsc22d3-cKO mice, both under the SD condition. j,k, Flow cytometric analysis of immune infiltrate of MCA205 fibrosarcomas in the context of DO-based chemotherapy in unstressed Tsc22d3-Tg mice (n = 7) versus littermate control mice (n = 9) (j), or MTX-based chemotherapy in stressed WT (n = 15) mice versus Tsc22d3-cKO mice (n = 13) (k). A representative result of two independent experiments is shown. Two-tailed Mann–Whitney test was used for statistical analyses (P values are shown between indicated groups). l, After MTX-based chemotherapy, tumor-antigen OVA-specific OT1 cell proliferation in tumor draining lymph nodes of WT and Tsc22d3-cKO mice, under SD or Ctrl conditions, were compared (n = 5 for Tsc22d3-cKO SD, n = 6 for all the other groups). A representative result from two independent experiments is shown. Two-tailed Mann–Whitney test was used for statistical analyses (P values are shown between indicated groups). All dot plots are shown as means ± s.e.m. * P < 0.05, ** P < 0.01, *** P < 0.001, ns, not significant.

Extended Data Fig. 7 Clinical investigations on plasma cortisol and ACTH levels and TSC22D3 expression in patients with cancer.

a,b, Independent cohorts of healthy volunteers (n = 79) and patients with colorectal cancer (n = 133) from the Sun Yat-Sen University Cancer Center were studied. Plasma cortisol levels (a) and age (b) were analyzed with unpaired two-tailed t test with Welch’s correction. c,d, Plasma ACTH levels in healthy volunteers (i = 78) and patients with colorectal (n = 98) or lung (n = 106) cancer were determined by ELISA kit. P values are calculated with unpaired two-tailed t test. Results in ac are shown as mean ± s.e.m. ** P < 0.01, ns, not significant. The correlation between ACTH concentrations and cortisol levels was analyzed by the linear regression test (d). e, TSC22D3 expression in tumor-infiltrating immune cells was analyzed using six published scRNA-seq studies55,56,57,58,59,60. The single-cell expression matrices were downloaded from the NCBI GEO DataSets (GSE103322, GSE114725, GSE120575, GSE127465), the EBI ArrayExpress database (E-MTAB-6149) and supplemental data online. f, The tumor immune estimation resource tool was used to explore the relationship between TSC22D3 transcription and the abundance of immune cell infiltrate in 23 cancer types (abbreviations according to the tumor cell genome atlas, TCGA). Results are expressed as partial Spearman correlations in the form of a heat map. g,h, The overall survival of patients with lung cancer (smokers), patients with colorectal cancer and patients with gastric cancer was compared between individuals bearing CXCL9high and CXCL9low tumors (g), or CCL5high and CCL5low tumors (h). Hazard ratio, 95% CI of ratio and log-rank test P values are shown.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Further reading