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Association between pretreatment emotional distress and neoadjuvant immune checkpoint blockade response in melanoma

Nature Medicine volume 29, pages 3090–3099 (2023)Cite this article

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Abstract

Neoadjuvant immune checkpoint blockade (ICB) outperforms adjuvant ICB for treatment of stage IIIB–D melanoma, but potential biomarkers of response, such as interferon-gamma (IFNγ) signature and tumor mutational burden (TMB), are insufficient. Preclinical studies suggest that emotional distress (ED) can negatively affect antitumor immune responses via β-adrenergic or glucocorticoid signaling. We performed a post hoc analysis evaluating the association between pretreatment ED and clinical responses after neoadjuvant ICB treatment in patients with stage IIIB–D melanoma in the phase 2 PRADO trial (NCT02977052). The European Organisation for Research and Treatment of Cancer scale for emotional functioning was used to identify patients with ED (n = 28) versus those without (n = 60). Pretreatment ED was significantly associated with reduced major pathologic responses (46% versus 65%, adjusted odds ratio 0.20, P = 0.038) after adjusting for IFNγ signature and TMB, reduced 2-year relapse-free survival (74% versus 91%, adjusted hazard ratio 3.81, P = 0.034) and reduced 2-year distant metastasis-free survival (78% versus 95%, adjusted hazard ratio 4.33, P = 0.040) after adjusting for IFNγ signature. RNA sequencing analyses of baseline patient samples could not identify clear β-adrenergic- or glucocorticoid-driven mechanisms associated with these reduced outcomes. Pretreatment ED may be a marker associated with clinical responses after neoadjuvant ICB in melanoma and warrants further investigation. ClinicalTrials.gov registration: NCT02977052.

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Fig. 1: Pathologic response and survival of PRADO according to ED status.
Fig. 2: Effects of ED on immune cell populations in the tumor microenvironment.

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Data availability

RNA sequencing data generated during the study will be deposited in the European Genome-phenome Archive (EGA) under the accession code EGAS00001007601. To minimize the risk of patient re-identification, de-identified individual patient-level clinical data are available under restricted access. Upon scientifically sound request, data access can be obtained via the Netherlands Cancer Institute’s (NKI) scientific repository at repository@nki.nl, which will contact the corresponding author (L.V.v.d.P.-F.). Data requests will then be reviewed by the institutional review board of the NKI and will require the requesting researcher to sign a data access agreement with the NKI.

References

  1. Ascierto, P. A. et al. Adjuvant nivolumab versus ipilimumab in resected stage IIIB-C and stage IV melanoma (CheckMate 238): 4-year results from a multicentre, double-blind, randomised, controlled, phase 3 trial. Lancet Oncol. 21, 1465–1477 (2020).

    CAS  PubMed  Google Scholar 

  2. Dummer, R. et al. Five-year analysis of adjuvant dabrafenib plus trametinib in stage III melanoma. N. Engl. J. Med. 383, 1139–1148 (2020).

    CAS  PubMed  Google Scholar 

  3. Eggermont, A. M. M. et al. Longer follow-up confirms recurrence-free survival benefit of adjuvant pembrolizumab in high-risk stage III melanoma: updated results from the EORTC 1325-MG/KEYNOTE-054 trial. J. Clin. Oncol. 38, 3925–3936 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  5. Patel, S. P. et al. Neoadjuvant–adjuvant or adjuvant-only pembrolizumab in advanced melanoma. N. Engl. J. Med. 388, 813–823 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Versluis, J. M. et al. Survival update of neoadjuvant ipilimumab + nivolumab in macroscopic stage III melanoma: the OpACIN and OpACIN-neo trials. J. Clin. Oncol. 40, 9572 (2022).

    Google Scholar 

  7. Reijers, I. L. M. Personalized response-directed surgery and adjuvant therapy after neoadjuvant ipilimumab and nivolumab in high-risk stage III melanoma: the PRADO trial. Nat. Med. 28, 1178–1188 (2022).

    CAS  PubMed  Google Scholar 

  8. Rozeman, E. A. et al. Survival and biomarker analyses from the OpACIN-neo and OpACIN neoadjuvant immunotherapy trials in stage III melanoma. Nat. Med. 27, 256–263 (2021).

    CAS  PubMed  Google Scholar 

  9. Reijers, D. P. et al. 6P response and survival according to the interferon-gamma (IFN-γ) signature and tumor mutational burden (tmb) in the PRADO trial testing neoadjuvant ipilimumab and nivolumab in stage III melanoma. Immuno-Oncol. Technol. 16, 100111 (2022).

    Google Scholar 

  10. Boesch, M. et al. Call for a holistic framework for cancer immunotherapy. Cancer 128, 3772–3774 (2022).

    PubMed  Google Scholar 

  11. Niedzwiedz, C. L., Knifton, L., Robb, K. A., Katikireddi, S. V. & Smith, D. J. Depression and anxiety among people living with and beyond cancer: a growing clinical and research priority. BMC Cancer 19, 943 (2019).

    PubMed  PubMed Central  Google Scholar 

  12. Beesley, V. L. et al. Anxiety and depression after diagnosis of high-risk primary cutaneous melanoma: a 4-year longitudinal study. J. Cancer Surviv. 14, 712–719 (2020).

    PubMed  Google Scholar 

  13. Liu, Y.-Z., Wang, Y.-X. & Jiang, C.-L. Inflammation: the common pathway of stress-related diseases. Front. Hum. Neurosci. 11, 316 (2017).

    PubMed  PubMed Central  Google Scholar 

  14. Eckerling, A., Ricon-Becker, I., Sorski, L., Sandbank, E. & Ben-Eliyahu, S. Stress and cancer: mechanisms, significance and future directions. Nat. Rev. Cancer 21, 767–785 (2021).

    CAS  PubMed  Google Scholar 

  15. Tian, W. et al. Chronic stress: impacts on tumor microenvironment and implications for anti-cancer treatments. Front. Cell Dev. Biol. 9, 777018 (2021).

    PubMed  PubMed Central  Google Scholar 

  16. Cole, S. W., Nagaraja, A. S., Lutgendorf, S. K., Green, P. A. & Sood, A. K. Sympathetic nervous system regulation of the tumour microenvironment. Nat. Rev. Cancer 15, 563–572 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Volden, P. A. & Conzen, S. D. The influence of glucocorticoid signaling on tumor progression. Brain Behav. Immun. 30, S26–S31 (2013).

    CAS  PubMed  Google Scholar 

  18. Sommershof, A., Scheuermann, L., Koerner, J. & Groettrup, M. Chronic stress suppresses anti-tumor TCD8+ responses and tumor regression following cancer immunotherapy in a mouse model of melanoma. Brain Behav. Immun. 65, 140–149 (2017).

    CAS  PubMed  Google Scholar 

  19. Yang, H. et al. Stress–glucocorticoid–TSC22D3 axis compromises therapy-induced antitumor immunity. Nat. Med. 25, 1428–1441 (2019).

    CAS  PubMed  Google Scholar 

  20. Acharya, N. et al. Endogenous glucocorticoid signaling regulates CD8+ T cell differentiation and development of dysfunction in the tumor microenvironment. Immunity 53, 658–671.e6 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Bucsek, M. J. et al. β-Adrenergic signaling in mice housed at standard temperatures suppresses an effector phenotype in CD8+ T cells and undermines checkpoint inhibitor therapy. Cancer Res. 77, 5639–5651 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Qiao, G. et al. Chronic adrenergic stress contributes to metabolic dysfunction and an exhausted phenotype in T cells in the tumor microenvironment. Cancer Immunol. Res. 9, 651–664 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Schmidt, D., Peterlik, D., Reber, S. O., Lechner, A. & Männel, D. N. Induction of suppressor cells and increased tumor growth following chronic psychosocial stress in male mice. PLoS ONE 11, e0159059 (2016).

    PubMed  PubMed Central  Google Scholar 

  24. Guereschi, M. G. et al. Beta2-adrenergic receptor signaling in CD4+ Foxp3+ regulatory T cells enhances their suppressive function in a PKA-dependent manner. Eur. J. Immunol. 43, 1001–1012 (2013).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  26. Zhou, Q. et al. Chronic psychological stress attenuates the efficacy of anti-PD-L1 immunotherapy for bladder cancer in immunocompetent mice. Cancer Invest 39, 571–581 (2021).

    CAS  PubMed  Google Scholar 

  27. Menzies, A. M. et al. Pathological response and survival with neoadjuvant therapy in melanoma: a pooled analysis from the International Neoadjuvant Melanoma Consortium (INMC). Nat. Med. 27, 301–309 (2021).

    CAS  PubMed  Google Scholar 

  28. Wu, F. et al. Correlation of psychological distress with quality of life and efficacy of immune checkpoint inhibitors in patients with newly diagnosed stage IIIB-IV NSCLC. J. Clin. Oncol. 40, 12001 (2022).

    Google Scholar 

  29. Bi, Z. et al. Negative correlations of psychological distress with quality of life and immunotherapy efficacy in patients with advanced NSCLC. Am. J. Cancer Res. 12, 805–815 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Feng, Z. et al. Chronic restraint stress attenuates p53 function and promotes tumorigenesis. Proc. Natl Acad. Sci. USA 109, 7013–7018 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Hara, M. R. et al. A stress response pathway regulates DNA damage through β2-adrenoreceptors and β-arrestin-1. Nature 477, 349–353 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhang, X. et al. Chronic stress promotes gastric cancer progression and metastasis: an essential role for ADRB2. Cell Death Dis. 10, 788 (2019).

    PubMed  PubMed Central  Google Scholar 

  33. Obradović, M. M. S. et al. Glucocorticoids promote breast cancer metastasis. Nature 567, 540–544 (2019).

    PubMed  Google Scholar 

  34. 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).

    CAS  PubMed  Google Scholar 

  35. Xie, H. et al. Chronic stress promotes oral cancer growth and angiogenesis with increased circulating catecholamine and glucocorticoid levels in a mouse model. Oral Oncol. 51, 991–997 (2015).

    CAS  PubMed  Google Scholar 

  36. Nagaraja, A. S. et al. Sustained adrenergic signaling leads to increased metastasis in ovarian cancer via increased PGE2 synthesis. Oncogene 35, 2390–2397 (2016).

    CAS  PubMed  Google Scholar 

  37. Ben-Eliyahu, S., Shakhar, G., Page, G. G., Stefanski, V. & Shakhar, K. Suppression of NK cell activity and of resistance to metastasis by stress: a role for adrenal catecholamines and beta-adrenoceptors. Neuroimmunomodulation 8, 154–164 (2000).

    CAS  PubMed  Google Scholar 

  38. Rosenne, E. et al. In vivo suppression of NK cell cytotoxicity by stress and surgery: glucocorticoids have a minor role compared to catecholamines and prostaglandins. Brain Behav. Immun. 37, 207–219 (2014).

    CAS  PubMed  Google Scholar 

  39. Matyszak, M. K., Citterio, S., Rescigno, M. & Ricciardi-Castagnoli, P. Differential effects of corticosteroids during different stages of dendritic cell maturation. Eur. J. Immunol. 30, 1233–1242 (2000).

    CAS  PubMed  Google Scholar 

  40. Hou, N. et al. A novel chronic stress-induced shift in the Th1 to Th2 response promotes colon cancer growth. Biochem. Biophys. Res. Commun. 439, 471–476 (2013).

    CAS  PubMed  Google Scholar 

  41. Franchimont, D. et al. Inhibition of Th1 immune response by glucocorticoids: dexamethasone selectively inhibits IL-12-induced Stat4 phosphorylation in T lymphocytes. J. Immunol. 164, 1768–1774 (2000).

    CAS  PubMed  Google Scholar 

  42. Mohammadpour, H. et al. β2 adrenergic receptor-mediated signaling regulates the immunosuppressive potential of myeloid-derived suppressor cells. J. Clin. Invest. 129, 5537–5552 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Colon-Echevarria, C. B., Lamboy-Caraballo, R., Aquino-Acevedo, A. N. & Armaiz-Pena, G. N. Neuroendocrine regulation of tumor-associated immune cells. Front. Oncol. 9, 1077 (2019).

    PubMed  PubMed Central  Google Scholar 

  44. Qiao, G., Chen, M., Bucsek, M. J., Repasky, E. A. & Hylander, B. L. Adrenergic signaling: a targetable checkpoint limiting development of the antitumor immune response. Front. Immunol. 9, 164 (2018).

    PubMed  PubMed Central  Google Scholar 

  45. Ben-Eliyahu, S., Page, G. G., Yirmiya, R. & Shakhar, G. Evidence that stress and surgical interventions promote tumor development by suppressing natural killer cell activity. Int. J. Cancer 80, 880–888 (1999).

    CAS  PubMed  Google Scholar 

  46. Lutgendorf, S. K. et al. Depressed and anxious mood and T-cell cytokine expressing populations in ovarian cancer patients. Brain Behav. Immun. 22, 890–900 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Sanders, V. M. et al. Differential expression of the beta2-adrenergic receptor by Th1 and Th2 clones: implications for cytokine production and B cell help. J. Immunol. 158, 4200–4210 (1997).

    CAS  PubMed  Google Scholar 

  48. Ramer-Quinn, D. S., Swanson, M. A., Lee, W. T. & Sanders, V. M. Cytokine production by naive and primary effector CD4+ T cells exposed to norepinephrine. Brain Behav. Immun. 14, 239–255 (2000).

    CAS  PubMed  Google Scholar 

  49. Taves, M. D. & Ashwell, J. D. Glucocorticoids in T cell development, differentiation and function. Nat. Rev. Immunol. 21, 233–243 (2021).

    CAS  PubMed  Google Scholar 

  50. Daher, C. et al. Blockade of β-adrenergic receptors improves CD8+ T-cell priming and cancer vaccine efficacy. Cancer Immunol. Res. 7, 1849–1863 (2019).

    PubMed  Google Scholar 

  51. Messina, G. et al. Efficacy of IL-2 immunotherapy in metastatic renal cell carcinoma in relation to the psychic profile as evaluated using the Rorschach test. Anticancer Res. 27, 2985–2988 (2007).

    CAS  PubMed  Google Scholar 

  52. Simoni, Y. et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).

    CAS  PubMed  Google Scholar 

  53. Attrill, G. H. et al. Higher proportions of CD39+ tumor-resident cytotoxic T cells predict recurrence-free survival in patients with stage III melanoma treated with adjuvant immunotherapy. J. Immunother. Cancer 10, e004771 (2022).

    PubMed  PubMed Central  Google Scholar 

  54. Danielsen, J. T. et al. Psychological and behavioral symptoms in patients with melanoma: a systematic review and meta-analysis. Psychooncology 32, 1208–1222 (2023).

    PubMed  Google Scholar 

  55. Kokolus, K. M. et al. Beta blocker use correlates with better overall survival in metastatic melanoma patients and improves the efficacy of immunotherapies in mice. Oncoimmunology 7, e1405205 (2018).

    PubMed  Google Scholar 

  56. Kennedy, O. J. et al. Prognostic and predictive value of β-blockers in the EORTC 1325/KEYNOTE-054 phase III trial of pembrolizumab versus placebo in resected high-risk stage III melanoma. Eur. J. Cancer 165, 97–112 (2022).

    CAS  PubMed  Google Scholar 

  57. Gandhi, S. et al. Phase I clinical trial of combination propranolol and pembrolizumab in locally advanced and metastatic melanoma: safety, tolerability, and preliminary evidence of antitumor activity. Clin. Cancer Res. 27, 87–95 (2021).

    CAS  PubMed  Google Scholar 

  58. Zhao, C. et al. The effects of acceptance and commitment therapy on the psychological and physical outcomes among cancer patients: a meta-analysis with trial sequential analysis. J. Psychosom. Res. 140, 110304 (2021).

    PubMed  Google Scholar 

  59. Faller, H. et al. Effects of psycho-oncologic interventions on emotional distress and quality of life in adult patients with cancer: systematic review and meta-analysis. J. Clin. Oncol. 31, 782–793 (2013).

    PubMed  Google Scholar 

  60. Xunlin, N. G., Lau, Y. & Klainin-Yobas, P. The effectiveness of mindfulness-based interventions among cancer patients and survivors: a systematic review and meta-analysis. Support Care Cancer 28, 1563–1578 (2020).

    CAS  PubMed  Google Scholar 

  61. Paley, C. A. et al. Non-pharmacological interventions to manage psychological distress in patients living with cancer: a systematic review. BMC Palliat. Care 22, 88 (2023).

    PubMed  PubMed Central  Google Scholar 

  62. Machingura, A. et al. Clustering of EORTC QLQ-C30 health-related quality of life scales across several cancer types: validation study. Eur. J. Cancer 170, 1–9 (2022).

    CAS  PubMed  Google Scholar 

  63. Schulte, T., Hofmeister, D., Mehnert-Theuerkauf, A., Hartung, T. & Hinz, A. Assessment of sleep problems with the Insomnia Severity Index (ISI) and the sleep item of the Patient Health Questionnaire (PHQ-9) in cancer patients. Support Care Cancer 29, 7377–7384 (2021).

    PubMed  PubMed Central  Google Scholar 

  64. Hofmeister, D., Schulte, T. & Hinz, A. Sleep problems in cancer patients: a comparison between the Jenkins Sleep Scale and the single-item sleep scale of the EORTC QLQ-C30. Sleep. Med. 71, 59–65 (2020).

    PubMed  Google Scholar 

  65. Tetzlaff, M. T. et al. Pathological assessment of resection specimens after neoadjuvant therapy for metastatic melanoma. Ann. Oncol. 29, 1861–1868 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Aaronson, N. K. et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J. Natl Cancer Inst. 85, 365–376 (1993).

    CAS  PubMed  Google Scholar 

  67. Fayers, P. M. et al. The EORTC QLQ-C30 Scoring Manual 3rd edn (European Organisation for Research and Treatment of Cancer, 2001).

  68. Giesinger, J. M. et al. Thresholds for clinical importance were established to improve interpretation of the EORTC QLQ-C30 in clinical practice and research. J. Clin. Epidemiol. 118, 1–8 (2020).

    PubMed  Google Scholar 

  69. Giesinger, J. M. et al. Thresholds for clinical importance for four key domains of the EORTC QLQ-C30: physical functioning, emotional functioning, fatigue and pain. Health Qual. Life Outcomes 14, 87 (2016).

    PubMed  PubMed Central  Google Scholar 

  70. Tavoli, A., Tavoli, Z. & Montazeri, A. The relationship between emotional functioning of the EORTC QLQ-C30 and a measure of anxiety and depression (HADS) in cancer patients. Int. J. Cancer Manag. 12, e94568 (2019).

    Google Scholar 

  71. Oort, Q. et al. Is the EORTC QLQ-C30 emotional functioning scale appropriate as an initial screening measure to identify brain tumour patients who may possibly have a mood disorder? Psychooncology 31, 995–1002 (2022).

    PubMed  PubMed Central  Google Scholar 

  72. Calderon, C. et al. Emotional functioning to screen for psychological distress in breast and colorectal cancer patients prior to adjuvant treatment initiation. Eur. J. Cancer Care 28, e13005 (2019).

    Google Scholar 

  73. Rodriguez-Gonzalez, A. et al. Using the emotional functioning in clinical practice to detect psychological distress in patients with advanced thoracic and colorectal cancer. Health Qual. Life Outcomes 21, 15 (2023).

    PubMed  PubMed Central  Google Scholar 

  74. van der Willik, K. D. et al. Inflammation markers and cognitive performance in breast cancer survivors 20 years after completion of chemotherapy: a cohort study. Breast Cancer Res. 20, 135 (2018).

    PubMed  PubMed Central  Google Scholar 

  75. Jiang, H., Lei, R., Ding, S.-W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15, 182 (2014).

    PubMed  PubMed Central  Google Scholar 

  76. Andrews S. Fastqc: A Quality Control Tool For High Throughput Sequence Data (Babraham Institute, 2010).

  77. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  78. Putri, G. H., Anders, S., Pyl, P. T., Pimanda, J. E. & Zanini, F. Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics 38, 2943–2945 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  81. Becht, E. et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 17, 218 (2016).

    PubMed  PubMed Central  Google Scholar 

  82. Zhu, A., Ibrahim, J. G. & Love, M. I. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35, 2084–2092 (2018).

    PubMed Central  Google Scholar 

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Acknowledgements

We thank all patients and their families for participation in the trial. We gratefully acknowledge the contributions of all participating study teams, and the support of all involved colleagues from the Netherlands Cancer Institute, Melanoma Institute Australia, Royal Prince Alfred Hospital, Royal North Shore and Mater Hospital, University Medical Center Utrecht, Erasmus Medical Center, Leiden University Medical Center and University Medical Center Groningen. We thank N. M. J. van den Heuvel and A.H. Boekhout for their contribution on the collection and analysis of the HRQoL data, H. Shehwana for assessment of the TMB calculation, and L. G. Grijpink-Ongering, A. Torres Acosta, R. Zucker, M. J. Gregorio, K. de Joode, A.M. van Eggermont, E. H .J. Tonk and J. Kingma-Veenstra for administrative support and data management. A.M.M.M. is supported by a National Health and Medical Research Council (NHMRC) Investigator Grant (no. 2021/GNT2009476), Melanoma Institute Australia and Nicholas and Helen Moore. G.V.L. is supported by an NHMRC Investigator Grant (no. 2021/GNT2007839) and the University of Sydney Medical Foundation. Financial support for the trial (NCT02977052) was provided by Bristol Myers Squibb.

Author information

Author notes

  1. These authors contributed equally: Itske Fraterman, Irene L. M. Reijers.

Authors and Affiliations

  1. Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Itske Fraterman & Lonneke V. van de Poll-Franse

  2. Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Irene L. M. Reijers & Christian U. Blank

  3. Molecular Oncology and Immunology, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Petros Dimitriadis & Christian U. Blank

  4. Core Facility and Molecular Pathology & Biobanking Department, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Annegien Broeks

  5. Melanoma Institute of Australia, The University of Sydney, Sydney, New South Wales, Australia

    M. Gonzalez, A. M. M. Menzies & Georgina V. Long

  6. Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia

    A. M. M. Menzies & Georgina V. Long

  7. Department of Medical Oncology, Royal North Shore and Mater Hospitals, Sydney, New South Wales, Australia

    A. M. M. Menzies & Georgina V. Long

  8. Department of Biometrics, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Marta Lopez-Yurda

  9. Department of Medical Oncology, Leiden University Medical Center, Leiden, the Netherlands

    Ellen Kapiteijn

  10. Departments of Medical Oncology and Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands

    Astrid A. M. van der Veldt

  11. Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands

    Karijn P. M. Suijkerbuijk

  12. Department of Medical Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

    Geke A. P. Hospers

  13. Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia

    Georgina V. Long

  14. Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands

    Christian U. Blank

  15. Department of Research and Development, Netherlands Comprehensive Cancer Organization, Utrecht, the Netherlands

    Lonneke V. van de Poll-Franse

  16. Department of Medical and Clinical Psychology, Center of Research on Psychological and Somatic Disorders (CoRPS), Tilburg University, Tilburg, the Netherlands

    Lonneke V. van de Poll-Franse

Authors
  1. Itske Fraterman
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  2. Irene L. M. Reijers
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  3. Petros Dimitriadis
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  4. Annegien Broeks
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  5. M. Gonzalez
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  6. A. M. M. Menzies
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  7. Marta Lopez-Yurda
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  14. Lonneke V. van de Poll-Franse
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Contributions

L.V.v.d.P.-F. designed the clinical emotional distress analysis. C.U.B. designed the clinical trial and wrote the trial protocol. G.V.L. reviewed the protocol. I.F., I.L.M.R., M.G., A.M.M.M., E.K., A.A.M.v.d.V., K.P.M.S., G.A.P.H., G.V.L. and C.U.B. recruited and treated patients and/or collected data. A.B. coordinated patient tumor sample processing and biobanking. I.F. and I.L.M.R. performed statistical analysis of the clinical data. P.D. performed RNA sequencing analyses. I.F., I.L.M.R., C.U.B. and L.V.v.d.P.-F. wrote the first draft of the manuscript. All authors interpreted the data, reviewed the manuscript and approved the final version.

Corresponding author

Correspondence to Lonneke V. van de Poll-Franse.

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No author has received financial support for the work on this paper, and no medical writer was involved at any stage of the preparation of this paper. I.L.M.R. and P.D. report financial interest in Signature Oncology and will receive some possible revenues if the IFNγ signature is being developed as a clinical companion diagnostic. A.M.M.M. has served on advisory boards for Bristol Myers Squibb (BMS), Merck Sharp & Dohme (MSD), Novartis, Roche, Pierre Fabre and QBiotics. E.K. received honoraria for consultancy/advisory relationships (all paid to the institute) from BMS, Novartis, Merck, Lilly and Pierre Fabre, and received research grants not related to this paper from BMS, Pierre Fabre and Delcath. A.A.M.v.d.V. received compensation for advisory roles and honoraria (all paid to the institute) from BMS, MSD, Merck, Roche, Eisai, Pfizer, Sanofi, Novartis, Pierre Fabre and Ipsen. K.P.M.S. received compensation for advisory roles and honoraria (all paid to the institute) from BMS, MSD, Novartis, Pierre Fabre and Abbvie, and received research funding from Novartis, TigaTx and BMS. G.A.P.H. received compensation for consulting and advisory roles (all paid to the institute) from Amgen, Roche, MSD, BMS, Pfizer, Novartis and Pierre Fabre, and received research grants (paid to the institute) from BMS and Seerave. G.V.L. is consultant advisor for Agenus, Amgen, Array Biopharma, AstraZeneca, Boehringer Ingelheim, BMS, Evaxion, Hexal AG (Sandoz Company), Highlight Therapeutics, Innovent Biologics, MSD, Novartis, Oncosec, PHMR Ltd, Pierre Fabre, Provectus, QBiotics and Regeneron. C.U.B. reports receiving compensation for advisory roles from BMS, MSD, Roche, Novartis, GlaxoSmithKline, AstraZeneca, Pfizer, Eli Lilly, Genmab, Pierre Fabre and Third Rock Ventures, and receiving research funding from BMS, MSD, Novartis, 4SC and NanoString. Furthermore, C.U.B. reports to be co-founder of Immagene BV. All compensations and funding for C.U.B. were paid to the institute, except for Third Rock Ventures and Immagene. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Flowchart for the Emotional Distress analyses of the PRADO trial.

Patients were defined as having emotional distress (emotional functioning score≤71) or no emotional distress (emotional functioning score >71) according to established clinically relevant thresholds using the EORTC QLQ-C30 questionnaire. HRQoL = health-related quality of life.

Extended Data Fig. 2 Baseline cortisol levels in patients with and without ED.

a, Cortisol levels in nmol/L as measured in the peripheral blood at baseline of patients with emotional distress (n = 26, purple) and without emotional distress (n = 53, orange). Patients with unknown baseline cortisol levels or unknown time of blood withdrawal were excluded. b, Two-tailed linear regression analysis (n = 79 patients) showing the association between cortisol levels and emotional distress status or time of blood withdrawal. c, Gene set enrichment analysis (n = 70 patients) using gene sets based on the Gene Ontology database showing the normalized enrichment score (NES) and corresponding unadjusted two-sided p-value of adrenergic and glucocorticoid-associated pathways. Orange bars indicate enrichment of pathways in patients without ED (n = 47), and purple bars indicate enrichment of pathways in patients with ED (n = 23). No corrections for multiple testing were performed.

Extended Data Fig. 3 Inflammation and T cell activation markers in patients with and without ED.

a-c, Comparison of genes associated with inflammation: COX2 (PTGS2), prostaglandin E2 (PGE2) and IL6 in the tumor. d-j, Comparison of T cell activation markers as measured by RNA sequencing. a-j, Patients with available RNA sequencing data (n = 23 patients with ED, n = 47 patients without ED) were included. P-values were calculated using two-tailed unpaired Student’s t-test. Bars represent mean +/− S.D.

Extended Data Table 1 Baseline clinical and tumor characteristics of patients without missing data in any of the variables (n = 48)
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Extended Data Table 2 Univariable and multivariable logistic regression analysis of MPR in total patient population with imputed data (n = 88)
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Extended Data Table 3 Comparison of multiple models with ED/MPR and preselected (stronger) predictors in terms of corrected AIC
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Extended Data Table 4 Univariable and multivariable Cox regression analysis of RFS in total patient population with imputed data (n = 82)
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Extended Data Table 5 Univariable and multivariable Cox regression analysis of DMFS in total patient population with imputed data (n = 82)
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Extended Data Table 6 Baseline median blood cell ratios and inflammation markers in ED vs No ED cohorts (n = 88)
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Extended Data Table 7 Logistic regression analysis of inflammation markers and association with MPR in total patient group (n = 88)
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Fraterman, I., Reijers, I.L.M., Dimitriadis, P. et al. Association between pretreatment emotional distress and neoadjuvant immune checkpoint blockade response in melanoma. Nat Med 29, 3090–3099 (2023). https://doi.org/10.1038/s41591-023-02631-x

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