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:

Early memory differentiation and cell death resistance in T cells predicts melanoma response to sequential anti-CTLA4 and anti-PD1 immunotherapy

Genes & Immunity volume 22, pages 108–119 (2021)Cite this article

Subjects

A Correction to this article was published on 01 December 2022

This article has been updated

Abstract

Immune checkpoint blockers (ICBs)-based immunotherapy has revolutionised oncology. However, the benefits of ICBs are limited to only a subset of patients. Herein, the biomarkers-driven application of ICBs promises to increase their efficacy. Such biomarkers include lymphocytic IFNγ-signalling and/or cytolytic activity (granzymes and perforin-1) footprints, whose levels in pre-treatment tumours can predict favourable patient survival following ICB-treatment. However, it is not clear whether such biomarkers have the same value in predicting survival of patients receiving first-line anti-CTLA4 ICB-therapy, and subsequently anti-PD1 ICB-therapy (i.e., sequential ICB-immunotherapy regimen). To address this, we applied highly integrated systems/computational immunology approaches to existing melanoma bulk-tumour transcriptomic and single-cell (sc)RNAseq data originating from immuno-oncology clinical studies applying ICB-treatment. Interestingly, we observed that CD8+/CD4+T cell-associated IFNγ-signalling or cytolytic activity signatures fail to predict tumour response in patients treated with anti-CTLA4 ICB-therapy as a first-line and anti-PD1 ICB-therapy in the second-line setting. On the contrary, signatures associated with early memory CD8+/CD4+T cells (integrating TCF1-driven stem-like transcriptional programme), capable of resisting cell death/apoptosis, better predicted objective response rates to ICB-immunotherapy, and favourable survival in the setting of sequential ICB-immunotherapy. These observations suggest that sequencing of ICB-therapy might have a specific impact on the T cell-repertoire and may influence the predictive value of tumoural immune biomarkers.

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: Systems biology and immunotherapy response-predictive efficacy of lymphocytic effector/cytolytic activity (LECA) signature.
Fig. 2: An overview of the melanoma patient-derived tumour single-cell (sc)RNAseq profiles of infiltrating immune cells.
Fig. 3: LECA-signature’s genes in T single-cells infiltrating melanoma tumours at baseline or during immune checkpoint blockade (ICB) immunotherapy.
Fig. 4: LECA-signature’s genes in T single-cells infiltrating melanoma tumours from immune checkpoint blockade (ICB) immunotherapy responsive or non-responsive patients.
Fig. 5: Differential gene-enrichment analyses in T single-cells infiltrating melanoma tumours from immune checkpoint blockade (ICB) immunotherapy responsive or non-responsive patients.
Fig. 6: Systems biology and immunotherapy response-predictive efficacy of immunotherapy-responsive early memory T-cell signature.

Similar content being viewed by others

Change history

References

  1. Tang J, Pearce L, O’Donnell-Tormey J, Hubbard-Lucey VM. Trends in the global immuno-oncology landscape. Nat Rev Drug Discov. 2018. https://doi.org/10.1038/nrd.2018.202.

  2. Versluis JM, Long GV, Blank CU. Learning from clinical trials of neoadjuvant checkpoint blockade. Nat Med. 2020;26:475–84.

    Article  CAS  PubMed  Google Scholar 

  3. Wei SC, Duffy CR, Allison JP. Fundamental mechanisms of immune checkpoint blockade therapy. Cancer Disco. 2018;8:1069–86.

    Article  Google Scholar 

  4. Galluzzi L, Vitale I, Warren S, Adjemian S, Agostinis P, Martinez AB, et al. Consensus guidelines for the definition, detection and interpretation of immunogenic cell death. J Immunother Cancer. 2020;8. https://doi.org/10.1136/jitc-2019-000337.

  5. Nghiem P, Bhatia S, Lipson EJ, Sharfman WH, Kudchadkar RR, Brohl AS, et al. Durable tumor regression and overall survival in patients with advanced merkel cell carcinoma receiving pembrolizumab as first-line therapy. J Clin Oncol. 2019;37:693–702.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Emens LA, Ascierto PA, Darcy PK, Demaria S, Eggermont AMM, Redmond WL, et al. Cancer immunotherapy: opportunities and challenges in the rapidly evolving clinical landscape. Eur J Cancer. 2017;81:116–29.

    Article  CAS  PubMed  Google Scholar 

  7. McDermott D, Lebbé C, Hodi FS, Maio M, Weber JS, Wolchok JD, et al. Durable benefit and the potential for long-term survival with immunotherapy in advanced melanoma. Cancer Treat Rev. 2014;40:1056–64.

    Article  PubMed  Google Scholar 

  8. Shekarian T, Valsesia-Wittmann S, Caux C, Marabelle A. Paradigm shift in oncology: targeting the immune system rather than cancer cells. Mutagenesis. 2015;30:205–11.

    Article  CAS  PubMed  Google Scholar 

  9. Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell. 2017;168:707–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Garg AD, Vandenberk L, Van Woensel M, Belmans J, Schaaf M, Boon L, et al. Preclinical efficacy of immune-checkpoint monotherapy does not recapitulate corresponding biomarkers-based clinical predictions in glioblastoma. Oncoimmunology. 2017;6:e1295903.

    Article  PubMed  PubMed Central  Google Scholar 

  11. James JL, Balko JM. Biomarker predictors for immunotherapy benefit in breast: beyond PD-L1. Curr Breast Cancer Rep. 2019;11:217–27.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Tray N, Weber JS, Adams S. Predictive biomarkers for checkpoint immunotherapy: current status and challenges for clinical application. Cancer Immunol Res. 2018;6:1122–8.

    Article  CAS  PubMed  Google Scholar 

  13. Vanmeerbeek I, Sprooten J, De Ruysscher D, Tejpar S, Vandenberghe P, Fucikova J, et al. Trial watch: chemotherapy-induced immunogenic cell death in immuno-oncology. Oncoimmunology. 2020;9:1703449.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Luchini C, Bibeau F, Ligtenberg MJL, Singh N, Nottegar A, Bosse T, et al. ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1/PD-L1 expression and tumour mutational burden: a systematic review-based approach. Ann Oncol. 2019;30:1232–43.

    Article  CAS  PubMed  Google Scholar 

  15. Loupakis F, Depetris I, Biason P, Intini R, Prete AA, Leone F, et al. Prediction of benefit from checkpoint inhibitors in mismatch repair deficient metastatic colorectal cancer: role of tumor infiltrating lymphocytes. Oncologist. 2020;25:481–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Simoni Y, Becht E, Fehlings M, Loh CY, Koo S-L, Teng KWW, et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature. 2018;557:575–9.

    Article  CAS  PubMed  Google Scholar 

  17. Cristescu R, Mogg R, Ayers M, Albright A, Murphy E, Yearley J, et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science. 2018;362. https://doi.org/10.1126/science.aar3593.

  18. Camidge DR, Doebele RC, Kerr KM. Comparing and contrasting predictive biomarkers for immunotherapy and targeted therapy of NSCLC. Nat Rev Clin Oncol. 2019;16:341–55.

    Article  CAS  PubMed  Google Scholar 

  19. Gnjatic S, Bronte V, Brunet LR, Butler MO, Disis ML, Galon J, et al. Identifying baseline immune-related biomarkers to predict clinical outcome of immunotherapy. J Immunother Cancer. 2017;5:44.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sprooten J, Ceusters J, Coosemans A, Agostinis P, De Vleeschouwer S, Zitvogel L, et al. Trial watch: dendritic cell vaccination for cancer immunotherapy. Oncoimmunology. 2019;8:e1638212.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Finn OJ. Immuno-oncology: understanding the function and dysfunction of the immune system in cancer. Ann Oncol. 2012;23:viii6–9.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Sprooten J, De Wijngaert P, Vanmeerbeerk I, Martin S, Vangheluwe P, Schlenner S, et al. Necroptosis in immuno-oncology and cancer immunotherapy. Cells. 2020;9. https://doi.org/10.3390/cells9081823.

  23. Lim M, Xia Y, Bettegowda C, Weller M. Current state of immunotherapy for glioblastoma. Nat Rev Clin Oncol. 2018;15:422–42.

    Article  CAS  PubMed  Google Scholar 

  24. Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Disco. 2019;18:197–218.

    Article  CAS  Google Scholar 

  25. Sampson JH, Gunn MD, Fecci PE, Ashley DM. Brain immunology and immunotherapy in brain tumours. Nat Rev Cancer. 2020;20:12–25.

    Article  CAS  PubMed  Google Scholar 

  26. Sprooten J, Agostinis P, Garg AD. Type I interferons and dendritic cells in cancer immunotherapy. Int Rev Cell Mol Biol. 2019;348:217–62.

    Article  CAS  PubMed  Google Scholar 

  27. Garg AD, Vandenberk L, Koks C, Verschuere T, Boon L, Van Gool SW, et al. Dendritic cell vaccines based on immunogenic cell death elicit danger signals and T cell-driven rejection of high-grade glioma. Sci Transl Med. 2016;8:328ra27.

    Article  PubMed  Google Scholar 

  28. Wauters E, Van Mol P, Garg AD, Jansen S, Van Herck Y, Vanderbeke L, et al. Discriminating mild from critical COVID-19 by innate and adaptive immune single-cell profiling of bronchoalveolar lavages. Cell Res. 2021;31:272–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. McKean WB, Moser JC, Rimm D, Hu-Lieskovan S. Biomarkers in precision cancer immunotherapy: promise and challenges. Am Soc Clin Oncol Educ Book. 2020;40:e275–91.

    Article  PubMed  Google Scholar 

  30. Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24:1550–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gohil SH, Iorgulescu JB, Braun DA, Keskin DB, Livak KJ. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nat Rev Clin Oncol. 2021;18:244–56.

    Article  PubMed  Google Scholar 

  32. Billan S, Kaidar-Person O, Gil Z. Treatment after progression in the era of immunotherapy. Lancet Oncol. 2020;21:e463–76.

    Article  CAS  PubMed  Google Scholar 

  33. Callahan MK, Wolchok JD. At the bedside: CTLA-4- and PD-1-blocking antibodies in cancer immunotherapy. J Leukoc Biol. 2013;94:41–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chae YK, Arya A, Iams W, Cruz MR, Chandra S, Choi J, et al. Current landscape and future of dual anti-CTLA4 and PD-1/PD-L1 blockade immunotherapy in cancer; lessons learned from clinical trials with melanoma and non-small cell lung cancer (NSCLC). J Immunother Cancer. 2018;6:39.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Subrahmanyam PB, Dong Z, Gusenleitner D, Giobbie-Hurder A, Severgnini M, Zhou J, et al. Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J Immunother Cancer. 2018;6:18.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Liu D, Schilling B, Liu D, Sucker A, Livingstone E, Jerby-Arnon L, et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med. 2019;25:1916–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Miao D, Van Allen EM. Genomic determinants of cancer immunotherapy. Curr Opin Immunol. 2016;41:32–38.

    Article  CAS  PubMed  Google Scholar 

  38. Łuksza M, Riaz N, Makarov V, Balachandran VP, Hellmann MD, Solovyov A, et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature. 2017;551:517–20.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Mushti SL, Mulkey F, Sridhara R. Evaluation of overall response rate and progression-free survival as potential surrogate endpoints for overall survival in immunotherapy trials. Clin Cancer Res. 2018;24:2268–75.

    Article  CAS  PubMed  Google Scholar 

  40. Darabi S, Braxton DR, Eisenberg BL, Demeure MJ. Predictive biomarkers for immunotherapy response beyond PD-1/PD-L1. Oncology. 2020;34:321–7.

    Article  PubMed  Google Scholar 

  41. Pomaznoy M, Ha B, Peters B. GOnet: a tool for interactive Gene Ontology analysis. BMC Bioinforma. 2018;19:470.

    Article  CAS  Google Scholar 

  42. Gide TN, Quek C, Menzies AM, Tasker AT, Shang P, Holst J, et al. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/anti-CTLA-4 combined therapy. Cancer Cell. 2019;35:238. e6

    Article  CAS  PubMed  Google Scholar 

  43. Fu J, Li K, Zhang W, Wan C, Zhang J, Jiang P, et al. Large-scale public data reuse to model immunotherapy response and resistance. Genome Med. 2020;12:21.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 2017;127:2930–40.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Shukla SA, Bachireddy P, Schilling B, Galonska C, Zhan Q, Bango C, et al. Cancer-germline antigen expression discriminates clinical outcome to CTLA-4 blockade. Cell. 2018;173:624. e8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sade-Feldman M, Yizhak K, Bjorgaard SL, Ray JP, de Boer CG, Jenkins RW, et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell. 2018;175:998–1013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Le T, Phan T, Pham M, Tran D, Lam L, Nguyen T, et al. BBrowser: making single-cell data easily accessible. BioRxiv 2020. https://doi.org/10.1101/2020.12.11.414136.

  48. He X, Xu C. Immune checkpoint signalling and cancer immunotherapy. Cell Res. 2020;30:660–9.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Shin DS, Zaretsky JM, Escuin-Ordinas H, Garcia-Diaz A, Hu-Lieskovan S, Kalbasi A, et al. Primary resistance to PD-1 blockade mediated by JAK1/2 mutations. Cancer Disco. 2017;7:188–201.

    Article  CAS  Google Scholar 

  50. Benci JL, Johnson LR, Choa R, Xu Y, Qiu J, Zhou Z, et al. Opposing functions of interferon coordinate adaptive and innate immune responses to cancer immune checkpoint blockade. Cell. 2019;178:933. e14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Thomas A, Giaccone G. Why has active immunotherapy not worked in lung cancer? Ann Oncol. 2015;26:2213–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Sekine T, Perez-Potti A, Nguyen S, Gorin J-B, Wu VH, Gostick E, et al. TOX is expressed by exhausted and polyfunctional human effector memory CD8+ T cells. Sci Immunol. 2020;5. https://doi.org/10.1126/sciimmunol.aba7918.

  53. Schreibelt G, Bol KF, Westdorp H, Wimmers F, Aarntzen EH, Duiveman-de Boer T. et al. Effective clinical responses in metastatic melanoma patients after vaccination with primary myeloid dendritic cells. Clin Cancer Res. 2016;22:2155–66.

    Article  CAS  PubMed  Google Scholar 

  54. Sprooten J, Garg AD. Type I interferons and endoplasmic reticulum stress in health and disease. Int Rev Cell Mol Biol. 2020;350:63–118.

    Article  CAS  PubMed  Google Scholar 

  55. Patel SJ, Sanjana NE, Kishton RJ, Eidizadeh A, Vodnala SK, Cam M, et al. Identification of essential genes for cancer immunotherapy. Nature. 2017;548:537–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Inoue H, Park J-H, Kiyotani K, Zewde M, Miyashita A, Jinnin M, et al. Intratumoral expression levels of PD-L1, GZMA, and HLA-A along with oligoclonal T cell expansion associate with response to nivolumab in metastatic melanoma. Oncoimmunology. 2016;5:e1204507.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Zamagni E, Tacchetti P, Pantani L, Cavo M. Anti-CD38 and anti-SLAMF7: the future of myeloma immunotherapy. Expert Rev Hematol. 2018;11:423–35.

    Article  CAS  PubMed  Google Scholar 

  58. Klebanoff CA, Scott CD, Leonardi AJ, Yamamoto TN, Cruz AC, Ouyang C, et al. Memory T cell-driven differentiation of naive cells impairs adoptive immunotherapy. J Clin Invest. 2016;126:318–34.

    Article  PubMed  Google Scholar 

  59. Busch DH, Fräßle SP, Sommermeyer D, Buchholz VR, Riddell SR. Role of memory T cell subsets for adoptive immunotherapy. Semin Immunol. 2016;28:28–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Fiorenza S, Kenna TJ, Comerford I, McColl S, Steptoe RJ, Leggatt GR, et al. A combination of local inflammation and central memory T cells potentiates immunotherapy in the skin. J Immunol. 2012;189:5622–31.

    Article  CAS  PubMed  Google Scholar 

  61. Takeuchi Y, Tanemura A, Tada Y, Katayama I, Kumanogoh A, Nishikawa H. Clinical response to PD-1 blockade correlates with a sub-fraction of peripheral central memory CD4+ T cells in patients with malignant melanoma. Int Immunol. 2018;30:13–22.

    Article  CAS  PubMed  Google Scholar 

  62. Mackenzie KJ, Nowakowska DJ, Leech MD, McFarlane AJ, Wilson C, Fitch PM, et al. Effector and central memory T helper 2 cells respond differently to peptide immunotherapy. Proc Natl Acad Sci USA. 2014;111:E784–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Le K-S, Amé-Thomas P, Tarte K, Gondois-Rey F, Granjeaud S, Orlanducci F, et al. CXCR5 and ICOS expression identifies a CD8 T-cell subset with TFH features in Hodgkin lymphomas. Blood Adv. 2018;2:1889–1900.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Shin H-J, Lee J-B, Park S-H, Chang J, Lee C-W. T-bet expression is regulated by EGR1-mediated signalling in activated T cells. Clin Immunol. 2009;131:385–94.

    Article  CAS  PubMed  Google Scholar 

  65. Becht E, Giraldo NA, Dieu-Nosjean M-C, Sautès-Fridman C, Fridman WH. Cancer immune contexture and immunotherapy. Curr Opin Immunol. 2016;39:7–13.

    Article  CAS  PubMed  Google Scholar 

  66. Berger C, Jensen MC, Lansdorp PM, Gough M, Elliott C, Riddell SR. Adoptive transfer of effector CD8+ T cells derived from central memory cells establishes persistent T cell memory in primates. J Clin Invest. 2008;118:294–305.

    Article  CAS  PubMed  Google Scholar 

  67. Gargett T, Yu W, Dotti G, Yvon ES, Christo SN, Hayball JD, et al. GD2-specific CAR T cells undergo potent activation and deletion following antigen encounter but can be protected from activation-induced cell death by PD-1 blockade. Mol Ther. 2016;24:1135–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Otano I, Alvarez M, Minute L, Ochoa MC, Migueliz I, Molina C, et al. Human CD8 T cells are susceptible to TNF-mediated activation-induced cell death. Theranostics. 2020;10:4481–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Chhabra A, Mukherji B, Batra D. Activation induced cell death (AICD) of human melanoma antigen-specific TCR engineered CD8 T cells involves JNK, Bim and p53. Expert Opin Ther Targets. 2017;21:117–29.

    Article  CAS  PubMed  Google Scholar 

  70. Chhabra A, Mehrotra S, Chakraborty NG, Dorsky DI, Mukherji B. Activation-induced cell death of human melanoma specific cytotoxic T lymphocytes is mediated by apoptosis-inducing factor. Eur J Immunol. 2006;36:3167–74.

    Article  CAS  PubMed  Google Scholar 

  71. Contreras A, Sen S, Tatar AJ, Mahvi DA, Meyers JV, Srinand P, et al. Enhanced local and systemic anti-melanoma CD8+ T cell responses after memory T cell-based adoptive immunotherapy in mice. Cancer Immunol Immunother. 2016;65:601–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Blattman JN, Greenberg PD. PD-1 blockade: rescue from a near-death experience. Nat Immunol. 2006;7:227–8.

    Article  CAS  PubMed  Google Scholar 

  73. Escobar G, Mangani D, Anderson AC. T cell factor 1: a master regulator of the T cell response in disease. Sci Immunol. 2020;5. https://doi.org/10.1126/sciimmunol.abb9726.

  74. Jackson CM, Choi J, Lim M. Mechanisms of immunotherapy resistance: lessons from glioblastoma. Nat Immunol. 2019;20:1100–9.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study is supported by Research Foundation Flanders (FWO) (Fundamental Research Grant, G0B4620N; Excellence of Science/EOS grant, 30837538, for ‘DECODE’ consortium), KU Leuven (C1 grant, C14/19/098 and POR award funds, POR/16/040), VLIR-UOS (iBOF grant, iBOF/21/048, for ‘MIMICRY’ consortium), and Kom op Tegen Kanker (KOTK/2018/11509/1; and KOTK/2019/11955/1), to ADG. IV is supported by FWO-SB PhD Fellowship (1S06821N). DMB is supported by the Belgian Federation against Cancer grant nos. 2018-127 and 2016-133 and by a grant from Fondation Roi-Baudouin to ST. ST is further supported by a Senior Clinical Investigator award of FWO.

Author information

Author notes

  1. These authors contributed equally: Isaure Vanmeerbeek, Daniel M. Borras, Jenny Sprooten

Authors and Affiliations

  1. Laboratory of Cell Stress & Immunity (CSI), Department of Cellular & Molecular Medicine, KU Leuven, Leuven, Belgium

    Isaure Vanmeerbeek, Daniel M. Borras, Jenny Sprooten & Abhishek D. Garg

  2. Laboratory of Experimental Oncology, Department of Oncology, KU Leuven, Leuven, Belgium

    Oliver Bechter

  3. Department of General Medical Oncology, UZ Leuven, Leuven, Belgium

    Oliver Bechter

  4. Laboratory for Molecular Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium

    Sabine Tejpar

Authors
  1. Isaure Vanmeerbeek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel M. Borras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jenny Sprooten
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Oliver Bechter
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sabine Tejpar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Abhishek D. Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhishek D. Garg.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vanmeerbeek, I., Borras, D.M., Sprooten, J. et al. Early memory differentiation and cell death resistance in T cells predicts melanoma response to sequential anti-CTLA4 and anti-PD1 immunotherapy. Genes Immun 22, 108–119 (2021). https://doi.org/10.1038/s41435-021-00138-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41435-021-00138-4

This article is cited by

Search

Advanced search

Quick links