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CD8 + T cell-based molecular subtypes with heterogeneous immune landscapes and clinical significance in acute myeloid leukemia

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Abstract

Background

Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy. Although high-dose chemotherapy is the primary treatment option, it cannot cure the disease, and new approaches need to be developed. The tumor microenvironment (TME) plays a crucial role in tumor biology and immunotherapy. CD8 + T cells are the main anti-tumor immune effector cells, and it is essential to understand their relationship with the TME and the clinicopathological characteristics of AML.

Methods

In this study, we conducted a systematic analysis of CD8 + T cell infiltration through multi-omics data and identified molecular subtypes with significant differences in CD8 + T cell infiltration and prognosis. We aimed to provide a comprehensive evaluation of the pathological factors affecting the prognosis of AML patients and to offer theoretical support for the precise treatment of AML.

Results

Our results indicate that CD8 + T cell infiltration is accompanied by immunosuppression, and that there are two molecular subtypes, with the C2 subtype having a significantly worse prognosis than the C1 subtype, as well as less CD8 + T cell infiltration. We developed a signature to distinguish molecular subtypes using multiple machine learning algorithms and validated the prognostic predictive power of molecular subtypes in nine AML cohorts including 2059 AML patients. Our findings suggest that there are different immunosuppressive characteristics between the two subtypes. The C1 subtype has up-regulated expression of immune checkpoints such as CTLA4, PD-1, LAG3, and TIGITD, while the C2 subtype infiltrates more immunosuppressive cells such as Tregs and M2 macrophages. The C1 subtype is more responsive to anti-PD-1 immunotherapy and induction chemotherapy, as well as having higher immune and cancer-promoting variant-related pathway activity. Patients with the C2 subtype had a higher FLT3 mutation rate, higher WBC counts, and a higher percentage of blasts, as indicated by increased activity of signaling pathways involved in energy metabolism and cell proliferation. Analysis of data from ex vivo AML cell drug assays has identified a group of drugs that differ in therapeutic sensitivity between molecular subtypes.

Conclusions

Our results suggest that the molecular subtypes we constructed have potential application value in the prognosis evaluation and treatment guidance of AML patients.

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Data availability

All data used in this work can be acquired from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) and The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/).

References

  1. Newell L, Cook R. Advances in acute myeloid leukemia. BMJ (Clin Res ed). 2021;375:n2026. https://doi.org/10.1136/bmj.n2026.

    Article  Google Scholar 

  2. Jongen-Lavrencic M, et al. Molecular minimal residual disease in acute myeloid leukemia. N Engl J Med. 2018;378:1189–99. https://doi.org/10.1056/NEJMoa1716863.

    Article  CAS  PubMed  Google Scholar 

  3. Kayser S, Levis MJ. Updates on targeted therapies for acute myeloid leukaemia. Br J Haematol. 2022;196:316–28. https://doi.org/10.1111/bjh.17746.

    Article  CAS  PubMed  Google Scholar 

  4. Daver N, Alotaibi AS, Bücklein V, Subklewe M. T-cell-based immunotherapy of acute myeloid leukemia: current concepts and future developments. Leukemia. 2021;35:1843–63. https://doi.org/10.1038/s41375-021-01253-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Krupka C, et al. CD33 target validation and sustained depletion of AML blasts in long-term cultures by the bispecific T-cell-engaging antibody AMG 330. Blood. 2014;123:356–65. https://doi.org/10.1182/blood-2013-08-523548.

    Article  CAS  PubMed  Google Scholar 

  6. Haubner S, et al. Coexpression profile of leukemic stem cell markers for combinatorial targeted therapy in AML. Leukemia. 2019;33:64–74. https://doi.org/10.1038/s41375-018-0180-3.

    Article  CAS  PubMed  Google Scholar 

  7. Morsink LM, Walter RB, Ossenkoppele GJ. Prognostic and therapeutic role of CLEC12A in acute myeloid leukemia. Blood Rev. 2019;34:26–33. https://doi.org/10.1016/j.blre.2018.10.003.

    Article  CAS  PubMed  Google Scholar 

  8. Vishwasrao P, Li G, Boucher JC, Smith DL, Hui SK. Emerging CAR T cell strategies for the treatment of AML. Cancers (Basel). 2022. https://doi.org/10.3390/cancers14051241.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Abaza Y, Zeidan AM. Immune checkpoint inhibition in acute myeloid leukemia and myelodysplastic syndromes. Cells. 2022. https://doi.org/10.3390/cells11142249.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Anderson NM, Simon MC. The tumor microenvironment. Current Biol. 2020;30:R921-r925. https://doi.org/10.1016/j.cub.2020.06.081.

    Article  CAS  Google Scholar 

  11. van der Leun AM, Thommen DS, Schumacher TN. CD8(+) T cell states in human cancer: insights from single-cell analysis. Nat Rev Cancer. 2020;20:218–32. https://doi.org/10.1038/s41568-019-0235-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dolina JS, Van Braeckel-Budimir N, Thomas GD, Salek-Ardakani S. CD8(+) T cell exhaustion in cancer. Front Immunol. 2021;12:715234. https://doi.org/10.3389/fimmu.2021.715234.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Morris AB, Adams LE, Ford ML. Influence of T cell coinhibitory molecules on CD8(+) recall responses. Front Immunol. 2018;9:1810. https://doi.org/10.3389/fimmu.2018.01810.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Williams P, et al. The distribution of T-cell subsets and the expression of immune checkpoint receptors and ligands in patients with newly diagnosed and relapsed acute myeloid leukemia. Cancer. 2019;125:1470–81. https://doi.org/10.1002/cncr.31896.

    Article  CAS  PubMed  Google Scholar 

  15. Le Dieu R, et al. Peripheral blood T cells in acute myeloid leukemia (AML) patients at diagnosis have abnormal phenotype and genotype and form defective immune synapses with AML blasts. Blood. 2009;114:3909–16. https://doi.org/10.1182/blood-2009-02-206946.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Xu L, et al. PD-1 and TIGIT are highly co-expressed on CD8(+) T cells in AML patient bone marrow. Front Oncol. 2021;11:686156. https://doi.org/10.3389/fonc.2021.686156.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jia B, et al. Bone marrow CD8 T cells express high frequency of PD-1 and exhibit reduced anti-leukemia response in newly diagnosed AML patients. Blood Cancer J. 2018;8:34. https://doi.org/10.1038/s41408-018-0069-4.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Knaus HA, et al. Signatures of CD8+ T cell dysfunction in AML patients and their reversibility with response to chemotherapy. JCI Insight. 2018. https://doi.org/10.1172/jci.insight.120974.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Tang L, et al. Characterization of immune dysfunction and identification of prognostic immune-related risk factors in acute myeloid leukemia. Clin Cancer Res. 2020;26:1763–72. https://doi.org/10.1158/1078-0432.Ccr-19-3003.

    Article  CAS  PubMed  Google Scholar 

  20. Szczepanski MJ, et al. Increased frequency and suppression by regulatory T cells in patients with acute myelogenous leukemia. Clin Cancer Res. 2009;15:3325–32. https://doi.org/10.1158/1078-0432.Ccr-08-3010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang X, et al. Increased population of CD4(+)CD25(high), regulatory T cells with their higher apoptotic and proliferating status in peripheral blood of acute myeloid leukemia patients. Eur J Haematol. 2005;75:468–76. https://doi.org/10.1111/j.1600-0609.2005.00537.x.

    Article  PubMed  Google Scholar 

  22. Zhou Q, et al. Program death-1 signaling and regulatory T cells collaborate to resist the function of adoptively transferred cytotoxic T lymphocytes in advanced acute myeloid leukemia. Blood. 2010;116:2484–93. https://doi.org/10.1182/blood-2010-03-275446.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhang L, Gajewski TF, Kline J. PD-1/PD-L1 interactions inhibit antitumor immune responses in a murine acute myeloid leukemia model. Blood. 2009;114:1545–52. https://doi.org/10.1182/blood-2009-03-206672.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Newman A, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453–7. https://doi.org/10.1038/nmeth.3337.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wang Y, et al. An immune risk score predicts survival of patients with acute myeloid leukemia receiving chemotherapy. Clin Cancer Res. 2021;27:255–66. https://doi.org/10.1158/1078-0432.Ccr-20-3417.

    Article  CAS  PubMed  Google Scholar 

  26. Yoshihara K, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612. https://doi.org/10.1038/ncomms3612.

    Article  ADS  CAS  PubMed  Google Scholar 

  27. Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005. https://doi.org/10.2202/1544-6115.1128.

    Article  MathSciNet  PubMed  Google Scholar 

  28. Fu J, et al. Large-scale public data reuse to model immunotherapy response and resistance. Genome Med. 2020;12:21. https://doi.org/10.1186/s13073-020-0721-z.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Rutella S, et al. Immune dysfunction signatures predict outcomes and define checkpoint blockade-unresponsive microenvironments in acute myeloid leukemia. J Clin Invest. 2022. https://doi.org/10.1172/jci159579.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Roh W, et al. Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci Transl Med. 2017. https://doi.org/10.1126/scitranslmed.aah3560.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kantarjian H, et al. Acute myeloid leukemia: current progress and future directions. Blood Cancer J. 2021;11:41. https://doi.org/10.1038/s41408-021-00425-3.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Horowitz MM, et al. Graft-versus-leukemia reactions after bone marrow transplantation. Blood. 1990;75:555–62.

    Article  CAS  PubMed  Google Scholar 

  33. Vago L, Gojo I. Immune escape and immunotherapy of acute myeloid leukemia. J Clin Invest. 2020;130:1552–64. https://doi.org/10.1172/jci129204.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chow A, Perica K, Klebanoff CA, Wolchok JD. Clinical implications of T cell exhaustion for cancer immunotherapy. Nat Rev Clin Oncol. 2022;19:775–90. https://doi.org/10.1038/s41571-022-00689-z.

    Article  PubMed  Google Scholar 

  35. Greenwald RJ, Freeman GJ, Sharpe AH. The B7 family revisited. Annu Rev Immunol. 2005;23:515–48. https://doi.org/10.1146/annurev.immunol.23.021704.115611.

    Article  CAS  PubMed  Google Scholar 

  36. Jiang P, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24:1550–8. https://doi.org/10.1038/s41591-018-0136-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Daver N, Schlenk RF, Russell NH, Levis MJ. Targeting FLT3 mutations in AML: review of current knowledge and evidence. Leukemia. 2019;33:299–312. https://doi.org/10.1038/s41375-018-0357-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Shimony S, Stahl M, Stone RM. Acute myeloid leukemia: 2023 update on diagnosis, risk-stratification, and management. Am J Hematol. 2023;98:502–26. https://doi.org/10.1002/ajh.26822.

    Article  PubMed  Google Scholar 

  39. Abaza Y, et al. Long-term outcome of acute promyelocytic leukemia treated with all-trans-retinoic acid, arsenic trioxide, and gemtuzumab. Blood. 2017;129:1275–83. https://doi.org/10.1182/blood-2016-09-736686.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was funded by the National Natural Science Foundation of China (82160405, 82160038, 82260035) and the Natural Science Foundation of Jiangxi Province (20232BAB216037).

Funding

Natural Science Foundation of Jiangxi Province, 20232BAB216037, National Natural Science Foundation of China, 82160405, 82160038, 82260035.

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Authors and Affiliations

Authors

Contributions

B.H. and X.W. contributed to conceptualization, supervision, and project administration; F.Z., B.H., X.W., J.L., and F.Y. performed funding acquisition; F.Z. and X.W. provided resources; F.Z., J.J., F.Y., J.L., and X.Y. did validation and visualization; F.Z. was involved in methodology, data curation, formal analysis, and writing—original draft and was responsible for software; B.H. and X.W. contributed to writing—review and editing. All authors edited and approved the final version of the submitted manuscript.

Corresponding authors

Correspondence to Bo Huang or Xiaozhong Wang.

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Zhong, F., Yao, F., Jiang, J. et al. CD8 + T cell-based molecular subtypes with heterogeneous immune landscapes and clinical significance in acute myeloid leukemia. Inflamm. Res. 73, 329–344 (2024). https://doi.org/10.1007/s00011-023-01839-4

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