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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1408109
This article is part of the Research Topic Immunotherapy in Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) View all 8 articles

A CD8 + T Cell Related Immune Score Predicts Survival and Refines the Risk Assessment in Acute Myeloid Leukemia

Provisionally accepted
Zeyi Li Zeyi Li 1Peng Jin Peng Jin 2Rufang Xiang Rufang Xiang 3*Xiaoyang Li Xiaoyang Li 2Jie Shen Jie Shen 2*Mengke He Mengke He 2*Xiaxin Liu Xiaxin Liu 2Hongming Zhu Hongming Zhu 2Shishuang Wu Shishuang Wu 4*Fangyi Dong Fangyi Dong 4*Huijin Zhao Huijin Zhao 2*Han Liu Han Liu 2*Zhen Jin Zhen Jin 2*Junmin Li Junmin Li 2*
  • 1 Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai, China
  • 2 Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicin, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai, China
  • 3 Department of General Practice, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
  • 4 Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai, China

The final, formatted version of the article will be published soon.

    Although advancements in genomic and epigenetic research have deepened our understanding of acute myeloid leukemia (AML), only one-third of patients can achieve durable remission. Growing evidence suggests that the immune microenvironment in bone marrow influences prognosis and survival in AML. There is a specific association between CD8+ T cells and the prognosis of AML patients. To develop a CD8+ T cell-related immune risk score for AML, we first evaluated the accuracy of CIBERSORTx in predicting the abundance of CD8+ T cells in bulk RNA-seq and found it significantly correlated with observed single-cell RNA sequencing data and the proportions of CD8+ T cells derived from flow cytometry. Next, we constructed the CTCG15, a 15-gene prognostic signature, using univariate and LASSO regression on the differentially expressed genes between CD8+ THigh and CD8+ TLow groups. The CTCG15 was further validated across six datasets in different platforms. The CTCG15 has been shown to be independent of established prognostic markers, and can distill transcriptomic consequences of several genetic abnormalities closely related to prognosis in AML patients. Finally, integrating this model into the 2022 European LeukemiaNet contributed to a higher predictive power for prognosis prediction. Collectively, our study demonstrates that CD8+ T cell-related signature could improve the comprehensive risk stratification and prognosis prediction in AML.

    Keywords: Acute Myeloid Leukemia, CD8 + T cell, the European LeukemiaNet, prognosis, CIBERSORTx

    Received: 27 Mar 2024; Accepted: 26 Aug 2024.

    Copyright: © 2024 Li, Jin, Xiang, Li, Shen, He, Liu, Zhu, Wu, Dong, Zhao, Liu, Jin and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Rufang Xiang, Department of General Practice, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
    Jie Shen, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicin, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai, China
    Mengke He, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicin, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai, China
    Shishuang Wu, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai, China
    Fangyi Dong, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai, China
    Huijin Zhao, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicin, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai, China
    Han Liu, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicin, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai, China
    Zhen Jin, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicin, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai, China
    Junmin Li, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicin, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai, China

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