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Comprehensive characterization of IFNγ signaling in acute myeloid leukemia reveals prognostic and therapeutic strategies

Author

Listed:
  • Bofei Wang

    (The University of Texas MD Anderson Cancer Center)

  • Patrick K. Reville

    (The University of Texas MD Anderson Cancer Center)

  • Mhd Yousuf Yassouf

    (The University of Texas MD Anderson Cancer Center)

  • Fatima Z. Jelloul

    (The University of Texas MD Anderson Cancer Center)

  • Christopher Ly

    (The University of Texas MD Anderson Cancer Center)

  • Poonam N. Desai

    (The University of Texas MD Anderson Cancer Center
    The University of Texas Health Science Center)

  • Zhe Wang

    (The University of Texas MD Anderson Cancer Center)

  • Pamella Borges

    (The University of Texas MD Anderson Cancer Center
    University of Houston)

  • Ivo Veletic

    (The University of Texas MD Anderson Cancer Center)

  • Enes Dasdemir

    (The University of Texas MD Anderson Cancer Center
    University of Houston)

  • Jared K. Burks

    (The University of Texas MD Anderson Cancer Center)

  • Guilin Tang

    (The University of Texas MD Anderson Cancer Center)

  • Shengnan Guo

    (Harbin Medical University)

  • Araceli Isabella Garza

    (The University of Texas MD Anderson Cancer Center)

  • Cedric Nasnas

    (The University of Texas MD Anderson Cancer Center)

  • Nicole R. Vaughn

    (The University of Texas MD Anderson Cancer Center)

  • Natalia Baran

    (The University of Texas MD Anderson Cancer Center)

  • Qing Deng

    (The University of Texas MD Anderson Cancer Center)

  • Jairo Matthews

    (The University of Texas MD Anderson Cancer Center)

  • Preethi H. Gunaratne

    (University of Houston)

  • Dinler A. Antunes

    (University of Houston)

  • Suhendan Ekmekcioglu

    (The University of Texas MD Anderson Cancer Center)

  • Koji Sasaki

    (The University of Texas MD Anderson Cancer Center)

  • Miriam B. Garcia

    (The University of Texas MD Anderson Cancer Center)

  • Branko Cuglievan

    (The University of Texas MD Anderson Cancer Center)

  • Dapeng Hao

    (Harbin Medical University)

  • Naval Daver

    (The University of Texas MD Anderson Cancer Center)

  • Michael R. Green

    (The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center)

  • Marina Konopleva

    (The University of Texas MD Anderson Cancer Center
    Albert Einstein College of Medicine)

  • Andrew Futreal

    (The University of Texas MD Anderson Cancer Center)

  • Sean M. Post

    (The University of Texas MD Anderson Cancer Center)

  • Hussein A. Abbas

    (The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center)

Abstract

Interferon gamma (IFNγ) is a critical cytokine known for its diverse roles in immune regulation, inflammation, and tumor surveillance. However, while IFNγ levels were elevated in sera of most newly diagnosed acute myeloid leukemia (AML) patients, its complex interplay in AML remains insufficiently understood. We aim to characterize these complex interactions through comprehensive bulk and single-cell approaches in bone marrow of newly diagnosed AML patients. We identify monocytic AML as having a unique microenvironment characterized by IFNγ producing T and NK cells, high IFNγ signaling, and immunosuppressive features. IFNγ signaling score strongly correlates with venetoclax resistance in primary AML patient cells. Additionally, IFNγ treatment of primary AML patient cells increased venetoclax resistance. Lastly, a parsimonious 47-gene IFNγ score demonstrates robust prognostic value. In summary, our findings suggest that inhibiting IFNγ is a potential treatment strategy to overcoming venetoclax resistance and immune evasion in AML patients.

Suggested Citation

  • Bofei Wang & Patrick K. Reville & Mhd Yousuf Yassouf & Fatima Z. Jelloul & Christopher Ly & Poonam N. Desai & Zhe Wang & Pamella Borges & Ivo Veletic & Enes Dasdemir & Jared K. Burks & Guilin Tang & S, 2024. "Comprehensive characterization of IFNγ signaling in acute myeloid leukemia reveals prognostic and therapeutic strategies," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45916-6
    DOI: 10.1038/s41467-024-45916-6
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    References listed on IDEAS

    as
    1. Jeffrey W. Tyner & Cristina E. Tognon & Daniel Bottomly & Beth Wilmot & Stephen E. Kurtz & Samantha L. Savage & Nicola Long & Anna Reister Schultz & Elie Traer & Melissa Abel & Anupriya Agarwal & Auro, 2018. "Functional genomic landscape of acute myeloid leukaemia," Nature, Nature, vol. 562(7728), pages 526-531, October.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Hussein A. Abbas & Dapeng Hao & Katarzyna Tomczak & Praveen Barrodia & Jin Seon Im & Patrick K. Reville & Zoe Alaniz & Wei Wang & Ruiping Wang & Feng Wang & Gheath Al-Atrash & Koichi Takahashi & Jing , 2021. "Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    4. Suoqin Jin & Christian F. Guerrero-Juarez & Lihua Zhang & Ivan Chang & Raul Ramos & Chen-Hsiang Kuan & Peggy Myung & Maksim V. Plikus & Qing Nie, 2021. "Inference and analysis of cell-cell communication using CellChat," Nature Communications, Nature, vol. 12(1), pages 1-20, December.
    5. Jaewoong Lee & Mark E. Robinson & Ning Ma & Dewan Artadji & Mohamed A. Ahmed & Gang Xiao & Teresa Sadras & Gauri Deb & Janet Winchester & Kadriye Nehir Cosgun & Huimin Geng & Lai N. Chan & Kohei Kume , 2020. "IFITM3 functions as a PIP3 scaffold to amplify PI3K signalling in B cells," Nature, Nature, vol. 588(7838), pages 491-497, December.
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