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Relapse of acute myeloid leukemia (AML) after bone marrow transplant is linked to immune evasion. Our study identifies key transcription factors (TFs) that regulate this process, offering new insights into AML evolution.

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Area of Science:

  • Hematology
  • Immunology
  • Computational Biology

Background:

  • Relapse of acute myeloid leukemia (AML) post-allogeneic bone marrow transplantation is often associated with immune evasion.
  • Reduced expression of major histocompatibility complex class II (MHCII) genes is a known factor in immune evasion during AML relapse, but the underlying mechanisms remain unclear.

Purpose of the Study:

  • To identify the transcriptional regulators responsible for reduced MHCII expression in relapsed AML.
  • To elucidate the mechanisms of immune evasion and AML evolution under immune pressure after transplantation.

Main Methods:

  • Development of CORENODE, a computational algorithm for genome-wide transcription network decomposition.
  • Analysis of transcription factor (TF) networks regulating MHCII expression in AML cells.
  • Investigation of TF combinatorial changes and their impact on gene expression programs.

Main Results:

  • A tetrad of TFs (IRF8, MYB, MEF2C, MEIS1) was identified as critical regulators of MHCII expression in AML.
  • Combinatorial TF expression changes, particularly opposing roles of MYB and IRF8, drive reduced MHCII expression at relapse, independent of the IFNγ/CIITA pathway.
  • MYB and IRF8 antagonistically regulate a broader genetic program for cytokine signaling and T-cell stimulation, which is downregulated at relapse.
  • Cells with silenced MHCII expression and altered TF abundance are detectable at initial diagnosis, suggesting early selection.

Conclusions:

  • AML evolution after transplantation involves an adaptive transcriptional mechanism where TF fluctuations under immune pressure select for cells with silenced T-cell stimulation programs.
  • These findings reveal a novel mechanism of immune evasion in AML relapse driven by specific TF networks.
  • Early identification of cells with altered TF abundance may predict relapse risk.