Multi-omics and machine learning reveal context-specific gene regulatory activities of PML::RARA in acute promyelocytic leukemia

William Villiers, Audrey Kelly, Xiaohan He, James Kaufman-Cook, Abdurrahman Elbasir, Halima Bensmail, Paul Lavender, Richard Dillon, Borbála Mifsud () and Cameron S. Osborne ()
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William Villiers: King’s College London
Audrey Kelly: King’s College London
Xiaohan He: King’s College London
James Kaufman-Cook: King’s College London
Abdurrahman Elbasir: Hamad Bin Khalifa University
Halima Bensmail: Hamad Bin Khalifa University
Paul Lavender: King’s College London
Richard Dillon: King’s College London
Borbála Mifsud: Hamad Bin Khalifa University, Education City
Cameron S. Osborne: King’s College London

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract The PML::RARA fusion protein is the hallmark driver of Acute Promyelocytic Leukemia (APL) and disrupts retinoic acid signaling, leading to wide-scale gene expression changes and uncontrolled proliferation of myeloid precursor cells. While known to be recruited to binding sites across the genome, its impact on gene regulation and expression is under-explored. Using integrated multi-omics datasets, we characterize the influence of PML::RARA binding on gene expression and regulation in an inducible PML::RARA cell line model and APL patient ex vivo samples. We find that genes whose regulatory elements recruit PML::RARA are not uniformly transcriptionally repressed, as commonly suggested, but also may be upregulated or remain unchanged. We develop a computational machine learning implementation called Regulatory Element Behavior Extraction Learning to deconvolute the complex, local transcription factor binding site environment at PML::RARA bound positions to reveal distinct signatures that modulate how PML::RARA directs the transcriptional response.

Date: 2023
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DOI: 10.1038/s41467-023-36262-0

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