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Regulatory interactions mediated by transcription factors (TFs) make up complex networks that control cellular behavior. Fully understanding these gene regulatory networks (GRNs) offers greater insight into the consequences of disease-causing perturbations than can be achieved by studying single TF binding events in isolation. Chromosomal translocations of the lysine methyltransferase 2A (KMT2A) produce KMT2A fusion proteins such as KMT2A-AFF1, causing poor prognosis acute lymphoblastic leukemias (ALLs) that sometimes relapse as acute myeloid leukemias (AMLs). KMT2A-AFF1 is thought to drive leukemogenesis through direct binding and inducing aberrant overexpression of key gene targets, such as the anti-apoptotic factor BCL2 and the proto-oncogene MYC However, studying direct binding alone does not allow for network generated regulatory outputs, including the indirect induction of gene repression. To better understand the KMT2A-AFF1 driven regulatory landscape, we integrated ChIP-seq, patient RNA-seq and CRISPR essentiality screens to generate a model GRN. This GRN identified several key transcription factors, including RUNX1, that regulate target genes downstream of KMT2A-AFF1 using feed-forward loop (FFL) and cascade motifs. A core set of nodes are present in both ALL and AML, and CRISPR screening revealed several factors that help mediate response to the drug venetoclax. Using our GRN, we then identified an KMT2A-AFF1:RUNX1 cascade that represses CASP9, as well as KMT2A-AFF1 driven FFLs that regulate BCL2 and MYC through combinatorial TF activity. This illustrates how our GRN can be used to better connect KMT2A-AFF1 behavior to downstream pathways that contribute to leukemogenesis, and potentially predict shifts in gene expression that mediate drug response.

Original publication




Journal article


Genome Res

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