Childhood Cancer

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MLL Oncogene-Dependent Enhancer Reprogramming Promotes Leukemogenesis

Institution: 
Stanford University
Researcher(s): 
Feng Pan, PhD
Grant Type: 
Young Investigator Grants
Year Awarded: 
2019
Type of Childhood Cancer: 
Acute Myeloid Leukemia (AML)
Project Description: 

Background: Rearrangement of the MLL gene (MLLr) is one of the most commonly recurring genetic events in acute myeloid leukemia (AML) associated with poor prognosis and survival. Our published and preliminary data have described key features of MLLr leukemia and shown that leukemia cells form a functional hierarchy wherein a minor group of cells serves as functional leukemia stem cells (LSCs). LSCs undergo self-renewal and are responsible for disease maintenance, resistance and relapse. Thus, LSCs would be a key target for therapeutic intervention. Emerging whole-genome sequencing data provide an unprecedented view of AML with aberrant genomic and/or epigenetic architecture, leading to dysregulated gene expression. As non-coding regulatory DNA sequences, enhancers control the expression of other genes. Together with increasingly recognized roles of these gene regulatory elements in the AML development, it is necessary to determine the role of master gene regulatory elements in leukemia stem cell function.

Project Goal: I will test the hypothesis that the pathogenic gene expression programs in leukemia stem cells are driven by aberrant activity of these regulatory elements.

Project Update 2023: I used a highly sophisticated approach to annotate regulatory elements genome-wide in leukemia stem cells. These efforts focus on a model of leukemia developed in our laboratory that allows for easy isolation of leukemia stem cells. The combination of my leukemia model and state-of-the-art genome scanning technology allows me to define the regulatory landscape in leukemia stem cells. Subsequently, I used cellular assays to examine the biological and pathogenic functions as well as relevance for the annotated elements. My research generated large datasets that require analysis and integration using specialized bioinformatics skills. Several genes have been identified from these datasets and displayed critical roles in the maintenance of leukemia stem cells, suggesting the disruption of the genes as a promising therapeutic strategy in acute myeloid leukemia.