Comprehensive Informatic Analyses of AML Genomes and Epigenomes

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Abstract Over the last decade, genomic studies have revealed the landscape of mutations that appear in Acute Myeloid Leukemias (AML). In each case, one of these mutations represents the initiating even for that tumor. Initiating mutations can create a fitness advantage when they occur in hematopoietic stem/progenitor cells (HSPC), resulting in clonal hematopoeisis, a state that increases the likelihood of leukemic transformation. Although we have characterized some consequences of these initiating mutations, including transcriptional changes and focal hypomethylation phenotypes for the DNMT3A mutations, our understanding of how these changes promote AML is largely incomplete. Since these initiating events occur in every cell of the tumor, unlike later- acquired subclonal events, they also are attractive targets for therapy. Thus, the first goal of this research program is to define the molecular mechanisms by which initiating mutations cause AML, and to use this information to develop novel, molecularly-targeted therapies. My role will be to distill large genomic and epigenomic datasets into intuitive, comprehensible models, generating testable hypotheses about the process of cellular transformation into AML. Doing so will require creative algorithmic development and statistical modeling, areas of bioinformatics in which I am proficient. This knowledge can then guide us in prioritizing targets and approaches for novel therapeutics. A second theme of our research program is to identify the reasons for progression of AMLs with "missing" mutations. Most initiating mutations are insufficient to produce overt AML on their own, but about 5% of AML cases appear to have no clear cooperating mutations. We will therefore use new technologies and analytical approaches to search the "dark matter" of the genome for AML-relevant genomic and epigenomic changes, using long-read sequencing to query previously unresolvable portions of the genome, whole genome sequencing to explore non-coding regions and structural variation, and algorithmic development to reveal difficult to ascertain events in AML. Because of my interdisciplinary background, I have expertise in designing algorithms and statistical models, as well as a deep understanding of the biology of AML. My training, experience, and record of productive and impactful research make me uniquely suited to push the informatics and analysis aspects of this research program forward.