# Comprehensive Informatic Analyses of AML Genomes and Epigenomes

> **NIH NIH R50** · WASHINGTON UNIVERSITY · 2022 · $195,422

## Abstract

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.

## Key facts

- **NIH application ID:** 10517065
- **Project number:** 2R50CA211782-06
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Christopher A Miller
- **Activity code:** R50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $195,422
- **Award type:** 2
- **Project period:** 2017-09-20 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10517065

## Citation

> US National Institutes of Health, RePORTER application 10517065, Comprehensive Informatic Analyses of AML Genomes and Epigenomes (2R50CA211782-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10517065. Licensed CC0.

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