# COMPREHENSIVE INFORMATIC ANALYSES OF AML GENOMES AND EPIGENOMES

> **NIH NIH R50** · WASHINGTON UNIVERSITY · 2021 · $107,259

## Abstract

Abstract
A decade of genomic studies has revealed the landscape of mutations appearing in the genomes of Acute
Myeloid Leukemias (AML). A subset of these mutations have been identified as AML-initiating events that
create growth advantages in a hematopoietic stem/progenitor cell (HSPC). This leads to the early stages of
disease, but the mechanisms by which these mutations act is poorly understood. This research program will
study key AML-initiating events in both primary tumors and mouse models by generating comprehensive
whole-genome transcriptomic and epigenomic data (RNA-seq, bisulfite-seq, ATAC-seq, single-cell RNA-seq,
et al). My role will be to translate these large, complex data sets into conclusions and testable hypotheses
about the precise molecular mechanisms by which these specific genetic mutations initiate AML. Doing so will
require the development of new algorithms and statistical models, both areas of bioinformatics in which I am
proficient. We will then leverage this knowledge to develop novel therapies.
A second key question focuses on the 50% of AML patients who initially experience complete remission after
chemotherapy, but ultimately relapse. Our previous work has shown that genome sequencing can often
identify persistent leukemia-associated mutations in post-chemotherapy biopsies from these patients, even
when they are in morphological remission. This lack of mutation clearance was strongly associated with risk of
relapse, but was not absolutely predictive: a few patients with persistent mutations experienced long
remissions, and others who cleared all mutations quickly relapsed. Two remaining questions with important
clinical implications are: 1) Can sequencing of biopsies from additional post-treatment timepoints better define
the trajectory of mutation clearance and offer additional prognostically useful information? 2) Could ultra-deep
sequencing have detected the residual disease responsible for relapse, thus prompting earlier interventions?
Having co-led the original mutation clearance study, and authored tools for tracking a tumor's clonal evolution
through therapy, I have the required expertise to deign these studies, and analyze, visualize, and interpret
these data. We are beginning clinical trials that use clearance to assign patients to more or less aggressive
treatments and providing more clarity on the above questions will be a key part of translating these findings
into robust clinical tests.
My interdisciplinary skillset couples a deep understanding of the biology of AML with statistical acumen and
expertise in designing new algorithms. My training, experience, and record of successful scientific contributions
make me uniquely suited to drive the informatics and analysis aspects of these projects forward.

## Key facts

- **NIH application ID:** 10246931
- **Project number:** 5R50CA211782-05
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Christopher A Miller
- **Activity code:** R50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $107,259
- **Award type:** 5
- **Project period:** 2017-09-20 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10246931

## Citation

> US National Institutes of Health, RePORTER application 10246931, COMPREHENSIVE INFORMATIC ANALYSES OF AML GENOMES AND EPIGENOMES (5R50CA211782-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10246931. Licensed CC0.

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