# Identification of epigenetic subclones in lymphomas and leukemias

> **NIH NIH F30** · WASHINGTON UNIVERSITY · 2020 · $47,730

## Abstract

Abstract
Epigenetic modifications, such as DNA methylation, play a significant role in human disease. DNA methylation
is altered in cancer, and DNA methylation-induced silencing of promoters of tumor-suppressors is thought to
play a key role in tumorigenesis. Though there is an increasing number of methylation sequencing datasets
generated from cancer, they are confounded by tumor heterogeneity. Understanding the subclonal architecture
underlying heterogeneity is important because individual cancer subclones impact cancer growth, treatment
resistance, and metastasis. Increased “epigenetic heterogeneity,” or variation in DNA methylation patterns,
was determined to be correlated with worse prognosis in patients with diffuse large B-cell lymphoma (DLBCL)
and acute myeloid leukemia (AML). However, these studies only profiled epigenetic heterogeneity at a global
scale and did not analyze methylation of individual subclones. Many computational methods have been
developed for this purpose in epigenome-wide association studies (EWAS), but these methods are not
applicable for methylation sequencing data. As such, there is a need to develop new computational
methods to study subclonal methylation profiles in cancer. I will develop a novel computational method
(DXM) that deconvolves DNA methylation of heterogeneous clinical samples into their major subpopulations,
their prevalence, and their respective DNA methylation profiles (Aim 1). DXM will be developed on simulations
generated from data available from both the Roadmap Epigenomics Project and the Blueprint Epigenome
Project and will be validated with methylation sequencing on DLBCL samples. I will then leverage DXM to
study subclonal methylation profiles in AML (Aim 2). I hypothesize that methylation can confer fitness
advantages in epigenetic subclones of AML, leading to clonal expansion following therapeutic
intervention and subsequent relapse. Using DXM, I will first identify epigenetic subclones from whole
genome bisulfite sequencing data from AML patients with well-characterized genetic subclonal architecture
and compare the epigenetic subclonal architecture to that of the genetic subclones. In a second dataset
containing methylation sequencing data from paired diagnosis-relapse samples, I will apply DXM, identify
subclonal methylation profiles, and characterize all genes impacted by subclonal methylation to determine if
they are expected to confer fitness advantages. Finally, I will test if mutations known to impact DNA
methylation in AML (DNMT3A, IDH1/2) alter epigenetic subclonal expansion. Taken together, this proposal will
result in a new computational approach to interpret epigenetic data from heterogeneous clinical samples as
well as identification of DNA methylation changes in epigenetic subclones of AML that contribute to disease
progression and correlate with prognosis.

## Key facts

- **NIH application ID:** 9931169
- **Project number:** 5F30CA224687-03
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Jerry Fong
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $47,730
- **Award type:** 5
- **Project period:** 2018-07-01 → 2021-05-21

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9931169

## Citation

> US National Institutes of Health, RePORTER application 9931169, Identification of epigenetic subclones in lymphomas and leukemias (5F30CA224687-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9931169. Licensed CC0.

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