# Local Promoter Methylation Disorder algorithm reveals bidirectional epigenetic disruption in DNMT3A-mutated AML and predicts azacitidine treatment response

## TL;DR

A new algorithm called LPMD reveals how DNA methylation is disrupted in a type of leukemia caused by DNMT3A mutations and can predict how well patients respond to a drug called azacitidine.

## Contribution

The LPMD algorithm introduces a novel approach to quantify methylation instability and identifies a predictive biomarker panel for azacitidine response in AML.

## Key findings

- DNMT3A mutations are linked to bidirectional methylation instability, with most regions showing aberrant stabilization and a smaller subset showing maintenance failure.
- A 5-DMDR panel predicted azacitidine response with 77.7% accuracy, 81% sensitivity, and 73% specificity.
- LPMD levels decreased significantly 3–5 months after treatment, suggesting a critical window for assessing drug efficacy.

## Abstract

DNMT3A mutations occur in 20-25% of acute myeloid leukemia (AML) cases and are associated with poor prognosis, yet the epigenetic mechanisms underlying treatment response remain poorly understood. Traditional methylation analyses focus on mean levels, overlooking the heterogeneity that may be central to therapeutic vulnerability. We developed a Local Promoter Methylation Disorder (LPMD) algorithm to quantify methylation instability and evaluate its clinical utility in predicting azacitidine response.

The LPMD algorithm was developed using the GSE62298 dataset (68 AML patients: 15 DNMT3A-mutant, 53 wild-type) to quantify local methylation heterogeneity through a 1-kb sliding window across CpG sites. Algorithm performance was validated in an independent WGBS cohort (20 AML samples) and further confirmed via R882-stratified analysis in the TCGA-LAML cohort (n = 194). Clinical predictive value was assessed in GSE152710 (63 high-risk MDS/secondary AML patients receiving azacitidine), where differentially methylated disorder regions (DMDRs) were identified and a consensus feature selection strategy was employed to construct a predictive panel. Longitudinal samples (n = 153) enabled treatment dynamics analysis.

The LPMD algorithm effectively captured DNMT3A mutation-associated epigenetic instability (Cohen’s d = 0.8, p < 0.001), with the strongest effects observed in the 5’UTR-Exon1 region (d = 0.74) and a gradient pattern from CpG islands to shores (d: 0.59→0.54→0.43). Genome-wide scanning identified 7,097 DMDRs exhibiting a striking bidirectional pattern: 85.3% showed decreased disorder (aberrant stabilization) while 14.7% showed increased disorder (maintenance failure), with the latter enriched in promoters (91% of high-priority DMDRs). Although genome-wide LPMD failed to predict azacitidine response, a 5-DMDR panel derived from multi-algorithm consensus achieved AUC = 0.777, 81% sensitivity, and 73% specificity. Treatment monitoring revealed a significant LPMD decrease at 3–5 months (-3.7%, p < 0.001), defining a critical window for efficacy assessment.

The LPMD algorithm reframes DNMT3A-mutant AML from a hypomethylation paradigm to a methylation disorder paradigm, revealing dual mechanisms of aberrant stabilization and maintenance failure at distinct genomic regions. The 5-DMDR panel offers a practical tool for azacitidine response prediction, while dynamic LPMD monitoring provides a potential biomarker for therapeutic guidance. These findings establish methylation disorder as a clinically actionable dimension of epigenetic dysregulation in myeloid malignancies.