LPMD: Methylation Disorder Data for DNMT3A-mutant AML and Azacitidine Response

Local Promoter Methylation Disorder (LPMD) algorithm results from 68 AML patients in the GSE62298 dataset, validated in cohorts totaling over 250 samples. The data reveals a bidirectional epigenetic disruption pattern and includes a 5-feature panel for predicting azacitidine treatment response. Author Xin Liu published the findings on figshare in March 2026 under a CC-BY-4.0 license.

Use Cases

• Predicting azacitidine treatment response based on a 5-DMDR panel derived from multi-algorithm consensus.
• Analyzing methylation instability patterns associated with DNMT3A mutations using a 1-kb sliding window algorithm.
• Monitoring treatment dynamics by tracking LPMD changes over time in longitudinal samples.
• Identifying differentially methylated disorder regions (DMDRs) exhibiting aberrant stabilization or maintenance failure.

Strengths

• Algorithm validated across multiple independent cohorts including TCGA-LAML (n=194) and a WGBS cohort (20 samples).
• Predictive panel for azacitidine response achieved an AUC of 0.777 with 81% sensitivity and 73% specificity.
• Longitudinal analysis includes 153 samples for treatment dynamics assessment.

Limitations

• Dataset is a 28.2 KB PDF; the underlying raw data files and column-level documentation are not provided.
• Row count and specific data structure for the derived features are unknown.
• Data may reflect bias inherent to the specific patient cohorts and methylation platforms used in the original studies.

Provenance

Source

Analysis derived from public datasets GSE62298, TCGA-LAML, and GSE152710.

Collection Method

Algorithm development and validation using methylation data from AML and MDS patient cohorts.

Freshness

Last updated 2026-03-18 05:21:25