️ Episode 100: Epigenomic Diagnosis and Prognosis of Acute Myeloid Leukemia

In this episode of PaperCast Base by Base, we explore a groundbreaking study that maps the epigenomic landscape of Acute Myeloid Leukemia (AML) and develops powerful diagnostic and prognostic tools for clinical use. The research introduces the Acute Leukemia Methylome Atlas (ALMA), integrating large-scale DNA methylation data, machine learning, and long-read sequencing to refine subtype classification and predict patient outcomes.

Study Highlights:

The authors harmonized DNA methylation profiles from 3,314 patient samples across 11 cohorts to build ALMA, an interactive atlas that visualizes hematopoietic heterogeneity and enables precise AML subtype classification. Using this resource, they developed two prognostic models: AML Epigenomic Risk, based on genome-wide methylation patterns, and a concise 38-CpG signature, both predicting five-year overall survival across pediatric and adult cohorts. They implemented a specimen-to-result protocol using nanopore sequencing, achieving high concordance between epigenomic predictions and genomic lesions, and demonstrated robustness across diverse clinical scenarios. While rare subtypes remain challenging due to limited training data, both models significantly outperformed current risk stratification systems.

Conclusion:

This work paves the way for faster, more affordable, and more accurate diagnosis and prognosis in AML, with potential to transform personalized treatment strategies.

Reference:

Marchi, F., Shastri, V. M., Marrero, R. J., Nguyen, N. H. K., Öttl, A., Schade, A-K., Landwehr, M., Krali, O., Nordlund, J., Ghavami, M., Sckaff, F., Mansinghka, V. K., Cao, X., Slayton, W., Starostik, P., Cogle, C. R., Ribeiro, R. C., Rubnitz, J. E., Klco, J., Elsayed, A., Gamis, A. S., Triche, T. J., Ries, R., Kolb, E. A., Aplenc, R., Alonzo, T., Pounds, S., Meshinchi, S., & Lamba, J. K. (2025). Epigenomic diagnosis and prognosis of Acute Myeloid Leukemia. Nature Communications, 16, 6961. https://doi.org/10.1038/s41467-025-62005-4

License:

This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

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