Epigenomic classification of acute leukemia | LC 25

Biography

Dr Salvatore Benfatto is a postdoctoral researcher at the Dana-Farber Cancer Institute in Boston, Massachusetts. As a computational biologist, he works at the intersection of artificial intelligence and cancer research. In a multidisciplinary team, he develops machine learning models to foster the next generation of rapid DNA methylation– and AI-powered cancer diagnostics.

Abstract

Acute leukemia (AL) is an aggressive form of blood cancer that requires precise molecular classification and urgent treatment. However, standard-of-care diagnostic tests are time- and resource-intensive and do not capture the full spectrum of AL heterogeneity. Here, we developed a machine learning framework to rapidly classify AL using nanopore-based genome-wide DNA methylation profiling.

We first assembled a comprehensive reference cohort (n = 2,540 samples) and defined 38 distinct methylation classes across AL lineages and age groups. Methylation-based classification closely matched lineage classification by standard pathology evaluation in most patients and revealed disease heterogeneity beyond that captured by standard genetic categories.

Using this reference, we developed a specialized deep neural network model (MARLIN) for rapid AL classification. MARLIN classification was concordant with pathology diagnoses in 18/19 (94.7%) retrospective cases profiled with nanopore sequencing, including refinement of the diagnosis in 7/19 (36.8%) cases. We further evaluated real-time MARLIN classification during nanopore sequencing in prospective patients with suspected AL, achieving an accurate methylation class prediction in less than two hours from the time of sample receipt.

In summary, we show that epigenetic profiling effectively resolves the biological heterogeneity of AL and is a valid surrogate for many conventional diagnostic assays. Our machine learning– and nanopore-based framework is fast, affordable and easy to implement, making it suitable for high-tech laboratories but also in remote settings, and provides a foundation for future developments in molecular AL diagnostics.