Rapid epigenomic classification of acute leukemia

Tuesday 07 October 2025
08:00 [PDT] 11:00 [EDT] 16:00 [BST]
Alternative time zones

Unlock the potential for faster, more comprehensive leukemia characterization with this webinar from Dana-Farber Cancer Institute expert Salvatore Benfatto.

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, making diagnosis and basic research investigations into AL a challenge. Researchers at Dana-Farber Cancer Institute (MA, USA) have developed MARLIN (methylation and AI-guided rapid leukemia subtype inference), a machine learning tool currently for research use only that enables the rapid classification of acute leukemia using real-time DNA methylation data generated by Oxford Nanopore sequencing. In both retrospective and prospective studies, Oxford Nanopore sequencing enabled MARLIN to deliver accurate molecular subtyping within two hours. This approach highlights the potential of nanopore technology to support faster, more comprehensive leukemia characterization.
REGISTER NOWWhat will you learn?Who may this interest?Speaker

What will you learn?

  • How the leukemia classification approach developed by Dana Farber offers a framework that can be easily implemented anywhere in the world.
  • How research with Oxford Nanopore sequencing is unlocking the potential to shorten the diagnosis and classification of acute leukemia to within hours.
  • Why rapid and accurate treatment for acute leukemias is crucial to survival rates.

Who this may interest?

  • Cancer researchers: basic and translational
  • Core lab directors, managers and technicians
  • Clinical lab directors, managers and technicians

Speaker

Salvatore Benfatto

Postdoctoral researcher

Dana-Farber Cancer Institute (MA, USA)

Salvatore Benfatto is a postdoctoral researcher at the Dana-Farber Cancer Institute. 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.

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