EHA 2026 | Genomic profiling is redefining risk stratification in acute lymphoblastic leukemia
Anthony Moorman, PhD, Newcastle University, Newcastle, UK, discusses how advances in genomic and cytogenetic profiling are reshaping risk stratification in acute lymphoblastic leukemia (ALL). Prof. Moorman explains how the identification of numerous new ALL subtypes is enabling increasingly personalized treatment approaches, while also highlighting the challenge of integrating genomic data with established clinical risk factors such as age, white cell count, immunophenotype, and treatment response. This interview took place at the 31st Congress of the European Hematology Association (EHA) in Stockholm, Sweden.
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Transcript
So it’s true that the increasing availability of genetic and genomic data has provided a greater level of granularity about the heterogeneity that exists within acute lymphoblastic leukemia. So if you go back kind of 20, 30 years ago, you were talking about maybe five or six well-defined subtypes, and we’re now up to about 27 defined subtypes in ALL and probably about another 14 or so in T-ALL...
So it’s true that the increasing availability of genetic and genomic data has provided a greater level of granularity about the heterogeneity that exists within acute lymphoblastic leukemia. So if you go back kind of 20, 30 years ago, you were talking about maybe five or six well-defined subtypes, and we’re now up to about 27 defined subtypes in ALL and probably about another 14 or so in T-ALL. So this offers a huge amount of opportunity to break down the cohort of ALL patients into distinct genetic subtypes and look individually at how patients with those particular subgroups vary on therapy. Of course some of them are quite small so with the opportunity to kind of dissect it into different subtypes comes the challenge of creating very large cohorts in order to have a sufficient number of patients in each of the subgroups to understand exactly how that group of patients will respond to a particular therapy. But from what we’re looking at now, a very large proportion of patients will be treated according to their genetic subtype. So it’s probably not 100% yet, but it soon will be. And that will provide a greater granularity in terms of risk prediction. The challenge, of course, is how do you integrate that genetic data with all the other risk factors? Because we know age is a major risk factor, immunophenotype is a risk factor, as is white cell count, and of course, your response to initial therapy. So the real challenge is how to integrate all those different factors into a kind of unified system whereby each patient gets the optimal treatment regimen that he or she needs.
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