Researchers have announced a new technique which in the future could help classify acute myeloid leukaemia (AML) and identifying high-risk patients, based on analysis of sphingolipids.
Reporting in Blood Advances, the researchers at the University of Virginia, USA, measured the levels of various families of sphingolipids, which are known to play a key role in resistance to treatment.
By using machine learning based upon the sphingolipid profiles of patient samples, they identified two AML subtypes, with one subtype associated with twice the risk of treatment failure than the other subtype.
They suggest that patients in the high-risk subtype might be directed towards trials of new treatments rather than attempting chemotherapy, which might fail. The researchers also believe sphingolipids might be a target for treatment.
Other researchers in the project come from Eastern Carolina University, Penn State, and Memorial Sloan Kettering.
Dr Thomas Loughran, director of the University of Virginia Cancer Center, said: “More research needs to be done to confirm our findings, but we believe this lipid-based subtyping could lead to tailored treatments to AML patients.
“We’re currently in an exciting phase of studying how lipids affect blood cancers, as we’re about to start a clinical trial using ceramide nanoliposomes for AML.”
Fellow researcher Dr Kevin Janes said: “Lipids are more difficult to measure than genes, but this study suggests it is worth the effort. By using the data to learn the relationship between genes and sphingolipid subtype, we showed how subtypes could be accurately inferred in many more patients than we measured directly.”
Paudel BB, Tan SF, Fox TE, Ung J, Golla U, Shaw JJP, Dunton W, Lee IS, Fares W, Patel S, Sharma A, Viny AD, Barth BM, Tallman MS, Cabot MC, Garrett-Bakelman FE, Levine RL, Kester M, Feith DJ, Claxton DF, Janes KA, Loughran TP. (2024) “Acute myeloid leukemia stratifies as two clinically relevant sphingolipidomic subtypes.” Blood Advances, doi: 10.1182/bloodadvances.2023010535
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