The Universities of Edinburgh and Glasgow have announced a blood test that can help predict a patient’s risk of developing leukaemia years before diagnosis by identifying blood cell production changes.
Researchers from the two universities analysed how changes in fitness advantage, which occur in blood production, might provide clues to a patient’s risk of developing the disease depending on the occurrence of different mutation types.
Changes in blood samples taken every three years over a 12-year period from 83 older individuals of the Lothian Birth Cohorts 1921 and 1936 were analysed.
The researchers used combined knowledge from mathematicians, biologists and genome scientists to understand the changes and what they could mean for the risk of developing leukaemia as a person grows older.
University of Glasgow Institute of Cancer Sciences senior lecturer and co-lead author Dr Kristina Kirschner said: “In knowing an individual patient’s risk of developing leukaemia, clinicians can schedule shorter gaps between appointments in those most likely to develop the disease and provide early treatment, which is more likely to be successful.”
By combining the complex genomic data with a machine-learning algorithm, the researchers linked the different mutations with the growth speeds of blood stem cells that carried mutations.
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They found that some mutations have distinct fitness advantages to stem cells in people without leukaemia.
This helps forecast the rapid growth of the mutated cells and determine the disease risk.
University of Edinburgh Centre for Regenerative Medicine chancellor’s fellow Dr Linus Schumacher said: “To understand leukaemia risk, we need to consider the balance between the different cells involved in blood cell production and how this balance changes as we grow older.
“By linking genomic data with machine learning we have been able to predict the future behaviour of blood cells based on the mutations they develop.”
The researchers noted that further research is needed to validate the data in a larger population, as the current study has a limited sample size.