Researchers revealed clues as to how natural mutations that occur during aging can lead to blood cancer in some people.
The joint American and Canadian team say their findings could help to advance the early detection and treatment of acute myeloid leukaemia (AML) by identifying who is at high risk of the disease. The research team was co-led by Dr Philip Awadalla from the Ontario Institute for Cancer Research (OICR) in Toronto, Canada, and Dr Quaid Morris from Memorial Sloan Kettering Cancer Center (MSK) in New York, USA.
Age-related clonal haematopoiesis (ARCH) is when mutations build up in blood stem cells as a person ages. ARCH can be a risk factor for AML – although not everyone who develops ARCH goes on to develop AML.
The team studied how the interplay of positive, neutral and negative evolutionary selection acting on mutations in ageing blood stem cells can lead to AML in some individuals with ARCH.
They sequenced the genomes of blood samples from 92 individuals who went on to develop AML, and 385 who did not despite the presence of ARCH. Using computers to identify patterns of evolutionary processes within blood cell populations, they illustrated how negative selection present in individuals who did not go on to develop AML prevents disease-related cells from coming to dominate the cell population.
In the future, these discoveries could allow doctors to distinguish between those with ARCH who are at increased risk of developing AML and those who are not.
Dr Awadalla said: “We have shown that the constellation of evolutionary forces at play within hematopoietic stem cells can be a robust indicator of those who are at increased risk of blood cancers such as AML.
“Being able to accurately classify patients based on risk can allow for more frequent and intensive screening for those with ARCH mutations with a concerning evolutionary signature.”
The researchers showed these alternative evolutionary models were predictive of AML risk over time, and also identified genes where mutations that are damaging to stem cells can accumulate.
First author Kimberly Skead, of the Department of Molecular Genetics at the University of Toronto and the Vector Institute for Artificial Intelligence, said: “Our novel application of deep learning tools and population genetic models to genomic sequencing allowed us to classify the evolutionary interactions within a blood sample with a very high degree of accuracy.
“This level of resolution enabled us to understand how both positive and negative selection shape the ageing blood system and to establish strong links to individual health outcomes, which bodes well for potential clinical use.”
Skead K, Ang Houle A, Abelson S, Agbessi M, Bruat V, Lin B, Soave D, Shlush L, Wright S, Dick J, Morris Q, Awadalla P. (2021) “Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood.” Nature Communications, doi: 10.1038/s41467-021-25172-8
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