Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood

Kimberly Skead, Armande Ang Houle, Sagi Abelson, Mawusse Agbessi, Vanessa Bruat, Boxi Lin, David Soave, Liran Shlush, Stephen Wright, John Dick, Quaid Morris () and Philip Awadalla ()
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Kimberly Skead: Ontario Institute for Cancer Research
Armande Ang Houle: Ontario Institute for Cancer Research
Sagi Abelson: Ontario Institute for Cancer Research
Mawusse Agbessi: Ontario Institute for Cancer Research
Vanessa Bruat: Ontario Institute for Cancer Research
Boxi Lin: Ontario Institute for Cancer Research
David Soave: Ontario Institute for Cancer Research
Liran Shlush: Weizmann Institute of Science
Stephen Wright: University of Toronto
John Dick: Ontario Institute for Cancer Research
Quaid Morris: University of Toronto
Philip Awadalla: Ontario Institute for Cancer Research

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract Age-related clonal hematopoiesis (ARCH) is characterized by age-associated accumulation of somatic mutations in hematopoietic stem cells (HSCs) or their pluripotent descendants. HSCs harboring driver mutations will be positively selected and cells carrying these mutations will rise in frequency. While ARCH is a known risk factor for blood malignancies, such as Acute Myeloid Leukemia (AML), why some people who harbor ARCH driver mutations do not progress to AML remains unclear. Here, we model the interaction of positive and negative selection in deeply sequenced blood samples from individuals who subsequently progressed to AML, compared to healthy controls, using deep learning and population genetics. Our modeling allows us to discriminate amongst evolutionary classes with high accuracy and captures signatures of purifying selection in most individuals. Purifying selection, acting on benign or mildly damaging passenger mutations, appears to play a critical role in preventing disease-predisposing clones from rising to dominance and is associated with longer disease-free survival. Through exploring a range of evolutionary models, we show how different classes of selection shape clonal dynamics and health outcomes thus enabling us to better identify individuals at a high risk of malignancy.

Date: 2021
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DOI: 10.1038/s41467-021-25172-8

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